diff --git a/examples/colab/tpu/evalrx_tpu.ipynb b/examples/colab/tpu/evalrx_tpu.ipynb index 86ad4ac..2c8ca03 100644 --- a/examples/colab/tpu/evalrx_tpu.ipynb +++ b/examples/colab/tpu/evalrx_tpu.ipynb @@ -1,576 +1,34647 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# EvalRX on a Colab TPU\n", - "\n", - "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/evalvitals/evalrx/blob/ruinan/examples/colab/tpu/evalrx_tpu.ipynb)\n", - "\n", - "- **What it does:** runs the full EvalRX workflow (baseline, M1–M5, repair confirmed on a held-out split) on a Gemma model on a TPU.\n", - "- **How to run it:**\n", - " 1. Select a TPU runtime.\n", - " 2. Add `GEMINI_API_KEY` to Colab Secrets.\n", - " 3. Run all cells. A run takes about an hour.\n", - "- **No install:** it downloads a prebuilt environment image from Google Drive (5 GB, about a minute) and unpacks it. The image holds Python 3.12, jax/libtpu, `agy` and EvalRX. It uses no pip, git, GitHub or PyPI, and does not touch the Colab kernel's packages.\n", - "- **Network:** Google Drive (the image), the model's weights source (`gs://gemma-data`), and the Gemini API (`agy`).\n", - "- **Options (section 1):**\n", - " - `MODEL` and `DATASET` choose what to run.\n", - " - `EVALRX_WEIGHTS` names a weights tar, for a runtime that cannot read the weights source.\n", - " - `EVALRX_STORE` names your own image folder. If the folder is empty, the notebook builds a new image there; building needs GitHub and PyPI." - ] + "cells": [ + { + "cell_type": "markdown", + "id": "WHUA_GTaRXIe", + "metadata": { + "id": "WHUA_GTaRXIe" + }, + "source": [ + "# EvalRX on a Colab TPU\n", + "\n", + "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/evalvitals/evalrx/blob/ruinan/examples/colab/tpu/evalrx_tpu.ipynb)\n", + "\n", + "- **What it does:** runs the full EvalRX workflow (baseline, M1–M5, repair confirmed on a held-out split) on a Gemma model on a TPU.\n", + "- **How to run it:**\n", + " 1. Select a TPU runtime.\n", + " 2. Add `GEMINI_API_KEY` to Colab Secrets.\n", + " 3. Run all cells. A run takes about an hour.\n", + "- **No install:** it downloads a prebuilt environment image from Google Drive (5 GB, about a minute) and unpacks it. The image holds Python 3.12, jax/libtpu, `agy` and EvalRX. It uses no pip, git, GitHub or PyPI, and does not touch the Colab kernel's packages.\n", + "- **Network:** Google Drive (the image), the model's weights source (`gs://gemma-data`), and the Gemini API (`agy`).\n", + "- **Options (section 1):**\n", + " - `MODEL` and `DATASET` choose what to run.\n", + " - `EVALRX_WEIGHTS` names a weights tar, for a runtime that cannot read the weights source.\n", + " - `EVALRX_STORE` names your own image folder. If the folder is empty, the notebook builds a new image there; building needs GitHub and PyPI." + ] + }, + { + "cell_type": "markdown", + "id": "_mKv-3PGRXIe", + "metadata": { + "id": "_mKv-3PGRXIe" + }, + "source": [ + "## 1. Settings\n", + "\n", + "Usually nothing to change." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c9ObmSFXRXIf", + "metadata": { + "id": "c9ObmSFXRXIf" + }, + "outputs": [], + "source": [ + "import os\n", + "MODEL = os.environ.get('EVALRX_MODEL', 'gemma-4-e2b') # a model with a JAX spec in evalrx/specs.py, e.g. 'gemma-4-e4b'\n", + "DATASET = os.environ.get('EVALRX_DATASET', 'bbh_word_sorting') # or 'cruxeval_output'\n", + "RUN_TAG = 'launch'\n", + "\n", + "# Our prebuild image\n", + "EVALRX_STORE = os.environ.get('EVALRX_STORE', '')\n", + "\n", + "# Optional: a specific image tar (a path, a Drive file link, gs:// or https:// URL).\n", + "EVALRX_IMAGE = os.environ.get('EVALRX_IMAGE', '')\n", + "# Optional: a weights tar (tools/colab/build_weights.sh), for runtimes that cannot read the model's\n", + "# registered source. '' = use one from EVALRX_STORE if it has one, else the registered source.\n", + "EVALRX_WEIGHTS = os.environ.get('EVALRX_WEIGHTS', '')\n", + "EVALRX_IMAGE_SHA256 = EVALRX_WEIGHTS_SHA256 = '' # '' = read .sha256 next to the tar\n", + "\n", + "# Building an image only: where the EvalRX source comes from.\n", + "EVALRX_REF = os.environ.get('EVALRX_REF', 'ruinan') # branch, tag or commit\n", + "EVALRX_GIT_URL = os.environ.get('EVALRX_GIT_URL', 'https://github.com/evalvitals/evalrx.git')\n", + "EVALRX_BUNDLE = os.environ.get('EVALRX_BUNDLE', '') # or an offline bundle (tools/colab/build_bundle.sh)\n", + "EVALRX_REBUILD = os.environ.get('EVALRX_REBUILD', '0') == '1' # build a new image even if the store has one\n", + "EVALRX_BUILD_WEIGHTS = os.environ.get('EVALRX_BUILD_WEIGHTS', '0') == '1' # also pack MODEL's weights into the store" + ] + }, + { + "cell_type": "markdown", + "id": "w0FdmpaMRXIf", + "metadata": { + "id": "w0FdmpaMRXIf" + }, + "source": [ + "## 2. Find the image\n", + "\n", + "The public image, or the newest one in `EVALRX_STORE`. If `EVALRX_STORE` is empty, the notebook builds an image there." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "icCzyudvRXIf", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "icCzyudvRXIf", + "outputId": "4345722f-a48f-4275-ddf3-84c24e8f1365" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Prebuilt image, nothing to install: https://drive.google.com/file/d/1dkVwb02WnR15NBfDy20m5lOeT_oENMdV\n", + "Weights: gemma-4-e2b loads from its registered source\n" + ] + } + ], + "source": [ + "import hashlib, html, http.client, json, re, shutil, subprocess, sys, tarfile, tempfile, time, urllib.parse, urllib.request\n", + "from pathlib import Path\n", + "\n", + "CONTENT = Path('/content')\n", + "ENV, REPO = CONTENT / 'evalrx-env', CONTENT / 'evalrx'\n", + "DEMO_ROOT = CONTENT / 'evalrx_examples'\n", + "WEIGHTS_NAME = f'evalrx-colab-weights-tpu-{MODEL}.tar'\n", + "# The public copy of the image (Google Drive, shared with anyone who has the link) and its SHA-256.\n", + "PUBLIC_IMAGE = ('https://drive.google.com/file/d/1dkVwb02WnR15NBfDy20m5lOeT_oENMdV', # commit e6e8ec4\n", + " '1a2048c8fc8a6e7effe659ffda4c86a065439d557ada54fc79bb918ddeb36f21')\n", + "# The Colab kernel exports settings for its own Python (PYTHONPATH, UV_*, PIP_*);\n", + "# they must not reach the image's Python, which every later cell runs.\n", + "for key in [k for k in os.environ if k.startswith(('UV_', 'PIP_')) or k in ('PYTHONPATH', 'PYTHONHOME')]:\n", + " del os.environ[key]\n", + "if any(p.startswith('/content/drive/') for p in (EVALRX_STORE, EVALRX_IMAGE, EVALRX_WEIGHTS)) \\\n", + " and not Path('/content/drive/MyDrive').is_dir():\n", + " from google.colab import drive\n", + " drive.mount('/content/drive')\n", + "\n", + "\n", + "def run_visible(command):\n", + " with subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True) as process:\n", + " for line in process.stdout:\n", + " print(line, end='', flush=True)\n", + " if process.wait():\n", + " raise subprocess.CalledProcessError(process.returncode, command)\n", + "\n", + "class Download:\n", + " \"\"\"An HTTP(S) download as a stream. A connection that stalls for a minute or drops early is\n", + " reopened with a Range request at the current byte, so a long download survives both.\"\"\"\n", + "\n", + " def __init__(self, open_at):\n", + " self.open_at, self.pos, self.retries = open_at, 0, 0\n", + " self.response = open_at(0)\n", + " self.size = int(self.response.headers.get('Content-Length') or 0)\n", + "\n", + " def read(self, n=-1):\n", + " parts, want = [], n if n >= 0 else 1 << 62\n", + " while want > 0:\n", + " try:\n", + " chunk = self.response.read1(min(want, 1 << 20))\n", + " except (OSError, http.client.HTTPException) as exc: # socket.timeout is an OSError\n", + " self.reopen(exc)\n", + " continue\n", + " if not chunk:\n", + " if self.size and self.pos < self.size:\n", + " self.reopen('connection closed early')\n", + " continue\n", + " break\n", + " parts.append(chunk)\n", + " self.pos += len(chunk)\n", + " want -= len(chunk)\n", + " return b''.join(parts)\n", + "\n", + " def reopen(self, reason):\n", + " self.retries += 1\n", + " if self.retries > 20:\n", + " raise RuntimeError(f'download failed at byte {self.pos}: {reason}')\n", + " print(f' connection lost at {self.pos / 1e9:.2f} GB ({reason}); resuming', flush=True)\n", + " self.response.close()\n", + " time.sleep(min(60, 2 ** self.retries))\n", + " self.response = self.open_at(self.pos)\n", + " if self.pos and self.response.status != 206:\n", + " raise RuntimeError(f'the server cannot resume a download (HTTP {self.response.status})')\n", + "\n", + " def close(self):\n", + " self.response.close()\n", + "\n", + " def __enter__(self):\n", + " return self\n", + "\n", + " def __exit__(self, *exc):\n", + " self.close()\n", + "\n", + "\n", + "def http_open(url, offset=0):\n", + " headers = {'Range': f'bytes={offset}-'} if offset else {}\n", + " return urllib.request.urlopen(urllib.request.Request(url, headers=headers), timeout=60)\n", + "\n", + "\n", + "def open_drive_file(file_id, offset=0):\n", + " \"\"\"A Google Drive file shared with anyone who has the link. For a large file Drive first answers\n", + " with a virus-scan notice; the notice's form carries the parameters of the actual download.\"\"\"\n", + " response = http_open(f'https://drive.usercontent.google.com/download?id={file_id}&export=download&confirm=t', offset)\n", + " if response.headers.get_content_type() != 'text/html':\n", + " return response\n", + " page = response.read().decode('utf-8', 'replace')\n", + " action = re.search(r']*action=\"([^\"]+)\"', page)\n", + " fields = dict(re.findall(r']*name=\"([^\"]+)\"[^>]*value=\"([^\"]*)\"', page))\n", + " if not action or 'id' not in fields:\n", + " raise RuntimeError(f'Google Drive did not serve file {file_id}: it must be shared with \"Anyone with the link\", '\n", + " 'and Drive refuses a file for a while after too many downloads in a day (\"quota exceeded\")')\n", + " query = urllib.parse.urlencode({k: html.unescape(v) for k, v in fields.items()})\n", + " return http_open(html.unescape(action.group(1)) + '?' + query, offset)\n", + "\n", + "\n", + "def open_source(src):\n", + " \"\"\"A binary stream of a local or Drive path, a Google Drive file link, an https:// URL, or a gs:// object.\"\"\"\n", + " drive_file = re.search(r'drive\\.google\\.com/(?:file/d/|open\\?id=|uc\\?(?:.*&)?id=)([\\w-]+)', src)\n", + " if drive_file:\n", + " return Download(lambda offset: open_drive_file(drive_file.group(1), offset))\n", + " if src.startswith(('http://', 'https://')):\n", + " return Download(lambda offset: http_open(src, offset))\n", + " if src.startswith('gs://'):\n", + " tool = shutil.which('gcloud') or shutil.which('gsutil')\n", + " if tool:\n", + " cmd = [tool, 'storage', 'cat', src] if tool.endswith('gcloud') else [tool, 'cat', src]\n", + " return subprocess.Popen(cmd, stdout=subprocess.PIPE).stdout\n", + " # No Cloud SDK (e.g. Colab TPU runtimes): the kernel's GCS client, with your\n", + " # credentials (google.colab.auth.authenticate_user()), else anonymous.\n", + " from google.cloud import storage\n", + " bucket, _, name = src[len('gs://'):].partition('/')\n", + " try:\n", + " blob = storage.Client(project=os.environ.get('GOOGLE_CLOUD_PROJECT', '_')).bucket(bucket).blob(name)\n", + " blob.reload()\n", + " except Exception:\n", + " blob = storage.Client.create_anonymous_client().bucket(bucket).blob(name)\n", + " return blob.open('rb')\n", + " return open(src, 'rb')\n", + "\n", + "\n", + "def with_gcs(op):\n", + " \"\"\"op(client) with your credentials (google.colab.auth.authenticate_user()), else anonymously.\"\"\"\n", + " from google.cloud import storage\n", + " try:\n", + " return op(storage.Client(project=os.environ.get('GOOGLE_CLOUD_PROJECT', '_')))\n", + " except Exception:\n", + " return op(storage.Client.create_anonymous_client())\n", + "\n", + "\n", + "def store_files(store):\n", + " \"\"\"{file name: modification time} of the store's top level.\"\"\"\n", + " if store.startswith('gs://'):\n", + " bucket, _, prefix = store[len('gs://'):].partition('/')\n", + " prefix = prefix.strip('/') + '/' if prefix.strip('/') else ''\n", + " return with_gcs(lambda client: {b.name[len(prefix):]: b.updated.timestamp()\n", + " for b in client.list_blobs(bucket, prefix=prefix, delimiter='/')})\n", + " folder = Path(store)\n", + " return {p.name: p.stat().st_mtime for p in folder.iterdir() if p.is_file()} if folder.is_dir() else {}\n", + "\n", + "\n", + "def save_to_store(files, store):\n", + " if store.startswith('gs://'):\n", + " bucket, _, prefix = store[len('gs://'):].partition('/')\n", + " from google.cloud import storage\n", + " client = storage.Client(project=os.environ.get('GOOGLE_CLOUD_PROJECT', '_'))\n", + " for p in files:\n", + " print('uploading', p.name, flush=True)\n", + " client.bucket(bucket).blob(f\"{prefix.strip('/')}/{p.name}\".lstrip('/')).upload_from_filename(str(p))\n", + " else:\n", + " Path(store).mkdir(parents=True, exist_ok=True)\n", + " for p in files:\n", + " if p.parent != Path(store):\n", + " print('copying', p.name, 'to', store, flush=True)\n", + " shutil.copy(p, Path(store) / p.name)\n", + "\n", + "\n", + "store = EVALRX_STORE.rstrip('/')\n", + "found = store_files(store) if store else {}\n", + "images = sorted((t, n) for n, t in found.items()\n", + " if n.startswith('evalrx-colab-tpu-image-') and n.endswith('.tar') and n + '.sha256' in found)\n", + "if not EVALRX_IMAGE and images and not EVALRX_REBUILD:\n", + " EVALRX_IMAGE = f'{store}/{images[-1][1]}'\n", + "if not EVALRX_WEIGHTS and WEIGHTS_NAME in found and WEIGHTS_NAME + '.sha256' in found:\n", + " EVALRX_WEIGHTS = f'{store}/{WEIGHTS_NAME}'\n", + "if not store and not EVALRX_IMAGE:\n", + " EVALRX_IMAGE, EVALRX_IMAGE_SHA256 = PUBLIC_IMAGE\n", + "\n", + "if EVALRX_IMAGE:\n", + " print('Prebuilt image, nothing to install:', EVALRX_IMAGE)\n", + "else:\n", + " # Building an image: the only step that downloads packages. It needs GitHub (or EVALRX_BUNDLE) and PyPI.\n", + " assert not store.startswith(('http://', 'https://')), \\\n", + " 'EVALRX_STORE has no image and is not writable: set EVALRX_STORE to a Drive, gs:// or local folder'\n", + " print(f'No image in {store}: installing the environment and building the image (about 15 minutes)')\n", + " assert not ENV.exists(), 'install on a fresh runtime: /content/evalrx-env already exists (Runtime > Disconnect and delete runtime)'\n", + " local = not store.startswith(('gs://', '/content/drive/'))\n", + " work = Path(store) if local else CONTENT / 'evalrx-image-out'\n", + " if EVALRX_BUNDLE:\n", + " with open_source(EVALRX_BUNDLE) as stream, tarfile.open(fileobj=stream, mode='r|') as archive:\n", + " unpack = Path(tempfile.mkdtemp(dir=CONTENT))\n", + " archive.extractall(unpack, **({'filter': 'data'} if hasattr(tarfile, 'data_filter') else {}))\n", + " (unpack / 'evalrx').rename(REPO)\n", + " else:\n", + " assert shutil.which('git'), 'git is not available: set EVALRX_BUNDLE to an offline bundle, or build the image on public Colab'\n", + " run_visible(['git', 'init', '-q', str(REPO)])\n", + " run_visible(['git', '-C', str(REPO), 'fetch', '-q', '--depth', '1', EVALRX_GIT_URL, EVALRX_REF])\n", + " run_visible(['git', '-C', str(REPO), 'checkout', '-q', 'FETCH_HEAD'])\n", + " run_visible(['bash', str(REPO / 'tools/colab/bootstrap.sh'), 'tpu'])\n", + " run_visible(['bash', str(REPO / 'tools/colab/build_image.sh'), 'tpu', str(work)])\n", + " if EVALRX_BUILD_WEIGHTS and not EVALRX_WEIGHTS: # it does not depend on the EvalRX revision\n", + " run_visible(['bash', str(REPO / 'tools/colab/build_weights.sh'), 'tpu', str(work), MODEL])\n", + " EVALRX_WEIGHTS = f'{store}/{WEIGHTS_NAME}'\n", + " built = sorted(p for p in work.iterdir() if p.is_file() and p.suffix in ('.tar', '.sha256')\n", + " and (p.name.startswith('evalrx-colab-tpu-image-') or p.name.startswith(WEIGHTS_NAME)))\n", + " save_to_store(built, store)\n", + " image_tar = max((p for p in built if p.name.startswith('evalrx-colab-tpu-image-') and p.suffix == '.tar'),\n", + " key=lambda p: p.stat().st_mtime)\n", + " EVALRX_IMAGE = f'{store}/{image_tar.name}'\n", + " # This runtime already holds the environment the image was packed from: section 3 keeps it.\n", + " (ENV / '.image-sha256').write_text(Path(str(image_tar) + '.sha256').read_text().split()[0] + '\\n')\n", + " for p in built:\n", + " if p.suffix == '.sha256':\n", + " print(p.read_text().strip())\n", + "print('Weights:', EVALRX_WEIGHTS or f'{MODEL} loads from its registered source')" + ] + }, + { + "cell_type": "markdown", + "id": "Xp7paTscRXIg", + "metadata": { + "id": "Xp7paTscRXIg" + }, + "source": [ + "## 3. Restore the environment\n", + "\n", + "Unpacks the image (and the weights tar, if one is given) to `/content`, checking each SHA-256." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "qJ9pYfiVRXIg", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qJ9pYfiVRXIg", + "outputId": "3edb2cbb-88a9-4e28-b0dc-1dfd70fa8fea" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "restoring https://drive.google.com/file/d/1dkVwb02WnR15NBfDy20m5lOeT_oENMdV\n", + " 1 GB, 9 s\n", + " 2 GB, 17 s\n", + " 3 GB, 25 s\n", + " 4 GB, 32 s\n", + " verified sha256 1a2048c8fc8a6e7e..., 4.8 GB in 35 s\n", + "Image: {\n", + " \"accel\": \"tpu\",\n", + " \"commit\": \"e6e8ec4db68786dcf259991ac4f2b79ad4d0b5e0\",\n", + " \"python\": \"3.12.15\",\n", + " \"packages\": {\n", + " \"evalrx\": \"0.1.2\",\n", + " \"jax\": \"0.7.2\",\n", + " \"jaxlib\": \"0.7.2\",\n", + " \"libtpu\": \"0.0.21.1\",\n", + " \"gemma\": \"4.0.1\",\n", + " \"flax\": \"0.11.2\",\n", + " \"torch\": \"2.9.0+cpu\"\n", + " },\n", + " \"built_on\": \"release-colab-external-images_20261008-060111_RC00\",\n", + " \"accelerator_type\": \"v5e-1\",\n", + " \"built_at\": \"2026-10-10T15:58:02+00:00\"\n", + "}\n", + "Model: gemma-4-e2b from the source registered in evalrx/specs.py (gs://gemma-data for Gemma)\n" + ] + } + ], + "source": [ + "def expected_sha256(src, given):\n", + " if given:\n", + " return given.strip().split()[0]\n", + " try:\n", + " with open_source(src + '.sha256') as f:\n", + " return f.read().decode().split()[0]\n", + " except Exception as exc:\n", + " raise RuntimeError(f'no checksum for {src}: put {Path(src).name}.sha256 next to it, '\n", + " 'or set the *_SHA256 variable in section 1') from exc\n", + "\n", + "\n", + "def restore(src, sha256, staging):\n", + " \"\"\"Stream src through SHA-256 into `tar -x` in staging; raise if the checksum differs.\"\"\"\n", + " shutil.rmtree(staging, ignore_errors=True)\n", + " staging.mkdir(parents=True)\n", + " digest, done, start, step = hashlib.sha256(), 0, time.time(), 1 << 30\n", + " tar = subprocess.Popen(['tar', '-x', '-C', str(staging)], stdin=subprocess.PIPE)\n", + " with open_source(src) as stream:\n", + " while True:\n", + " chunk = stream.read(8 << 20)\n", + " if not chunk:\n", + " break\n", + " digest.update(chunk)\n", + " tar.stdin.write(chunk)\n", + " if (done + len(chunk)) // step > done // step:\n", + " print(f' {(done + len(chunk)) / 1e9:.0f} GB, {time.time() - start:.0f} s', flush=True)\n", + " done += len(chunk)\n", + " tar.stdin.close()\n", + " if tar.wait():\n", + " raise RuntimeError(f'tar failed on {src}')\n", + " if digest.hexdigest() != sha256:\n", + " shutil.rmtree(staging)\n", + " raise RuntimeError(f'checksum mismatch for {src}: got {digest.hexdigest()}, expected {sha256}. Copy it again.')\n", + " print(f' verified sha256 {sha256[:16]}..., {done / 1e9:.1f} GB in {time.time() - start:.0f} s')\n", + "\n", + "\n", + "image_sha = expected_sha256(EVALRX_IMAGE, EVALRX_IMAGE_SHA256)\n", + "marker = ENV / '.image-sha256'\n", + "if marker.exists() and marker.read_text().strip() == image_sha:\n", + " print('image already restored:', image_sha[:16])\n", + "else:\n", + " print('restoring', EVALRX_IMAGE)\n", + " staging = CONTENT / '.evalrx-image-staging'\n", + " restore(EVALRX_IMAGE, image_sha, staging)\n", + " for name in ('evalrx', 'evalrx-env'): # fixed paths: the environment refers to them\n", + " shutil.rmtree(CONTENT / name, ignore_errors=True)\n", + " (staging / name).rename(CONTENT / name)\n", + " shutil.rmtree(staging)\n", + " marker.write_text(image_sha + '\\n')\n", + "\n", + "model_path = ''\n", + "if EVALRX_WEIGHTS:\n", + " weights_sha = expected_sha256(EVALRX_WEIGHTS, EVALRX_WEIGHTS_SHA256)\n", + " weights_root = CONTENT / 'evalrx-weights'\n", + " wmarker = weights_root / '.sha256'\n", + " if not (wmarker.exists() and wmarker.read_text().strip() == weights_sha):\n", + " print('restoring', EVALRX_WEIGHTS)\n", + " shutil.rmtree(weights_root, ignore_errors=True)\n", + " restore(EVALRX_WEIGHTS, weights_sha, weights_root)\n", + " wmarker.write_text(weights_sha + '\\n')\n", + " (model_dir,) = [p for p in weights_root.iterdir() if p.is_dir()]\n", + " model_path = str(model_dir)\n", + "\n", + "# Datasets frozen into the image; agy reads GEMINI_API_KEY when its provider is \"gemini\".\n", + "shutil.copytree(REPO / '.bundle/data', DEMO_ROOT / 'data', dirs_exist_ok=True)\n", + "settings = Path.home() / '.gemini/antigravity-cli/settings.json'\n", + "config = json.loads(settings.read_text()) if settings.exists() else {}\n", + "settings.parent.mkdir(parents=True, exist_ok=True)\n", + "settings.write_text(json.dumps({**config, 'modelProvider': 'gemini'}, indent=2))\n", + "\n", + "os.environ.update(EVALRX_ENV=str(ENV), EVALRX_DEMO_ROOT=str(DEMO_ROOT), EVALRX_DATASET=DATASET,\n", + " EVALRX_RUN_TAG=RUN_TAG, EVALRX_MODEL=MODEL, EVALRX_MODEL_PATH=model_path,\n", + " PATH=f\"{ENV / 'bin'}:{os.environ['PATH']}\",\n", + " OMP_NUM_THREADS='2', PYTHONUNBUFFERED='1', TF_CPP_MIN_LOG_LEVEL='2',\n", + " MALLOC_ARENA_MAX='2', MALLOC_TRIM_THRESHOLD_='0')\n", + "os.chdir(REPO)\n", + "image = json.loads((ENV / '.image').read_text())\n", + "print('Image:', json.dumps(image, indent=1))\n", + "print('Model:', MODEL, 'from', model_path or 'the source registered in evalrx/specs.py (gs://gemma-data for Gemma)')" + ] + }, + { + "cell_type": "markdown", + "id": "56NfL4SsRXIg", + "metadata": { + "id": "56NfL4SsRXIg" + }, + "source": [ + "## 4. Check the TPU and agy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "PZD9Q4RiRXIg", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "PZD9Q4RiRXIg", + "outputId": "477e729a-a87b-4f00-aacc-fb1c0b3c875f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/content/evalrx-env/lib/python3.12/site-packages/jax/_src/cloud_tpu_init.py:86: UserWarning: Transparent hugepages are not enabled. TPU runtime startup and shutdown time should be significantly improved on TPU v5e and newer. If not already set, you may need to enable transparent hugepages in your VM image (sudo sh -c \"echo always > /sys/kernel/mm/transparent_hugepage/enabled\")\n", + " warnings.warn(\n", + "[('TPU_0(process=0,(0,0,0,0))', 'TPU v6 lite')]\n", + "jax 0.7.2\n", + "Source revision: e6e8ec4db68786dcf259991ac4f2b79ad4d0b5e0\n", + "Runtime: release-colab-external-images_20261008-060111_RC00\n", + "Gemini API key: ··········\n", + "Agent: antigravity\n", + "{\"event\":\"init\",\"conversation_id\":\"dfdf95cd-1bcf-4aa1-85f3-37cd5452dc98\",\"init\":{\"model\":\"gemini-3.8-flash-low\",\"cwd\":\"/content/evalrx_examples/agent_check\",\"tools\":[\"ask_custom_permission\",\"ask_permission\",\"ask_question\",\"browser_click_element\",\"browser_drag_pixel_to_pixel\",\"browser_get_dom\",\"browser_get_network_request\",\"browser_input\",\"browser_list_network_requests\",\"browser_mouse_down\",\"browser_mouse_up\",\"browser_move_mouse\",\"browser_press_key\",\"browser_refresh_page\",\"browser_resize_window\",\"browser_scroll\",\"browser_scroll_dom\",\"browser_select_option\",\"browser_subagent\",\"call_mcp_tool\",\"capture_browser_console_logs\",\"capture_browser_screenshot\",\"click_browser_pixel\",\"command_status\",\"define_subagent\",\"delete_knowledge\",\"execute_browser_javascript\",\"find_by_name\",\"finish\",\"generate_image\",\"grep_search\",\"invoke_subagent\",\"list_browser_pages\",\"list_dir\",\"list_permissions\",\"list_plugin_accounts\",\"list_resources\",\"manage_inbox\",\"manage_subagents\",\"manage_task\",\"multi_replace_file_content\",\"notebook_edit\",\"notebook_execution\",\"open_browser_url\",\"read_browser_page\",\"read_resource\",\"read_url_content\",\"replace_file_content\",\"run_command\",\"run_workflow\",\"schedule\",\"search_marketplace\",\"search_web\",\"sed_file\",\"send_command_input\",\"send_message\",\"view_file\",\"wait\",\"wait_5_seconds\",\"write_to_file\"],\"permission_mode\":\"always-proceed\"}}\n", + "{\"event\":\"step_update\",\"step_update\":{\"conversation_id\":\"dfdf95cd-1bcf-4aa1-85f3-37cd5452dc98\",\"step_index\":0,\"state\":\"DONE\",\"step_type\":\"user_input\"}}\n", + "{\"event\":\"step_update\",\"step_update\":{\"conversation_id\":\"dfdf95cd-1bcf-4aa1-85f3-37cd5452dc98\",\"step_index\":1,\"state\":\"DONE\",\"step_type\":\"agent_response\",\"duration_seconds\":1.964069185,\"usage\":{\"input_tokens\":12537,\"output_tokens\":90,\"thinking_tokens\":0,\"cache_read_tokens\":0,\"total_tokens\":12627}}}\n", + "{\"event\":\"step_update\",\"step_update\":{\"conversation_id\":\"dfdf95cd-1bcf-4aa1-85f3-37cd5452dc98\",\"step_index\":2,\"state\":\"ACTIVE\",\"step_type\":\"tool\",\"tool_name\":\"write_to_file\",\"tool_info\":{\"name\":\"write_to_file\",\"parameters\":{\"TargetFile\":\"/content/evalrx_examples/agent_check/agent_check.py\"}}}}\n", + "{\"event\":\"step_update\",\"step_update\":{\"conversation_id\":\"dfdf95cd-1bcf-4aa1-85f3-37cd5452dc98\",\"step_index\":2,\"state\":\"DONE\",\"step_type\":\"tool\",\"tool_name\":\"write_to_file\",\"duration_seconds\":0.016961851,\"tool_info\":{\"name\":\"write_to_file\",\"parameters\":{\"TargetFile\":\"/content/evalrx_examples/agent_check/agent_check.py\"}}}}\n", + "{\"event\":\"step_update\",\"step_update\":{\"conversation_id\":\"dfdf95cd-1bcf-4aa1-85f3-37cd5452dc98\",\"step_index\":3,\"state\":\"DONE\",\"step_type\":\"agent_response\",\"duration_seconds\":1.329839907,\"usage\":{\"input_tokens\":12739,\"output_tokens\":69,\"thinking_tokens\":0,\"cache_read_tokens\":0,\"total_tokens\":12808}}}\n", + "{\"event\":\"step_update\",\"step_update\":{\"conversation_id\":\"dfdf95cd-1bcf-4aa1-85f3-37cd5452dc98\",\"step_index\":4,\"state\":\"ACTIVE\",\"step_type\":\"tool\",\"tool_name\":\"run_command\",\"tool_info\":{\"name\":\"run_command\",\"parameters\":{\"CommandLine\":\"python3 agent_check.py\"}}}}\n", + "{\"event\":\"step_upd\n", + "print(\"AGENT_READY\")\n", + "\n", + "AGENT_READY\n" + ] + } + ], + "source": [ + "run_visible([str(ENV / 'bin/python'), '-c',\n", + " \"import jax; assert jax.default_backend() == 'tpu', jax.default_backend(); \"\n", + " \"print([(str(d), d.device_kind) for d in jax.devices()]); print('jax', jax.__version__)\"])\n", + "print('Source revision:', image['commit'])\n", + "print('Runtime:', os.environ.get('COLAB_RELEASE_TAG', 'non-Colab environment'))\n", + "if not os.environ.get('GEMINI_API_KEY'):\n", + " try:\n", + " from google.colab import userdata\n", + " os.environ['GEMINI_API_KEY'] = userdata.get('GEMINI_API_KEY')\n", + " except Exception:\n", + " import getpass\n", + " os.environ['GEMINI_API_KEY'] = getpass.getpass('Gemini API key: ')\n", + "assert os.environ['GEMINI_API_KEY']\n", + "agent_dir = DEMO_ROOT / 'agent_check'\n", + "run_visible([str(ENV / 'bin/python'), '-c', f\"\"\"\n", + "from pathlib import Path\n", + "from evalrx.agent_runtime.providers.antigravity import AntigravityAgent\n", + "agent_dir = Path({str(agent_dir)!r})\n", + "agent = AntigravityAgent(binary_path={str(ENV / 'bin/agy')!r}, model='gemini-3.8-flash-low',\n", + " timeout_sec=180, extra_args=['--output-format', 'stream-json'])\n", + "result = agent.run('Create agent_check.py that prints AGENT_READY. Run it with Python and report its output. Work only in this directory.', agent_dir)\n", + "print('Agent:', result.provider_name)\n", + "print(result.raw_output)\n", + "assert not result.error, result.error\n", + "assert (agent_dir / 'agent_check.py').exists()\n", + "print((agent_dir / 'agent_check.py').read_text())\n", + "\"\"\"])\n", + "run_visible([str(ENV / 'bin/python'), str(agent_dir / 'agent_check.py')])" + ] + }, + { + "cell_type": "markdown", + "id": "Ly-onT5QRXIg", + "metadata": { + "id": "Ly-onT5QRXIg" + }, + "source": [ + "## 5. Run EvalRX\n", + "\n", + "Baseline, M1–M4 analysis, and M5 repair. About an hour on a TPU v5e." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "Gwu7t-75RXIh", + "metadata": { + "colab": { + "background_save": true + }, + "id": "Gwu7t-75RXIh", + "outputId": "360ff41a-2cd4-4013-8dcf-71bce76d7008" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[model] Gemma 4 E2B-it, spec gemma-4-e2b-it = google/gemma-4-E2B-it; backend=jax_local; benchmark=bbh_word_sorting; n=128\n", + "judge: agy model=gemini-3.8-flash-low effort=high\n", + "[model] weights loaded in 117s (attn_impl=None); generation={'max_new_tokens': 512, 'do_sample': False}\n", + "[device] tpu: TPU_0(process=0,(0,0,0,0)) (TPU v6 lite)\n", + "Baseline: PASS=52, FAIL=76, UNKNOWN=0, accuracy=0.406 (379s, 3.0s/case)\n", + "\n", + "================================================================\n", + "VLDiagnoseLoop model=gemma-4-e2b max_cycles=1\n", + "================================================================\n", + "RUN loop ·········· starting · model=JaxLocalModel(key='gemma-4-e2b-it', adapter=GemmaJaxAdapter, loaded) · n_cases=64\n", + "M1 probe ········· cycle 0 · 8 analyzers run (771.5s)\n", + "M2 explore ······· cycle 0 · explore report ready (73.9s)\n", + "M2 explore ······· cycle 0 · 1 finding(s) · severity=medium · 1 figure(s) (8.9s)\n", + "M3 diagnose ······ cycle 0 · 3 falsifiable hypotheses proposed (8.7s)\n", + "M4 verify ········ cycle 0 · ✗ The generation token limit causes premature termination during mult... — inconclusive (6.2s)\n", + "M4 verify ········ cycle 0 · ✗ Failures on completed sequences are driven by an inability to const... — inconclusive (6.2s)\n", + "M4 verify ········ cycle 0 · ✗ Multi-step sorting trajectories are brittle to superficial input ph... — inconclusive (6.2s)\n", + "RUN loop ·········· done · 1 cycle(s) · resolved=False · stopped_by=max_cycles · 3 hypotheses\n", + "Diagnosis: stopped_by=max_cycles, cycles=1, verified=0\n", + "M5: skipped by --skip-m5 (tiered fix search remains enabled)\n", + "[fix] label quarantine: 22 run-dir file(s) held in memory for the fix stage (restored afterwards; see fix_quarantine.json)\n", + "M5 intervene ····· L2 expanded_budget_self_refine ✗ +0.047 (n_fixed=5, n_broken=2)\n", + "M5 intervene ····· 1 candidate(s) tried — none selected\n", + "Fix [L2] expanded_budget_self_refine: fixed=False, repairs=5, breaks=2, effect=+0.047\n", + "Done. Tidy M1-M5 logs: /content/evalrx_examples/runs/gemma-4-e2b/bbh_word_sorting.launch/logs\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/content/evalrx-env/lib/python3.12/site-packages/jax/_src/cloud_tpu_init.py:86: UserWarning: Transparent hugepages are not enabled. TPU runtime startup and shutdown time should be significantly improved on TPU v5e and newer. If not already set, you may need to enable transparent hugepages in your VM image (sudo sh -c \"echo always > /sys/kernel/mm/transparent_hugepage/enabled\")\n", + " warnings.warn(\n", + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "I0000 00:00:1791677831.905212 1423 port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "I0000 00:00:1791677835.949378 1423 port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "WARNING:absl:Grain multiprocess worker profiling requires jaxlib extension version 448 or later (jaxlib >= 0.11.0). Current version: 373.\n", + "E1011 00:17:31.587550 1837 google_auth_provider.cc:188] Could not find the credentials file in the standard gcloud location [/content/.config/application_default_credentials.json]. You may specify a credentials file using $GOOGLE_APPLICATION_CREDENTIALS, or to use Google application default credentials, run: gcloud auth application-default login\n", + "/content/evalrx-env/.python/cpython-3.12.15-linux-x86_64-gnu/lib/python3.12/subprocess.py:1885: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", + " self.pid = _fork_exec(\n" + ] + } + ], + "source": [ + "%%bash\n", + "set -euo pipefail\n", + "\"$EVALRX_ENV/bin/evalrx\" run \\\n", + " --modality llm --model \"$EVALRX_MODEL\" --dataset \"$EVALRX_DATASET\" \\\n", + " --backend jax_local --device tpu --dtype bfloat16 \\\n", + " --limit 128 --download-limit 128 --seed 0 \\\n", + " --max-new-tokens 512 --temperature 0 --analyzer-max-cases 8 \\\n", + " --judge-provider agy --judge-model gemini-3.8-flash-low \\\n", + " --fix-tier L2 --fix-repair-rounds 2 --fix-validation-cases 64 \\\n", + " --allow-codegen --explore --m2-codegen --skip-surgery \\\n", + " --data-dir \"$EVALRX_DEMO_ROOT/data\" --run-dir \"$EVALRX_DEMO_ROOT/runs\" \\\n", + " --run-tag \"$EVALRX_RUN_TAG\" \\\n", + " ${EVALRX_MODEL_PATH:+--model-path \"$EVALRX_MODEL_PATH\"}" + ] + }, + { + "cell_type": "markdown", + "id": "d37cd7yDRXIh", + "metadata": { + "id": "d37cd7yDRXIh" + }, + "source": [ + "## 6. Result\n", + "\n", + "A repair counts only if it passes the independent CONFIRM split." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "gwXN6gVdRXIh", + "metadata": { + "colab": { + "background_save": true + }, + "id": "gwXN6gVdRXIh", + "outputId": "a42569d4-8a2e-4b2d-96fa-f11d21c61b23" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Baseline: 52/128 (40.6%)\n", + "Verified hypotheses: 0\n", + "Frozen candidate: expanded_budget_self_refine\n" + ] + }, + { + "data": { + "text/html": [ + "
splitnbaseline_correctcandidate_correctrepaired_casesregressed_casese_valuerequired_e_valueverdictvalidated_fix
CONFIRM642629520.761904761904763220.0partialFalse
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/json": { + "baseline_override_min_support": 0, + "generation_kwargs": { + "do_sample": false, + "max_tokens": 1536 + }, + "image_ops": [], + "n_samples": 1, + "name": "expanded_budget_self_refine", + "output_key_pattern": "Answer:\\s*(.*)", + "prompt_template": "{prompt}", + "strategy": "self_refine" + }, + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No statistically confirmed accuracy repair.\n" + ] + }, + { + "data": { + "text/markdown": "### EXPLORE candidates (selection only)", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
nameheadlinetiern_pairsn_fixedn_brokeneffecte_valuecoverageverdict
letter_bucket_sortDirects the model to group words by their starting letter from A to Z before assembling the complete alphabetical list.L164870.0156250.318259518259518151.0partial
two_letter_code_sortInstructs the model to extract the first two letters of each word, sort those letter prefixes, and use them to construct the ordered word list.L164611-0.0781250.58837894132011981.0unsafe
direct_ordered_selectionTells the model to repeatedly find and cross off the alphabetically earliest remaining word one by one until all words are ordered.L1641070.0468750.37442296265825671.0partial
structured_letter_pass_with_budgetGives the model extra output space and asks it to group the words from A to Z before outputting the final sorted list.L164760.0156250.340992340992340941.0partial
iterative_minimum_extractionGives the model a larger token budget and instructs it to repeatedly find and cross off the alphabetically earliest remaining word until none are left.L1641050.0781250.68198468198468081.0partial
concise_verification_passDirects the model to write out the sorted list once, verify every adjacent pair, and then output the final answer line without repeating the entire list.L1641220.1562512.0029304029303731.0partial
expanded_budget_self_refineGives the model a larger token budget so it does not run out of room, then has it inspect its list and correct any misplaced words before finishing.L2641100.171875170.666666666667061.0fixed
verified_letter_chain_votingGenerates multiple independent attempts with extra token space and verifies word adjacencies across samples to vote on the most consistent sorted order.L2641550.156253.2206005209101121.0partial
least_to_most_alphabetical_drainBreaks the sorting task into smaller sub-problems by repeatedly selecting the alphabetically earliest remaining words from the list.L264880.00.299538370126604061.0no_effect
self_consistency_5Asks the same question five times and keeps the answer the model gives most often.L264750.031250.39782439782439871.0partial
coded_pipelinePrompts the model to verify alphabetical order concisely with extra token budget and keeps an agreeing answer if it differs from the baseline.L2641030.1093752.04595404595403751.0partial
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", 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\n", 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jhqpWrWr5qVKligoWLKiDBw9qwoQJKd5inB6jR4/W33//Len2ieEXX3yh33//3WpMSnO9GzVqpOrVq1seL1u2TDExMZbH6bkqnx5t27ZVkSJFLI979+5tc4Kbmqz+3Do4OKht27ZWbTNmzNCKFSssj//44w99/PHHVmOeffbZNFX1z6i0fP7ulFXv7eXLl9W7d2+VLVtW77//vv7++2+rW/OvXr2qN954w2obg8Fgs4rBg8ju7y57/V4DyAPsVX0QAJC33b0KQEoVs++WmJhoVZE9+adUqVLm4ODgVKvvnzlzxrIPe6wCMGLECJsxv//+u83zDB48ONOO02w2mwcNGmQzJiAgwFylShVzlSpVzCtXrjSbzWZzaGio2c/Pz2asj4+PuVy5cuZixYrZVE9fsWJFml+PlF7T5J/ixYtbVdW/sz0qKirF/X377bep7ispKSnNcd0tpVUA7nT27FnzwYMHzQcPHjTHxMRY9d1rFYDs+NyeP3/eXLBgQZsxBQsWNAcEBNi0u7m5mf/+++807/9uaRmf1s/fnbLivV2xYoXN/jw8PMxlypQxlyxZMsWVAdq0aZPu492/f3+qY7LjM3C3rP69BpA3cQcAACDHcHJySnHt8nPnzuns2bMyGo3q0KGDHSJLv/r166t+/fpWbbNmzdK1a9cy7Th79uxpc0vvhQsXdPjwYR0+fNgyJ7x48eL66aefbG6vvnHjhk6cOKHw8HCbCuUPqlOnTpKksLAwm0rsXl5eWrRoUaq3YHfq1En+/v427Zld/O9upUqVslw5dXd3T/N22fG5LVGihNauXWuzFv3169dtCrt5e3tr6dKleuSRRx7oOe8nrZ+/O2XXexsTE6PTp0/r/PnzNp/tKlWqaOrUqZn2XJJ9vrvs8XsNIPcjAQAAyFH69u2rBQsWWC0LKN2eLz579mx9/vnndoos/UaOHGn1ODo6Wt98842kzDnOGjVqaOnSpapYseJ9x9asWVMHDx7U+PHjVb16dZuTLUdHR5UuXVrdu3fXnDlz9OSTT953n/cye/ZsTZ061aqSuqOjo5599lnt2LHjnnOVXV1dbZZgc3BwUK9evR4opqyUFZ/bu4vO1atXTwcPHtTo0aNVoUIFm/ElS5bUkCFDdOjQIZspKFkhPZ+/ZFnx3rZo0UI///yzhg4dqscffzzFaQ/Ozs6qWbOmvvjiC+3duzfFJMSDssd3V3b/XgPI/Qxms51LJwMA8qQLFy5YFahycHBQ5cqV07WPixcvKjIyUoULF5afn58kKSkpSceOHbMaV6FCBTk7O0u6XeX67or7VapUscznvl9/Whw+fNiq4FmRIkVSPKEwm806cuSI1VhPT0+b4ngZOc67Xb16VZcvX7a64hcUFCRfX98Ux8fGxurSpUuKj49XoUKFVLhw4XS9Bne632t66dIl3bhxQwEBAWmek37y5EmrE6nmzZtbFYzLiLvft1KlSqU5ntOnT1vNV/f397eqGXCnjLyfv//+uxo2bGjpc3V1VVxc3D1jioqK0uXLl2U0GuXn56eCBQumOja9n/v0jk/P5y8r3tu7Xbt2TdevX1dCQoI8PT0VEBAgFxeXVMen5Xjj4uJ08uTJe45JlhXfXWmRmb/XAPImEgBAFjv+7zGtWf2jTbtvwUJ6tVc/O0QEADmbyWTS8OHDNWHCBEvbqlWr9MILL9gxqqxjMpnUv39/TZ8+3dL28MMP659//rFjVFkjv723AJDTsAwgkMXMZpOMRqNe6zNQBQp4W9rJyAOAtS5dumj//v26cOGC1fzxihUrqkWLFnaMLOs88sgjCgkJsZkvf/cSf7ldfnxvASAnIgEAZBMnRyc5OfErBwCpOXXqlI4ePWrV5uTkpGnTpuXZ9csPHz4so9Fo1VamTBkNHTrUThFljfz43gJATkQRQEDSpo3r9O3s6YqOvqUVPy7WxK8+1cwZk3Xk8EF7hwYA+VaNGjW0adOmfFO4zNXVVe3bt9f27dvl7e19/w1ysfz23gJATkENAEDShvVrFBJyTgEBgapRo5YKFS6sPbt3as/uXXqlVz8VLuyX4X3/e+yIVq1cpgIFvBUfHycPD0+VLVtO9Rs+KTe3tC8zBQB5XXKhPUdHRwUFBaW5QF9ullyY0M3NTUFBQXJzc7N3SFkiP763AJATkQAAdDsBcPDAfnXu0lNBJW6vp2s0GjVl0pd6+JFH9WSjpyVJR44c0oZ1q+67v4oVq6jF860lSSdP/KuIiHBVrFhZHp5eCg05p19++Ulurm7q1qPXPasSAwAAAEBmYUIy8P9cXFwsJ//S7bVzCxYqrBs3Ii1tlSpVUfny91/v+M4Cfw+Vq6CHyv23XnOFipXl7u6hRQvn6p+/96l2nbqZcwAAAAAAcA8kAID/5+llezuiq4ur4uP/W4fZYDBkSiG/kqWC5eLqqvCw0AfeFwAAAACkBQkA4P+luijfHZNkMjIFIPXnM4gZOAAAAACyCwkAIB0yMgUgJeHhoYqPj1NAYLHMCg0AAAAA7okEAJAOGZkCsH7dKpUrX1HFi5eQi4uLwkJDtPGntfL29lH1GjWzKFIAAAAAsEYCAMhiNWs9pj93/aFNP61VTEyMPDw8VKZseTV8ohHLAAIAAADINiwDiFyjVq1akqS9e/dm+r5NJpPMZrMcHR2t2o1GoyTZtGeU2Wy+7/QAAAAAAMgK3AGAXOPChQtZtm8HB4cU2zPrxD8ZJ/8AAAAA7CXlsx4AAAAAAJCnkAAAAAAAACAfIAEAAAAAAEA+QAIAAAAAAIB8gAQAAAAAAAD5AAkAAAAAAADyARIAuYTRaNTevXu1ZcsWhYeH33d8UlKSDhw4oN27dysyMjJTx4eEhGjXrl26ceNGqmMuX76sLVu2KCYm5r7PDQAAAADIeiQAcrgNGzaoe/fu8vf3V+3atdW4cWOtXr36ntt8//33CgwM1COPPKLHHntMRYsWVZ8+fRQXF/fA4wcOHKgKFSrotddeU/HixTV//vwU99mjRw+NHj1a7u7u6T/oXMJsNutyVJJOX4rX5agkmc1me4cEAAAAAKlysncAuLexY8fqzz//lKOjo/z9/XXx4sV7jl+6dKl69uwpSSpbtqwKFiyo/fv3a8aMGYqOjrY5YU/P+NWrV2vKlCnatWuX6tSpow8//FB9+vTRM888I39/f8u4JUuW6JdfftH+/ftlMBgy54XIQeITTVq1/4YW7bqufyPiLe0VAl3V8fGCalXDR67O5NYAAAAA5CycpeRwzz33nObOnauLFy9q+PDh9xxrMpk0bNgwSdLkyZN18uRJ7dmzRwcPHpSfn58WLFig3bt3Z3j8ggULVKNGDdWpU0eSNGDAAMXExGjFihWWMTdv3tQbb7yhYcOGqUqVKpn2OuQUV6KS1HXaOY1dccHq5F+S/o2I19gVF9R12jldiUqyU4QAAAAAkDISADncu+++q27duqlw4cL3HfvHH38oJCRE9evXV//+/S3tlSpV0ttvvy1JWrhwYYbHnz17ViVLlrQ89vX1la+vr86cOWNpe/vtt+Xi4qLRo0dn4GhztvhEk/p9F6Ij4SlPpUh2JDxO/b8PUXyiKZsiAwAAAID7IwGQh+zZs0eS1LJlS5u+F154wWpMRsZ7e3srNjbWalxMTIx8fHwkSXv37tWUKVM0efJkeXh4PMih5Eir9t+478l/ssNhcVq9P/UiiQAAAACQ3UgA5CHnzp2TJJUrV86mLzg4WM7OzpYxGRlfs2ZN7d27V4mJiZJuJwcSEhJUs2ZNGY1G9e3bV23bttVzzz2npKQk/fXXXzp48GCeKI5nNpu1aNf1dG2zcNf1PHHsAAAAAPIGigDmIdHR0ZKkAgUK2PQ5ODjI09NTt27dyvD4IUOGaPr06erevbvatWun999/X/Xq1VPTpk319ddf6/jx41q1apWOHTum5557TiaTSXFxcQoKCtKGDRtUpEiR+x5DUFBQqn0REREKDAxUUlL2z6+/EpVkM+f/fv6NiNfFyHj5FeDXDAAAAEDaOTllzTkEZyZ5SPKHJLUT5KSkJKtl+dI7PiAgQDt27NBnn32m6dOnq3nz5hoxYoTCw8M1evRojRs3TsWLF1fjxo1VokQJ/frrr4qJiVH16tX1zjvvaPr06Q98jCaTSZGRkQ+8n/QKu5qxpEP45Ug5Gfk1AwAAAJB2fn5+WbJfzkzykORCgRcuXLDpu3Xrlm7dumV1hT2946XbBQLnzJlj1darVy+VK1dOAwYMUHh4uLZs2aK5c+fKyclJ3t7e6tq1q7788ss0JQBCQ0NT7UuOxdfX9777yWxJjkmS0jcFQJKKFfGVL3cAAAAAAMgBODPJQypXrixJ2r17t1555RWrvuTl/CpVqpTh8SlZv369VqxYoT///FOOjo46e/asJFmtFlCqVClFR0fr8uXLaZoGcD9ZdTvMvfj7OqpCoGu6pgEYJP11Pl7PPeKWdYEBAAAAQBpRBDAPefrpp+Xg4KDFixfr8uXLVn1ff/21JKlp06YZHn+32NhYDRw4UP3791etWrUkybIiwJ2rBURHR8tgMMjb2/sBjs6+DAaDOj5eMF3bmCX9b1G4Pt9wUUlGigECAAAAsC8SADncmTNntGXLFm3ZskWnTp2SJB0/ftzSFhISYhkbEBCgDh06KDIyUo0bN9b8+fO1bt06denSRatXr1bRokXVuXPnDI+/2/vvv6+4uDh9+OGHlrayZcvKx8dHO3bssLT98ccfqlq1qlxdXTPzpcl2rWr4qHKx9F/N/3bbNfX9LkSR0dlfvBAAAAAAkhnMrFOWo73zzjtWJ9h3++yzzzR8+HDL4ytXrujJJ5/UkSNHrMZ5eHho1apVatKkiVV7escnO3z4sGrUqKEFCxaoXbt2Vn3jx4/XBx98oK+++krXrl3TqFGjtGzZMrVu3Toth5yq5BoA96oTkNWuRCWp//chOhwWl+qYYr7OunQzUUkm6/biBZ01sVuQKgYyJQAAAABA9iMBkMPNnj1b8+bNS7W/X79+6tChg1VbTEyMZsyYoa1btyo+Pl6VK1dW37599dBDD6W4j/SOl24nJq5cuaJp06al2D9r1iytWrVKzs7O6tmzp1544YU0HO295YQEgCTFJ5q0ev8NLdx13aomQMVAV3V8vKBeqOGjfyPiNXhBqC7dtL7q7+Zs0PttA9Wiuk92hw0AAAAgnyMBgFwjpyQAkpnNZl25ZdStOKO83Bzl5+Uog8Fg6b8claQ3FoRq/7lYm217NiykN5oWlZOjwaYPAAAAALICCQDkGjktAZAWCUlmjV93UYt22S4h+HhZD33eqbgKerIYBwAAAICsRxFAIAu5OBk0ulWAPngxUM53Xe3fdSpG7Sed1dHw1OsJAAAAAEBmIQEAZIO2tXw1t08p+XtbX+0Pj0xU12lntfbvG3aKDAAAAEB+QQIAyCYPl3DXkoGlVTPY3ao9LtGsEYvDNX7tRSUZmZEDAAAAIGuQAACykV8BJ83uVUpd6ha06Zv7xzW9Nue8rt1KSmFLAAAAAHgwJACAbObsaNCoFwI07qVAuThZ1wXYfTpG7Sef0ZEw25UDAAAAAOBBkAAA7KRNTV/N61NKAT7WdQEiIpPUddo5rf6LugAAAAAAMg8JAMCOqgbdrgtQu7SHVXt8kllvLQ3Xx2suKJG6AAAAAAAyAQkAwM4Kezlp5qsl1bWebV2A+Tuu67XZ53WVugAAAAAAHhAJACAHcHY06K3nA/RRu0C53lUXYM+ZGLWfdEaHQqkLAAAAACDjSAAAOUirR301r69tXYALN5LUbfo5rfor0j6BAQAAAMj1SAAAOUyV4u5aOrC0apexrguQkGTWqKUR+mg1dQEAAAAApB8JACAHKuTlpFmvlFT3+oVs+hbsvK5es8/rShR1AQAAAACkHQkAIIdycjRoREt/fdK+mE1dgL1nYtRh8hkdDKEuAAAAAIC0IQEA5HDP1/DR/L6lVMzX2ar9wo0kdZ9xTiv2RdonMAAAAAC5CgkAIBeoXNxdSwYG67GytnUB3lkWoXGrLighiboAAAAAAFJHAgDIJQp6OmnGyyXVo4FtXYCFu67r1dnnqAsAAAAAIFUkAIBcxMnRoDdb+Gt8h2Jyc7auC/DX2Vi1n3RGB85TFwAAAACALRIAQC7UsrqP5vcNVvGC1nUBLt68XRfgxz2R9gkMAAAAQI5FAgDIpSoVc9OSAcF6/K66AIlGs95dHqH3V0ZQFwAAAACABQkAIBfz9XTS9JdL6uUnbOsCLP4zUq/MOqfL1AUAAAAAIBIAQK7n5GjQ8Ob++qyjbV2A/edu1wX4h7oAAAAAQL5HAgDII557xEcL+gUr6K66AJduJqnHjHNatue6nSIDAAAAkBOQAADykIqBblo8IFj1HvK0ak80mjVm+QWNXRGhhCSTnaIDAAAAYE8kAIA8xtfTSdNeLqFXnihs07dkd6Rennlel28m2iEyAAAAAPZEAgDIgxwdDBrWvKi+6FRc7nfVBfj7fKzaTTqr/edi7BQdAAAAAHsgAQDkYc0e9tYP/YNVopB1XYDLUUnqOfOclvxJXQAAAAAgvyABAORx5QPctGhAadUvZ10XIMkojV15QWOWUxcAAAAAyA9IAAD5gK+Ho6b2LKHXGtnWBVi2J1I9Z5zXJeoCAAAAAHkaCQAgn3B0MGhI06L6snNxubtY1wX4JyRW7Sad0V9nqQsAAAAA5FUkAIB8pmk1by3sZ1sX4EqUUS/PPKdFu67LbDbbKToAAAAAWYUEAJAPlQtw0+KBpdWw/F11AUzSB6tu1wWIT6QuAAAAAJCXkAAA8ikfd0dN7lFCvVOoC/Dj3hvqMeOcLtygLgAAAACQV5AAAPIxRweDBjctqgldbOsCHAyNU/tJZ7TvDHUBAAAAgLyABAAAPVvVW4v6l1apwi5W7VdvGfXKrHP6Yec16gIAAAAAuRwJAACSpIf8XbVoQLCeqGBbF+DD1Rc1+kfqAgAAAAC5GQkAABbe7o6a3L2E+j7lZ9O3Yt8NdZ9xThGR1AUAAAAAciMSAACsODgY9PozRfR11+LycLH+ijgUGqcOk89oL3UBAAAAgFyHBACAFDWp4q1FA4IV7GdbF+DVWee0YAd1AQAAAIDchAQAgFSVLXq7LsCTFb2s2pNM0kdrLurtZRGKoy4AAAAAkCuQAABwTwXcHDWpW5D6P21bF2DVXzfUffo5hVMXAAAAAMjxSAAAuC8HB4MGNCmib7oFydPV+mvjcFicOkw6o92no+0UHQAAAIC0IAEAIM2eqlxAi/oHq3QR67oA16KN6jX7vOb9QV0AAAAAIKciAQAgXcoUddWi/sFqXMm6LoDRJH2y9qJGLaUuAAAAAJATkQAAkG5ebo6a2DVIA5rY1gVYvf+Guk47q/Dr1AUAAAAAchISAHnQb7/9ppYtW+qhhx5SYGCg6tevr6+++koJCQkpjj916pS6deumkiVLqmjRomrcuLFWr16d4ti1a9eqevXq8vPzU9OmTfXvv/+mOO7tt99WhQoVFB3NvPC8ysHBoP5PF9Gk7kHyuqsuwNHweLWffEZ/nuL9BwAAAHIKg5kJu3nKhx9+qHfeeSfFvjp16mj79u1ycflv/vaRI0dUv359RUZG2oyfPHmy+vfvb3l89OhRVatWTePGjVOnTp00ePBgHThwQP/++6+cnZ0t4w4fPqwaNWpowYIFateuXaYdW1BQkCQpNDQ00/aJzHHmcrwGzQvV6cvWSSZHB2lY86LqXr+QDAaDnaIDAAAAIHEHQJ5y7do1jR07VpI0ZswYnT59WhcuXNDy5cvl7++v3bt3a8GCBVbb9OvXT5GRkWrdurWOHj2qCxcu6IsvvpCDg4OGDx+u8PBwy9ivv/5aQUFBGjlypEqVKqXPP/9cZ86c0fLlyy1jzGaz+vbtqyZNmmTqyT9yttJFXLWwf7CermxbF+DTdZc0ckm4YhOoCwAAAADYEwmAPOTIkSNKTExUkyZN9N5776l06dLy9/dXmzZtNG7cOEnSgQMHLOOPHz+ubdu2qVSpUlq0aJEqVqwof39/DR06VP3791dsbKzmzZtntf+qVataHj/00EPy8PDQkSNHLG2zZ8/Wvn37NHny5Gw4YuQkXm6O+qpLkF5/pojuvti/9u+b6jrtrMKupzwNBQAAAEDWIwGQhxQtWlSS5OrqatOX3JY8RpK2bt0qSerQoYPNNj169JAkbd682dJmNBrl4GD9kXFwcJDRaJQkXblyRSNGjNDo0aNVunTpBz0c5EIODgb1fcpPk1OoC3AsIl7tJ53VrpPUBQAAAADsgQRAHlK+fHk9+eST2rBhg6ZPn66YmBgZjUbt3LlTY8aMkYeHh7p06WIZf+LECUlStWrVbPZVrVo1GQwGy5jk/R87dszyODQ0VLdu3VL58uUlScOGDVNAQICGDx+eVYeIXOLJigW0eECwyhZ1sWqPjDHqtTnn9d32q6L8CAAAAJC9KAKYx0RGRmrw4MFatGiREhIS5OjoKKPRqMcee0xff/21HnvsMcvY3r17a+bMmdqwYYOaNWtmsy9vb2+5uLjoypUrkqQ9e/aoTp06mjNnjjp16qT+/ftr/fr1On36tHbv3q2nnnpKW7ZsUe3atTVs2DCtXLlSzs7O6tmzp8aMGWNz90BKkgv9pSQiIkKBgYE6e/Zs+l8Y2EV0vEmjl1/Qr0dsr/o3r+alMa395e5CHhIAAAC4k5OTU9bsN0v2CrsJCQlRaGiokpKSJMnq9vwjR45YJQCSpVad3cHBQSbTf4XbateurRkzZmjkyJHq1auXKleurBUrVsjJyUn9+vXTyy+/rCeeeEL9+vXTunXrtH79el29elWtW7eWr6+v3njjjQc+PpPJlOKKBci5RjdzV+lCZs3+PUZ3Zhs3HLylExdiNa6VjwJ9HO0WHwAAAJDT+Pn5Zcl+uQMgDwkPD1elSpUkSV9++aWaNGkid3d3HTp0SKNGjdKff/6pOXPm6OWXX5Z0+5b9L7/8UgsXLlTHjh2t9pWYmCg3NzeVKFEixSvuiYmJlqX/PvjgA3399deW5QD9/Pw0fvx4ywl/v379tHbtWoWEhDzQ8SXfHcAdALnT9uPRemvpBUXFWa8G4OPuoPHtA1T3IU87RQYAAADkLNwBgPuaP3++bt68qa+++kqvvvqqpf2pp57SypUrFRgYqEmTJlkSAGXLlpUkHTp0yGZfR48elclk0kMPPZTicyWf/J86dUofffSRpk6dqsKFC+uvv/5SYmKiVV2BatWqadq0aYqKilKBAgUe+Diz6pcBWatxZR8tHuCuQfNDdfJivKX9RqxJ/eeG641mRfVyw0Kp3pECAAAA4MEw+TYPuXTpkiTJzc3Nps/FxUUGg8EyRpIaNGggSVq2bJllqkCyhQsXSpIaNmx4z+fs37+/6tSpo549e0r6b8rBnfP9HR0drfqQf5Xyc9EP/YL1bFXrRJDJLH2x4ZL+tyhcMQmmVLYGAAAA8CBIAOQhDz/8sCRpzJgxWrduneLj42UymXT48GF17NhRZrPZMiZ5/KOPPqp///1Xffv2VWRkpIxGoxYuXKgvv/xSzs7O6tq1a6rPt2jRIm3ZskXTpk2ztJUtW1YODg5WqwUcPXpURYoUka+vb+YfNHIdT1cHfdm5uIY0LaK7L/ZvOHBTXaaeVci1BPsEBwAAAORh1ADIQ+Lj4/XYY4/pn3/+kXS7uJ+jo6OlIKC7u7u2bt2q2rVrW7bZvXu3nnzyScXFxUm6fXt98vixY8fq3XffTfG5bty4oYoVK+qVV17Rhx9+aNXXtWtX7dmzR5s3b9b169f1xBNPaMiQIRo9evQDHV9yDYDQ0NAH2g9yju3/3tKbi8J08666AN7uDvqsY3E1KO9lp8gAAACAvIc7APIQV1dXbd26Vf/73/9UpkwZSVJSUpIKFiyotm3baufOnVYn/5JUp04dbd++XU8//bRcXFxkNBpVoUIFTZ8+PdWTf0kaNWqUPDw89M4779j0TZw4UZUqVVJwcLBq1aqll156SSNGjMjcg0We0LCClxYPLK1y/q5W7TdjTer3XYhmbb0icpQAAABA5uAOgDzMaDTKaDTKxcUlTePNZrOMRmOaiuzFxMTIycnpnvs2Go0yGAxW9QAeBHcA5F3R8Sa9+2O4fjoYZdPXtFoBffBiMXm6kq8EAAAAHgQJAOQaJADyNrPZrDnbrumrjZdkuutbqZy/qyZ2C1LJwmlLZgEAAACwxSU1ADmCwWDQq08W1rSeJeTtbv3VdOJivDpMOqPt/96yU3QAAABA7kcCAECOUr+8l5YMLK3yAXfVBYgzqd/3IZq5hboAAAAAQEaQAACQ45Qo5KIF/YLV/GFvq3azWfpq42W98UOYouNNqWwNAAAAICUkAADkSB4uDvqsYzENb15UDgbrvp8PRanz1LM6dyXBPsEBAAAAuRAJAAA5lsFg0MtPFNaMV0rKx93Rqu/kxXh1mHxG245RFwAAAABICxIAAHK8ug95asnAYFUItK4LEBVnUv+5IZq+mboAAAAAwP2QAACQKwQVctGCvsF67hHbugATN13WkAVhio432ik6AAAAIOcjAQAg13B3cdCnHYrpf8/Z1gX45XCUOk45q7OX4+0THAAAAJDDkQAAkKsYDAb1bFhYM18pKV8P67oApy8lqMPks9pyNMpO0QEAAAA5FwkAALnS4/9fF6DiXXUBbsWbNHBeqKb+elkmE3UBAAAAgGQkAADkWsULumh+32C1rG5bF2DSL1c0ZEGobsVRFwAAAACQSAAAyOXcXRz0SftiGtHCX453faP9euSWOk05qzPUBQAAAABIAADI/QwGg7o3KKSZr5RUQc+76gJcTlDHyWf12xHqAgAAACB/IwEAIM94rKynlgworcrF3Kzab8Wb9Pq8UE35hboAAAAAyL9IAADIU4oVdNa8vqX0Qg0fm77Jv17RoPmhiqIuAAAAAPIhEgAA8hw3Zwd91C5Qb7W0rQuw+ejtugCnL1EXAAAAAPkLCQAAeZLBYFDX+oU0+9WSKnRXXYAzlxPUcQp1AQAAAJC/kAAAkKfVLuOpxQNLq0px67oA0f9fF2DSz9QFAAAAQP5AAgBAnlfM11lz+5RSq0dt6wJM/e2KBs4L1c1Y6gIAAAAgbyMBACBfcHN20IcvBWrU8/5yuuubb+uxW+o4+axOXqQuAAAAAPIuEgAA8g2DwaAu9Qppdq9SKuxlXRfg3NUEdZpyVr8cvmmn6AAAAICsRQIAQL5Tq7SHFg8orapB1nUBYhJMGjw/TBM3XaIuAAAAAPIcEgAA8qVAX2fN7V1KbWra1gWYvvmqBswNoS4AAAAA8hQSAADyLVdnB33wYqDeecG2LsC2f6PVYfIZ6gIAAAAgzyABACBfMxgM6lS3kOa8ZlsX4PzVRHWcckabDlEXAAAAALkfCQAAkFQz2ENLBpZWtbvqAsQmmPXGgjB9vfGSjNQFAAAAQC5GAgAA/l+Aj7Pm9imlF2vZ1gWYseWq+n8fohvUBQAAAEAuRQIAAO7g4uSgsW0D9W7rADlZzwjQ78ej1WHSGZ24EGef4AAAAIAHQAIAAO5iMBjU4bGC+rZXKfkVsM4ChFxLVKepZ7XxIHUBAAAAkLuQAACAVDwa7KGlA0vrkRLuVu2xCWYN/SFME36iLgAAAAByDxIAAHAPRb2d9V3vknqptq9N36ytV9XvuxBFxlAXAAAAADkfCQAAuI/kugBj2tjWBfjjRLQ6Tj6jfyOoCwAAAICcjQQAAKRR+zoF9f1rpVSkgJNVe8i1RHWZelYbDlAXAAAAADkXCQAASIfqpTy0ZGCwqpe8qy5AolnDF4bp8w0XqQsAAACAHIkEAACkU1FvZ337Wil1eMzXpu/bbdfU99sQRUYnZX9gAAAAwD2QAACADHBxMujd1oEa2zZAzo4Gq74dJ6PVYfJZHaMuAAAAAHIQEgAA8ABeql1Q3/cupaLe1nUBQq/frguw/p8bdooMAAAAsEYCAAAe0CMl3bVkYGnVKGVdFyAu0az/LQrX5+svKslIXQAAAADYFwkAAMgERQo4aU6vVOoCbL+mPt+epy4AAAAA7IoEAABkkuS6AO+3DbSpC7DrVIzaTz6ro+HUBQAAAIB9kADIIJPJpEmTJqlGjRry8PCQwWDQsWPHJElTpkzRkCFDdPLkSTtHCcAeXqztq7l9Ssn/rroAYdcT1XXaWa39m7oAAAAAyH4kADKoe/fuev3113XgwAElJiZa9cXExOjrr7/W7Nmz7RQdAHt7uMTtugA1g23rAoxYHK7xa6kLAAAAgOxFAiADVq5cqQULFqh06dI6d+6cypYta9Xfvn17SdKSJUvsER6AHMKvgJNmvVpKnesWtOmb+8c19f72vK7doi4AAAAAsgcJgAxYuHChJGnkyJEKCgqSwWA917dEiRJyc3PT6dOnFRkZaYcIAeQULk4Gvf1CgMa9FCgXJ+vvij9Pxaj95DM6EhZrp+gAAACQnzjdfwjudvz4cUlSjRo1Uuw3GAwKCAjQ2bNndfXqVfn6+mZjdLfduHFDS5cu1V9//SWTyaRHH31UXbp0kaenp81Yo9GoZcuWaevWrYqLi1OVKlXUvXt3FSlSxGbshQsXNGnSJIWGhqpmzZrq3bu3XF1dbcatXbtWS5cu1ZQpU1J8TiC/aVPTVw8VddWQBaG6cOO/q/4RkUnqOu2cxrYN1PM1fOwYIQAAAPI6EgAZkHzF38HBwerxna5cuSJJdjn5/eWXX9SlSxddunTJqv2dd97R0aNHVbhwYUtbdHS0mjVrpt9//91q7CeffKINGzaoVq1alrbIyEjVrl1bFStWVOvWrfXFF1/o559/1urVq622vXHjhl577TW9+uqrnPwDd6j2/3UBhv4Qpr1nYizt8UlmjVwSrsNhcRrWvKjNCgIAAABAZmAKQAaULl1aknTq1ClJtgmAo0eP6tatW/L19ZW/v3+2xvbPP//o+eef16VLl9SwYUN9/fXX+u677zRo0CDFxsYqNtb6VuNhw4bp999/V4kSJfTZZ59p1qxZevrpp3XlyhW99NJLio+Pt4ydOHGibt68qVWrVmnAgAGaPXu21qxZo+3bt1vt86233pKHh4fefvvtbDlmIDcp7OWkWa+WVJcU6gLM++OaXptDXQAAAABkDRIAGfDCCy9IkubMmSOz2bqKt9FotJz4tmrVKsW7A7LSoEGDFBcXp0GDBmnbtm0aNGiQevTooa+//lpnzpyRn5+fZey1a9c0e/ZseXp6avv27Ro+fLheffVV/fzzz2rcuLHOnTunxYsXW8bv2LFDderUkYeHhySpUaNGcnJy0o4dOyxjdu/erenTp2vKlClyd7eufg7gNmdHg0a9EKAPU6gLsOd0jNpNOqPD1AUAAABAJiMBkAGdO3dWjRo1tHHjRrVu3VrXr1+XJH3//fd64okntGLFChUoUEDvvvtutsZ14sQJbdu2TUWLFtX48eNt+v38/OTm5mZ5/MsvvygpKUnt2rVTqVKlLO0Gg0HDhw+XJK1fv97SfvXqVRUoUMBqXIECBXT16lVJt5Mfffr0Ubt27dS0adNMPz4gr2ld01fz+5ZSgI/1bKwLN27XBVj1V6R9AgMAAECeRA2ADHB2dtb69evVvn17q/nvn3zyiSTJ399fS5YsUZkyZbI1ruR5/K1atVJCQoImTZqkQ4cOqXDhwmrevLmaNGliNf7QoUOSpLp169rsq379+lZjJKlkyZIKCwuzPI6OjlZkZKRKliwpSfr66691+vRpq6QBgHurUtxdSweW1tCFYdpz+r+6AAlJZo1aGqHDoXH6Xwt/6gIAAADggZEAyKCAgABt27ZNmzdv1i+//KKIiAh5eHioZs2aateunby8vLI9pvPnz1tiq127tmW1Akn68ssv1b59e/3www9ydHSU9F+hwmLFitnsy8fHRx4eHrp8+bKlrU2bNurRo4eOHTumihUrau7cuXJyclLLli0VGhqqMWPG6JNPPlFgYKDWr1+vlStXytnZWV27dk0xyZCSoKCgVPsiIiIUGBiopCTmRyNv8XaTpnYvpq82XtH8nZFWfQt2XtexiDh91iFAhb34ygYAAMgPnJyy5u8+/pp8QI0bN1bjxo3tHYYkWQr8TZo0Sa6urho7dqyCgoJ06NAhTZs2TUuWLFHNmjX15ptvSpKlwJ+Li0uK+3N1dbUqAti5c2ctW7ZMDRo00KOPPqrt27fr448/VnBwsNq0aaNKlSqpX79+mj59ul5//XW9++67unr1qho2bKiNGzfq6aeffuBjNJlMioyMfOD9ADnRa/WcVcq3gD77OUoJd+S59p2NVYfJ5/RBK29VCnC2X4AAAADIFnfWbstMJAAyoE6dOjp+/Lj27NmjcuXKZXhMZkteci8pKUn//POPSpQoYelr1qyZmjZtqm+//daSAEgeHx0dbbMvs9ms6Ohoqw+eg4ODVq5cqd27dyssLEzTpk1TmTJltGbNGq1Zs0Z79uxRUlKS3n33XQ0aNEjvvPOOJOnkyZMaNWqU/vzzz/seQ2hoaKp9yXcH+Pr63nc/QG7Vvp5ULdhXb/wQoYgb/2UBLt8yadDiG3rn+aJq9ai3HSMEAABAbkUCIANu3rypGzduyGg0PtCYzJZ8wl+vXj2rk39JevbZZ1WoUCGdPHnS0pZ8Qp28nOGdQkNDlZCQYLMf6XZyI1l0dLRef/11vf7666pRo4aOHTumS5cuWd0V8fTTT2vYsGFKSEhI9W6D9Miq22GAnKJaSS8tGVhawxeF6c9T1nUB3l1xUccuJOhN6gIAAAAgnVgFIIskX1W/s+p+VqtVq5ak28mHuyUlJSkmJkaurq6Wtpo1a0qSNm7caDN+w4YNVmNS89577ykpKUnvv/++JFlWBLhztQBvb29u3QfSqZCXk2a8XFLd6xey6fth53W9OuucrkRRDwMAAABpRwIgjWJiYnTr1i3dunVLZrPZpi355/r161q3bp3CwsLk7OyswMDAbIuxatWqqlKlinbt2qVly5ZZ2k0mk9555x3FxcVZFeN74oknVKRIEf3yyy/68ccfLe1hYWEaN26cJKldu3apPt/Bgwf11VdfaeLEiZYT/uQVAcLDw6325+bmpiJFimTOgQL5hJOjQSNa+mt8+2Jyc7a+2r/vbKzaTzqjAyGxdooOAAAAuY3BnHw2i3uqWLGi/v3333Rt07FjRy1cuDCLIkrZTz/9pBYtWshkMunRRx9VUFCQDh8+rFOnTsnR0VGbN29Ww4YNLeNnzJihPn36yGAwqF69eipYsKC2bt2qqKgoNW/ePNUl/cxms+rXr69ChQpp7dq1Vn21a9dW0aJFtXbtWiUkJOjRRx9V9erVtWDBggc6tuQpC/eqEwDkVUfD4zRoXqjCIxOt2p0dDXq3dYDa1vK1T2AAAADINUgApNFzzz2n06dPS5LOnDmjhIQElS5d2mZOu6Ojo/z8/PTUU09p+PDhlkJ72emHH37QwIEDdf36dUtbYGCgpk6dqlatWtmMHz9+vMaMGWNV8f/555/XvHnz5OPjk+JzzJgxQ2+88YYOHz6s4OBgq749e/aoWbNmKlOmjG7duqWEhARt2bIlxXoC6UECAPnd9egkDV8Ypl131AVI1uExX41sGSAXJ+oCAAAAIGUkADIg+W6Ao0ePqmLFivYOJ0Xx8fHauXOnrl27poCAANWuXVvOzqkvHxYZGak///xT8fHxqly5sh566KF77n/jxo0qUKCA6tWrl2L/jRs39Pvvv8vZ2VlPPPFEptRCIAEASElGs77aeEnfbr9m01ejlLsmdAlSkQIUygQAAIAtEgAZ8NdffykmJkY1a9aUu7u7vcPJN0gAAP9Z/88Njf4xQnGJ1l/hRb2d9FWXID1Sku8mAAAAWCMBgFyDBABg7VjE7boAYddt6wK808pfL9UuaKfIAAAAkBORAHhACQkJCgsLU0xMjFJ6KcuXL29TJwAZQwIAsBUZnaT/LQrXjpPRNn3UBQAAAMCdSABkUEhIiIYOHaq1a9cqLi4u1XE5uU5AbkMCAEhZktGsrzdd1pxtV236qpdy11edi6uId+o1QAAAAJA/ONg7gNzo+vXratCggZYtWyZ/f39Lpf9atWpZquZXr15dTZs2lZeXlz1DBZAPODkaNKx5UX3eqbjcna2v9v99LlbtJp3V3+dsVw4AAABA/kICIAOmTp2q8+fPq2HDhjpx4oTlyvS8efN07tw5NWzYUBcvXtTkyZMtfQCQ1Zo/7K0F/YJVopD11f7LUUnqMfOcluy+nsqWAAAAyA9IAGTA5s2bJUkDBgywWVrPx8dHkydPVkREhAYNGmSP8ADkYxUC3bRoQGnVK+dp1Z5klMauuKAxyyOUkGSyU3QAAACwJxIAGXDx4kVJUrly5SRJjo6OkqSkpCRJUrVq1eTr66uNGzcqJobbbgFkL18PR03rWUK9nixs07dsT6R6zjivSzcTU9gSAAAAeRkJgAwoVKiQpNsrAEhSgQIFJEnXrl2zjPHx8ZHRaNSlS5eyP0AA+Z6jg0FvNCuqLzsXl7uLdV2Af0Ji1W7SGf11lgQlAABAfkICIAOSr/yfPn1akixV/vfv3y/pdiIgLCxMBoNBRYsWtU+QACCpaTVvLUyhLsCVKKNennVOi/+8nuISpgAAAMh7SABkQOvWrSVJa9askSS1adNGkjRu3Dh988036tSpk5KSklSnTh15eHjYK0wAkCSVC3DT4oGl1aC8bV2A91dSFwAAACC/MJi59JNuRqNRXbt2ldFo1Lx58+Ti4qL27dtr2bJlljGenp767bffVKdOHTtGmrckr6gQGhpq50iA3MloMmvSz5c1Y8tVm76HS7jpqy5B8vdxTmFLAAAA5AUkADKJ2WzWunXrtHfvXnl7e6tdu3YqUaKEvcPKU0gAAJlj06GbGrU0XLEJ1l//hb0cNaFLkGoGc+cSAABAXkQCALkGCQAg85y8GK/X54Xo/FXr1QCcHKSRLf3V8fGCMhgMqWwNAACA3IgaABnw/PPPq3Llyjpz5oy9QwGADHnI31WLB5TWExXuqgtgksatvqjRP0YoPpG6AAAAAHkJCYAMCAsL09GjRxUXF2fvUAAgw7zdHTW5ewn1aVzYpm/FvhvqPuOcLtxITGFLAAAA5EYkADLgoYcekiSdO3fOzpEAwINxcDBo0LNF9XXX4vJwsf4v4VBonNpPOqO9Z2LsFB0AAAAyEwmADHj55ZclSQsXLrRzJACQOZpU8dbC/sEK9nOxar96y6hXZ53Tgh3XRMkYAACA3I0EQAY0b95cH330kRYsWKDXX39d//77rxITuU0WQO72kL+rFg0I1pMVvazak0zSR2su6u1l1AUAAADIzVgFIAMqVqyof//9N01jjx49qooVK2ZxRPkDqwAA2cNkMmvKr1c09bcrNn1Virvp665BCvR1tkNkAAAAeBBO9g4gNwoICNCtW7fSNNbZmT+SAeQuDg4GDXymiCoVc9NbS8MVHf/fVf/DYbfrAnzRubjqlPG8x14AAACQ03AHAHIN7gAAst+pS/EaPD9UZy4nWLU7OkhvPuevLvUKymAw2Ck6AAAApAc1AAAAqSpb1FUL+wercSXrugBGk/Tx2osatTRCcdQFAAAAyBVIAAAA7qmAm6Mmdg3SgKf9bPpW77+hbtPOKTySQqgAAAA5HQkAAMB9OTgY1L9JEX3TLUhertb/dRwJv10X4M9T0XaKDgAAAGlBAgAAkGZPVS6gRQOCVaaIi1X79WijXptzXnN/vyZKywAAAORMJAAAAOlSusjtugBPVbatCzB+3UWNXBKu2ATqAgAAAOQ0JAAAAOnm5eaor7sEaWATP929CMDav2+q2/SzCruekPLGAAAAsAsSAACADHFwMKjf00U0KYW6AEfD49V+0lntOkldAAAAgJyCBAAA4IE0qlRAiwcEq0xR67oAkTG36wJ8t/0qdQEAAAByABIAAIAHFlzEVYv6B6tJlQJW7Saz9Nn6S3pzMXUBAAAA7M1g5rLMPUVFRcloNGZ4e29vbzk4kGfJDEFBQZKk0NBQO0cCIDUmk1kzt17VNz9f1t3/u1QIdNXErkEKKuSS8sYAAADIUiQA7qNixYr6999/M7z90aNHVbFixUyMKP8iAQDkHtuO3dKbi8MUFWd91d/H3VFfdC6uug952ikyAACA/MvJ3gHkdJUqVZKbm1uGt3+QbQEgt3qiopcWDyit1+eF6NSl/1YDuBFrVO855zW0WVH1bFhIhruXEAAAAECW4Q4A5BrcAQDkPtHxRr29LEI/H4qy6Wv+sLfefzFQHi5MkwIAAMgO/NUFAMgynq6OmtC5uIY0LaK7L/ZvOHBTXaaeVci1hJQ3BgAAQKYiAQAAyFIGg0GvNfLT1B4l5O1m/d/O8Qvxaj/pjP44fstO0QEAAOQfTAG4j+RVAO6s5p+elQFYBSDzMAUAyP3OXUnQ4PmhOnEx3qrdwSANaVpUrzxBXQAAAICsQgLgPpJXAbizmn96VgZgFYDMQwIAyBui400a/WO4Nh60rQvQrFoBffBSMeoCAAAAZAFWAbiP5FUA7qzmn56VAVgFAACsebo66ItOxVWl+DV9tfGSTHekoX86GKXTl8/q665BKlnYxX5BAgAA5EHcAYBcgzsAgLznj+O3NHxRmG7Gmqzavd0c9GnH4mpYwctOkQEAAOQ93GMJALCb+uW9tHhAaZUPcLVqvxlnUr/vQzRzyxWRpwYAAMgcJAAAAHZVsrCLFvQLVrNqBazazWbpq42XNfSHMEXHm1LZGgAAAGnFFIAHlJCQoLCwMMXExKR4lap8+fJycWEea2ZgCgCQt5nNZn27/Zom/GRdF0CSHvJ31cSuQSrlx/cpAABARpEAyKCQkBANHTpUa9euVVxcXKrjWAUg85AAAPKHHSduafjCcN2ItV5ulboAAAAAD4YpABlw/fp1NWjQQMuWLZO/v788PT0lSbVq1ZKPj48kqXr16mratKm8vOz7h2pYWJjmz5+v+fPn68aNG6mOu3btmtavX6/ly5ffd4nDXbt2admyZTp58mSqY86cOaP58+crOjo6w7EDyJ/qlfPSkoHBqdYFmL6ZugAAAAAZQQIgA6ZOnarz58+rYcOGOnHihOXK9Lx583Tu3Dk1bNhQFy9e1OTJky199vLyyy+rW7du6tatm0JCQlIc89FHHykwMFAtWrTQiy++qIoVK6pFixaKjIy0GmcymdS6dWu98MILmj17tqpVq6YvvvjCZn9ms1ldunTR4sWLLckRAEiPoEK36wI894i3VbvZLE3cdFlDFoQpOt6YytYAAABICQmADNi8ebMkacCAAXJ2drbq8/Hx0eTJkxUREaFBgwbZIzyLefPmacuWLapQoUKqY6ZNm6a3335bSUlJevLJJ9W6dWv5+Pho/fr16tixo9XYH374QWvXrtX27du1YcMGffnll3rrrbd09uxZq3EzZszQP//8o0mTJmXFYQHIJzxcHPRph2L633NF5WCw7vvlcJQ6TTmrc1cS7BMcAABALkQCIAMuXrwoSSpXrpwkydHRUZKUlJQkSapWrZp8fX21ceNGxcTE2CXGq1evaujQoXrzzTdTrUGQmJio0aNHy2Aw6Mcff9SWLVu0YsUKHT16VCVLltTGjRstyQ5JWr58uWrXrm1JKPTo0UNJSUlas2aNZcylS5f01ltvacyYMSpVqlTWHiSAPM9gMKhnw8Ka+UpJ+Xo4WvWdupSgDpPPaOuxKDtFBwAAkLuQAMiAQoUKSbq9AoAkFShwe+mqa9euWcb4+PjIaDTq0qVL2R+gpKFDh6pw4cIaPXp0qmO2bt2qK1eu6JlnnlHr1q0t7YGBgXrnnXckSUuXLrW0h4SEqFixYpbHHh4e8vX1tZpaMHToUBUvXlxDhw7NxKMBkN89/pCnlgwMVsVA67oAUXEmDZgbqmm/XZHp7qUDAAAAYIUEQAYkX/k/ffq0JFmusO/fv1/S7URAWFiYDAaDihYtmu3x/fbbb5o/f75mzpwpV1fXVMft27dPkvTss8/a9DVr1sxqjCQVLlzYqpCg2WxWVFSU/Pz8JEm//vqrfvjhB02fPl1OTk6ZciwAkKx4QRfN7xusltVt6wJ88/NlDVkQqltx1AUAAABIDWdpGdC6dWvNmjVLa9asUefOndWmTRt9//33GjdunBwcHLR27VolJSXpsccek4eHR7bGFhcXpz59+qh3795q2LDhPceGhYVJksqUKWPTFxQUJBcXF6sl9+rVq6fPP/9c0dHR8vT01JYtW5SUlKS6desqPj5e/fv3V69evVSvXj1FRkZq27ZtcnZ2VqNGjeTu7p6m+O9VNDEiIkKBgYGWqRYA8h9nB2lc26KqGOiiCRuvyGj6r+/XI7fUcfIZfdW5mIKLuNgvSAAAgAeUVRdUSQBkQLNmzdSxY0cZjUbFx8frhRde0EsvvaRly5ZZCv95enpq4sSJ2R7b+++/r9jYWI0fP/6+Y5OX6EupUr/BYJCnp6fVMn6DBg3SjBkz9MILL6hVq1aaMGGCWrZsqYYNG+q9997T9evX9cknn+jPP/9U8+bNVaFCBUVFRSk6OlpbtmzJlJoAJpPJZnUCAPnP85UNKu7lo/fW3tSN2P9u/T9zJVGdp53X288VUP2yqd8BBQAAkJMl32Wd2UgAZICjo6MWLlxo1bZkyRKtW7dOe/fulbe3t9q1a6cSJUpka1yHDh3S559/rqVLl8rb2/u+45OnByTXMrhbfHy8vLy8LI99fX21d+9eTZkyRf/884+GDx+uXr166cSJE/rkk080c+ZMFSpUSAMGDFD9+vW1Zs0axcfHq2bNmho5cqTNa5aSO+84uFvy3QG+vr733Q+AvO8pX6liyYJ644cIHYuIt7RHJ5g1auVN9WtcSL0bFZLD3UsIAAAA5FMkANKgYsWK+vfff3X06FGrivrr16/XzZs31aJFCxUoUEAtW7ZUy5Yt7RZn3759VbJkSUVFRWn+/PmW9uST6rVr1+rvv/9W69at5eXlpSJFikiSwsPDbfYVGRmpmJgYlS5d2qo9ICBA77//vlVbv379VK9ePXXr1k3nz5/Xvn37tGjRIkm3kwwdO3bUhx9+KLPZLIPhwf8Qp74AgGQl/Zy0oF+w3lseoTV/37Tqm7r5mo5dSNAn7YvJy80xlT0AAADkH5xJPYChQ4emmBiwl7///lvR0dHq1q1biv1vvfWWJOnEiRN66KGHVKVKFUnSjh071Lt3b6uxf/zxhySpatWq93zO+fPna/v27Tpw4IAkWVYEuHO1gGLFiikuLk6XL1+2S1FEAHmbm7ODPm5fTFWD3PXp+otWdQE2H72ljlPOamLXIJUpypQAAACQv5EAyEM6duyouLg4m/atW7cqNDRULVu2lI+Pj2XZwmeeeUbOzs5aunSpxowZY7nabzKZ9Nlnn0mSWrRokerzXb9+XcOGDdPIkSNVoUIFSbdXCpBktVrAjRs35OjoqIIFC2bOgQLAXQwGg7rWL6Tyga4a+kOYrkf/txrAmcsJ6jjlrD5pX0xPVS5gxygBAADsiwRAHjJr1qwU21u3bq3Q0FB9/PHHVlf0CxYsqF69emnq1Klq0KCBXn/9dRUsWFCLFi3S1q1bFRwcrPbt26f6fCNHjpS3t7flzgJJKlu2rPz9/bV582bLdIhffvlFtWrVkrOzcyYdKQCkrE4ZTy0ZWFpD5ofqcNh/CdHoeJNenxeqfk/5qf/TftQFAAAA+RIJgHzus88+0+HDh7Vt2zarE/kiRYroxx9/tBQKvNvOnTs1a9Ys/fTTT3Jzc7O0Ozs7a9y4cerXr58KFCiga9euadOmTfr555+z/FgAQJKK+Tprbp9SGrviglbvv2HVN/W3KzoSHqfxHYqpAHUBAABAPkMCIB9o1KiRvLy8Uqye7+npqc2bN2v58uXaunWr4uPjVblyZXXt2vWeS09s375dY8eO1TPPPGPT16tXLwUFBWnVqlVydnbWjh07VLt27cw8JAC4JzdnB33ULlBVg9w0fp11XYCtx26p4+SzmtgtSGWpCwAAAPIRg9lsNt9/WP6WvArAnDlzrJb2e+WVVxQSEmLTfqd69erJw8Mju0LN05KXAbzXUoEAcLc9p6M19IcwXbujLoAkebg46JP2xfR0FeoCAACA/IEEQBokJwAyIqesEJAXkAAAkFERkYkasiBUh0JtC6X2aVxYA5sUoS4AAADI85gCkAZVq1aVl5dXhrZ1d3fP5GgAAOkV6Ousub1L6f1VF7Ryn3VdgOmbr+poeJzGdygub3fqAgAAgLyLOwCQa3AHAIAHZTabtWjXdX2y9qKSTNZ9pQq7aGK3ID3kT10AAACQNznYOwAAALKLwWBQp7qFNKdXKRX2sr7af+5qgjpNOaufD920U3QAAABZiwQAACDfqVnaQ0sGlla1IDer9pgEk4YsCNPXGy/JaOIGOQAAkLeQAAAA5EsBPs76vncpta3lY9M3Y8tVDfg+RDdijSlsCQAAkDuRAAAA5Fuuzg56v22gRrcKkNNd/yNuPx6tDpPO6MQF25UDAAAAciMSAACAfM1gMKjj4wU15zXbugAh1xLVaepZbTxIXQAAAJD7kQAAAEBSzWAPLR1YWo+UsF6+NTbBrKE/hGnCT9QFAAAAuRsJAAAA/p+/j7O+611SL9b2tembtfWq+n0XosgY6gIAAIDciQQAAAB3cHG6XRdgTJsAOVnPCNAfJ6LVcfIZHacuAAAAyIVIAAAAkIL2dQrqu9dKya+AbV2AzlPO6qcD1AUAAAC5CwkAAABSUaPU7boA1UveVRcg0axhC8P0xQbqAgAAgNyDBAAAAPdQ1NtZ375WUu3r+Nr0zdl2VX2pCwAAAHIJEgAAANyHi5ODxrQJ1HttAuTsaLDq23EiWh0mndGxCOoCAACAnI0EAAAAadSuTkF937uUihRwsmoPvZ6oLlPPav0/N+wUGQAAwP2RAAAAIB0eKemupQODVaOUdV2AuESz/rcoXJ+vv6gkI3UBAABAzkMCAACAdCri7aw5vUqpw2O+Nn3fbr92uy5AdFL2BwYAAHAPJAAAAMgAFyeD3m0dqPfbBtrUBdh5MlrtJ5/V0XDqAgAAgJyDBAAAAA/gxdq+mtu7lPy9resChF1PVNdpZ7X2b+oCAACAnIEEAAAAD+jhku5aPLC0Hg22rQswYnG4Pl1HXQAAAGB/JAAAAMgERQo4afarpdTp8YI2fd//fk29vz2v69QFAAAAdkQCAACATOLiZNA7rQI07sVAuThZ1wX481SM2k86qyNhsXaKDgAA5HckAAAAyGRtaqVcFyA8MlFdp53T2v3UBQAAANmPBAAAAFmgWgl3LRlYWrVKe1i1xyeZNWJJuD5ZS10AAACQvUgAAACQRfwKOGnWqyXVpa5tXYB5f1xTrznnde0WdQEAAED2IAEAAEAWcnY0aNQLAfrwJdu6AHtOx6jdpDM6TF0AAACQDUgAAACQDVrX9NW8PqUU4GNdF+DCjSR1m3ZOq/+iLgAAAMhaJAAAAMgmVYNu1wWonUJdgLeWhuvjNReUSF0AAACQRUgAAACQjQp7OWnmqyXVtZ5tXYD5O67rtdnndZW6AAAAIAuQAAAAIJs5Oxr01vMB+rhdMbneXRfgTIzaTzqjQ6HUBQAAAJmLBAAAAHbywqM+mt+3lAJ9U6gLMP2cVuyLtE9gAAAgTyIBAACAHVUu7q4lA0qrThnrugAJSWa9syxCH66mLgAAAMgcJAAAALCzQl5OmvlKSXWvX8im74ed1/XqrHO6EkVdAAAA8GBIAAAAkAM4ORo0oqW/xre3rQuw72ys2k86owMh1AUAAAAZRwIAAIAcpGUNHy3oF6xivs5W7RdvJqn79HNavjfSPoEBAIBcjwQAAAA5TKVibloyMFiPl7WuC5BoNGv0jxH6YNUFJSRRFwAAAKQPCQAAAHKggp5Omv5ySfVsaFsXYNGu63p19jldpi4AAABIBxIAAADkUE6OBv3vOX992qGY3Jyt6wL8lVwX4Dx1AQAAQNqQAAAAIIdrUf12XYDiBa3rAly6maTuM87pxz2R9gkMAADkKiQAAADIBSoGumnJgGDVfcjTqj3RaNa7yyP0/soI6gIAAIB7IgEAAEAu4evppGk9S+jlJ2zrAiz+M1IvzzqnyzcT7RAZAADIDUgAAACQizg5GjS8ub8+71Rc7nfVBfj7XKzaTTqrv8/F2Ck6AACQk5EAAAAgF2r+sLfm9wtW0F11AS5HJanHzHNasvu6nSIDAAA5FQmAPCw2NlZmc9rng5pMJsXHx6dpbFxc3D37k5KSFBkZKaPRmObnBwCkT8VANy0eWFr1ylnXBUgySmNXXNB7KyKUkGSyU3QAACCnIQGQh0RFRem7775T06ZNVaRIEXl4eMjNzU0NGzbUypUrU91u9+7daty4sVxdXeXm5qZy5cpp6tSpKY6dOXOmihQpIi8vL1WpUkW7du1KcdyAAQNUp04dJSWxRjUAZCVfD0dN61lCrz5Z2KZv6e5I9ZxxXpeoCwAAACQZzOm5RIwcbdasWXrttdcsj11dXa2u6E+YMEFDhgyx2mbXrl1q1KiR4uPjZTAY5OzsrISEBEnSmDFj9N5771nG7tmzR3Xq1NHcuXPVoUMH9evXT+vWrdPp06fl4eFhGbdz5041aNBAGzduVJMmTTLt+IKCgiRJoaGhmbZPAMhLfjpwU+8sC1dsovV/7X4FHPVVlyDVKOWRypYAACA/4A6APMTb21s9e/bUpk2bdOnSJcXFxenixYvq06ePJGnUqFG6deuW1Tb9+/dXfHy8+vbtq8jISMXGxmrx4sVydXXVhx9+qJMnT1rGTpkyReXLl1e3bt3k4uKisWPH6uLFi1q6dKllTFJSkvr06aOOHTtm6sk/AOD+mj3srR/6B6tEIeu6AFeijOo585yW/Hk9XVPDAABA3kICIA9p3769vv32Wz3zzDMqUqSIJKlo0aKaOnWqHn74YcXGxurYsWOW8f/884/279+vSpUqafLkyfL29paDg4Pat2+vYcOGKSkpSfPnz7eMP378uCpWrGh5HBQUpAIFCujEiROWtgkTJigkJERffvllNhwxAOBu5QNu1wWon1JdgJUXNGbFBeoCAACQT5EAyAcMBoOCg4MlSYUL/zdH9I8//pAkvfjii3JwsP4odOzYUZL0+++/W9qcnJxsivoZjUY5OTlJks6fP6+xY8fq448/lr+/f6YfBwAgbXzcHTW1Zwm91si2LsCPeyLVY8Y5XbxBXQAAAPIbEgD5wIULF/Trr7+qbt26Kl26tKX91KlTkqQqVarYbFOxYkU5OjpaTQGoVKmSDh48aLl99OTJk4qJiVGlSpUkSQMHDlS1atXUu3dvSVJiYiKrAACAnTg6GDSkaVF92bm43F0MVn0HQuLUbtIZ7TsbY6foAACAPTjZOwBkrYSEBHXq1Elms1nTp0+36ouKipIkFSxY0GY7Z2dneXl56ebNm5a2wYMHa9asWfroo4/UqVMnDRs2TKVLl1bbtm21YsUKbdiwQXv37tX169f18ssv66effpKDg4O6dOmiKVOmyNXV9b7xJhf6S0lERIQCAwNZWQAA0uHpSh4q1buE3vghQuev/XfV/+oto16ZeU5vPldE7ev4yGAw3GMvAAAgOyXfZZ3p+82SvSJHiI+PV4cOHbRjxw6tWLFC1apVS3FcagWhTCaT1QevUqVKWrlypd59911NnDhRjz76qDZs2KD4+HgNHjxYgwcP1iOPPKLOnTvrxIkTCgkJ0fXr19WgQQOVKFHCakWBjDKZTIqMjHzg/QBAfuLnKk3p5K0P1kfpzzMJlvYkk/TR2sv6+2yUhjztJVcnkgAAAOQEfn5+WbJfEgB51K1bt9S6dWv9/vvvWrFihZ577jmbMb6+vpKkK1eu2PTFx8fr1q1bVlMGJKlly5Zq2bKlVdvQoUMlSWPHjtW1a9e0ePFiTZ48Wf7+/vL391e3bt00efLkNCUA7rXEX/LdAclxAwDSzlfS1J4FNfW3q5q59bpV3/pDcTp33awJnQLl7+Oc4vYAACD3IwGQB129elXNmzfXwYMHtXLlSjVr1izFceXLl5d0ezWArl27WvUdOHBAZrPZMiY1f//9tyZOnKgff/xRnp6eOnLkiEwmkypUqGAZU6lSJV25ckWRkZGZcvKeVbfDAEBe5yRpSLMAVQny0KilEYpJ+G81gMNh8eo0LUQTOgepZmkP+wUJAACyDEUA85jQ0FA1bNhQhw4d0po1a1I9+ZekJ598UpK0ZMkSxcfHW/V99913kqTGjRunur3JZFLfvn3VsmVLtWrVStJ/J+d3Fv9LnrPPiTsA5AzPVPXWwv7BKlXYxar96i2jXpl1Tj/svJbq9DAAAJB7kQDIQ06dOqUGDRro1KlTWrBggWrVqqXIyEirn8TE/wpAlStXTo0aNdL58+fVrl07HTp0SGFhYfrss880bdo0eXh42NwZcKdp06bp0KFD+uabbyxtZcuWlYuLiw4cOGBpO3DggEqUKCEvL6+sOXAAQLo95O+qRQOC9WQF6+/mJJP04eqLeufHCMUnmlLZGgAA5EYGMyn+PGPcuHEaPXr0PcesWLFCrVu3tjw+duyY6tWrp+vXr9uMnTZtmvr06ZPifi5cuKCKFStq9OjRGjZsmFXfgAEDtGrVKq1cuVLXrl1T27Zt9dFHH2nQoEHpP6g7JNcAuFedAABA+phMZk3+9Yqm/WZbD6ZKcTd93TVIgb7UBQAAIC/gnuw8xM3NTT4+Pvcc4+xs/UdcxYoVtW/fPo0dO1Zbt25VfHy8KleurKFDh6ZYODDZ2LFjVbFiRQ0ePNim7/PPP5fBYFCbNm3k7OysESNG6PXXX8/YQQEAspSDg0GvP1NElYu56a2l4YqOv7MuQJzaTzqjLzsXV+0ynnaMEgAAZAbuAECuwR0AAJC1Tl2K16B5oTp7JcGq3dFBevM5f3WpV1AGA0sFAgCQW1EDAAAASJLKFr1dF6BRJeu6AEaT9PHaixq1NEJx1AUAACDXIgEAAAAsCrg56puuQRrwtJ9N3+r9N9Rt2jmFRyamsCUAAMjpSAAAAAArDg4G9W9SRN90C5Knq/WfCkfCb9cF2H062k7RAQCAjCIBAAAAUvRU5QJa1D9YZYq4WLVfjzaq1+zzmvfHNVFKCACA3IMEAAAASFWZoq5a2D9YjVOoC/DJ2ot6a0m4YhOoCwAAQG5AAgAAANyTl5ujJnYN0oAmtnUB1vx9U92mn1X4deoCAACQ05EAAAAA9+XgYFD/p4tocvcged1VF+BoeLzaTTqjXaeoCwAAQE5GAgAAAKRZo0oFtHhAsMoUta4LEBlj1Guzz+u77VepCwAAQA5FAgAAAKRLcBFXLewXrKcrW9cFMJmlz9Zf0ojF1AUAACAnIgEAAADSzcvNUV91CdKgZ4vIYLDuW/fPTXWddlah1xLsExwAAEgRCQAAAJAhDg4G9Wnsp8ndg1TAzfpPimMR8Wo/6ax2nqQuAAAAOQUJAAAA8ECerFhAiweUVtm76gLciDWq95zz+nYbdQEAAMgJSAAAAIAHVsrPRQv7B+uZqgWs2k1m6fMNl/S/ReGKoS4AAAB2RQIAAABkCk9XR03oXFyDU6gLsOHATXWZSl0AAADsiQQAAADINAaDQb0b+2lqjxLyvqsuwPELt+sC7Dhxy07RAQCQv5EAAAAAma5hBS8tGlBaD/m7WrXfiDWqz7chmr2VugAAAGQ3EgAAACBLlPJz0Q/9gvVsCnUBvvzpkoYvDKMuAAAA2YgEAAAAyDKerg76snNxvdHMti7ATwej1GXqWZ2/Sl0AAACyAwkAAACQpQwGg3o96adpPVOuC9Bh8hn9fpy6AAAAZDUSAAAAIFs0KO+lxQNLq3yAdV2Am7Em9f0uRDO3XKEuAAAAWYgEAAAAyDYlC7toQb9gNatmXRfAbJa+2nhZQ38IU3Q8dQEAAMgKJAAAAEC28nBx0Oedimtos6JyuKsuwKZDUeo89azOXaEuAAAAmY0EAAAAyHYGg0GvPln4dl0Ad+s/R05ejFfHyWe0/V/qAgAAkJlIAAAAALupX95LS1KqCxBnUr/vQzRjM3UBAADILAYz/6silwgKCpIkhYaG2jkSAEBmi0kw6d0fI7ThwE2bviZVCuijdoHydHW0Q2Rpc+nSRV2ICFdSUpIK+/mpZMlgGe5e9xAAkGvFxsbo33+PKiY6WlWqPCwfX197h5QhTvYOAAAAwMPFQZ91LKaqQW76YsMlme64PPHL4SiduRyvb7qVUCk/F/sFmYKbN29o1YqlunLlssqXryQXVxft3LldBQp4q02b9irg7W3vEAEADyAhIUFrVi/XhYhwFSpcWCHnz6lY8aBcmwBgCgAAAMgRDAaDejYsrBmvlJSvh/XV/lOXEtRh8hltPRZlp+hSdivqdjyv9uqvFs+31jPPPqdXXu2rW7eitHHjWjtHBwDIDA8/UkN9+w9W7Tp17R3KAyMBAAAAcpS6D3lqycBgVQy0rgsQFWfSgLmhmvbbFZlMaZ/BaDabteOPbYoID5PRaNTRI4e0+88dCgsNeeBYfXx91aFTd3n7+Fja3N099NBD5XX2zGmZTCxpCABZ7cKFCO34Y5uMRqNuREZq397d+nv/PkVHP3gxWRcXF5UrV0GOjjl3Glp6kAAAAAA5TvGCLprfN1gtHrG+hd5slr75+bLe+CFM0fHGNO3LbDbr9+1bdPbsaS1ftkhnz55RRES4Fsz/Vnt273qgOD09veTiYjstITY2VpKoAwAA2SAiIky/b9+i06dPau2a5bp+/Zr2/7VHc2ZN1Y3ISHuHl6NQAwAAAORI7i4OGt+hmCoXT7kuwOnL8ZrYNUili7imvpM7/P33PrXv0FWFC/tJkn77dZN27tim6jVqytnZWZJ06OA/unnzxn33VaXqw/Lx8U2x7/r1azp54l8FB5chAQAA2ej4v0fVqUtPOTg4KDExUTOmf6Pdu3fomWefk3S7kN/+v/bedz/u7u6q8WjtrA7XLrgDAAAA5FjJdQFmvmpbF+D0pQR1nHxWW46mrS5AyRLBlpN/SSpfoaLi4uJ09eoVS5vRaJTRmHTfn9QWUTIajVq3ZoUk6clGT6f3cAEAD+Dhh2vIweH2Ka6zs7NKly6rsLD/VhAzm5Wm7/ikpLTdYZYbcQcAAADI8R4v66mlA0tr0PwQHQ2Pt7Tfir9dF2BAEz/1bewnB4fUr7gXKlzY6rGnp9ftfdyKkhQoSXqk+qMZjtFsNmvd2pUKDw9Ti+fbqEhR/wzvCwCQfoXuSPJKt7/no2+dtDz28PBQwyeeyu6wchQSAAAAIFcoVtBZ8/sG673lEVrz902rvsm/XNHR8Dh93K6YvNxSLtSUfJt/MgfD7atE5jsK9T3IFIDfft2kY0cP6+kmTVWlSrW0HBIAIBPZfM87GKyKsTIFgCkAAAAgF3FzdtDH7YtpZEt/Od71V8xvR26p45SzOn0pPuWN0yCjUwB27fxd+/b+qfoNnlTNWo9l+PkBAFmHKQDcAQAAAHIZg8GgbvULqUKgq4b+EKbr0f/9oXbmcoI6TjmrT9oX01OVC6R73xmZAnDgn/3atvU31az1mOo3eDLd2wMAsgdTAEgAAACAXKpOGU8tGVhag+eF6kh4nKU9Ot6k1+eFqv/Tfur3lN899vDgLl6I0Maf1srd3UPu7u7a8cc2q/6atR6Tq2vaVikAAORMf+3brbi4OF27dlWSdOTwQYWHhcrdw0M1atSyc3TpQwIAAADkWsV8nTWvbymNXXFBq/dbz92f8usVHQmP08ftAlW3XgMFFitu1e/q5qa69RqoUCHr4oDp4eTsrMfr1pd0+7ZSAED2Cwi4/T3v6GhdA6ZEyWA5O7s88P6Tkm5PD/Px8VHdeg0k/f9Uglw4VcBgTm0dGyCHCQoKkiSFhobeZyQAIL8xm836Yed1jV93UUaTdV+wn4smdgtS2aJciQcA5G8UAQQAALmewWBQl3qFNPvVkirkaX0F6OyVBHWacla/Ho6yU3QAAOQMJAAAAECeUfv/6wJUKe5m1R4db9Kg+aH65ufLMpm4+REAkD+RAAAAAHlKoK+z5vUppdY1fWz6pv12RQPnhupmbO6btwkAwIMiAQAAAPIcV2cHjXsxUG+/4C+nu/7a2frvLXWcfFYnL8bbJzgAAOyEBAAAAMiTDAaDOtctpDm9Sqmwl3VdgHNXb9cF+PnQTTtFBwBA9iMBAAAA8rSapT20ZGBpVQ2yrgsQk2DSkAVh+nrTJRmpCwAAyAdIAAAAgDwvwMdZc3uXUpsU6gLM2HxVA74PoS4AACDPIwEARUdH64svvtALL7ygZ599Vm+88YaOHz+e4tijR4+qZ8+eatKkiUaMGKHIyMgUx33//fdq1qyZoqOjszByAADSztXZQR+8GKjRrQJs6gJsPx6tDpPP6MSFOEub2WzW5agknb4Ur8tRSTKbuUsAAPKS/Pg9bzDnh6NEqi5fvqwnnnhCx44ds2p3d3fXypUr9eyzz1raLly4oEqVKqlFixZq166d3nvvPXl4eOiPP/6w2vbChQuqWLGiRo8erWHDhmVarEFBQZKk0NDQTNsnACB/2nc2Rm8sCNXVW9ZX/d1dDHqvTaBiEkxatOu6/o34r1BghUBXdXy8oFrV8JGrM9dQACC3ik80adX+G/nye54EQD7XsWNHLV68WNWqVdOoUaNUsGBBLViwQPPmzVORIkV06tQpFShQQJI0cuRIzZ49W+Hh4XJ2dtaePXtUp04dbdiwQc2aNbPss1OnTjp69Kj27t0rJyenTIuVBAAAIDNdvJGoIQtCdSAk7v6D71C5mJum9iwhvwKZ938cACB7XIlKUr/vQnQkPPXv/rz8PZ830xpIk4iICC1ZskQFCxbUb7/9po4dO6pp06aaO3eu2rRpo8uXL2vBggWW8Xv37lXNmjXl7OwsSapdu7ZcXFy0b98+y5hNmzZpyZIlmjZtWqae/AMAkNn8fZz1fe9SerG2b7q2OxIep/7fhyg+0ZQ1gQEAskR8oum+J/9S3v6eJwGQj/36668ym83q0KGD/Pz8rPpef/11SdLGjRstbVFRUXJ3d7ca5+HhoZs3by+hFBcXp/79+6t37956/PHHszh6AAAenIuTg95vG6gxrQPkYEj7dofD4rR6/42sCwwAkOlW7b9x35P/ZHn1e55LtPnY0aNHJd2+kn+3OnXqWI2RpODgYJ08edLyODIyUpGRkSpdurQk6cMPP9StW7f08ccfZ2XYAABkunZ1fPXt9qs6fzUxzdu8v/KCPl9/UQZDOjIHAAC7MJvNiklI3+z3hbuu66Xavnnqe54EQD529epVSVJAQIBNn6enp7y8vHTt2jVLW5cuXdSqVSvt2rVLjz/+uCZPniwPDw+1adNGx44d06effqpvv/1WPj4+mjFjhlauXClnZ2f17NlTbdq0SVNMyfP8UxIREaHAwEAlJSWl80gBALi3K1FJ6Tr5lySTWboVb5ZEOSUAyIv+jYjXxch4u9QCyKrp1CQA8rHkE+nUPlxOTk5KTPzvj6EXXnhB/fv3V+PGjVW6dGmdP39e06dPl7+/vzp27KgnnnhCnTt31kcffaSPP/5Y33zzja5evap27dpp0aJFeumllx44ZpPJlOrSgwAAZFTYVZLLAABb4Zcj5WTM/tPmu6doZxYSAPmYl5eXJFnm8N/JZDLp1q1bNncHTJ48WaNGjVJ4eLgqVKggb29vff/999q5c6cOHjyouLg4ffrppxo6dKh69uwp6XbxwPfeey9NCYB7VfhPvjvA19c3jUcIAEDaJDkmSbpu7zAAADlMsSK+8s1DqwHknSNBupUsWVKSdOLECZu+M2fOKCkpSaVKlbLpK168uIoXLy5Junbtmv73v/9p1KhRKleunA4dOqQbN26obt26lvF169bV4sWLFRcXJzc3tweOm9UFAACZzd/XURUCXa3Wg76fcgGu+v61knlqbigA5FVms1k9ZpzXiYtp/56vGOgqf1/XPPU9z5lUPpZc6G/NmjV66623rPpWr15tNSY1b775pgoVKqQRI0ZIur1SgCSr1QI8PT1lNpsVFRWVKQkAAAAym8FgUMfHC2rsigtp3qZL3YLy8eBPKQDILTrXS9/3fMfHC+apk3+JZQDztXr16qlkyZLauXOnJk6caGk/dOiQPvzwQ0lS586dU93+jz/+0Jw5czR16lS5urpKur1SgCSdP3/eMu7s2bPy8vJSkSJFsuAoAADIHK1q+KhysbQlqqsUd9MLNXyyOCIAQGbie14ymM1mStfmYz/++KNlbn5wcLB8fX118OBBGY1Gde/eXd9//32K2yUmJurRRx9VjRo1NHfuXKu+p556SomJidq8ebOio6NVvXp1NW/eXFOmTHmgWJNrANyrTgAAAA/iSlSS+n8fosNhqa8TXaW4m6b0KGGXqtAAgAeT37/nSQBA8+fP1/Dhw3Xx4kVJkouLi1599VVNmDDBcmX/bp988ok+/fRTHTt2TEWLFrXq+/fff9WiRQvFx8crLi5OZcuW1bp161S4cOEHipMEAAAgO8QnmrR6/w0t3HXdqiZAxUBXdXy8oF6o4SNXZ26iBIDcKj9/z5MAgCTJaDTq2LFjio+PV9myZeXjc+/bXf766y95eXmpfPnyqe7v0KFDcnZ2VuXKlTMlRhIAAIDsZDabdeWWUbfijPJyc5Sfl2OemwsKAPlZfvyeJwGAXIMEAAAAAABkXN68rwEAAAAAAFghAQAAAAAAQD5AAgAAAAAAgHyABAAAAAAAAPkACQAAAAAAAPIBVgFAruHi4iKj0ajAwEB7hwIAAAAAWSYgIEB79+7N9P06ZfoegSzi7Oxs7xBsRERESBJJCQDIo/ieB4C8Lb99z3MHAPAAgoKCJEmhoaF2jgQAkBX4ngeAvC2/fc9TAwAAAAAAgHyABAAAAAAAAPkACQAAAAAAAPIBEgAAAAAAAOQDJAAAAAAAAMgHSAAAAAAAAJAPsAwgAAAAAAD5AHcAAAAAAACQD5AAAAAAAAAgHyABAAAAAABAPkACAAAAAACAfIAEAAAAAAAA+QAJAAAAAAAA8gESAAAAAAAA5AMkAAAAAAAAyAdIAAAAAAAAkA+QAEC+FxYWJqPRaO8wAABZwGQyKSQkxN5hAACyyOXLlxUbG2vvMHINEgDI1/766y+VK1dOfn5+6tChg9auXSuTyWTvsAAAmWTIkCEqWbKkqlSpopEjR+r06dP2DgkAkEmuXLmiWrVqqVChQmrWrJm+/fZbxcXF2TusHI0EAPK1Rx99VHv37tWgQYO0detWPf/88ypXrpzmzZtn79AAAJng888/1+rVq1W5cmV99tlnKleunFq3bq3jx4/bOzQAwAPy8/PTzp07NX78eJ0/f16vvPKKgoKC9OGHHyo+Pt7e4eVIBrPZbLZ3EEBOEBcXp9mzZ2vMmDG6evWqGjRooB9++EElSpSwd2gAgExw4sQJjR07VgsWLJCLi4vefvttjR49WgaDwd6hAQAekNls1sqVKzVy5EgdP35cZcuW1fz58/X444/bO7QchQQAcJfLly9r4MCBWrJkiQICArRmzRrVqlXL3mEBADLJL7/8oldeeUUhISF66aWXNG/ePLm5udk7LABAJoiPj9f777+vTz75RC4uLvr222/VsWNHe4eVY5AAQL5nNpt15coVFSlSxKr9gw8+0LvvvqvChQvrzz//VNmyZe0UIQAgs124cEHPP/+89u7dq06dOumHH36wd0gAgEy0fPlyde3aVQkJCVq3bp2aNm1q75ByBGoAIF+5deuWOnbsqFOnTkm6ffI/YMAA1alTR+fOnbMaO3r0aH3++ee6evWqnn/+eeYRAUAeEhAQoM2bN6tGjRpauHChPvvsM3uHBADIRG3bttXq1avl4OCg9u3b6+zZs/YOKUcgAYB8ZePGjVqyZIkaNWqkkydPasCAAZo6daoiIiJ04sQJm/HDhg1Tnz59dPToUX3++ed2iBgAkFW8vLy0du1aFStWTO+9957Onz9v75AAAOkQGxurH3/8UeHh4Sn2N2nSRFOmTNHNmzf1xhtvZHN0ORNTAJDvTJkyRQMHDpSrq6vi4uLk6uqqVatWpXpbUGxsrKpWraoLFy7o/PnzKly4cDZHDABIL6PRqJUrV+rPP/9UgQIF9MILL+iRRx5Jcezq1avVqlUrde/eXd9//302RwoAyIhVq1apT58+unjxosqXL6+tW7cqICAgxbHPP/+81q5dq23btqlhw4bZHGnOQgIA+Y7ZbFajRo20bds2SdL06dPVu3fve27z008/qXnz5vr44481cuTI7AgTAJBBYWFhatWqlfbt22dpMxgM6t+/v7766is5OTnZbNO6dWtt2LBB58+fl7+/f3aGCwBIp/nz56tHjx4ymUx69tln9eabb6pRo0ZydHRMcXxISIjKly+vFi1aaNmyZdkcbc5CAgD5zvHjx1WzZk0lJCQoMTFRxYsX15YtW+5b5O+xxx7T5cuXdfr06WyKFACQXrdu3dLjjz+uw4cPKzg4WO+9956CgoI0ePBgHT58WG3atNGyZcvk4GA9C3L//v169NFHNX78eL355pt2ih4AcD8nT55UtWrVFBcXp3fffVfvvfeeZTlXs9mshIQEubq62mw3YMAATZ8+XeHh4SpatGh2h51jUAMA+U758uW1fv16rV69WpMmTVJYWJgaNWpkKQyYbMOGDVaFAd98802dOXNGR44cye6QAQBpNH78eB0+fFgVK1bU3r171aNHD129elXHjh2TJFWpUiXF7WrUqKEmTZpo7dq12RkuACCdpk6dqri4OLVr105jx46VwWBQbGysPvjgAxUrVkzu7u565ZVXZDQarbYbPny4TCaTfvrpJztFnjOQAEC+1LBhQzVt2lT9+/e3SgIkFwJcsWKFWrdurUaNGunq1auSbs8d8vHx0ebNm+0ZOgDgHhYsWCBJWrRokQoXLqxly5apS5cuMhqNeuedd/TBBx+oc+fOVtMDknXp0kW7du1SbGxsdocNAEijS5cuSbo9dUuSdu/erRo1amj79u2aOnWq6tSpo2+//VaLFy+22q506dKqV69evv9b3nYSHJAHzZ8/X99//71MJpNefPFF9enTxzJHqH///pKkgQMHqk6dOnryySe1Zs0amUwmtW7d2lL0z8XFRePGjVP37t3tdhwAgHsLCwtTpUqV9Mgjj+jHH39Up06dlJSUZDn5P3r0qJYsWaKTJ09q7969Vtu2bt1asbGxcnd3t1P0AID76d69uxYsWKBRo0Zp3rx52rVrlz777DP16tVLkrR+/Xr9+eefunDhgs22gwcPVo0aNbI75ByFGgDI80aMGKFPP/3Uqq1hw4ZatWqVChYsaGmbN2+e+vTpo9jYWDk4OOjdd9/VmDFjsjtcAMA9HDx4UFWqVLHM4T937pymT5+uDz/8UAaDQeXLl9e1a9f01Vdf6eWXX7Y6+Y+Li1O9evW0f/9+TZgwQUOGDLHvwQAA7mvmzJnasmWL5s6da7mA9+uvv2ru3LkqW7as+vbta5nTv2/fPtWvX18JCQk6cOCAqlatas/QcyQSAMjT9u7dq9q1a8vZ2Vlvv/22HnvsMc2ZM0dLly7Vo48+ql9++cUqCRASEqJffvlFderUSXWeKADAPvbs2aNGjRqpTZs2mjt3rkJCQvTkk0/q3LlzllVaxo0bp9GjR1u2ST75j4mJUefOnbVq1SpLfQBPT087Hg0A4H7MZrOef/55rVu3Th07dtT8+fNTrfS/ceNGdenSRVevXlWfPn00bdq0bI42dyABgDzJbDbrypUrmjlzpt5++21999136tGjh6V/5MiRGj9+fIpJAABAzhQTE6OWLVtq8+bNevHFF7V3716dO3dOjz32mDZu3CgfHx/FxsaqYcOG2rdvn8qWLauFCxfq7NmzGjt2rA4fPiwfHx/t2LFDlStXtvfhAADSID4+Xm3bttX69etTTALs3btX33zzjebPny+TyaQXXnhBS5culYuLix2jzrlIACBP6t+/vzZs2KDmzZvrt99+s1R/vhNJAADIfWJiYtS8eXNt27ZNkqxO/pNduXJF7dq105YtW6y2rVq1qhYuXMgtoQCQy9wrCfDBBx/o3XffVeHChTVq1Ci98cYblmUBYYsigMiTypUrp6lTp2ratGlq0KBBimM++eQTSbeXjGrSpIm2bdvG7aAAkMPFxsZaLdFapkwZFShQwGqMn5+fNm/erI0bN1qSAHXr1tVzzz0nJyf+9AGAnMxkMlnqvCRzdXXV8uXL1bZtWy1atEiSLEmA0aNHq379+qpbty5FXNOAOwCQZ02YMEFDhw6Vi4uL9u7dq2rVqqU4buTIkYqPj9eECROyOUIAQEYMGTJEu3btkoeHhzZv3qwuXbpo7ty5Vn8wJiUlydHRkatAAJCLzJw5UwsXLtTatWvl4eFh0x8dHa1HHnlEp06dum9NAKSMBADytOQkQHBwsLZs2aJSpUrZOyQAQCaIiYmRJEtNgDuTAElJSerUqZO8vb01a9YskgAAkEv07NlT33//vZ588kmtX78+xSTA8OHD9dVXX8loNJIEyACH+w8Bcj6z2aywsDBFRUVZtb/xxhv68ssvdfbsWTVq1MjqtlEAQO7l4eEhDw8PrV27Vo0bN9aCBQvUrl07/fXXX2rVqpWWLVumH3/8USdPnrR3qACANJozZ466deumrVu36rnnnrMke+8UHh6uZs2a6bnnntPVq1eVmJhoh0hzLxIAyPVmzZqloKAgBQUFycfHR88++6yOHDli6ScJAAC5V0xMjMaMGaOqVauqevXq+uyzz5SUlGTpT04CPPPMM1q+fLlq1qyp9evXq1ChQtq0aZPKlStnx+gBAOnh4OCg7777zioJEB0dbenfvXu3li1bJnd3dy1fvlyrV6+Wm5ubHSPOfZgCgFxtxIgR+vTTTyVJNWvWVEBAgDZu3Cg3NzetWLFCTZo0sYy9czrA3r17VbhwYXuFDQBIg7CwMDVt2lSHDx+Wo6OjihYtqoiICDVv3lzLly+3+qMvMTFRH330kebPn6/KlSvriy++0EMPPWTH6AEAGWUymdSzZ0/NmzdPVatW1ahRo3Tx4kW99957unHjhmbOnKlevXrZO8xciQQAcq3Vq1erVatWcnZ21pw5c9S1a1ddunRJDRo00IkTJ1SrVi3t3r3bau7nhAkTdP78eQr+AUAOl5iYqIYNG+rPP/9U5cqVNW/ePL377rtat26dJOmZZ57hyg8A5GL//POPPv30U+3fv18FChRQy5Yt9cYbb8jLy0vS7STA66+/rilTplht17JlS61atcpmpQCkDQkA5Fr16tXTzp079c0332jgwIG6fPmyGjdurMOHD6ty5cravHmzdu3apRYtWlAYBAByma+//lpDhgxR+fLltW3bNr3yyitav369ChcurBo1auiXX34hCQAAudS8efP06quv2szfL1OmjNavX68KFSpY2tavX6+ZM2fq5s2bev755zVw4ECWdH0AJACQK5nNZrm4uMjf31/nzp3T9evX9dRTT+ngwYOWk38PDw8VL15cbdq00XfffWfvkAEA6VCxYkWdPn1ae/bs0VdffaXvvvtOhQoV0m+//aaSJUuqRIkSio6OJgkAALnM77//rsaNGyspKUndu3dX//79ZTQaNXHiRC1evFglSpTQvn37VKRIEXuHmieROkGuZDAY5OzsLD8/P0VGRurpp5+2OvkvWrSoRowYoZs3b/JHIQDkErdu3bLc+tmkSROFhobq4sWL+u677+Tu7q7169frkUcekSQFBQUpKipKP//8s7p27aply5bZM3QAQBq99dZbSkpK0sCBA/XNN99Y2uvVqycfHx/NmDFD77zzjqZPn27HKPMu7gBArvXcc89pw4YNKlu2rE6dOmV18r9s2TK1b99eTk5OOnDggCpWrGjvcAEA93D58mU9/vjj6t27t0aMGCFJOn/+vEaNGqUFCxbo888/17BhwyRJFy5cUFBQkN5//32dPn1ar776qurWrWvP8AEAaZCUlCRXV1eZTCZduXLFpij3tWvX5OfnJy8vL924ccOqlhcyB5UTkGu9//77cnJy0qlTp1SuXDlt3rxZhQoV0hdffKGOHTvKbDbryy+/5OQfAHKBvn376vTp01q2bJni4+MlSSVLlrQs/1SvXj1Jt4sD9ujRQ0ajUSVLltSsWbM4+QeAXCT5Tt7kO77u5O3tLVdXV0VFRVkt/4fMQwIAOd6uXbv05ZdfatWqVVaFQmrVqqXZs2fLyclJp0+fVtu2bVW6dGkNHz5cRqNRo0aN0sCBA+0YOQDgfhITExUdHa2VK1eqfPny2rRpk1xdXS39L774oiRpyJAhmjhxourVq6dNmzZZarwAAHIPJycnPfHEE0pMTNTixYtt+levXq24uDgVL148xQQBHhwJAORob775purWrathw4apdevWqlatmg4ePGjp7969u7Zt26annnpK+/bt09WrV9WkSRP98ssv+vDDD+0YOQDgfs6fP69q1app6dKlMplMGjdunAoWLGg1pmvXrurdu7d2796twYMHa+/evSpcuLBWrVolT09PO0UOAMiocePGydHRUQMHDtSaNWss7b/99pt69+4tSVzEy0LUAECOtXr1arVq1UoODg566aWXZDQatXz5cnl7e2vTpk2qU6eOvUMEADyAffv2qUmTJoqOjpbRaNTWrVvVoEGDFMeuXbtWmzZtkr+/v3r16iV/f/9sjhYAkB4XLlzQzp07FRQUpNq1a1v1zZw5U3379pXJZFK5cuXk5OSkY8eOyWw2q0WLFlq1ahXLeGcREgDIsdq3b6+lS5dqyZIlateunaTbSYH27dvLzc2NJAAA5AF79uzRs88+q8jISA0ePFhfffWVvUMCADygBQsWqFevXoqLi5MkPfbYY5o/f74eeughy5gNGzZo0KBBOnnypCTJ1dVVgwYN0ocffihnZ2e7xJ0fkABAjtWsWTM5Ojpq3bp1Vu0//fSTWrduTRIAAPKI5CTAjRs3tGzZMrVt29beIQEAMujAgQOqVauWEhMT9cgjj8jDw0M7d+6Un5+ffv31Vz388MOWsWazWYcOHdLNmzdVrVo1eXt72zHy/IEaAMhRVqxYoZdfflmDBg2S2WxWhQoVbMY0a9ZMK1euVFxcnJ599lnt3r3bDpECADJL7dq1tWnTJvn4+Khjx45auXKlvUMCAGTQt99+q8TERL311lv6+++/tWPHDs2fP183btzQU089pQMHDljGGgwGVatWTfXr1+fkP5uQAECOMWrUKLVt21bfffedvvnmG23atEkbNmywLAd1pzuTAKtXr7ZDtACA9Lh+/boGDx6swMBAeXh4qEGDBlZ3eCUnATw9PdW+fXuSAACQS126dElVqlTRuHHjLG1dunTRokWLdPPmTZskALIXUwDwf+3deVyN6f8/8NdpE5JsJWuiRbYkS/hMokLGLlvEYOy7DBJjG0TZ9y0ySWqMslcijMq+tIkka5IUUtru3x/z63yd6URRnZNez8djHvNwX9d9n9c5Zx7mXO/7uq5bLty5cwfGxsZQVlaGg4MD9PT0sGnTJty+fRvDhw/HwYMHoaCQv14VGRkJIyMjGSQmIqLCSkhIgLm5OWJiYqCgoAAjIyPEx8fj/fv3WLhwocSPxLzlAGlpafD29kbfvn1lmJyIiAojIiICf//9N1RUVBAREQEAOHDgQL5+R48exdChQ6Guro6goCCJ5QBUOlgAIJkSBAFxcXHw8/PDrFmzcODAAdjb2wMAMjIy0K9fP5w9exZ2dnZwd3eXWgQgIiL59tNPP+HSpUto0aIFfHx8oK+vj61bt4of83TmzBl0795d3P/atWsYOHAgvLy8YGZmJqvYRERUCJ6enhg1ahSysrLEx/T09BAeHg4VFZV8/fOKAAMHDoSnp2dpRiWwAEAyNn36dLi7u8PW1hb+/v6Ij4+XaGcRgIiobPP390f37t1Rt25d3LhxA1paWvD29sbw4cORnZ0NJycnTJ8+HRkZGahfv774vMzMTKk/HImISH68fv0aDRs2RHp6Ovr27QsTExO4u7sjNjYWY8aMwZ49eyASifKdFxwcjHbt2qFixYoySF2+cSRFMmVpaYmPHz9iz549qFy5cr52VVVVHDt2DN27d4eHhwfs7e2Rm5srg6RERCRNbm4uZs2ahcjISKntwcHBAAAnJydoaWnBx8dHYvC/fPly7Nq1C126dMHz58/F53HwT0Qk31JSUhAYGIj09HTMmTMHx44dw+LFi3Hz5k389NNP2LdvHyZOnAhp95vNzc05+JcRFgBIpvr06QMfHx8oKysjKioKZ86cydfn8yJArVq1OAOAiEiOhIWFYfPmzejatavUIkBOTg4AwMjICEePHsWwYcMkBv+vX7/Gli1bEB8fj+zs7NKOT0REXxATEyN1w749e/ZAX18fERERqFixIv744w9xm7q6Ok6dOoWffvoJu3btKrAIQLLBJQAkF/z8/DBo0CBUqlQJ/v7+aNeuXb4+WVlZUFZWlkE6IiL6ksOHD2PEiBGoWbMmgoKCJDZn9fX1Rb9+/dCxY0dcu3YNWVlZ4sF/dnY2evbsicDAQNja2uLIkSMyfBdERPS5T58+wcDAAB8+fMDly5dhaGgobsv7ex8ANDU18eLFi3znp6WlwcbGBhcvXsT48eOxY8cOqcsBqHTxViqVqk+fPuHWrVu4f/++xPG8mQAfP36EtbU1rl69mu9cDv6JiOTT0KFD8eeffyIpKSnfTICff/4ZrVq1wpUrVyQG/0lJSeLBf506dbB582YZvgMiIvqvChUqYN26dWjdurXEHi3A//29DwAvX76Ev79/vvMrV64sngkQFxeHzMzMUslNX8YZAFRqNm7ciN9//x2pqakA/t0ddPPmzRI7PxdmJgAREcmngmYCxMbG4n//+x9evnyJbt26oXbt2vDz88P79+9Rs2ZNnD59GqampjJOT0REX7J//36YmJhIPLov7+99DQ2NAh/rl5aWBgUFBa75lxMsAFCpmDt3LlxcXAD8u/Gfrq4uvL29kZqaim3btmHChAnivp8XAW7fvg0dHR0ZpSYiosL4+PEjDh48iIiICFy7dg2hoaHQ0tKSKAI8e/YMM2fOhK+vL7Kzs6GiooJBgwbB2dkZ9erVk/E7ICKiL3ny5An09PRQpUqVfAP9whQBSH6wAEAl7syZM+jZsyeUlJRw6NAh2NraIiEhARYWFoiOjkbLli1x7do1iR2f/fz8cOnSJaxdu1aGyYmI6GsSEhJgaWmJiIgIqKqqonXr1nj+/DmePHmSrwgAAO/fv8fr169Rp04dqKqqyjA5EREVxdGjRzF06FCoq6uzCFCGsQBAJc7a2hoBAQFYv349Zs6ciVevXsHCwgJRUVFo0aIFzp07B39/f/Tu3Rvq6uqyjktEREXQo0cPnD17Fi1atMCxY8egq6sL4N9lX7NmzYKmpma+IgAREZVNhS0C/PPPPzAwMJBhUioICwBU4jQ1NZGdnY2EhASkpKTAwsICkZGR4sF/lSpVUL9+fRgZGYmfF01ERPLvxYsXqFu3LhQVFREVFQU9PT2J9tmzZ2P9+vVSZwIQEVHZ9LUiwIEDB3D06FGu+ZdTfAoAlbjKlStDVVUVb9++Fe8OnTf4r1WrFpYuXYqkpCQ0bNhQ1lGJiKgIEhMTAQCGhob5Bv8AMHLkSACQ+nQAIiKSXyEhIRgxYgT69u2LLVu2ICsrS9w2YMAAHD58GO/evUPXrl1x9+5dcdvQoUNx+vRpDv7lGAsAVOK6d++Oly9fomXLloiIiJAY/Ht5ecHZ2RnKyspwcHCQdVQiIioCXV1dKCsr4+nTpxI/DvO8ffsWALBixQpkZmaKnwJDRETyy9PTE//73//g4eEBPz8/TJs2DWZmZkhISBD3+VIRgOQbCwBU4pycnKChoYHExETUq1cP586dg5qaGpYvX47hw4dDEAS4uLhwsxAiojJGXV0dQ4cOxbt377BgwQKJtuzsbPz+++8AAFtbWzx69AhmZmayiElERIWUnJyMcePGIScnByNGjMDhw4dhZ2eHGzduwNzcHC9fvhT3/bwIsGrVKhmmpqLgHgBUbC5cuIADBw7g5cuXaNq0KSZOnCje/OPSpUvo06cPUlJS0KhRI7x9+xYpKSkQiUT4/fffxT8SiYiobElMTES7du0QHx+PoUOHYty4cUhLS8PKlSsRFhaGZs2aITw8XNYxiYjoK969ewd/f3/Y2tpi1qxZWLdunbht7dq1+O2336Cvr48LFy5AW1tb3BYcHIx27dpx2n8ZwQIAFYu5c+fCxcVF4piKigq2bNmCX3/9FQAQHx+PP/74A+fOnUN2djbat2+P2bNno0OHDrKITEREhZSWloYNGzbA19cXHz58gImJCWbPng0TExMAwKNHj9CrVy9ER0dLnFe9enUEBQWhVatWsohNRESFtHv3bixevBgTJ07E6tWrkZycnG9A/6UiAJUdLADQd1u3bh3mzJkDZWVlLFu2DKNHj0ZoaCj69+8PAHB3dxdvBEVERGXL8+fPYW1tnW8DPwUFBaxbtw4zZswAAGRkZGDnzp04fvw43r17h7Zt22LevHlo0KCBLGITEVERHDhwAGPGjIFIJELt2rXx7Nkzqf0+LwJcvHgRWlpapZyUvhcLAPRdkpOTUb9+faSnp+PIkSMYNGgQXr16BQsLC0RFRYk3/KtQoQLU1dVlHZeIiIogJycH7du3x40bN6Crq4s1a9bA0NAQ58+fh6OjI96/fw83NzeMHj1a1lGJiOg75RUBcnNzcf78eXTp0kVqv7Vr1yIgIAC+vr6c9l8GsQBA38XLywtDhw7F0KFD4enpicTERFhYWEg86u/+/fuYMWMGAgMDUa1aNVlHJiKiQvLw8MCIESOgra2NO3fuoFatWuK2q1evonPnzqhUqRIeP34MDQ0N2QUlIqJikVcEqFmzJs6fPw8jIyOp/XJycqCoqFjK6ag48CkAVGSCICA3NxfA/z3iydraGq9fvxY/5/nzR/25uLjg5s2bOHv2rCxjExHRf+Tm5mLUqFFwdXWV2h4cHAwAmDZtmsTgHwDatWuH/v37IzU1FSdOnCjxrEREVPJGjRqFffv2ISkpSfy7XhoO/ssuFgCoyGbOnIkRI0YgOzsbTZs2BQCcPn0aXbt2RUREhMTgf/v27fD19UXt2rUxYMAAGScnIqI8giDgl19+gbu7OxwcHKQWARQU/v2ZUNDdfU1NTQCQeDY0ERHJt8jISIwePRpt2rTByJEjcf/+fYn2vCLA5zf36MfBAgAVyV9//YVNmzbh6NGjuHPnDv73v/+hRYsW8Pb2Rnh4uMTg383NTbw51NatW6GioiLj9ERElEckEiE8PBwVKlRA1apVpRYBunbtCgA4dOiQeOZXnrS0NJw8eRIAxI98JSIi+ebp6QkTExMcOHAA0dHR8PT0hKmpKfz9/SX6/bcI8PDhQxklpuLGAgAVyZ49eyASieDj44M2bdpAQUEBf/75J6pUqQIAaNu2LU6dOoXevXtjzJgxyMrKgpOTE+/+ExHJoVmzZuHTp0+YP38+NDQ08hUBBg4ciDZt2uDy5csYN24c0tPTAfy7AezgwYMRFxcHPT099OjRQ1ZvgYiICunKlSuwt7fHp0+fMHHiRJw5cwbq6ur48OED+vbtm2+5bl4RwNjYGHXr1pVRaipu3ASQvkoQBEyZMgWampoICAhA3bp1ceTIEYk+t2/fxvDhwxEVFSU+Vr16daxduxZjxowp7chERFQIWVlZaNSoEVq2bImlS5fC2toaKSkpcHFxwZw5cwAAT58+hbm5OeLi4lC1alXo6+sjMjISaWlpqFq1KoKDg9GqVSsZvxMiIvoSQRBgaGiImJgYODo6wtbWFl27dsXbt2/RpUsXhISEQCQS4dixY+jevXu+c0UikYySU3HjDAD6qpSUFAQFBWHp0qUIDw+Hnp5evj7Gxsa4d+8eLly4gL179+LkyZN4/vw5B/9ERHJMWVkZU6dOxZkzZ1C5cmX4+/vnmwlQv359hIaGYvDgwXj//j2uXbuGtLQ0dOzYESEhIRz8ExGVAf7+/oiJiYGFhQXGjx+PHj164O3bt+jduzfOnj2L+fPnIyMjA/369cs3E4CD/x8LZwBQoSQkJKBLly64f/8+TExMcO3aNfHmUEREJN++dPfm7du3qF+/PoYNG4bdu3fj2rVrUmcCAMCrV6/w4MEDaGtro3HjxqUVn4iIvlN4eDgsLCzg5eWFDRs24Pjx4zA3N8fZs2dRoUIFnDhxAra2tlBSUkJ2djaCgoJgZmYm69hUAjiCI6kEQUB4eLj4z7Vr18aFCxdgYGCAmzdvwtHRUYbpiIiosHJzc8W7PR86dAjZ2dkS7dWqVcPo0aNx8OBBJCYmom3btlJnAgCAlpYWOnfuzME/EVEZ07x5c1y+fBlGRkY4fvw41NXV4eXlhQoVKgAAwsLCkJmZCT8/P1hbW3N21w+MBQCSau7cuWjbtq3EFKDPiwDOzs5YsmSJ7AISEdFXff6ov/DwcNjZ2UFXVxcuLi5ITU0V95s5cyaysrKwdetWAPhiEYCIiMomAwMDfPjwAQDQsmVLaGlpAQDu3LkDV1dXaGhowMLCAr6+vqhUqZIso1IJYgGApLK1tYWKikq+dUCfFwGWLl3KIgARkRwTiURo0qQJAEBNTQ0rV66Enp4e5s6di/r162PWrFl4/PgxmjRpgt69e2Pbtm3inf7/WwTYtGmTLN8KEREVUmhoKIYNGwYbGxu4uroiIyND3Na4cWMYGhriypUrcHZ2hrOzM8zNzZGeno6JEyfKMDWVFu4BQBJ27NiBDRs24Pnz51BSUkJKSgpUVVXz7Qj6+Z4Av//+OwsBRERybPny5Vi8eDFq1KiBoKAgCIIAV1dXHD58GLm5uRg4cCDatm2LuXPnYseOHZgwYYL43GvXrmHgwIHw8vLielAiIjl35MgRDB8+HDk5OeJjzZs3x8mTJ9GgQQMA/z69q1u3bkhOThb36dGjB/z8/KCsrFzqmal0sQBAYgsXLsTKlSuhoKCA8ePHo3Pnzrh+/Tq2bNkCJSWlAosAPXv2xPr162WYnIiI8uTm5mLq1KlwdXVFxYoVxcf/WwRo2bIlnj9/js2bN2Pnzp1ISUkBAPTq1QsnTpyQuGZmZiZUVFRK820QEVERpaamol69evjw4QPs7OzQv39/nDlzBnv27IGOjg4uXLiAhg0bAgCePXuGLVu24Pnz57CwsMCoUaOgqKgo43dApYEFAAIAPHz4EAYGBsjNzcXhw4cxZMgQcdv58+dhbW0ttQiQmpqKqlWryiIyERFJ8fLlS9SpUwdHjx5F//79JdqkFQEA4MOHD9i3bx9Onz6NAwcOQFNTUxbRiYjoG6WkpODSpUvo06cPpk6dis2bN4vbtm/fjilTpqBhw4YSRQAqn7gHAAH4d5Cfm5sLS0tLicE/AFhYWGDy5MniZ4MGBQWJ2zj4JyKSL9ra2mjZsiX+/PPPfG2LFi3CsmXL8ObNG3Tt2hV3794F8O/+ANOnT8fp06c5+CciklNxcXES6/nzuLm5QU9PD7du3YKysjJWrlwp0T5p0iRs2bIF8fHx6NKlC+Lj40srMskhFgAIAMTPh9bV1ZXabmJigmrVqkFFRQX9+/dHTExMacYjIqIi6NOnD44dO4YHDx7kayuoCEBERPIrNjYWnTt3Rp8+ffIVAdTU1JCSkoJly5ahSpUqqFKlSr7zJ0+eLFEEePbsWWlFJznDAgABAMzNzQEAAQEB4h2gP3fx4kVkZ2fDx8cH79+/x7hx40o7IhERFdKvv/4KkUiEpUuXSm1nEYCIqGypUaMGtLW1ERAQkK8IYGtri0OHDkEkEiE5ORmnTp2Seo28IkDjxo1RvXr10opOcoZ7AJCYvb09Dh48CFtbWxw4cEC8eZS7uztGjx4NAwMDREVFoXv37vD398etW7dgbGws29BERCTV4MGD4e3tjZMnT8LGxkZqn4L2BCAiIvmTkpICS0tL3LhxA1ZWVvDz84Oqqqq43dvbG8OHD4e6ujoCAwPRunVrqdfJzc2FggLvA5dX/ObLoZycHDx+/BgfP36UOL5t2za0bdsW3t7eMDIywrhx49ClSxeMGjUKgiDAwcEBAKCjowMAuHPnTmlHJyKiQlqzZg0qVqyIsWPH4smTJ1L7fD4ToF+/fsjKyirllEREVFgaGhoIDAxEmzZtvjgT4N27d7C0tMStW7ekXoeD//KN33454+bmhvr166NRo0aoUaMGFi5cKH5OqJqaGoKCgmBvb4/4+Hjs3bsXwcHBUFJSwtKlSzF27Fh8+vQJ/v7+AIA6derI8q0QEdEX6OjoYMWKFUhISICNjQ3evn0rtd+iRYvg7OwMDw8PPv+ZiEjOFVcRgMovLgEoR+bPnw9nZ2cAgJWVFWJjY/Ho0SMMGDAAhw8flvjhFxsbi0uXLkEQBFhYWEBHRwfZ2dn49ddfsX//fujp6SEiIoI/FomI5Nwvv/yC/fv3w9DQEKdOnUKjRo1kHYmIiL4gJSUFGhoaX+2TtxzA2toaJ06ckPhd/vlygEuXLsHIyKiEU1NZwQJAOfHXX39h0KBBUFZWxt69e6GiogI7Ozvx3X9pRYA8GRkZCAgIwIoVK3D16lVUrlwZFy5cgKmpaWm/DSIiKqLs7GxMnjwZu3fvRs2aNbFly5Z8j3slIiL5EBMTAwsLC8yePRtz5sz5Yt+UlBR069YNN2/exIQJE7Bjxw6Jdm9vb+zZswd///03KlWqVJKxqQxhAaAcyM3NRZMmTRAXF4f9+/dDTU0NQ4YMQU5ODhYsWIDz588jNDS0wCJAeHg4LCwskJSUhObNm+Pw4cNo1qyZjN4NERF9i+3bt2PevHl4//49evbsid9//x3t27eXdSwiIvrMxYsX0bNnT3z8+BEuLi5fLQK8evUKrVq1wqtXr3DlyhWYmZmVUlIqq7gHQDlw+fJlxMXFYcCAAWjdurX4zv+CBQuwcuVK/PHHHwCAo0ePYujQofk2gWrevDkuXryIy5cv4+7duxz8ExHJgKenJ9q1a4eqVauiffv2CAgIKNL5kyZNQnR0NMaOHYvz58+jQ4cOMDc3x86dO5GYmFhCqYmIqCh++uknnDp1CpUqVYKDgwNcXV2/2F9LSwsLFy4EAOzbt680IlIZxwJAOdC5c2eMHDkSU6ZMwZw5c/Dp0yfY29tj5cqVAICqVasC+PcvnKNHj+LgwYP5rtG0aVN06tQJIpGoVLMTEZV3giBgwoQJGD58OK5du4Z3797h6tWr6NGjBwIDA4t0rTp16mDPnj149uwZXF1doaioiBkzZuDIkSMllJ6IiL4mJiZGvE8XAJibmxepCNCmTRsAwIMHD0o0J/0YuASgnMjNzUVmZibU1NRQsWJFvHjxAlWqVAEAzJ07Fy4uLoiIiMDly5cxfvx4GaclIqI8y5cvx+LFi1GhQgWsWrUKffr0QUBAAKZMmYLWrVvj+vXr33X9zMxMqKioFFNaIiIqqoULF8Ld3R1PnjyRuNkWHBwMGxubry4HcHZ2xvz58zF06FB4enqWVmwqo1gAKEdycnKgoaGBChUq4OnTp6hYsSKOHDmCYcOGoWrVqkhISOCPQCIiOZKYmIj69esjMzMTvr6+6NOnj7jN1tYW/v7+SE1NlWFCIiL6XhcvXoS5uTkuXLgAc3NzibbPiwCrVq3C/PnzJdrv3r2Lzp074/379zh58iRsbGxKMzqVQVwCUI4oKipi6tSpePPmDdq2bQtzc3MMGTIEubm5WLJkCQf/RERyJjg4GJmZmWjbtq3E4B/4t6hrbGyMoKAgbNiwASdOnEBubq6MkhIR0bfq2LEjatSogTVr1uRry1sOULlyZSxYsAB2dnaIjY3Fp0+f4OHhgS5duuD9+/cYNmwYB/9UKJwBUM5kZ2dj/PjxcHNzA/BvUcDR0RHLli2TcTIiIvovX19f9OvXDy1atMDdu3fFxy9evIhu3bpBSUkJGRkZ4uNWVlbw8/ODqqqqLOISEdE3mjdvHtasWYPQ0FCpT2i5evUq+vTpg1evXuVr69+/Pzw9PVGhQoXSiEplHAsAPwBBEHDnzh0YGxsX+pzIyEjExMSgdevWaNiwYcmFIyKib5aWlgZ9fX28ePECkyZNwtixY3H58mU4Ojri48eP6NChA5ycnFCxYkXMmjULd+/exbx587B69WpZRycioiKIj4+Hvr4+9PT0cPPmTakzc5OTk7FixQp4eXkhMTERTZs2xbRp0zBu3Dhu1E2FxgLAD8DR0RF79uzBgwcPxDv6ExHRj+H69evo168fnj9/DgBQUlJCdnY2bGxs8Pfff4t/JAYHB6NLly7Q1dVFbGysLCMTEdE3cHR0xKpVqzBp0iRs27ZN1nHoB8U9AH4ArVq1wuvXr7F8+XJZRyEiomJmamqK6Oho+Pr64tixY5g5cyZUVVXh7u4ucYcoPT0dwL+zwoiIqOxxcnKCoaEhtm/fLnU/AKLiwBkAP4jOnTvj6tWriIiIgJ6enqzjEBFRCTExMYFIJMKNGzfExzIzM9GlSxeEhIRgypQp2LJliwwTEhHRt4qNjUX79u3x5s0bzJ8/HytXruT0fipWnAFQBgmCgBcvXkgc27BhA7Kzswt8PigREf0YRCIRwsPDcevWLQD/rgnt378/QkJCoKmpicWLF8s4IRERfavGjRvj/PnzaNCgAVavXo3evXuLl4ARFQcWAMoYQRAwZcoU6OrqYv78+Xj37h2Af6eI2tvb4/jx4wgICJBxSiIiKinjxo1DZmYmzMzM0K5dO9SrVw+nTp2ChoYGTpw4AU1NTVlHJCKi79CiRQuEhYXBxsYGJ0+ehJGREVavXo3U1FRZR6MfAJcAlDHJyclo1KgRPn36hNzcXFSvXh3Lly/H2LFj8erVK+jr60NHRwe3b9+GoqKirOMSEVEJcHR0xJo1a5CTkwMAMDMzg5ubGwwMDGScjIiIAODjx49wd3fHtWvXUKVKFVhbW6Nnz55Fns7/999/Y+nSpbhz5w7U1dUxcuRIDBo0CD/99BMUFHgvl4qOBYAyaO3atfjtt99w4MAB+Pj44Pjx42jVqhXWrVuHkJAQODk5YevWrZg8ebKsoxIRkRRubm7YsmUL4uPjoaOjg8GDB2PKlCmoXLlyoa/x7Nkz3Lp1Cw0aNECrVq1KMC0RERXF48eP0bNnT0RHR0scb9WqFdzc3NC6desiX/PSpUvw8PDA2bNnkZubi+DgYOjo6BRTYipPWAAogzIzM2FkZISaNWsiJCQE586dw6xZsxAeHo7u3bvjwoULqFKlCh48eAANDQ1ZxyUios9MnjwZ27dvz3e8Xr16+PPPP2Fubi6DVEREVBxycnLQunVr3Lt3D0ZGRpg6dSqysrJw8OBBXL9+HSoqKnBzc8Pw4cO/+TUyMjKgqqpajKmpPOG8ETknCALmzp2Lbdu2iad6qqiowMXFBWFhYfD09ISlpSVu376NrVu34saNG/j06ROSkpJw/PhxGacnIqLP/fXXX9i+fTsqVqyIffv2ISkpCaGhoRg0aBCePXsGS0tLHDx4sMDzc3JyxP8vICIi+XP8+HHcu3cPjRo1QkhICCZNmoTp06fj6tWr2LRpE3JzczFixAi4ubl982tw8E/fgzMA5Fx0dDR69uyJx48fo1mzZnB1dUX37t0BAN26dcODBw9w//59VKxYEQCQkpKCZcuWQUFBAS4uLrKMTkRE/9GrVy+cOnUKGzZswIwZMyTa3NzcMGHCBGRnZ8PT0xNDhgyRaM/JyYGdnR1EIhH+/PNP7vNCRCSHVqxYgUWLFsHR0RF//PFHvvYTJ05g4MCByMnJwenTp2FlZSWDlFSecQaAnDM0NERUVBRWrFiB+Ph49OjRA7169UJ0dDQ2bNiAFy9eYO3ateL+GhoaWLduHQf/RERyKDExEQCkTvP/5Zdf4OPjA0VFRdjb2+PmzZsS7U+fPsX58+dx8uRJxMTElEpeIiIqmrwnsbx9+1Zq+88//ww3Nzfk5OTA3t6+wH5EJYUFgDJAVVUVCxcuRExMDEaNGoXTp0+jRYsW2L17NwYMGIA1a9bgxYsXso5JRERfkbdLf3h4uNT2Pn36YP369cjMzIS9vT1yc3PFbTo6OggKCoK/vz+aNm1aKnmJiKhoevbsCUVFRXh5eRU4uB8+fDjs7e2RkJCAdevWlXJCKu+4BEAOZWdn4/79+1BSUoKenl6+R3xcv34dM2fOxD///ANFRUVxBfHAgQMySkxERIVx+vRp2NjYwMjICLdu3YKKiorUfp06dcKVK1fg4+ODgQMHlnJKIiL6HuPHj8fu3btha2uLI0eOSO0THR2Npk2bonHjxnj48GEpJ6TyjDMA5Mz27duhra2N5s2bw9DQEDo6OvDw8JDoY2pqisuXL8PT0xN169YFANSsWVMWcYmIqAh69uwJS0tLREZGYtq0aQX2GzNmDADg1KlTpRWNiIiKiYuLCxo1agRvb284ODhI7WNgYAAFBQXExcWB92OpNLEAIEccHBwwefJkJCUlQVtbG3p6enj69ClGjBgBR0fHfP2HDh2K6OhoeHt7w9XVVQaJiYioqPbv34/atWtj165dmD9/vtQ+6urqAApeQ0pERPJLXV0dp06dQo0aNeDq6orx48cjMzNTok9QUBByc3NRu3ZtiEQiGSWl8ogFADnh5+cHV1dXKCsrY+/evXjx4gViYmJw5coV1K1bF6tWrcKff/6Z77yKFSti0KBBMkhMRETfom7dujhz5gyqVasGZ2dnjBw5Eu/fvxe3Z2dnY+vWrQCAVq1aySomERF9B0NDQ5w/fx7a2trYvXs3TE1N4ePjg8ePH8Pb2xvDhg0D8O9yAaLSxD0A5ES3bt0QFBSE5cuXw8nJSaItJCQEHTt2RP369fH48eN8ewIQEVHZExERgZ49e+Lp06eoXbs2fvnlF2hpaeHQoUO4evUqNDU1ce/ePfGO0kREVPY8f/4c9vb2CAoKytdmY2MDX19fKCkpySAZlVcsAMiJunXr4sWLF3j48CEaN26cr11fXx8PHjxAVFQUDA0NZZCQiIiKW3JyMqZNmwZPT0+JNaDa2trw8/ODqampDNMREVFx8fX1xf79+xEREYFq1arBzs4OU6ZMgaKioqyjUTnDAoCcaNOmDW7evImQkBB06NAhX3vt2rXx6tUrREREwMjISAYJiYiopERHR8PX1xcvX76EgYEBRowYgSpVqsg6FhERAXjw4AHU1NSgra0t6yhE343zTeTE8OHDcfPmTSxatAhnz56VmOa/fft2vHr1ClpaWuJnSBMR0Y/D0NCQs7uIiOTQw4cP0aVLF6ipqeHChQssAlCZx8XkJejKlStIT08vVN9p06ahTZs2CAwMhLW1Na5cuYLo6GgsXrxY/KiolStXcpoQEREREVEpmThxonhz7i5duuDly5dFOv+/u/8TyRqXAJSQixcvwtraGp06dcKJEydQsWLFr56TmJgIGxsb3LhxI1+bo6Mj/vjjj5KISkRERERE/3Hu3DlYWlqiQYMGMDExwbFjx6Cvr1/omQCRkZHo1asXdu/eDUtLy1JITPR1nAFQQpo3bw4jIyMEBQXh559/LtRMAE1NTfzzzz9Yu3YtTExMULduXVhZWeH06dMc/BMRlVE+Pj6IjIyUdQwiIioiR0dHAMCyZctw5MgR9OvXr0gzAfz8/PD48WNs3ry5pKMSFRpnABQzQRAgEokA/Lu7s6WlJW7duoWuXbsWeiYAERH9GLy8vGBnZ4eaNWvi7t27fKQfEVEZsmvXLmzduhW3bt2CgoICsrKyMHjw4CLNBNi/fz+GDBnCMQDJDRYAitHZs2cREhKCJUuWiI8VtQjg5+eHzMxMDBo0qBQSExFRSckb/Ofk5MDJyQnLly+XdSQiIiqit2/folq1auI/f60IkJmZCRUVFVlEJSoULgEoRqdOncKhQ4ckjlWvXh2BgYFo3br1V5cD+Pn5wdbWFsOGDcPt27dLITEREZUEX19fDv6JiH4Anw/+AUBZWbnA5QCRkZEwMDBAYGCgLKISFQoLAMWoR48eePDgAUJCQiSOF6YIkDf4z8zMxNSpU2FsbFyKyYmIqDi1bt0aDRo04OCfiOgHJK0IEBgYCAsLC675J7nHJQDFKCMjA9ra2jA3N8exY8fytRe0HODzwf/MmTOxfv360g9PRERShYSE4ODBgzAzM8PIkSMLfV5KSgo0NDRKLhgREcnU58sB8lhZWcHX15dr/klusQBQzGbNmoWNGzciJCQE7du3z9f+3yLA+PHjYW9vz8E/EZEcmjNnDtavXw9BEKCpqYmLFy/CwMBA1rGIiEhO3L59G6ampsjJyeHgn8oEFgCK2aNHj9C0aVM0adIEt27dkroJyOdFgDwc/BMRyZf169dj9uzZUFVVxZo1azB58mQoKirKOhYREcmJyMhIWFhYIDExkYN/KjO4B0Ax09XVxezZsxEZGYlJkyZJ7fP5ngAAB/9ERPImPT0dy5YtAwAcOnQI06ZN4+CfiIgkjBo1ioN/KnNYACgBixYtQvPmzbFv3z78/vvvUvvkFQFcXV05+CcikjNhYWFISUlB69at0b9/f/Fxf39//PzzzzA0NIS1tTV8fHxkmJKIiGTJy8sLo0aN4uCfyhQuASgh8fHxaN++PV69eoXJkydj06ZNvHtERFRG+Pr6ol+/fhg0aBC8vb0BAEuXLsWSJUvy9Z04cSK2b99eygmJiIiIio4zAEpIw4YNcfnyZejr62Pbtm0wNzdHdHS0rGMREVEh6OjoAADu3bsHALh48SKWLFkCJSUlODk54fr16zh48CC0tLSwY8cObNq0SYZpiYiIiAqHMwAK4eLFizA0NISmpmaRz3379i1mzJiBgwcPokKFCpg0aRLmzJmDevXqlUBSIiIqDrm5udDV1UV8fDzOnj0Lf39/uLq6ws3NDaNHjxb3u3r1Ktq3bw8tLS28ePECCgqsqxMREZH84i+VrwgICECPHj3QtWtXJCYmFvn8atWqwd3dHRcvXoS5uTk2btwIXV1dDBw4EJ6enkhLSyuB1ERE9D0UFBQwb948AMCUKVOQmpoKHR0dicE/ALRr1w7a2tp49eoVXr58KYOkRERERIXHGQBfYWNjgzNnzkAQBDRr1gxBQUHfNBMgT0xMDLy8vHD69GmoqKjgxIkTUFNTK8bERERUHARBgLW1NQIDA1G5cmXUq1cv31Ku169fQ1tbG4IgICUlBVWqVJFRWiIiIqKv4wyAr+jQoQMMDAzQr18/REREfPNMgDz6+vpYtGgRrly5ggsXLnDwT0Qkp0QiEXx8fGBiYoK0tDTcv38fR44cEbdnZGRg1KhRyMnJgbW1NQf/REREJPc4A+Ar8pYAvH79GmPHjsWxY8eKZSYAERGVDe/evYOtrS38/f2hoKCAkSNHokGDBvD29kZ0dDQ0NDQQFhYGfX19WUclIiIi+iIWAL7i/fv30NDQQEBAAP73v/9h8ODB+YoAhw8fxoABA6CioiLruEREVAIEQcDmzZuxfPlyJCUliY/r6enhyJEjMDY2ll04IiIiokJiAaAQWrVqhSFDhsDR0RFZWVkSRYDevXtj9erV6Nu3L44dOybrqERE9AVJSUm4ffs2KlWqBFNT0yIXbjMyMhAcHIwnT56gYcOG6Nq1K5SUlEooLREREVHxYgGgECZNmoTnz5/Dz88PACSKAACgqKgIDw8PDBkyRIYpiYioILm5uVi6dCmcnZ3x6dMnAEDVqlUxc+ZMzJ8/H6qqqjJOSERERFTyuAlgIZiZmSEsLEz8Z2VlZYnpnvXr14eFhYUMkhERUWHMmzcPy5YtQ2ZmJjp16oTOnTvjw4cPWLp0Kdq1a4fY2FhZRyQiIiIqcSwAFELHjh2RmJiIR48eAQBWrFiBJUuWQFFREe3bt8fjx4+/++kARERUMmJiYrBu3TooKyvj1KlTuHz5Mi5duoSYmBhYWVnh3r176NSpE8LDw6Wen5OTg8DAwFJOTURERFT8WAAohCZNmkBTUxOhoaFYsWIFFi1aJJ72f+nSJfEjAt3c3GQdlYiI/iMoKAi5ubmwtbVFjx49xMd1dXVx5swZTJ06Fa9evUL37t3x5MmTfOfb29vD2toa+/btK83YRERERMWOOxcVkpmZGRYuXIjHjx/nW/N/5MgRuLu7Y+zYsTJOSUREBalVq1a+YwoKCti8eTMyMzOxa9cuDB06FJcuXYKioqK4j7W1NU6dOoWmTZuWZlwiIiKiYsdNAAtp586dmDhxIjf8IyIqY+7du4eWLVuiSZMmiI6Olhjc58nOzkanTp1w9epV7N69G+PGjZNoT05ORvXq1UsrMhEREVGJKJcFgKysLCgqKkJBoWgrIObNmwcTExMO/omIyhgLCwtcuHABzs7O+O2336T2uXjxIszNzWFsbIxbt26VckIiIiKikleuCgC3bt3CnDlzcOnSJQiCADMzM8yYMQODBg2SdTQiIipBkZGRMDU1RVZWFnx9fWFjYyO1X+XKlfHx40d8+vQJKioqpZySiIiIqGSVmwLAlStXYGlpifT0dCgoKEBJSQmZmZkAgF69euHAgQOoUaOGjFMSEVFJOXToEEaMGIEKFSrAy8sLffr0kWh/8+YNtLS0IBKJkJaWxgIAERER/XDKxVMAsrOzMWLECKSnp2PkyJF48+YNPn78CD8/PxgaGuLkyZPo1KmT1N2f8yQlJZViYiIiKm7Dhw/H9u3bkZmZif79+2PevHn48OEDAODjx48YM2YMcnJyYGlpycE/ERER/ZDKxQyAK1euoFOnTtDV1cX9+/ehpPR/Dz9IS0vDiBEjcOzYMejp6eGff/7Jt1O0n58f7OzscOjQIfTu3bu04xMRUTE6evQoxowZg9TUVKipqaFZs2Z48OABkpOTUbVqVYSFhcHAwEDWMYmIiIiKXbmYAZCSkgIAaNGihcTgH/h3vaePjw8GDhyIBw8eYMiQIcjNzZXoEx0djQ8fPuDSpUulFZmIiErIgAEDcPfuXdjb2yMrKwthYWFITk5GmzZtcPnyZQ7+iYiI6IdVLmYAPH36FA0aNEC9evXw+PFjqY+AysjIgJmZGW7fvo2dO3di/PjxEu2BgYGwtLQsrchERFQK0tLScP/+fVStWhWNGzeWdRwiIiKiElUuCgAAYGVlhcDAQGzcuBHTp0+X2ic0NBRmZmbQ1dVFbGxsKSckIiIiIiIiKjnlpgBw584dtGvXDiKRCOfPn4eZmZnUfk2aNEFsbCwePXqERo0alXJKIiIiIiIiopJRLvYAAIBWrVph3bp1+PTpE3r16oWwsDCp/UQiEQCId4YmIiIiIiIi+hGUmwIAAEyZMgWLFi3C27dv0bVrV7i5uUm0e3h44OHDh6hWrRr09fVllJKIiIiIiIio+JWbJQCfW79+PebOnYucnBwYGxvDysoK8fHx8PHxQW5uLjZv3oypU6fKOiYRERERERFRsSmXBQAACAkJwZQpU3Dr1i3xMUVFRSxZsgROTk4yTEZERERERERU/MptAQAABEFAaGgorl69ChUVFfTo0YMb/xEREREREdEPqVwXAIiIiIiIiIjKix9uE8D09HRZRyAiIiIiIiKSOz9UASAsLAyNGzfG+fPnZR2FiIiIiIiISK78MAWAsLAwdO/eHS9fvsShQ4dkHYeIiIiIiIhIrvwQBYC8wX9qairs7OywY8cOAMDjx49lG4yIiIiIiIhITpT5AsB/B/8HDhyAoqIili5dimbNmnE5ABEREREREREAJVkH+B5fGvwvWbIESkpKePPmjaxjEhEREREREclcmZ0BUJjBv6enJwYNGiTrqEREREREREQyVyYLABz8ExERERERERVNmSsAcPBPREREREREVHRlrgDg7e3NwT8RERERERFREYkEQRBkHaIgqampUFdXh0gkEh8TBAEHDhzAyJEjOfgnIiIiIiIiKiS5LgBMnToVALBlyxap7Rz8ExERERERERWO3C4BSExMxL59+5CYmFhgn4SEBA7+iYiIiIiIiApBbmcAODo6YtWqVbh16xaMjY2l9hEEATdv3kSbNm1KNxwRERERERFRGSOXBYB3796hQYMG6Ny5M06cOCHrOERERERERERlnlwuAdi2bRtSU1Ph6Ogo6yhEREREREREPwS5mwGQkZGBRo0aoWnTpggKCpLaJyQkBB06dJB4OgARERERERERFUzuZgC4ubkhISEBCxculDguCAL++usvmJiYoGPHjli0aJGMEhIRERERERGVPXI1AyAnJwd6enrQ1NREaGio+JinpydWrlyJqKgoNGjQAHPmzMG4ceNQqVIlGScmIiIiIiIiKhuUZB3gc4cPH0ZcXBw2btyIzMxMuLu7Y/Xq1YiNjYWhoSHc3NxgZ2cHZWVlWUclIiIiIiIiKlPkpgAgCAKcnZ3RpEkTxMXFoXHjxnj27Bnatm2LNWvWoH///lzzT0RERERERPSN5GYJwPHjx9GnTx/xn7t164YFCxagW7duMkxFRERERERE9GOQmxkA6urqqFy5MqytrbFgwQK0bdtW1pGIiIiIiIiIfhhyMwMAANLS0lC5cmVZxyAiIiIiIiL64chVAYCIiIiIiIiISoaCrAMQERERERERUcljAYCIiIiIiIioHGABgIiIiIiIiKgcYAGAiIiIiIiIqBxgAYCIiIiIiIioHGABgIiIiIiIiKgcYAGAiIiIiIiIqBxgAYCIiIiIiIioHGABgIiIiIiIiKgcYAGAiIiIiIiIqBxgAYCIiIiIiIioHGABgIiIiIiIiKgcYAGAiIiIiIiIqBxgAYCIiIiIiIioHGABgIiIiIiIiKgcYAGAiIiICmX79u1wcHDA27dvf8jX+16urq5wcHBARkaGrKMUq6ioKDg4OOCvv/4q9DlPnjyBg4MDPDw8SjAZEREVlUgQBEHWIYiIiKhw3N3dcffuXQCAlZUVunfvXmDf1atXIykpCQAwatQotGjR4rteu0uXLggODkZcXBx0dHS+61ry+Hrfq3nz5oiIiMDbt2+hoaEh6zjF5sSJE+jduzcmTJiAHTt2FOqc0NBQmJmZYciQITh8+HAJJyQiosJSknUAIiIiKjw/Pz/xndhz584VWACIiorCggULxH/u0KHDdxcAJk2ahJ9//hnVq1f/rut87+tt3rwZ8fHxWLRoEapWrVoqWahoGjZsiLVr18LQ0FDWUYiI6DMsABAREZVBderUwe3bt3Hz5k2YmJjka9+7d6+434sXL4rlNYcMGVIs1/ne1/Pw8EBYWBhmzpzJAoCc0tbWhoODg6xjEBHRf3APACIiojJo1KhRAIB9+/bla8vKyoK7uzuaNWuGtm3blnY0IiIiklOcAUBERFQGtWzZEqampvDw8ICLiwtUVVXFbb6+vnj9+jUWLFiA4ODgAq+RkZGBc+fOISYmBklJSahVqxbMzc3RunVrqf23b9+O2NhYLFy4ENWqVcvXHh0djcDAQLx8+RKVK1eGiYkJrKysoKiomK+vq6srXr58iRUrViAnJwfHjx9HdHQ0lJSU4OTkJPX1EhIS4OLigidPngAAVqxYATU1NfE1nZ2d4eTkBEEQsGLFCigp5f+Zk5GRgcWLF0NVVRVLly6FSCQq8PPJ8/jxYwQEBOD58+eoWLEiGjVqhO7du39x9sHHjx/h6+uLqKgoVKhQAV26dEGnTp2k9i3q91CYzy5PaGgorly5gqSkJFStWhUdOnSAubn5V9/zl9y8eROBgYF4//499PT00K9fP6irq0v0efLkCTZt2oTWrVvDzs5OfDwqKgp79+6FmZkZBg4ciKioKJw6dQrJycnQ0dHBgAEDUKNGje/KR0REXyAQERFRmTFw4EABgODp6Sls375dACB4eHhI9OnevbugoqIivH79Wujbt68AQPD29pbos23bNkFDQ0MAkO+ffv36CR8/fsz32ubm5gIAIS4uTuJ4enq68MsvvwgikSjftQwMDIS7d+/mu1azZs0EAEJAQIBQt25dcX9tbe0CX+/evXtS8+b9IwiCMGTIEAGA4OPjI/Xzc3NzEwAI48eP/+pnnZubK0yaNElQVFTM91pqamqCm5ub1Pd06dIlQUdHJ985Y8aMyfca3/I9FOaze/TokWBmZib1uu3btxeePn361fef5/jx4wIAYcKECcLkyZPzXU9LS0u4fPmyxDkhISECAGHIkCEFXsvR0THffzPVqlUTQkNDC52NiIiKhjMAiIiIyqhhw4Zh9uzZ2Lt3L4YPHw4AePr0KQICAjBgwADUrFmzwHPDwsIAAIMHD4auri4qVqyIuLg4HDt2DMeOHcP8+fOxcePGQuWYPHky3NzcIBKJ0K9fP7Rs2RIJCQn4+++/cf/+fVhZWeHu3bvQ1NSU+h6UlJQwceJE6OjofPGuura2NtauXYvNmzfjyZMncHJyytd/5syZ8PLywtatWzFw4MB819i2bRtEIhFmzpz51ffl6+uL7du3Q0lJCba2tjAyMkJubi4ePnyIs2fP4vbt21LPGzBgAFRUVDB58mRoaWnh5s2b8PX1xb59+9CnTx/07dtX3Pd7voeCPrs3b97AwsIC8fHxMDIygrW1NWrVqoU3b97g+PHjCAsLQ69evXDz5k2pszMK4uPjgzdv3qBr167o2LEjUlJS4Ovri6dPn6J3794IDw9HnTp1CnWto0eP4vXr1+jRowdMTU2Rnp4Ob29vPHnyBKNHj0ZUVFShcxERURHIugJBREREhff5DABBEISRI0cKIpFIiI2NFQRBEJYsWSIAEM6cOSMIglDgDIDr168LHz58yHf958+fC1paWkKlSpWEzMxMiTZpMwDu378viEQiQSQSCSdPnpTon5CQIBgYGAgAhPnz50u05d3FNjY2FlJSUqS+14JmHLRv314AUOBd7Hbt2gkAhIiICInj165dEwAIPXr0kHref61YsUIAIKxduzZf24cPH4Q7d+5IfU+dO3fO99k6OTkJAISRI0dKHP+W7+Frn93cuXMFAIKjo2O+tuzsbKFfv34CAOHo0aMFv/nP5N21l/ZZpKamij/vOXPmiI9/bQYAAOHw4cMSbcnJyUKtWrWkfndERFQ8OAOAiIioDBs7diwOHjwINzc3LF26FG5ubqhfvz6srKy+eF6bNm2QlpYGX19fREZGIjU1FdnZ2QAANTU1vHr1CjExMWjWrNkXr3PmzBkIgoABAwbAxsZGok1LSwurV69G//79cfr0aaxatSrf+cuXLy/2nfxnzJgBOzs7bNu2DVu2bBEf37p1KwBg1qxZhbpO3mMTIyMj8enTJ1SoUEHcVrlyZbRs2VLqeStXrkTlypUljg0ePBgrVqxAXFycxPHv+R4K+uzyHhP54cMHzJ8/H4IgAID433nXDwsLQ//+/b/+Qfx/enp6mD17tsQxdXV1ODs7w8LCAmfOnIGLi0uhrtWxY8d8T3moVq0arKyscOjQIcTFxcHIyKjQ2YiIqHBYACAiIirDzM3N0aRJE+zfvx8dO3ZEfHw8Fi9eDAWFLz/ox8fHBxMmTEBycnKBfVJTU7/6+k+fPgUAmJqaSm1v164dACA+Pl5qe6tWrb76GkVla2uLuXPnwt3dHatXr4aamhqSk5Ph5eWFZs2awdraulDX6dOnD2bOnIlNmzbBx8cHZmZmMDY2RqdOnWBlZYWKFStKPa9p06b5jtWqVQvAvxv+fe57vgdpn11OTo64yLBp06YCr/ml6xbExMRE6n9Xbdq0AVDwdyyNtM8IKPhzIiKi4sECABERURk3ZswYODo6YtKkSRCJRBgzZswX+8fFxcHOzg6ZmZno1KkT2rVrh5o1a0JFRQUAcPDgQdy9exe5ubmFzpCTkyP1eN7d5oIKElWqVCn0axSWsrIyJk+eDCcnJxw8eBCTJk3Cvn37kJ6eXqi1/59bv349HBwccPbsWVy7dg0XLlyAq6srqlWrhv3796NXr175zpH29IE8eXfhge//Hr702SkoKGDVqlVfLAQZGxsX2CZN3nf5X3nffWGeqJDnS58RIPk5ERFR8WEBgIiIqIwbNWoUFi1ahPj4eFhZWaFhw4Zf7H/27FlkZmbi119/xa5du/K1u7u7F/q1dXV1AQD//POP1Pa843n9ikPeQPNLg8QJEyZgxYoV2Lp1KyZMmIAdO3agZs2aGDFiRJFfr27duhgzZoy4sBIfHw9TU1MMGzYMr1+/llgaUBTF+T3kUVRURKNGjfDo0SN07twZHTt2/KZs0ly/fh3Z2dn5Bu8hISEAivc7JiKikvHl+YFEREQk9+rUqQM3NzesXbtW6jr7/8rKypJ6XBAEODs74969e4V+bRsbGygqKuLMmTPYv3+/RFtsbCwWLFgAAOjdu3ehr/k1eXe+85YfSFOzZk3Y2dkhIiICjo6OiI2NxaRJk6Cqqlro1wkKCkJMTIzUa1eqVAnv378v0rT3/yrO7+FzQ4cOBQCMHj0aDx8+zNeenZ2N48ePIykpqUjXzVte8nnhJSEhAb/99huA4v2OiYioZHAGABER0Q9g5MiRhe5rYWEBkUiE3bt34969ezAxMUFaWhpCQ0MRExOD+vXrf3Fw/bmGDRti2rRp2LBhA3755Rfs2rULLVu2xKtXrxAQEIC0tDQ0bNgQ06dP/9a3lo+xsTECAgIwfPhw9OjRA2pqagCQbwO6GTNmYO/evXB2dhY/lq8ojhw5gp07d6Jly5bQ19dHnTp1kJKSgnPnzuH58+eoU6cOdHR0vvl9FOf38Ll58+bB19cXERERMDAwgLm5ORo3bgyRSIQnT57gxo0bSEpKQlRU1BcfFflf9erVw6pVq3DixAl06NABKSkpCAwMxNu3b6GtrV3k5RVERFT6WAAgIiIqZ5o3b46NGzdi/vz5CA0NRWhoKABAVVUVO3bsQFBQELy8vAp9PRcXFygrK2PDhg0ICQkRTwkHADMzM3h4eEBDQ6PY8k+fPh2HDx9GfHw8du7cKZHjcy1atICFhQXOnz+PYcOGoXbt2kV6nW7duiE4OBh3797F3bt3JdqMjY2xd+9e8Xr9b1Hc30MedXV1BAcHY/r06fDy8sL58+dx/vx5cbuysjL69Okj3nCvsHr16gVtbW0sXbpUYnaCvr4+jh49iho1ahQ5KxERlS4WAIiIiMqQUaNGoUOHDmjdunWh+v/yyy/o3LlzvkfWTZs2Dba2trh8+TISEhKgpaUFS0tLVKtWDXXq1IGpqSkaNWokcU7ezuz/HfQqKipizZo1cHBwwIULF5CQkIBKlSqhTZs2BeZ0cHBAUlJSgTvpA8CkSZPw888/o3r16hLH69Wrh/v37yMoKAiPHj3Cp0+fCrxGgwYNAOCb7k7b2trC1tYW4eHhiIqKQkJCAmrVqgVDQ0OpG+h96T2pq6tj7dq1+YoQ3/I9FOazq1GjBjw8PODq6op//vkHL168QKVKldCgQQO0adMm32f6JUZGRli7di2MjY1haWmJ0aNH48KFC3j//j309PTQtWtXKCsrS5zTsGFDrF27FoaGhgVeS5q+ffuiXr16BT5ikYiIvo9I4DarRERE9BVZWVnQ1tZGSkoK0tLSvnnju9J0584dtG3bFqamprhy5Yqs4xAREckcZwAQERFRgVJTU7F48WL8888/ePPmDTp37izXg/+PHz9i8eLFePfuHf766y9kZWVhzpw5so5FREQkFzgDgIiIiAr07Nkz1K9fH8C/09jPnj2LDh06yDhVwZKSkiTWttva2uLIkSMyTERERCQ/WAAgIiKiAn348AG7du2CpqYmunfvXuSN40pbeno6tm7dChUVFTRv3hxdu3aVdSQiIiK5wQIAERERERERUTmgIOsARERERERERFTyWAAgIiIiIiIiKgdYACAiIiIiIiIqB1gAICIiIiIiIioHWAAgIiIiIiIiKgdYACAiIiIiIiIqB1gAICIiIiIiIioHWAAgIiIiIiIiKgdYACAiIiIiIiIqB1gAICIiIiIiIioHWAAgIiIiIiIiKgdYACAiIiIiIiIqB1gAICIiIiIiIioHWAAgIiIiIiIiKgdYACAiIiIiIiIqB1gAICIiIiIiIioHWAAgIiIiIiIiKgdYACAiIiIiIiIqB1gAICIiIiIiIioHWAAgIiIiIiIiKgdYACAiIiIiIiIqB1gAICIiIiIiIioHWAAgIiIiIiIiKgdYACAiIiIiIiIqB1gAICIiIiIiIioHWAAgIiIiIiIiKgdYACAiIiIiIiIqB/4fT+pozpOnm1cAAAAASUVORK5CYII=\n", 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VYX0DqtraQ7aZWVFB/urSwHmq377NohXg7SVJMlssWn/4VIX2D6hONm9cb93u3ae/PD2dL3tSOEC7e+d2ZWZmVstjz/3eNitt+MjRJR4bOCs/P197t6+zlrv3HVriPl0vHmLd3rWl8q7RfHxt93H8/AMd1klLSbRu14qs51K7taJs9XZu/tNJTaDyhF3UWUZvb0mS2WTSmcXO09pnxZxQ2i7bPYjwvkUHXVREm6iZLBZLjfkBUDp8C6lh4uLi9M0332jJkiXaunWr4uPjlZubq4CAAHXs2FFXXHGF7rjjDjVo0MDlNhcuXKi5c+dqxYoVOnXqlLKyshQQEKDo6GhddtllGjNmjC699FKX2srOzrYGIz09PeXr6ytJSk1N1VdffaWvv/5a+/fvV1JSkgIDA9WuXTuNGjVK9913X7FB5Pz8fGVlZUmSXdD5bDk9Pb3IPoGBjr+8VKXyfJ0LS0hI0PTp0zVnzhzt379faWlpioiIUMuWLXXrrbdq7NixCggoSCmckZFh/bD19vaW978Xqucq7vd48uRJffLJJ5ozZ46OHDmitLQ01apVS126dNFNN92kW265xaWbI8W1n5CQoK+//lrffvutDh06pPj4eFksFk2ePFkPPfSQw7bWrVunb7/9VkuWLNGxY8eUlpYmHx8f1alTR/369dPIkSN1zTXXyGgs+zids+/fmTNnav/+/UpMTFRwcLDatGmjMWPGaMKECQoKYs3D81V2bp4Ox8Zbyz1aNipxn56tmli3j51JVFpmtoL8fSukf4W9O3epNX12q/p11K6R45ss6/cctm4P6d5evv8GLoCKFnt8l3Xb08tHtRt2LHGfBs17ae+/M53jCu3vrsy0BP0283Ed279O6Slx8vLxU2BwbdVv1kOtug5Tw5a9z4s2UXPsPp1g3e7WsE6J9b08PNSpfpTWHCoYzLEnNkHD1bxc+rLlWKzu/maxdp6KV0pWjkL8fNQgLEi9GtfTtV1aqkFY6bNkVESbQHEO7t9r3W7f0Xn2AEnq0MmWQcBisejwwX1q16FztTr24YP7tWWzbXBh4ZnUQElOHz+o3BxbtqQWbXuUuE+r9rbBq0cPld81WkkO7rVllGrcwvG1pI+fbVmkjDTX0mafTTMuSUcP7ixb54ByFtjWdm2Xse+ITKlF72GeK2n9Pwr6d7+gtkWvDSuiTQAACiPwXEPk5uZq0qRJmjJlisMR1BkZGVq7dq3Wrl2rl19+Wffee69efvllBQcXfwNoz549uvPOO7V69WqH7e3bt0/79u3TRx99pMGDB+uTTz4pMaA9cuRILV68WJI0ePBgLVq0SCtXrtQNN9yg06dP29VNT0/X+vXrtX79ek2bNk2LFy9Wq1ZF18H77bffNHz4cIfHe+ihhxwGJPPy8s6b0eEV8TqfNX/+fE2YMEFxcXF2j8fHxys+Pl5r1qzRq6++qjlz5qh79+5q1KiREhIKboq+8MILmjRpksN2Hf0eZ8+erQkTJigjwz4NZEJCgpYuXaqlS5dq6tSpmj17tlq2bOm0347anzt3rsaPH6+UlKJfKh2NTDtz5ozuuecezZkzp8hzOTk5iomJUUxMjGbOnKkuXbro008/VdeuXYvULcnq1at13XXXFXn/pqamWt+/U6ZM0ddff61LLrmkmFZQlY7ExluDuZLUpE5kifs0rmM/0/jQ6TPq1NT1AT2lkZ9v1j+Hj+mjX1bq62VrJUn+Pt6aev9NDtfNNZvN2nbouLV8Nkgel5yqLxav1h//7NGJ+CT5+Xgrulao+rRrrpsuu0jRtUIrpP+oWZJiD1q3QyMauzSoJyyqsXU7sdD+7kqOj9GaX9+1eyxOO3Vo5x/6c/4batSqj0b+52NF1HX+mVTRbaLmOJyQbN1uHO5aELZReLA18Fw4Tbe7lu2NKfLYthNntHDHIb306xrd3LOtXru6vwJ8XB+4VBFtAo7k5+fr1EnbtU79Bg1L3Mff31/htSKUmFAw2PBozJEyBX+r8thzCs127tKtp5o0JTAA150+fsi6bfTwUGSdkt+7taMbW7fTUhKUnpaswKDQCuidTVZGmpb/8rW1fMkQx+vN1om2DQQ+fmS3zpw+psg6xX8fSzxzUscKBc9PHqu8rFWAMwFNbX+LmYePubRP1hHb51BA86ID5yuiTQAACiPVdg2QmJioQYMG6bXXXnMYdD531mp+fr7ef/99zZ8/v0jdszZs2KC+ffs6DIZ6eRW9WbR48WL16dNHe/fuLfKcM6tWrdLgwYPtgnaO2o+JidFll12mpKSkUrV/vqvI1/mnn37SqFGjigSdJdkF3Y8eParLL79ce/bsKWXvbebNm6ebb77ZLujsqP+bN2/WpZdeqoMHSxdYmD9/vkaPHm0XdC58DucGno8dO6Z+/fo5DDo76teWLVvUv39/LVu2rFT92rZtW5H3r6MBDUePHtWQIUP055+k8zofpWRk2ZUjQ0qenR4WGCCPQgG11HPacMc1L05Ts3FPq9m4p9X4licVft2D6vPw69agc8vo2lr6+qPq266Fw/2TM7KUWyjNfbtG9bTw723qePckTZo5X39u36eDp85ox5ETWrxpp57/6me1vfM5Tfr6Z+Xnm8vtPFAzZWemWrcDQkoexCFJ/kERhfZ3bcZKeYjZu1ofPtOrXNN7V0SbuHCkZtmWSogIcm05mFoBtjXSC+9fkUxms75ct0OXT/lWiRnZJe9QRW2i5srISJfZbLtmCa8V4aS2TVhYuHU7PT3VSc3z79h5ubla8POP1vK1o28qdRuo2TIzbNdYQcHh8nBhIH5QiP37OzO94q/TPn/3cSUnxkqSmrTspH5XXO+wXvtu/eXpWfDd3mKx6NO3H7b72yzMYrHok//ZP5+VkUpqVZwXPAvdf8iJS3RS0yY33nZv1DO4aEbHimgTAIDCCDxf4PLz8zVq1Ci7gJafn58ee+wxrVu3ThkZGcrJyVFmZqbWr1+vF198UfXr13faZkJCgkaMGGGd+SpJXbp00dy5c5WcnKzc3FwlJCRo1qxZatHCFvg4duyYrr76apfXrEpMTNSYMWOUk5OjTp066bvvvlNCQoJyc3OVmZmp+fPnq3Xr1tb6J0+e1HPPPVekHU9PTwUEBFh/CvPx8bF77uyPo1mCla0iX+cjR45o7NixdmssDx8+XCtXrlRWVpZyc3N1/Phxvf/++4qIiFBqaqpuueWWYr+oORMfH6877rhDFotFzZs316xZs6y/x+TkZH3//fdq27attf7Jkyd13XXXubz+c0JCgm6//XaZzWa1bdtW33zzjc6cOaO8vDzFxsZq1qxZqlPHlq7SZDJp1KhRdsH5Bg0a6LPPPlNsbKx1XeiFCxeqT58+1jrp6ekaNWqUjh496lK/8vPzdcsttygjI0MRERF6//33deLECeXm5iojI0OLFy+2S42elZWlUaNGKT4+3kmrqArpWfYp+n29XcuG4FdoxlZ6do6TmqUTl5yqkwnJOpmQrNjkVLsg8m2DLtbK/z2hLs2Ln6GQkmH/v2HX0VMa88pHRQLsheWaTHrj+0Ua/84X7p8AarSc7DTrtpeXn5OaNl7etnqmvGzl57v2+VCckIiG6jnwLl1792ca93+LNfHNf/Tw5D2684UVGnLzm4qMtn0m5Wana9Y7o5SadNJJixXTJmqe9Bxb4NjPy8XPmkKfSem5eW4d3yCpa4PaenRgD824dagW3T9am58epw1P3qo5/xmpBy7tpvAA27IRu08n6NYvf6n0NoGSZGbYpwz19XFtuRNfX9vnTWZG2dZZrqpjL1+2WEmJBd8bAwICNWiI44xfQHGyMmzXaN4+rl2jFV5rWZKyM0tO1+uORXOma8m8z6zHfujFGcVmzwkMDtOlV91qLa9f8bNeuG+Idv+zWvn5+ZIKvrPv+WeNJt1/pdb+MdeuLbPZrJzssq+3DpQXzyDbfUxzlmuD8/Izbd/tPQMDijxfEW0CAFDY+ZFLGBXm9ddf14oVK6zlli1b6rffflPTpk3t6vn5+alnz57q2bOn/u///k9Tp05VSEiIwzYffvhhuxmco0eP1jfffGM3kzM8PFw33XSTRo4cqWHDhln7sHfvXr3wwgt66623Suz7hg0F61PdeOON+uqrr+za9/Pz0/Dhw3XxxRere/fuOnLkiCTpq6++0ptvvmm33vOQIUOs6zhnZ2fLz8/2Jerdd9/V3XffXWJfqkJFvs4PPvigXWDaUdrs6Oho3X///brmmmvUr18/bdy4sUznsWnTJklS3759tXjxYrvfTUhIiEaPHq3hw4dr+PDhWrp0qSRp69atev/99/Xwww+X2P7Zfl111VWaM2eO3Qz+qKgo3XST/Wj/d955x/rekqTu3btr6dKldu/3gIAADR06VEOGDNFdd92lTz/9VJKUkpKiu+66S7/99luJ/Tp9+rROnz6txo0ba9WqVYqOjrY+5+/vryuuuEKDBg3Sfffdpw8//FBSQZD+ySef1GeffVZi+6g85kIj3Q0Gg8vrfRee8ZxfhkEbZfHlkjWas2qTnhxzpR4ddYXDQTR5pny78nNfzpPZbFHj2rU08erLdXHb5goN8FNcSpqWb92jqfP/UPy/az59/+dG9e/YSuMH962U88GFx2Kx/S0YXPxbMho9im2jtGo37KDH3nOcVSO8djM1atVHva98QH/8+JJWzntVkpSVnqjlc/6rq+/8sNLaRM1U+PPG6OIgSI9C9dz9rJk4oJsevtzxmp4ta4drYOvGeviy7rr969+0Yl/BQLy/DhzXgm0HNLyj45S+FdEmUJJzB8u6/HnjYfu8yTfnO6l5/h177g+2NNtDhl1t950LcEWZrtE87K/RzGX8u3HFuhU/6+O3HpRU8J3svmenq1Hz9k73ufX+V7V94wqdPl5wnbZ943I9fedyefv4yj8gRJkZKXbrWg8dc59++fZ9a5kZzzgfGIy2az2Li9d6lkKpm9R6AADqJElEQVSZygweRf+eK6JN1EwWi0UW84X/v5LPA6D0CDxfwNLT0/X2229byyEhIVqyZIkaNnS+Vo+np6fDdY+lgtmos2fPtpZbtGihr7/+utj1kP39/TV37ly1bNnSOpNz+vTpev755xUUVHK62vbt22vGjBnFtl+rVi29+OKLuu222yRJaWlpWrFihYYOHVpi2+ezinydjxw5ogULFljLgwYNKnatZqkgAP3dd9+pV69eZf6gDQkJ0Zw5c4q9AeLr66sffvhBLVq0sPZ/ypQpevDBB10K8kVHR2v27NlF0safy2QyacqUKdZyYGCg5s2bV+wgC6PRqI8++kibNm3Sli1bJEmLFi3Sjh071L698y+5UsEX4u+//94u6Hzu81OnTtX69eu1efNmSdLMmTP1+uuvKzLStRS0xYmLi9OZM2dKtc+BA6xj5UiAr49122KxKDs3T77eJa8/mVlo5lqQn2uzXVzx0wv3Kfff4HFunklxKWnavD9GM5et1ZaDx5SelaPnvpyno3EJeu/eomkWA8/pS0Z2jnq3aaafJ92vIH/bc43rRKhnqyYaO7C3Bj75Px2JLZhF89q3v+r2K/qcF5khULWSzhzRpy8OcFqnx+X/0YBr/s9a9vaxpWXLy3UtBX3heh6e3vL0dP6/3plzg9jF1Rk45kWlJh7Tlj8L1hDcvvY7XTXuPXl4Fv3br4g2UTMF+Nje21l5rs3szyxUL8in7H8bkuTpwk3E8AA/zR4/XL3fmqkjCQUpVb/duLvYIHFFtIma59knH9S6NX8V+7ynp4cWLbcNLPX3t5+JlZPt2myu7Gzb501AQNnSiFbFsU+eOGb3+oy8zvGat6hZJoxoLrOTLDEde1ymByd9bi37+tned7nZrl2j5Z4zI9jPv+R7PGWxbvk8/e//bpI5P18Gg0F3PzVNlwy+ocT9gkLC9d8Pf9ebT92g/Ttt/yNyc7LtAs5GDw/dNvE11a7XxBp49vDwlJ8/6YRR9Uzptr8zDxfvK3gU+l5vSsso8nxFtAkAQGEMUbqAzZ49227N4+eff77EoHNJvv76a7sUyC+99JJ8fHyc7CGFhYXpySeftJbT0tL0448/OtnD5rnnnisxmHjttdfKo9BI261bt7rU9vmsIl/nWbNm2QWQX3rppRL706NHDw0fXvZ0bQ888ICioqKc1gkNDdVjjz1mLcfExNjN1nfm8ccfV2BgyV8KFy9erJMnbalNH3zwwWKDwmd5eHjo1VdftXvsiy9cSzc8cuRI9ejheJbPWUaj0e53kJubazfooKw++OADtW/fvlQ/I0eOdPu4F6IgP/u/vaT0klOuZWbn2s0sPjfY646o0GDVjwhT/YgwNa0bqYtaN9W9wy/V6slPa9LYEdZ6n/z2l5Zu3lVk/3OD4B5Goz575Da7oHNh0bVCNeUe2w3M4/FJ+ufQ8XI6G1Rn5nyTUhNPOP3Jzky228fHz3ZDMisjSa7IyrC14eNbMTc0Hek3wvaZmpOVplMxW8/LNnHhCCy0RENypmvBqpRCy0EEuhl4dpW/t5fu7tfZWl596MR52SYuHMlJiYqLPVXsT+zpU3b1zw3cpqa6tu5sWqF65y7R5KqqOPZPP862zrRu1rylOnXuVqr9cWFKiDuuhLgTxf6kpSTY1S8cNM5IT3Zp0HlGWrJ9GwHlf522askPeuvpG2Uy5clgMOg/T7ynwddOcHn/yDoN9fpnf+mRl79W196D7fro6xegiy69Rq9/ulJX3/ywTh7db30uMDisXM8DKKvCQV6vsGCX9vEKtdUzpTsIPFdAmwAAFEbg+QK2bNky67a3t7fGjx/vdpurVq2ybgcEBLgcqBo7dqzd7LjC7RTHy8tLI0aMKLFeYGCgXerwmJgYl/p0PqvI13n16tXW7UaNGumiiy5yqe0bbih5RHFxzk137Wo9V94nUkEaclec297YsWNd2u+KK65Q7dq1S90vV8978ODBCg8PL3X7qBwNo2rZlY/ElrwO97l1GkaFF1Oz/BgMBj15/ZUa1NW2luwXv68uUi/I31e1Cq3p1KlpAzWp43yG/cAubRRcKDC9+yhr06JsQiNsA+CSzxxxaZ/C9QrvX9Ei67WyC5SnJZ92Uvv/2bvr6Ciuh43jTxRIkEBwLe4ORVrc3b1GhUKxCtAWCpVfaaGFQrECNaBocStWIHix4m4FgpMQSAjxef/Iy5AlG9m4fD/n5HTv7J07d9JdsjvP3HuTr02kHYUjXPi76v0oVvtErFcoR9LdmFGt0LPPRQ/8A8yZOFJam0ifnJydlTPXs5teb3hei3Gf4OBg3b3z7N/kfPkLpopjh4WFafWKJWa5E6OdEUe58j37jBUcFKgH929FUzvcnZv/mY8zZnJVlmzuUVeOg23r5umH0a8qNDTEDJ1bdbV9qTQHBwfVb9FTY6as0yIPby3y8Na8Lbe1eKePPvnuT5WqUEuSdOH0s+XFipSomGDnAcTHkwjfvV1eiN3fh0wR6j25Gvm7e2K0CQBAREy1nYbt27fPfFylShW5ubnFu82n6/VKUrVq1ZQxY+xG8eXNm1dFixbV5cuXJSlW6wWXLVs21u3nypVLFy6E35366FHsLtSlZIn5e346ZbSkGEfjRmRL3Yjc3NxUpkyZWNUtVKiQ8uXLp1u3wr/kRvw9RCVfvnzKnz9/rNqP2F727Nlj3S97e3vVqlVLa9askRQ+qj40NNRipL01sQ31HR0dVb16dW3ZsiVSP5H8srlmUr4c2XTLO3wkyvHLnqpTtni0+xy78mxEcM6smZUrW9KFAa1frKgt/z/S+VQUXwjLFs6v3afC/83M7259qvmI7O3tlSd7Vj36/xF43kytBUnZcxfVsKlXoq0TMWSVpFwFypqPA5/4yvvOZeXIU+z53SxEHBWcq2DZqCsmAnuHZyNQo5uuMrnbRNpQOs+zm5RO3Ix5uQzDMHTq5rMbncrkSdgL/tF5fgrtkLAwOSvmaeeTuk2kDV+P/1EBgVHPAmCnyMt/FCteSvfv3ZUknTtzKsZjXDh32mJ95uIlSsWhp0l/7D07t+v2rfDPe45OTmrbsauNvUVa9fOaS9GOWnbOkMmiXPCFMrJ3cFBYaPhNP5fPH1OOXNF/z75y/pjF/gm5FM/G5bM0a/xgGYbx/9NrT1OLzv0SpO1MrlmU6bltwUGBOnbg2eCN0hVrJcixgPjyO/1sSbTMZYvL3tlJYUHB0e6Trcqza12+ZyIvqZYYbQIAEBHBcxp2+/azu6bLlSsXTc3Yi7hmbMmSJW3at3Tp0mYgevfu3Rjru7vH/uJZpkzPvjYExHIdrZQsMX/PEdsuUSL2a+cVK1ZMdnZ2Nq/zbMsxpPDzfRo837lzJ8b6tkwfH/H3EZff61NBQUHy8fGJ9jWaMWNGFSwY+9EKJUuWNIPn2Jx3TN57771YjwR/6uLFi0y3HYUapV7Q2n/CL6xsPXpG77ZpEG39bUfOWOyblOwjXPAJM8Ks1nmxTFEzePbxi90abj4RphjP5vr8pRqkR/b2DsrmbtuIsFz5yyhDpqwKfBJ+k9ilk38rR56oLyKGhYXp8ikPs1yweNxugooLv4d39MTP2yy7Zo1+ZoDkahNpR/XCec3Hl+756Jr3IxXOEfX0hydu3tO9CP82Vy+cJ8q6Ce38nWevYxdnR7k4x3+t8sRoE2mDW3bbZ46pWKmKDvwTPovQvj07Yqy/b89O83HBQkXidMzkOPaKpQvMxw0bN1eOHDlt6CnSMvfc0S8p9Twn5wwqUryCGSYf2/+3arzUKtp9ju7/23xcsnzCfUZbs3Cyfps0XFL4rE4DPp2h5p3eTrD2rdm7dbn8/Z5NeV+zfttEPR4QWz6HTpiPHTJlVPaXq8tr2z9R1ndyy6ps1Ss82//giUh1EqNNpFNhhoww264Tp0rp4RyBBMZU22nU48ePFRQUZJYTYrSzn5+fxbrD2bLFPEouooj1fXx8Yqwf09rOUbE1GE1pEvP37Ofnp9DQZ9MWZs0au7VcpPARj7FZR/l5thxDsv11kiVL7EeSRmwvPr/X59uyJj7n/fjxY4vXQFzkzp1b5cuXt+nH1psE0pMOdaqYjzccPKHr97yjrOvt+1gr9vxrdd+ksOP4efNxAXfra5N1qlvVfHzown96EMMaTUcvXde9h35muXj+6NdsB6Li4OikUlVbm+UDf8+Otv7Zw2vk+yB8JJednZ3K1uiYmN2zcHDrz+ZjBwcn5S1cKUW2ibSjTrH8ypn52Y09v+49Hm39X/c8e75cXncVz5V061HO+eek+TjiFNkprU2kX42bPftbc+XyRR3cvzfKuqGhoVr+57MAt0nz1lHWTUnH9rp/Tzs8ngV/TLON+KrdsKP52OOv+QoM8I+y7vUrZ3T6yK5n+zbqlCB9WPr7OIvQ+b1RMxM9dA4M8Nf8n8aY5eJlq6lU+RcT9ZhAbAXcuCOfg88+873wbvT/1hd6q5vsncJv3gv2eaT7W/dFqpMYbQIAEBHBcxr1/BRHCRHGxrfNiPUTcgqmtCYpf8+2th1xCrjE2idi/di8TmKa7jqiiO3F5/f6fFvWxOe8Y9M+klbbWpXl9v+jfENCwzR0xqIo/x8P/WmR/APDb/zJ6pJRHepWSapuasmOg1q599l0+k2rWZ/tokapF1TxhfBREAFBwfr0txVRthkcEqrhPy81yzmzZtaLpYomUI+RHlWr/5r5+PbVY9q3YYrVek/8HmjD/OFmuViFJjaPsI6rc/+u145V31ocO6OLbTcUJUWbSFsc7O3Vo/qzaQxn7jyiEzesT7m97/IN/XHg2RS+fV4sn+j9k8I/D322Zpf+ufJsKYd2FeN341pitAlUrFzVYsrqb74aqSdPrIdoM6f9IM/rVyWF32zbrqNtswYl17FXr1iikODwqVFz58mnui83jHunAUn1W/WSg0P4xIi+D731++QRVuuFhARrxjcDzO/IufO/oArVo58RKjYW/PS5FswYLSn8/TBo9M9q1uHNeLcbnaDAAI0f0V33bl01t/Xp/1WiHhOw1fW5K83H+bq0VO42Da3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\n", 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9+pg+fTrkcrnZ4nKGNqsPNzc3rfOXL19upUhMZ2p71CQlJQXNmjXDvHnzoFQqtZbNyMjAiBEj8PHHH6teO3bsGOrVq4f9+/frXFdOTg6++uorDB061GzfW6mpqWjfvj0+++wzZGRk6Cz/9OlTDBgwAJMmTTLL+u2Btdrz48eP0bBhQ/zyyy869xUAuHr1Klq1aoXFixcbtB5zS0lJwcGDB3WWmzFjhhWi+Z+ienyQy+UYNmwYhg4ditjYWBOj1Z89nuc563ttK/a+va1bt0ZYWJjG+VFRUQbFvW7dOigUCo3zhw0bVuA8yN7fI3tWuXJlnWWePXtmdP32cm7uaL89nW2f5jmd44qMjESLFi1w/fp1jWUs1XYSEhIwf/58VK9eHYcPHzZo2cTERNVvQX3OJ2/fvo2OHTti3rx5xoZLhejRo4fWx/Xs27cPcXFxete3ceNGrfNHjBgBkUikd31EREUFE89EdiQ7Oxtdu3bVeWJTmG+++QZ///23BaIyzIIFC9CuXTs8fvzYpHpu3bqFRo0amW2bTpw4gaFDh0Imk5mlPmPdv38fnTt3Nui5ZatWrUKjRo2K9LPOvvzyS3Tv3h2JiYkm1SMIAubNm4c2bdrolVjTxRnarL50DT335Zdf4rPPPjP5M7ImY9qjJtnZ2ejevTvOnj1r0HJz587Fjh07cP78eXTp0gVJSUkGLb9u3Tr89ttvBi1TmOzsbHTu3NngCywA8PPPP+ODDz4wOQZbs1Z7TkhIQKtWrbReTCuMQqHA+PHjbXqTR1RUFHJzc7WWqVGjBlq0aGGliIr28eHTTz/Fn3/+aUx4RrPH8zxnfa81scbFTXva3sKIRCK8/fbbGucLgoC1a9fqXd+aNWu0zh89enSB1+z9PbJn+iSMjN3P7eXc3BF/ezrTPs1zOsd18eJF9O3bF9nZ2TaN4/Hjx+jevTvOnTunV/nU1FS0bdtWr8fRvEoQBEyfPh0//PCDMWFSISQSCcaPH69xvlKpxLJly/SuT9v3iFgsxogRIwwJj4ioyGDimciOnD59Gv/884/Ry9u619nGjRvxwQcf6HWnrz4yMzPRr18/k3vEpKenY8iQITovlgOWu3P2VYmJiRg6dKheZXft2oW3335br9id1bJlyzBz5kyzfjZRUVEYPHiwyfuqo7dZQ9SrV0/rfIVCga+++gqlSpVCly5d8MMPP+DEiRPIysqyUoTGMaQ9anP69GkcO3bMqGXHjh2LPn36ID093ajlp0+fbnKi7PTp0zhx4oTRy8+fPx979uwxKQZbs1Z7HjduHG7evGn0ehYsWGD0sqY6c+aMzjLt27e3QiQvFeXjQ2xsLH788Uej6zeGvZ7nOeN7rY2lE8/2tr2ajBgxQutzOHUlk/NcuXIFly5d0ji/WbNmqFGjhtprjvIe2atbt27pLBMUFGRU3fZwbu6Ivz2dbZ/mOZ1jys3NxZAhQ5CZmamzrDXaTnp6OgYNGqR1RIw8H3zwAS5evGj0urgvmNfo0aPh6empcf6KFSv0+lyvXLmCq1evapzfrl07hISEGBUjEZGzY+KZyIncvHkTR44cscm6r127hhEjRmj9AVC5cmUsW7YMsbGxyMnJwYsXL7B9+3atCa20tDT06tXLpLvFz507Z5bhwry9vdGjRw/8+OOP2Lt3L+7cuYOEhATk5OQgNzcXL168wMWLF/Hbb7+hUaNGOmPauXOn1jJ37tzBgAED9DohdlYXLlzAuHHjLFL3jh07bD6slS3brKE6duyoV7nc3FxERkZiypQpaN68OXx9fREREYHx48dj/fr1ePTokVnisXZ7tKQnT57g3r17Ri+fmpqKDRs2mDEi44wZM8YsPUUdlT7tecuWLdi8ebN1ArIAfS6uNm7c2AqR8PiwZcsWjcM4isVivPvuuzhx4gRevHgBuVyOjIwMPH78GGfPnsWaNWswdepUNGjQABKJRK947Pk8z1T29l7rYunEs71trybBwcHo1q2bxvk3btzAv//+q7MeY3o7O8p7ZK/Wr1+vs4ytLuSb49zcEX97cp9WVxTO6fS1b98+iEQig/+mT59u8LquXbuGmJgYk2P28PBA586d8d1332H37t24desWnj9/juzsbMhkMiQlJeG///7D8uXLdY7Sc/PmTZ0jaPzzzz/svW5nAgICMHjwYI3zHz16pNdN05s2bdI6/6233jI4NiKiIsP6j5Umclx+fn4CAI1/HTp00Lr8rl27tC7/6l+LFi2EvXv3ComJiUJ6erpw4MABoU6dOjqXmzp1aoH1fvjhh3qvV5+/Ro0aFVhH7969tS7TqVMnISMjo9D3JScnR+jatavW5RctWmSW99XT01OYPn268O+//wpJSUlCRkaGcO3aNWHLli3C0KFDhT///LNA/U2bNhXWrVsn5OTkaP18X6VUKoVJkyZpjaV///5a6xgwYIBe21S1alXhp59+Ev777z8hOTlZyM3NFZ4+fSpER0cL3333ndCgQQNV2aysLFX91tgvdH02LVu21PoedOrUSed6S5UqJfz666/CvXv3hJycHCEhIUHYtWuX0KJFC53L+vn5CYmJiRrXb6s2m59MJtNZz+PHj3XWY6p69eqZZV+pV6+e8MMPPwgJCQkGx2Cr9rh+/Xq9tk0ikQhTpkwRrl+/LmRnZwuxsbHChAkTDHp/+vXrJ5w9e1bIzMwUnjx5Inz99deCSCTSukyvXr20xm/IvjxgwAAhKipKSE5OFjIyMoSLFy8KH3zwgSCVSnUu+9tvvxkdg67vA0svb432XL9+fZ11lCxZUli0aJEQFxcn5OTkCI8ePRJWrFghhIaG6hV7cHCw1hhM0bRpU53r//fffy22/lcV9eND//79NS43ZcoUvd/HlJQUYc2aNUKXLl2EjRs3aiznKOd5zvBe6zpeffbZZzrXtW3bNq11tG3b1m621xR79+7Vup0TJkzQurxCoRDKli2r9XsgMzOzwHKO9B6Zk6m/hQVBEL766iu92vLVq1cLXd7W3weGxuAovz2tvU/znM7253R5dLVrY/+mTZtWYF1RUVF6L+/m5iZMnjxZiI6OFhITE4XMzEzhxo0bwo4dO4RRo0YJixcvLlB/27ZthZUrVxb6va3NjBkztMai67utXbt2OrenWLFiwoIFC4S7d+8KOTk5wuPHj4U///xTqFy5st7vhza63tvatWvb3fKzZ8826z5XtWpVtfovX76stXyXLl20bpMgCEKVKlU0Lh8QECBkZ2frrIOIqKhi4pnIANZKPI8YMUJQKpUFlk9NTRUqVaqkddnWrVvr3A5dCcfZs2cb9L5cunRJa33BwcFCcnKy1joSEhIEHx8fjXWULVu20PdEEPR/X8uXLy/cuXPHoG0zRUZGhuDh4aExnpIlS2pc9sqVKzoTTQCETz/9VFAoFDpjOX/+vNCxY0etJ8bm3i8EwbSLCmfPntW5/dWrVxeePXtW6PJKpVIYPXq0zjpmzZpldPzWarP2kng+ffq04Orqqtd7os+fj4+P8Pnnn6vdEGEpprRHQdA/8bxmzZpCl3/99df1Wn7ixImFLq8reR0SEqI1fn335QULFmisY+/evYKLi4vW5WvVqmV0DPaSeLZUez59+rTOdVeqVEmIj48vdPnExEQhPDxcZx2WvEhZq1Ytneu/deuWxdafh8cHQWjdurXG5ZYtW2b4m6qFo5znOcN7LQi6E88zZszQWYcpiWdrb68pFAqF1gROiRIlBJlMpnH5f/75R+v79O677xa6nCO9R+ZkzG9hhUIhvHjxQvj777+F7t2769WWw8LCNMZg6+8DQ2JwpN+e1t6neU5n+3O6PPaYeC5VqpQQExNj8W3PI5PJhMDAQI3xeHt7azyWXL9+Xef2lClTRrh3716hy6enpwtNmjTRWYczJp7z05WI/vDDD3XWkZ+27zaJRCI8ePBA47IXLlzQGo+mcwQiInqJQ20T2ZlKlSphyZIlhQ6j5+Pjg3feeUfr8nfu3LFUaBpt27ZN6/xx48bBz89Pa5lixYqha9euGuc/fPgQFy5cMCo+AHB1dcWePXtQsWJFo+swlKenJ8qUKaNx/pMnT/DkyZNC523btk3nc4s+/PBDzJkzB2Kx7q/yiIgI7N27F25ubjrL2osdO3ZonS8Wi/Hnn3+iRIkShc4XiUT45ZdfEBYWZtJ6dHHENmushg0bYvny5VqfqWiItLQ0zJ49G02bNkVcXJxZ6tTElPaor86dO2PIkCGFzuvevbvO5cuUKYNvv/220HkjRozQuuyDBw9MHqq2Y8eOWp9p17FjR0yYMEFrHVeuXMGDBw9MisOWLNme9RnObeXKlShVqlSh8wICAvDnn3/adBjLnJwcnWW8vb0tHgePD9B67F+8eDFevHihf6A6OMJ5nrO81/rQ57zPUvXbYnu1EYvFGDlypMb5z58/x759+zTO1zXM9pgxYzSuVxN7e4+sqbAheSUSCYoVK4aOHTvq/ViTL774wqQ47OHc3NF+ezrjPs1zOsckFouxbds21KhRw2rrdHFx0dpW09PTcfv27ULnRUZG6qz/t99+Q/ny5Qud5+XlhbVr1zrUdRpHMnHiRI3zFAqF1iHSN27cqLVuDrNNRKQdE89Edmbq1KlwdXXVOD8iIkLr8snJyWaOSLf9+/drna/vs2GrVaumdf6BAwf0jim/0aNHm+XHy+PHj7F8+XK8++67aNeuHSpVqoSgoCB4eXlBLBYXuNii6QdKHk0/4v/++2+tywUHB2P27NlGb4cj0LVftWrVCnXr1tVaxs3NDePHj9da5uLFi0hISDA4vjyO2GZNMWTIEOzbtw8VKlQwW50XLlxAly5dkJaWZtBy1mqP+tL2vFld32/Ayx+vmi461KpVS2eywdR9afLkyTrLTJgwQefzRaOjo02Kw5Ys2Z5PnDihddkGDRrg9ddf11rmtddeQ7t27bSWsSR9Loqlp6dbPA4eH16eB2hy7tw5hIaGokePHpg1axbWrl2Lc+fOITU1VWfMhXGE8zxnea/1YelnPNvb9uoycuRIrckbTcnlrKwsbN26VeNyDRs2RHh4eKHzHO09ciStWrXCwIEDTarDHs7NHe23pzPu0zync0wDBw7U+cxyfTx//hyrV6/GhAkT0KFDB1SuXBnBwcHw9vYutO2cPXtWa32a2o6ufaFy5cpab7wDgAoVKqBnz55ay5BxunfvrvW6xfLly6FUKgud99dff2lcLjw8HPXq1TM5PiIiZ+Zi6wCISF2vXr20zi9ZsqTW+YYmbszh/PnzWuc3btzYLOuJiYkxetnhw4ebtO5jx45hxowZOHbsmM6eyIZITEws9HVd7+mgQYPg4eFhtjjska6eT/pe6O7QoYPW+YIg4OLFi0b/8LdGm3VxcTHrfmeq1q1b4+rVq/j111+xcOFCxMbGmlznlStX8NVXX2Hu3Lk6y1q7PepDLBajTZs2GucHBQXprKN9+/Ya57m6usLX11frRbC0tDSNPTx1kUqlaNWqlc5yFSpUQMWKFbX2Arl06RL69+9vVBy2Zsn2fOnSJa3L6vud1qlTJ609+CzJx8dHZxlr3EzD4wPQtGlTrFu3TuP8jIwM7Ny5s0APw9KlS6NOnTpo3Lgx2rVrh8aNG+tMZDrCeZ6zvNf6sHSPZ3vbXl3Kli2LTp06Yffu3YXO37lzJ1JTU+Hr66v2+o4dO7QmzkaPHq1xnqO9R44iIiIC27dvN/k9sYff047229MZ92me0zkmU9vOmTNn8Pnnn+PgwYMaE4rG0NR2zLkv6OphS4YTi8UYP348pk6dWuj8Bw8eYO/evejSpYva62fPntX6e1fbaCtERPQSezwT2ZHSpUvrTE54eXlpnW/t5FRaWhqys7Otsi5jex25u7vrvKNZE6VSiQkTJqBly5Y4evSo2d/fwoYtTU9PR1ZWltblmjRpYtY47E1qaipyc3O1ltG3F0HVqlV1XqR9/vy53rG9yhHbrLm4u7tjypQpuHv3Lv755x98+OGHCA8PN+li08KFC5GRkaFxvi3ao75CQ0Ph6empcb62eXl07dPaem0AMOniSqVKlXTWn6d69epa5zvicIyAZduzIAhISkrSuqy+32nWHHowv5CQEJ1lLP34AB4fXnrzzTf1+l7JLz4+HpGRkZgxYwaaNm2K0NBQzJ07F5mZmYWWd4TzPGd5r+2FI26vtiRxVlYWNm/eXOD1P//8U+MyPj4+GDBggMb5jvge2bsBAwbg4MGDOoft18Uezs0d7bcn4Hz7NM/pzKdDhw4QBMHgP31uJs5PJBKZdDPbp59+ikaNGmH//v1mTToDmtuOrt89zrQvOKq3335ba3tfunRpgde03QTg6uqKwYMHmyU2IiJnxsQzkR3RddctALM9W9VcjL0gawxjL0hWrFgRLi7GDfAwceJE/Prrr0Ytayx93lN99hVHps9nHRAQoFddEomkQC8XY9ZXGEdss+YmEonQpk0bfP/997h06RJevHiBPXv24JNPPkHLli3h7u6ud13p6ek4fvy4xvm2aI/6KlasmNb5uoYolkgkKF68uNYypiTGddG3PQGAv7+/1vmm9By3JUu259TUVCgUCq1l9P0MDPmszK1KlSo6y5w6dcqiMfD48L/6Z8yYYfTyeR48eICPP/4YderUKfT57I5wnucs77W9cMTt7dKlC0qXLq1xfv4ks65nPw8cOFDr8+od8T2yR3nnkJGRkVi/fj0CAwNNrtMezs0d7bcn4Hz7NM/pHFOpUqX0Gl2nMJ999hm+/vprM0eknSAISElJ0VqG+4Lt+fv7Y8iQIRrn79mzB/Hx8WqvaRtmu1u3bjp/txMRERPPRHZF2wWOPJYeXs+e6erhpImxd85HR0dj0aJFRi1rCkftAWvPLPWess0WFBAQgM6dO+Orr77CkSNHkJSUhO3bt2sdRvpV586dK/R1W7VHfenqJaKrJ7iuXhcAIJfLDYrJVuxliEVD2bo961u3Ld/fBg0a6CxjynN6bcGRjw/Tpk3DjBkztD7fVl+3bt1Cx44dIZPJTK7LWMae5xW191qffdbUXur2tL36kEgkeOuttzTOP3LkiFoSbP369VqPqWPGjNG5Tkd7j2xJLBbDz88P5cqVQ7169TBq1CgsWrQId+7cwT///INOnTqZbV22PpYDjvfbM48z7dO23g8c4ZzOHhnbdi5fvox58+aZORrz4L5gHyZOnKhxnkKhwIoVK1TTp06dwv379zWW5zDbRET6KVpXw4nsnD4nm/aWxDL2eaLWpO/wsfktWrRI58W9tm3bYsuWLXjw4AFycnIKDDFVtWpVg9erz7Ngnz59anC9jkSfO0h1DXGWR6FQ6HxWm7F3rDpim7U2d3d39OjRA/v379frgoCmHm+2ao/6MvViga0vNhjyXF5dZR31jn1LtmdfX1+dF3L1/Qz0/e6zhBYtWujsIRQTE4OoqCiLxcDjg7ovvvgCZ86cwcCBA40+38lz9epVrFy5Uu01RzjPc5b3Wl/6JOjNMfKEvWyvvkaNGqVxXxAEAWvXrlVNaxtmOyIiAvXq1dNrnY72HlmapiF5FQoFkpOTcf/+fZw9exbLli3DuHHjUKFCBbPHYA/n5o722/NVzrJP85zOMRm7zy1ZskTnDbpNmzbFhg0bcP/+fWRnZxdoO40aNTJ4vSKRSGeynPuCfahRowbatm2rcf6yZctUw7NrG2a7dOnS6NChg9njIyJyRkX7ajgRmczHx0fnELLx8fFGPRco/9/Fixets1H/b8+ePVrnv/322zh48CB69+6NsmXLFvpDSdvzajXx9vaGh4eH1jLR0dEG1+tIfH19df7wvHr1ql513bx5U+cznhzhwrozmDp1KsqWLau1jKYL6rZqj0XF7du39e5teO3aNa3zdQ07XhSJRCKdQ4jeuHFDr7r0/e6zBH9/f7Rr105nudmzZ1ssBh4fCqpbty7WrVuH58+fY8eOHfjggw/Qvn17lC1b1uCbWrZs2aI27cznecaw5HudR1c9+jw79fLlywbFook1ttdcKlSooPX7ac2aNQBetvt///1XYzl9eju/ypHeI7Jv9nKuy31aO2c5p3Mmu3fv1jq/X79+iIqKQv/+/VGuXLlCz2uMbTu6blDkvmA/tPV6vn//Pvbv3w9BELQOsz1s2DCzjApBRFQUMPFMRCaLiIjQOl/T0Ln2LCkpSefdqV999ZXW+ZmZmUb3TK5bt67W+evXrzd5GEV7p2u/0vZsPkPKiUQi1KlTR9+wyAQikQjly5fXWqawH++2bo9FgUwmw9GjR3WWi42Nxd27d7WWqV27trnCciq6vmcOHTqkVz179+41QzTGGzdunM4yBw4csOhwoTw+FM7X1xfdu3fHDz/8gP379+PBgwfIyMhATEwMtm3bhunTpyMkJERrHWfPni3wmjOe55nKUu81oPvRDU+ePNE6X6lUIjIyUvsGGMiS22tOo0eP1jjv6tWrOH/+vCoBXRgvLy8MGjTIqHU7yntE9skez3W5T2vmLOd0zkChUOh8nvicOXO09nBXKBSIi4szav3cFxxH165dUbFiRY3zly5diuPHj+PRo0cay2h7rAcREalj4pmoCNI1TKahz2LSNdTM+vXrDarPHmga7jePr6+vziGx9+3bZ/RzrTp27Kh1/pMnT/DZZ58ZVbcm5t4vTKXrmcCHDx/W2TsqNzdXZ/KjTp06Rg+lai1yuRwikUjrn64L0ab64osvMHnyZNy8edPoOuRyuc7lC2tXtm6PRcWCBQt0lvn11191DgPZtGlTM0XkXJo1a6Z1/tGjR3HlyhWtZa5cuWLzZyh37dpVr5sL3n//fWzYsMGodQiCgPXr12PatGmFzufxQX8eHh6oUaMGevbsiW+++QaXLl1CaGioxvJJSUlQKBRqrznjeZ4lmOO9BnQ/4/LChQta569fv97oC+iGMNf2mlPPnj21ng/88ccfakNu59e/f3/4+vqaLR5zvkdz587Veh44YsQIs8VN1uco57r22O5twVnO6ZxBYmKiztFrKlWqpHX+sWPHkJqaatT6de0LFy5c0Dla3b1797B9+3aj1u9MLH09SiwW491339U4f9euXfjpp580zm/evDmqVKliUgxEREUJE89ERZCPj4/W+bqGUc2vR48eWudv2LABp0+fNqjOPGlpafj2228xffp0o5Y3lpeXl9b56enpWp8LKZPJ8OWXXxq9/l69eukcyuyHH37A559/rvOHFvByWMEePXogJydHYxlz7xem6tmzp9b5SqUSQ4YM0XqhZtKkSbh165bWerp3725MeEVOUlISFixYgGrVqqFt27ZYvHix1ruB8xMEAVOnTsWzZ8+0lissaWnr9lhUREZG4tdff9U4/8CBA/j555+11lGrVi2dvV+Kqq5du2qdr1QqMXz4cI0XvpKTkzFkyBCbX8AViUT4+eefdR6jZDIZBg8ejI8++kjvi3k5OTlYv349GjVqhEGDBuH+/fuFluPxwXgBAQF4/fXXNc4Xi8UFPltnPM+zBmPeawAoV66c1nqvXbumsWfUlStXtA4laUnGbq85SaVSDB8+XOP83377Dffu3dM4X1uPaXOwh/eI7JOjnusW1X3aWc7pnIGutgMAjx8/1jhPqVRi5syZRq+/S5cuOvfxUaNGaTwnzcjIwODBg51+NDt9WON61KhRozTuM3K5XOvjAdjbmYjIMEw8ExVBuu6W3rZtGxYuXIhHjx5BLpfrrC8iIkLrxVmlUonu3bsbdFHy3Llz+PDDDxEaGopp06ZZvDdnfkFBQXB3d9c4X6lUYuHChYXOUygUGD16tEnPKqxZsyb69++vs9ycOXNQs2ZN/PLLL7hy5QpSU1Mhl8uRkJCAc+fOYdGiRWjfvj2qVauGnTt3au2paO79wlT16tXT2fM7JiYGderUweLFi3H//n3k5uYiMTERkZGRaN26NX777Tety/v6+trsAq2jEgQBhw4dwvjx4xESEoL69evj3XffxbJly3DmzBnExsYiJSUFCoUC6enpuHr1KpYvX45GjRrp7FEbHBxcaE9KW7fHomTChAkYMmQITpw4gbS0NGRlZeG///7D1KlT0aVLF513mmu7i7yoq1evHho1aqS1zPnz51G3bl388ccfiI+Ph0wmw+PHj7Fq1SrUqVMHly5dslK02rVo0QJTpkzRWU6pVOK7775DWFgYJkyYgL179+LevXvIyMhAVlYWHj16hAsXLmDp0qUYMWIEypQpg0GDBml9/irA4wMA/Pnnn3jzzTexbds2ZGVl6b1cdna21vOxkiVLFhiO0hnP8wxhzfcaABo0aKCz7n79+uHnn3/GkydPkJOTg1u3buHrr79GkyZNkJiYqHeMhbH29prb22+/rXGethswa9WqhcaNG+u1Dkd/j8j+2Ppcl/u0YZzpnM7ReXp6olixYlrL/PLLL4W+LggC3n//fURFRRm9/rCwMLzxxhtay1y/fh0RERH4/fff8fDhQ8hkMjx9+hTr1q1D3bp1cfLkSaPX70x0XY86ePAgvv/+e8TFxRl9PcrPzw/Dhg0zeDlvb2/069fPqHUSERVVLrYOgIisT9dzaORyOd577z289957hc5//vx5gaEnv/76a/z999/Izc0tdJlnz56hWbNm6N+/PwYMGIC6deuiePHiUCqVePHiBZ49e4aLFy/izJkzOHTokM5eSJbm4uKCli1ban3+46efforY2FiMGzcOYWFhyMnJQVRUFL7++mucOXPG5BjmzJmDnTt3IjMzU2u569evm+XiuCX2C1N99dVXOHjwoNYfFo8ePcL48eONqn/atGkIDAw0NrwiTxAEnDt3zmzP95wwYUKhd4zbQ3ssStauXat1KFJNSpcujaFDh1ogIufx8ccf6+yte+fOHa099uzF119/jevXr2PXrl06yz5//hy//vqr1h71hirqx4fs7Gxs3boVW7duhaurKxo1aoQWLVqgevXqqFKlCsqUKQNvb294eXlBLpcjPj4e0dHR+OGHH7SeY7Vq1arQ153tPM8Q1n6vK1WqhEqVKuHOnTsal01PT8ekSZMwadIkUzevAGtvr7lVqVIFLVu2xNGjRw1absyYMXqXdfT3iOyPrc91uU8bzpnO6Rxdu3btsHHjRo3zv/32Wzx+/BiTJk1ClSpVoFAoEB0djblz5+LYsWMmr3/69Ola2y4APHz40KDjTFGk63pU3ghqU6dOLXT+rVu3EBYWpnM9EydOxG+//abz8VGv6tu3L7y9vfUuT0RETDwTFUnh4eEIDAw0uUfEq2rWrIkVK1Zg6NChGk/gFAoF1q1bh3Xr1pltvZY0btw4rT8glEollixZgiVLllhk/ZUqVcKGDRvQq1cvqwzDZYn9wlR169bFL7/8gnHjxpm97q5duzrl0J6OKiQkBB988IHG+bZuj6Tb0qVL9Rrurijr0aMH+vXrh02bNtk6FJO5uLhg06ZN6N+/P3bu3Gn19fP48D+5ubmIiooyqcdOnpEjRxb6ujOe5xnDGu81ALzzzjv46KOPTF6Hqay1veY2evRogxLP7u7uGDJkiFHrctT3iOyPvZzrcp/WjzOd0zm6cePGaU08C4KAP/74A3/88YdF1t+qVSuMGTMGS5cutUj9RUWlSpUQEhKCBw8eWHQ91apVQ7t27Qx6xjqH2SYiMpzzjXlDRDq5urpa5MRp8ODB+OGHH5xmOK0ePXqgQ4cORi/fsWNHve641KZbt25YtmwZXF1dTapHH5baL0w1duxYfP7552ats2nTpli/fr3T7KuOLjAwEHv27IGHh4fGMvbQHp1Zw4YNC32+tr4mT56MLl26mDEi57Vo0SJUq1bN6OXff/998wVjInd3d2zbtg0ff/yxTb5PeXwwr7feegtt2rTRON/ZzvNsSdd7/e6776Jy5cpG1e3p6YnJkycbG5pF6Npec3vzzTcREBCgd/m+ffsaVN4SrP0ekf1xtnPdorBPO9M5nSNr2bKlScMgN2/eXGdvW12+//57REREGL28vR23bUEkElmtV7ghIwZWrlxZ67PsiYiocLxqQFREzZgxAxUrVjR7vZMnT8bhw4cREhJi9rptYePGjYU+c1aXDh06YMuWLZBIJCbHMGLECJw6dcqkH7X6stR+Yaovv/wS27Ztg7+/v8l1ffDBBzhy5AiHSjJQaGioRW6AeO2113Do0CG89tprOsvaQ3t0Vh4eHoiMjDRqSMQJEybgxx9/NH9QTqpYsWI4fPgwqlevbtByEokEixcv1tl7yNpJQbFYjK+//hrHjx9Hw4YNzVavRCJBjRo1dJbj8cE8hg0bplcvOmc7z7MFfd5rT09PbNiwweBkaFBQEA4ePIgWLVqYEqJZ6btvmZO7u7tBj34YPXq0BaPRzRbvEdknZznXLSr7tLOd0zmylStXGnUTbfPmzbF79264ubmZtH4fHx8cPHgQdevWNWg5kUiEefPm6Uw8F5V94cMPP9TruoCpunTpoveNOvbYOYOIyBEUjSMXERXg6+uLo0ePonnz5mavu0WLFoiJicH8+fNRqVIlo+spVqwYBg4ciPXr1+Onn34yY4T68/PzQ3R0NEaOHKnXyb6XlxfmzJmDPXv2wNPT02xxRERE4MKFC1i2bBnCw8PNVm9+ltwvTNWzZ0/cuHEDn376qcHP3ZRIJOjTpw/OnDmDH374AVKp1EJROq/Jkyfj+fPnWLduHQYOHIgyZcqYVF/VqlXx448/4vz583pfYLOX9uis/Pz8cODAAXzxxRdae5/nCQ4Oxvr16/Hzzz9bITrnUrJkSZw+fRoTJkzQa1+uUaMGjh49irFjxyI1NVVrWVs9l7hJkyY4ffo0IiMj0a1bN6O/ZytUqIBp06bh7t27mDFjhl7L8PhgvNq1a2PHjh1YvXq13tvuTOd51mToe123bl0cP35c74vY/fv3x3///YcmTZqYGqpZGLNvmZO+yeRq1arZrCeTrd8jsj+Ofq5bFPdpZzync0Senp44dOgQ3nvvPbi46H6qpJubGz755BMcOnQIfn5+ZokhMDAQx48fx7Rp0/SKISwsDPv27cNHH33EfeH/eXh44ODBg3jjjTcsuh6RSIR3331XZzmJRIJhw4ZZNBYiImfFZzwTFWFly5ZFVFQUjh07hr/++gtnz57FvXv3kJqaiqysLJPq9vHxwfvvv4+JEyfi9OnTOHHiBE6ePImbN28iOTkZSUlJyM7OhqenJ7y8vBAUFIQKFSqgYsWKqF27Nho3boyqVauaaUtN4+npieXLl2Pq1KlYtWoVDh8+jHv37iE5ORm+vr4IDg5GlSpV0KNHD/To0cNiQ/W5u7tj1KhRGDVqFC5cuIDjx48jOjoa165dQ1JSEpKSkpCVlQVfX18EBATA398fZcuWRUREBOrWrYu6devC3d1d53osuV+YKigoCHPmzMEnn3yCI0eO4Pjx4zh58iTi4+ORmJiI5ORkeHh4IDAwEMWLF0dERASaN2+Otm3bomzZsjaN3Rn4+vpi4MCBGDhwIAAgLi4OJ0+eRExMDO7cuYM7d+7gyZMnSE9PR0ZGBmQyGby9veHr64tixYqhZs2aqFOnDlq1aoX69esbFYO9tEdn5eLighkzZuDdd9/Ftm3bsH37dty6dQuPHz+GUqlEqVKlUL16dfTp0we9evWCj4+PrUN2WD4+Pvj5558xceJErFu3Dnv37kVcXBwSEhIQEBCAUqVKoVGjRujXrx9atWqlupj55MkTrfUGBQVZI3yNOnXqhE6dOiE1NRUHDhzA6dOn8d9//yE2NhZPnz5FZmYmFAqF6ruhTJkyqFmzJsLDw9G2bVvUrFnTqPUWtePDyJEj0b59e9y5cwe3b99WfQc/fvwYaWlpSE9PV/0rk8ng6ekJHx8flCpVCtWqVUOdOnXQtWtXo0dTcabzPF1s+V7XqFEDZ8+exa5du7B9+3ZER0er2lFwcDDKlSuHjh07on///kYPzW1P22tOtWrVQuPGjXHq1Cmt5Yzp7ews7xHZJ1uc63KfNo2zntM5Gjc3N/zyyy+YPHkyVqxYgcOHD+P27dtISkqCj48PgoODERYWhm7duqFnz54oUaKE2WPw8PDA3LlzMXbsWKxfvx579uxBbGwsnj9/Dn9/f5QqVQr16tVD37590a5dO1WCmvvC/wQFBWHfvn04ffo0Nm7ciDNnzuDu3btISUlBZmam2dYzcuRIfP7550hPT9dY5o033jD5hnsioqJKJAiCYOsgiIiIiMhydu/ejW7dummc37JlSxw5csR6AZHRpk2bhm+//Vbj/GHDhmH16tVWjIiIiIiIDMVzOsrzzTff4JNPPtE4v3v37tixY4cVIyoa3nvvPSxcuFDj/L/++gt9+vSxYkRERM6DQ20TERERETkAuVyODRs2aC3Tvn17K0VDRERERMbgOR3lEQQBa9eu1VqG+4JllCtXTuO8YsWKoXv37laMhojIuTDxTERERETkAObPn4+4uDiN88ViMTp27GjFiIiIiIjIUDynozzLli1DTEyM1jKdO3e2UjRFy8aNGzXOGzJkCFxdXa0YDRGRc2HimYiIiIjIShQKBZo0aYJ169YhJydH7+V+/fVXfPzxx1rLDB06FMWLFzc1RCIiIiLSged09KoWLVpg5cqVBj2HeNWqVZgwYYLWMj169EDFihVNDY/y2bFjB86fP1/oPLFYjHfffdfKERERORc+45mIiIjIyfEZz/ZDLpdDKpUCALy9vdGxY0e0bt0a4eHhqFSpEvz8/CCVSpGamoq7d+8iOjoaK1aswH///ae1Xk9PT9y8eRNlypSxxmYQERERFWk8p6NXeXt7IyMjAx4eHnjjjTfQtm1bhIeHo3LlyvD394erqytSU1MRGxuLkydPYvXq1fj333+11imVShETE4PKlStbaSucm0wmw8OHD7Fjxw589tlnyMjIKLRcr169sHXrVitHR0TkXJh4JiIiInJyTDzbj1cvUpqLWCzGunXr0L9/f7PWS0RERESF4zkdvSov8WxOS5YswZgxY8xaZ1EzZ84cfP7553qXl0qluHz5MqpWrWrBqIiInB+H2iYiIiIiclBisRhLly7lBUoiIiIiB8ZzOsojEonwww8/MOlsA5999hmTzkREZuBi6wCIiIiIiMhwNWvWxPLly9GoUSNbh0JERERERuI5HeWpVKkSli1bhlatWtk6lCKnd+/e+Oyzz2wdBhGRU2CPZyIiIiIiB9K4cWP8/vvvOH/+PC9QEhERETkontNRnrp162LhwoW4fPkyk85WJpFIMGXKFGzatAliMVMlRETmwB7PRERERERW4uLigidPniAmJgYxMTG4cuUKbt68iaSkJKSmpiI1NRVpaWkQiUTw9fWFn58fgoKCUKdOHdSrVw/NmzdHlSpVbL0ZREREREUaz+noVbGxsar9ICYmBjdu3EBiYqJqX0hNTQUA1b5QvHhx1K5dG/Xq1UOzZs1Qs2ZNG29B0SGVSuHn54dq1aqhVatWGD58OMLCwmwdFhGRUxEJgiDYOggiIiIiIiIiIiIiIiIiInJcHD+CiIiIiIiIiIiIiIiIiIhMwsQzERERERERERERERERERGZhIlnIiIiIiIiIiIiIiIiIiIyCRPPRERERERERERERERERERkEiaeiYiIiIiIiIiIiIiIiIjIJEw8ExERERERERERERERERGRSZh4JiIiIiIiIiIiIiIiIiIikzDxTEREREREREREREREREREJmHimYiIiIiIiIiIiIiIiIiITMLEMxERERERERERERERERERmYSJZyIiIiIiIiIiIiIiIiIiMgkTz0REREREREREREREREREZBIXWwdAZI+Sk5Nx9OhR1XRISAjc3NxsGBERERERERERERERERE5u5ycHDx48EA13bJlS/j7+9suIAMw8UxUiKNHj6Jnz562DoOIiIiIiIiIiIiIiIiKsO3bt6NHjx62DkMvHGqbiIiIiIiIiIiIiIiIiIhMwsQzERERERERERERERERERGZhENtExUiJCREbXr79u0ICwuzUTRERERERERERERERERUFNy+fVvtcbD5c1b2rEgknmNjY3HmzBlcvXoVsbGxePz4MTIyMpCVlQUPDw94eXmhdOnSKF++PGrUqIGGDRsiNDTU1mGTDbm5ualNh4WFoWbNmjaKhoiIiIiIiIiIiIiIiIqi/Dkre+aUieecnBzs3bsXu3fvxt69exEfH29wHWXKlEGnTp3QtWtXdOrUCa6urhaIlIiIiIiInIVMJsOdO3cKnVepUiVIpVIrR0REREREREREZD1OlXiOiYnBokWLsH79eiQlJZlU16NHj7Bs2TIsW7YMgYGBGDhwIMaPH48aNWqYKVoiIiIiInImGRkZ2LhxY6HzJk2aBH9/f+sGRERERERERERkRWJbB2AOUVFR6Ny5M1577TUsWrTI5KRzfomJiVi4cCFq1aqFzp07Iyoqyqz1EzkDQRCQm5ur+hMEwdYhEVkd2wER2wEREfFYQASwHRABbAdEANsBEdtA0ePQPZ4vXbqETz75BJGRkRrL+Pj4IDw8HLVr10aFChVQtmxZBAUFwcPDA25ubsjOzkZWVhaePXuGR48e4e7du7h06RL+++8/pKenq9UlCAL27t2LvXv3olOnTvjmm29Qu3ZtS28mkUOQyWRqw9qXLl2aQ9RTkcN2QMR2QEREPBYQAWwHRADbARHAdkDENlD0OGTiOTk5GZ999hkWL14MpVKpNk8kEqFRo0bo2bMn2rRpg7p160IikRi8DoVCgXPnzuHw4cPYsWMHTp06pXYnxt69e7Fv3z6MGzcOc+bM4bB5RERERERERERERERERFRkOWTiuWrVqnj27JnaaxUqVMDo0aMxZMgQhISEmLwOiUSChg0bomHDhpg2bRoePnyIP//8E7///jvu3r0LAFAqlVi4cCE2b96MJ0+emLxOIiIiIiIiIiIiIiIiIiJH5JDPeH416dy0aVPs2rULt2/fxscff2yWpHNhypYti+nTp+P27dvYs2cPmjVrppr39OlTi6yTiIiIiIgch0gkgpubG9zc3CCVSlV/bm5uEIlEtg6PiIiIiIiIiMiiHLLHMwA0atQI33zzDVq3bm3V9YpEInTu3BmdO3fGkSNHMH36dJw+fdqqMRARERERkf3x8/PD9OnTkZuby2dYEREREREREVGR45CJ540bN6Jfv362DgOtWrXCqVOnsGnTJluHQkRERERERERERERERERkMw451LY9JJ1fZW/xEBERERERERERERERERFZk0MmnomIiIiIiIiIiIiIiIiIyH4w8UxERERERERERERERERERCZh4pmIiIiIiIiIiIiIiIiIiEzCxDMREREREREREREREREREZnExdYBEBERERERERFpIwgClEolBEGwdShEelEoFFAqlWrTcrnchhERWR/bARHbAZEztQGRSASxWAyRSGTrUOwaE89EZBZSqRSlS5dWmyYqatgOiNgOiAC2AyJztAFBEJCdnY20tDSkpaUhNzfXnCESWcWrN0rExsbaLhAiG2I7IGI7IHK2NuDq6gofHx/4+PjA3d2dieh8nDbxXL58ebPW5+LiAj8/P/j5+SE0NBT169dH8+bNUbt2bbOuh8hRiUQiuLq62joMIptiOyBiOyAC2A6ITG0DmZmZiI+Ph0wmM2NURNbHi5BEbAdEANsBkbO1gdzcXLx48QIvXrxQ3XTr6elp67DshtMmnu/fv2/R+letWgUAqFevHj744AMMGjTIousjIiIiIiL7lp6ejs2bNxc6r0+fPvD29rZyRESOJzMzE3FxcRxSm4iIiIiI7J5MJkNcXBzKlSvH5PP/c9rEs7WcO3cOgwcPxqZNm7Bq1Sr4+/vbOiQiIiIiIrIBuVyu8QZYR32GFZE1MelMRERERESORhAEJp9f4bSJ5zJlygAAlEolHj9+XGiZvHHYxWIxUlJSND4zqlSpUhCLxcjKykJKSgoUCkWBMjt27MCbb76Jv//+m89wIyIiIiIiIjKAIAiIj48vkHSWSqXw9fWFt7c3pFKp0w3TR0REREREjkEQBMhkMqSnpyM1NVXt0UB5v2cqVapU5H+zOG3i+eHDh3j06BH69++vSjwHBQVh4MCB6Nq1K8LDwxEUFKS2zPPnz3Hp0iXs2bMH69atw7NnzwAAlSpVwoYNG1CmTBkIgoBbt24hKioKv//+O06fPq1a/tChQ5g9eza+/PJL620okZ1QKBRIS0tTTfv4+EAikdgwIiLrYzsgYjsgIiLjjgXZ2dkFnuns4+ODMmXKFPkLN+SYBEFQ67ggkUi4L1ORw3ZAxHZA5GxtQCqVwtPTEyVKlMCjR4/UfvfIZDLk5OTA3d3dhhHantjWAVjKw4cP0aRJE5w4cQIA8NFHH+Hu3btYsGAB2rVrVyDpDAAlSpRAu3btMH/+fNy9excfffQRAOD48eNo2rQp4uPjIRKJUKVKFYwaNQqnTp3CsmXL4Orqqqrjp59+QmJionU2ksiOKBQKJCcnq/4KGxmAyNmxHRCxHRARkXHHglcv2AAvL+gw6UyOLO8ia94fh5CnoojtgIjtgMhZ24BIJEKZMmUKjICcmppqo4jsh1MmngVBwODBg/HgwQMAwJIlSzBv3jx4eXnpXYeXlxfmzZuH3377DQAQFxeHIUOGFCg3atQoLFmyRDWdmpqKLVu2mLgFREREREREREVH/sSzr68vk85ERERERGS3RCIRfH191V7L/7umKHLKxHNUVBSOHTsGAOjevTvGjBljdF3vvPMOunfvDgA4fPgwoqOjC5QZMWIEmjRpopo+evSo0esjIiIiIiLH5O7ujpYtW6J58+aoXbu26q958+ZFfqgtIm0EQUBubq7aa97e3jaKhoiIiIiISD/5f7fk5uY6Ta9uYznlM57XrFmj+v+4ceNMrm/s2LHYuXMnAGD16tVo2rRpgTIjR47EyZMnAQAXLlwweZ1ERERERORY3N3d0apVK+Tm5iI+Pl71eunSpdUez0NE6pRKZYHX8g9ZR0REREREZG9cXAqmWZVKJSQSiQ2isQ9O2eP5zJkzqv/Xq1fP5PpereP06dOFlmnWrJnq/wkJCSavk4iIiIiIiKgoKKxHAIfZJiIiIiIieycWF0yzFvUez06ZeH706JHq//nHVzeGn59foXW/qnTp0qr/JyUlmbxOIiIiIiIiIiIiIiIiIiJH4ZSJ5/T0dNX/nzx5YnJ9rw6T92rdr/L09FT938PDw+R1EhERERERERERERERERE5CqdMPJcsWVL1/7///tvk+vbt26f6f6lSpQotk5iYqPp/QECAyeskIiIiIiIiIiIiIiIiInIUTpl4rlmzpur/3333HTIzM42uKyMjA99//71qukaNGoWWu3btmur/TDwTERERERERERERERERUVHiYusALKFv376IjIwEANy5cwf9+/fHpk2bDB4COysrC/3798edO3fU6i7MsWPHVP8vUaKEEVETOaa4uDjs2bMHly9fRkBAAORyOVxcXJCUlITXXnsNXbp0Qbly5WwdJhERWRiPB0REREREREREREWbUyae+/Xrhy+//BL37t0DAOzevRt169bFt99+iy5dukAs1t7RW6lUYs+ePfjoo49w/fp11esVK1bUmHhev3696v+aekUTOQO5XI7ffvsNq1atwt27d5Geng4RAF9vTzRu3ERV7tSpk1i+LBOTJk2Ct7c3KlasiJEjR2LMmDFwcXHKrx4ioiKFxwMiIiIiIiIiIiJ6lVNe7fP09MTvv/+ODh06QKFQAACuX7+O7t27o1SpUujYsSPCw8MREhICHx8fAEBaWhoePHiA//77D3///TceP36sVqdEIsHSpUvh6elZYH0HDhxQS1DXr1/fgltHZBtxcXGYMWMGdu3ahfS0NLiJFXAVCwiUCnARAx4uuRDlpKrK+7vkws1NBrkSkGcn4+aVC5j8/vv4/PPP0b17d3zxxRfs+UZORyQSQSqVqk0TORseD4h04/GAijq2AaKXuO8TsR0QAWwHRGwDRYtIEATB1kFYyl9//YVBgwZBLpebVI9UKsW6devQp0+fQuf37dsX//77r2r65MmTKFWqlEnrJNuKiYlBrVq1VNNXrlxRe3Z4UaJUKjFz5kzMnz8fClkOPMQKeEgEeLgA4SVEqBwgQvVAMaoGiuHnBkhEgEIAUnKAG4lKXEtU4laSgP+eC8iSA1kKEbKUEkikbvjggw8wa9YsnaMQEBGR7fF4QKSbQqHA8+fPC51XokQJSCQSK0dE5Bjkcjlu3bql9lrlypU5MgYVWYmJifj333/x4MEDJCcnIzc3F76+vihRogSqV6+OqlWrws3NzeHWZYzVq1erRjRs2rQp3njjDZvFYu/kcjliY2Nx//59JCUlISkpCcnJyZDL5fD390epUqVQv359lC1b1qj6Fy1ahGfPngEA2rdvj2bNmpkzfCIiIodkqd8yjpyjcurEM/AyCfz222/j6tWrRi1fs2ZN/P7772jSpInuwuQ0HLlRm9ONGzfQp08f3LxxA14SObxcBJT2BrpWkKBzJQl8XPW/UyktV0DkHQV231MgPh3IkIuQoXBBlapVsXnzZlStWtWCW0JERKbg8YBIP8nJyfjpp58KnTdp0iT4+/tbNyAiB1EUEs8pKSm4cuUKbt++jYyMDMhkMkilUnh5eSEsLAy1atWCn5+frcN0CAcPHsTx48e1lhGJRPD09IS/vz9Kly6NevXqoWTJkiat9+zZs9i9e7faa23atEGLFi1MqjdPZmYm1qxZgyVLluDChQtay7q6uqJZs2bo0KED+vXrhwoVKtjtukxx5swZNGnSBEqlEi4uLrhy5QrPFfOJj4/Hxx9/jAsXLuDatWt6db6pXLkyRo8ejXfeeQe+vr56r+vXX3/FhAkTAAChoaG4evVqoSNDEhERFSVMPBfk9IlnAMjNzcXatWvx+++/4+TJk3ot07hxY4wZMwZDhgxRGyKMDCeXy3H69GlcunQJz549gyAIKFGiBMLDw9GkSRO7fH8duVGby6ZNmzBy5EgIsmz4uigQ6A68W0eCtqESk4bGEAQB/9xXYOFFBRKzgVS5BCKpO1asWIF+/fqZcQuIiMgceDwg0h8Tz0TGccbEs1KpxIkTJ7B9+3ZcvHgRDx8+BADIlAooBQECABEAsUgEqfjlaAhly5ZFnTp10KtXLzRt2pQjgWgwffp0zJs3z+DlqlatirfffhujR482Ksnfo0cP7Ny5U+21Zs2a6UyC6+P48eMYMWIE7ty5Y/CyIpEILVu2xKZNm1CiRAm7WpcplEolGjRogPPnzwMA3n33Xfz6668WXacjOnv2LBo0aGDUsmXLlsXy5cv17kUuk8lQs2ZN1ff1J598gq+++sqodRMRETkLJp4LKhKJ51c9efIEZ86cwaVLl/DixQskJycDAPz8/FC8eHGEh4ejUaNGJt8Jaw2CIODx48e4c+cO7ty5g3v37iEnJ0c1v2LFihgzZozN4svKysL333+PX375ReOQg4GBgRg/fjymT58OLy8vK0eomSM3anNYvnw53nvvPbgKufCVKtGstAiT60sR4G6+ZzEkZguYf1aG6HgBqTIxckWuWLRoEd566y2zrYOIiEzD4wGRYZh4JjKOMyWeU1JSsHnzZmzYsAFxcXHIlOciRyFHrkIOmVKBwi7AiABIxRK4SlzgJnGBp4srypUrh4EDB+LNN99kT+h8jE085wkJCcGqVavQpk0bvZd59uwZypYtC5lMVmDerVu3EBYWZnQ8e/bsQa9evQrUHRQUhIYNG6JixYrw8fFBcnIyHj16hBMnThR6jeXatWuoVq2a3azLVKtWrVKdD7q6uuLu3bsoU6aMRdfpiApLPJcsWRIREREoX748AgICkJ2drfo8826CyePi4oK//voLPXv21Gt9q1evxogRIwAA7u7uuHnzJkJCQsyxKURERA6JieeCHO9XnIlKliyJ7t27o3v37rYOxSiLFi3C33//rUo0Z2VlaSzbsmVLmyWe79y5g27duuHatWtayyUmJmLOnDlYv349du3aherVq1spQtJk48aNeO+99+CGl0mG8XUk6F3F/F8Vge4izG7uiq035Vh0UYFUeS7Gjx8PLy8v9nQjIrIDPB4QERHpTxAE7N69G7Nnz0ZichLSZTlIl+UAUgl8q5WHb0hJeJUrCc+ywXDxcgfEYkCphDwjG5kPnyIj7gkyHzxB0o1YJGVkIuV2Fu7N/Qa//fYbZsyYgS5dupg00ogzi4iIKPQaT1paGu7du4ejR48iMTFR9fqDBw/QuXNn7N27F61bt9ZrHX/++WehSWfgZYJ0zpw5RsV+8+ZN9OnTR63usLAwfP/99+jatSskEkmBZQRBwPnz5/H7779jzZo1yMzMtLt1mSo3NxezZs1STY8YMYJJZx3Cw8MxaNAgdOvWDTVq1Ci0jCAI2LVrF9577z08ePAAwMuL5YMHD8bVq1cRGhqqcz2DBw/GrFmzEBsbi+zsbMyaNQvLly8367YQERGRYytyPZ4dXceOHbFv3z69yrZs2RJHjhyxbECFePjwIZo0aaJ2F6VIJEL79u0RHh4OiUSCmJgY7N27FwqFQlUmODgY0dHRqFixotVjzs+R7yYxxY0bN1CvXj1I5FnwkyoxpYEEHSvol2RQuHggo0Rt1bTX80uQyDXfGPGqvXfl+OGsAikyMRQuHjh37hyf20QOSSaT4dmzZ6rpoKAgu3ycAJEuPB4QGYc9noleMvScyNF7PCckJGDmzJk4cPAgUnOzkJqbDfegQJR4PQLFGtaEi4e73nXJs7Lx4kwMnkddQPazRPi6usPX1QPt27XDF198geLFi1twSxxD/h7Po0aNwrJlyzSWz8rKwvz58zFjxgy1axDFixfHnTt39HrGbXh4OC5fvgzg5fUNFxcXVQI3JCQEsbGxBYZGVyqVas/bdXFxKVCmW7duas+Nrlu3Lg4dOqR3L/ekpCR89dVX+Pnnn/Hff/9p7YVszXWZavHixRg/frxq+vr16zwn1ODp06e4e/cumjRpovcyjx49QuPGjdWu2w0dOhR//PGHXssvWLAAkydPBgBIJBLcvHlT47U8fdoBkbNjO6CiztnbAHs8F+Q8n24RJBKJUKpUKTRv3hzDhw9Hr169bB0SBEHAwIED1U5ew8LCcPnyZezbtw/fffcd5s6di127duHGjRsIDw9XlXv69Cn69esHpVJpi9CLPKVSiT59+rx8huf/92zTN8kAABCJoXD1Uv1BpP/XS6eKLhhfRwJfqRKCLBt9+vThfkAOSRAEyGQy1R/v7SJHxOMBERGZqiidE126dAldunRB5P59eJqZikxXESq+1R01Px2F4Jb1DEo6A4CLhzuCW9ZDzU9HoeJb3ZHpKsLTzFRE7t+HLl264NKlSxbaEufl4eGBTz75BCtWrFB7PSEhAQsWLNC5/Llz51RJZwBo0aIFOnXqpJp+8OAB/vnnn0KXFQRB9Zff06dPERkZqZoWiUT4448/DBpaPSAgAN9//z1iYmIQFBSksZw112UqpVKJH3/8UTXdunVrJp21CA4ONijpDABlypQpMFz99u3b1R7fp83w4cPh7v7yu02hUGD+/Play2trB0RFBdsBFXVsA0ULE88O5t1338WOHTtw5coVZGRkID4+HlFRUVi1apVdDEe5ZcsWHD9+XDVdokQJREVFFXonRqVKlXD06FGUL19e9dq5c+fw559/WiNUymfmzJm4eeMGfF0UaFZaZJHhVLXpXcUFTUuL4OuiwM0bN9SG1SIiIuvh8YDIeH5+fpg2bRomT56MgQMHqv4mT57MZ7QSOaHTp09j+PDhePT8KZ5mpsK7dmXU/GQUAiOqmTwstkgkQmBENdT8eBS8a4fhaWYqHj1/iuHDh+PMmTNm2oKiZdiwYQWG1t6yZYvO5VatWqU2PXjwYAwZMkTttZUrVxocz4EDB9RusKtbt67RvVgqV66MwMBAu1iXqXbt2oXbt2+rpkePHm2xdRVlvXr1gpubm2o6LS0N169f12vZgIAAvPnmm6rplStXIjk52dwhEhERkYNi4tnBdOvWDd27d0fNmjXh4eFh63DUKJVKzJgxQ+21H374ASVLltS4jL+/PxYuXKj22qxZszQ+O4ksIy4uDvPnz4eXRI5Ad+D9+rYZGnhyfSkC3QEviRw//vgj4uLibBIHEVFRxeMBkWlEIhHc3d3h7u4OV1dX1Z+7uzufzUrkZC5evIh33nkHz1KTkJiTidD+b6DSW90h9fY063qkPp6o9FYPhPZ/A4k5mXiWmoQxY8aw57ORBg8erDZ9+fJlpKSkaCyfm5uLdevWqaZdXV3Rt29fdOvWTW2I7u3bt2utpzD5z2+qVKli0PL2ui5Tvfq8YHd3d3Tr1s0s9SqVSkRHR2PNmjWYN28efv75Z2zevBkJCQlmqd/ReHh4FHhu9tOnT/Ve/tXOLxkZGdiwYYPZYiMiIiLHViQTz0qlEsnJyUhISDDqjwoXHR2Na9euqaZLlSqFgQMH6lyuc+fOas8GunfvHg4fPmyRGKlwM2fOhEKWAy8XAe/WkSDQ3TYXRgPdRRhfRwIvFwEKWQ57uRERWRmPB0RERLolJCTgnXfeQUJqMpJzslBhWFeUaFbHouss0awOKgzriuScLCSkJmPMmDG8PmGEOnXqqE0LgoAnT55oLL9z504kJiaqprt06QJ/f3+4u7ur9fjMysrCxo0bDYolKSlJbdqSNyhZc12meP78Of7++2/VdIcOHeDt7a3XsocOHcKsWbNUf3m9prOzs/HVV1+hQoUKaNasGYYNG4bp06dj0qRJ6Nu3L4KDg9G9e3fcvHnTIttkz/I/e9KQZ1F26NBB7eaL1atXmy0uIiIicmxFIvF88eJFTJ06Fa+//joCAgLg4uKCgIAAlChRwqg/KtyOHTvUpgcNGqT3SeuwYcPUprdv326usEgHuVyOnTt3wkOsQGlvoG2oxKbxtAuVoLQX4CFWYMeOHZDL5TaNh4ioqODxgIiISDdBEDBz5kw8S3yh6ulcrH4Nq6y7WP0a/+v5nPgCM2fO5HPyDPRqoiyPtp7K+YfQfrXHdP7e04YOt53/EQxXrlwxaHl7XZcpdu7cqTYCXocOHfRe9tChQ/jiiy9Uf7dv30ZcXBwaNmyIzz77TOMIOkqlErt27UL9+vVx7Ngxk7fBUchkMjx69EjttYoVK+q9vJubG1q2bKmaPnXqVIH6iIiIqGhy6sTz/fv30alTJ0REROD777/H8ePHkZyczB9mFrJ792616VatWum97Ksnq4XVRZazdOlSpKelwUMioGsFic3vfBaJROhSUQIPiYD0tDQsXbrUpvEQERUVPB4QERHptnv3bhw4eBCJ2RkIiKhm8Z7O+ZVoVgcBEVWRmJ2BgwcPYs+ePVZdv6NLTU0t8Jq/v3+hZR8/fox9+/appv38/NC1a1fVdOvWrVG6dGnV9KlTp/R+Ri7w8lnJr/rvv/9w9OhRvZc3hDXXZYrIyEi16fzP5DZEQkIC2rRpg8uXLwMASpcujSFDhmDatGl4//330apVK0gk/7vRMi0tDT179tTaA96Z7N+/HxkZGarpqlWronz58gbVkf/zyf/5ERERUdHktInnGzduoGHDhmpD9JDlZGVlFRiWqEmTJnovX69ePbi6uqqmHzx4oDacFVnOihUr4CZWwMMF6FzJtr3b8nSuKIGHC+AmVmDFihW2DoeIqEjg8YCIiEi7lJQUzJ49G6m5WRA8XVGuTzubxFGuT3sInq5Iyc3Cl19+afCzhYuyCxcuqE2LRCIEBwcXWnbNmjVQKBSq6T59+sDNzU01LRaLCzxebNWqVXrH0rp1a7XEJ/DyubmvJrvNxZrrMpYgCGqPXfPz81N7LJuhpkyZgjt37sDPzw+rVq3Cw4cPsWbNGsydOxfz58/H4cOH8e+//yIkJES1TFJSEj755BOTtsMRpKSkYOrUqWqvTZs2zeB68l/3++eff0yKi4iIiJyD/g/vcCAKhQK9e/fGs2fPCswrXbo0KlWqBF9fX4OeXULa3bhxA0qlUjUdGBiIYsWK6b28m5sbQkNDcevWLdVr169fR9OmTc0aJxV09+5duIoFhJcQwcfVPp7z5OsmQngJEY49EnD37l1bh0NEVCTweEBERKTdli1bkJichNTcbFQc3B1SH0+bxCH18US5Pu1xd9VOJCYnYevWrXjrrbdsEoujWbdundp0eHh4gWGo8+R/Zm3+obUBYMiQIfjhhx9U02vWrMFXX31VIMlbmKCgIAwePBh//PGH6rVnz56hY8eOaNSoEQYNGoT27dujWrVqJo9EY811Gev27dtqz6J+7bXXTKrv6dOn8PHxwZEjRwo82ztPREQEdu/ejbp166puMti4cSMWLFhQ6LDsr1qwYAGSk5NNilEfXbt2Rf369c1WX3R0NN555x1cu3ZN9Vrv3r0xYsQIg+t67bXXIBaLVdcDT58+ba4wiYiIyIE5ZeZ17dq1uHr1qmraw8MDH3zwAd5++22Dh40h/dy4cUNt+tU7RvUVEhLCxLOV3b9/H+np6QiUCqgcYF8DIFQOECE6XonE9HTExcWhXLlytg6JiMhp8XhARESknVKpxPr165Euy4F7UCAC6lS1aTwBEVXhHhmA9OQsrF+/HsOHD4dYbF/HcHvzxx9/qPWoBYBevXoVWvb06dNq15XKlClT4BFhAFCnTh3UrFkTMTExAID4+HgcOHAAHTt21Cum77//HkePHsX9+/cLrD8viRcYGIgGDRqgQYMGaNKkCZo1a6YxWW4v6zJG/t7oNWqY/uz077//XmPSOU94eDj69OmDjRs3AgAyMzNx5MgRdO/eXetyCxYsKPBeWkLJkiUNTjz/+uuvSEhIUE1nZ2fjyZMnOHXqlNr1O5FIhLFjx2LBggVG3XDg5eWF0NBQ3Lt3DwAQGxuLpKQkBAQEGFwXEREROQ+n/FWybds21f+9vLxw+PBhzJkzh0lnC8rfu/zV5xzpK/8yhfVYJ/OKjIyECICLGKgeaF9fB9UCxXARAyIAe/futXU4REROjccDIiIi7aKjoxEXF4d0WQ5KvB5hs16heUQiEUo0j0C6LAf3799HdHS0TeOxZ1lZWfjmm28watQotdeLFSuG999/v9Bl8g+ZPXDgQI2J/fw9oVeuXKl3bCVKlMDhw4e1JkcTExOxb98+zJkzB126dEFgYCBatmyJZcuWISsryy7XZYw7d+6oTRvToeFVpUqV0nskgB49eqhN50+CO5pff/0VX3zxhepv3rx5WL16tSrp7Orqirfeegvnzp3DokWL1B59Z6iyZcuqTXOUIiIiInLKHs/nz59X/X/69Olo1KiRDaMpGtLT09WmPT0NH3Is/zL56zTWs2fP8Pz5c4OWuX37ttq0TCZDbm6uzuVEIhGkUmmB12UyGQRB0Hv9EomkwNBcgiBAJpPpXQcASKXSAhdEFAqFagipy5cvw9fbE55SOSoHu0IhLeTiiaCERF7wB6bCxQMQiV+Z9oRS4qo2/SqRIhdipXr8AkRQSgvfV6oEC/D0zIWv1AWXLl1Svf+6tkkfjvY56YPbpJk1t0kmk0Eul6tNv8oRt0kXbpNmjrRNeh0PoN93ua7jAQCIZZkQQf29VIqlECQFL3ppOh4Uxc9JX9wmzay1TbqOB4DjbZM+uE2aFbVt0qcNAJq3SRAEKJVKtcc5aSMSiQrEIgiCQe+Lrnq2bt2KTHkuIJWgWMOaBtVrKcUa1sKj3ceQKc/F1q1bCx0xzFzvTWFJV30/H3PHkr/8+fPnMXPmzALl0tPTce/ePRw9ehSJiYlq81xdXbFhw4YCvXmVSiWys7OxYcMGtdcHDRpU6PaKRCIMGjQIn376qSquHTt24MWLF/D391eLNW+/zi80NBSnTp3CkiVLMHfuXDx+/Fjr9iuVShw7dgzHjh3DrFmzMHfuXAwaNAiA7s8pNDQUJ0+exJIlSzBv3jyT1qWLoZ93bGys2nSpUqWgVCr13vfy19u1a1dIJBIolUqdsdSsqd6mY2NjNX7eefW8//77SE5ONnj/zatHV/x56tatqzOWV+vQJ57c3Fzs27cP/v7+KF68eIEkvyHtMn8nkvv376NevXoAXn5O+evS1A5M3SZL1GNP33vcJs0cZZv0bQeA42yTIbhNlo3F3rcpL55X/6+rXnvfpldjyTvevUomkxVYj6G/CQ39fWZPnDLx/GpP2X79+tkwkqIjf5LYw8PD4Drc3d211mmsRYsW4YsvvjCpjmfPnsHf319nOalUijJlyhS6vCFfFP7+/gXWJ5PJEB8fr3cdwMsfAPnvXE1LS1M9hyggIACNGzeBl4sS4goSpIoLHhQkuRnwfXyqwOsZJWpD4eqlmhZEEihc//cMpPTgCIiE/31puiffhUfKPbU6lFJPpJZuXGjsIqWAuo0UyJCLERgYqNp2XdukD0f7nPTBbdLMmtukVCrVblJ5+vSp2gmOI26TLtwmzRxpm/Q5HgD6fZfrOh4AgG/8KUhkGWqv5fiURbZ/xQLr1HQ8KIqfk764TZpZepvS0tJw9uxZCIKg9uNRIpGgQYMGaue7jrJNzvg5cZssv026zonyaNomAJDL5XpfWJJIJHBxUb+8Ye5k+oULF5Atl8GnWnlI3N20xmZIUsmQOvLXI/Fwg0+V8si+HoeLFy8Wur3muumhsB6RhnxGgPk+p/wuXLhgUA/V0qVL4/fff0e7du0KzJPL5di8ebNa26xRowZq1Kih8Sai0NBQNG/eHFFRUQCAnJwcrFu3DmPGjFF7f169GSM/qVSKiRMn4t1338X+/fuxfft2HD58WO1xZIV59OgRhg4diujoaCxYsECvzylveOXRo0fj4MGD2LlzJ44ePVrgBnxt65o/f77WsnnbZMi+9+rznYGXnRPkcrne+17+emvXrq36zHTte/m/E5OTk3W2p7ze8nK53CztKTc3V2N70rTvadqmsWPHqg21nZGRgSdPnuDcuXOqfSo+Ph7z58/HsmXLsHjxYrWe+4Z8R3h5ealNv/o55n1O+rYDa3yX68vevve4TYVzpG3Stx040jbpi9ukWVHZJgB6t4E89r5Nr35Or7bxvNceP35c4PePob8JHXlEYKdMPLu6uiI7OxvAy7s5yfLy3u88xgzT4+bmprVOMr9Xv+Q15Bj0JhIUkGYl6C6oJ8kr8Tjy3T1UtIjFYqNGfCCyNR4PiMxDLpcXGCo0T0REhJWjIbIdZzsnSk5OxqNHj5CrVMCvXLCtw1HjVS4YCVfv4eHDh0hJSbHa83gdUVhYGIYPH47Ro0drfQbtmjVr1KYHDBigs+7BgwerEs/Ay+dJjxkzxuDnbkskEnTq1AmdOnWCXC7H48ePceHCBZw/fx4nTpxAdHQ0MjMzCyy3ePFilClTBp9++qlB6+rQoQM6dOgA4OXFzQsXLuDixYs4ceIEjh8/rnFdpUuXxtSpUw3aNl0yMtRvSjSmQ8OrihcvrnfZ/MnTwrbbkYwbN07jvIsXL+KTTz7BoUOHALy84D106FAolUoMHTrU4HXl/67P/zkChfc2Iypq2A6oqGMbKFqcMvFctmxZXL16FcDL5GX+hCaZX/4fBDk5OQbXkX8ZU39kkG6v3qmjFExPNpiT4pWbjgobfo+IiMyHxwMiIiLN8q4vyJQKeJYrZeNo1HmGlESu8mXPj6tXr6JJkyY2jsj6IiIi0LVrV7XhDEUiETw8PODr64vSpUujbt26KFVK92f38OFDVUIur57+/fvrXK5fv36YOHGiqqf/2bNncfXqVdSoUcOILfqfoKAgteRwRkYGNm/ejLlz5+LePfVRaGbPno3hw4cXeOauoevq3LkzXFxckJGRgU2bNmHOnDkFnts7Z84cDBw40Oh16cOYIaxfZcrNL6au257VqVMHkZGR+OCDD7Bo0SIAL7d33LhxaN26tcGfaf73StNoDURERFR0OGXiuW7duqofhjdu3EDDhg1tHJHzy393qDG9lfMv4+3tbVJMecaPH4++ffsatMzt27fRs2dP1XRQUFCB59YURtMJdlBQkMHDN+QnlUr1iiH/Mvn5+PiofoAlJSXh1KmTCJDKoQxwhb9H4c94LozX80tqz3jWRaQo+IxssSwTvvEFh/EGgMQsAedP5yJJ5oKQ8hVU265rm/SKxcE+J31wmzTjNmnGbSpcUdwmvY4HMPy7XBOxrGAvEre0h3DNLDiMkKbjQVH8nPTFbdLM0tuk7eaIoKAgtaE8HWWbnPFz4jbZ/zYBL2+KKmyYPn3r0fT8aEPrkUgkiI2Nhfz/fxd5lQ02KrFirmRM/nq8QkoCAOSCEvfu3UOLFi10rlcikZil14m+n4+2WIz5nPKrW7cuvvjiC7MkC9evX6+WwG7evDnCwsI0ls/bpoCAAHTq1Ak7duxQzVu7di2+/fZbvdar7+fk7++Pt99+GwMHDsTAgQOxZ88e1bycnBwsWbIEs2fPVlvG2M/Jy8sLb731Fvr164cBAwZg9+7dautauXIlvvzyS5O3KU/+a0C5ubkaYy/s9fzfbRKJRLVv6dr38u+DmvZLZ2pPP/30E06ePKkapj4jIwO//PIL5s2bZ9A25e9E8ur1QVt9RzjT56StHm6TZtwmbpOh9XCbNOM26d6mV58BnTcScKlSpQqcmxj6+8mQxzLZG6dMPA8bNgx//vknAGDz5s1MPFtB/h8IxgxLlH84HnMlnoOCghAUFGRSHVKp1Kjhw19d3lSanklgKIlEovrSe+2117B8WSbc3GS49TQHTcsUvAilsR55lsmxiCAUeMZnnptPFcjMlCM1R4ratWtr3fZXt8kU9vo5mYLbVDhuk2bcJs2ceZtMOR5o+y43hFgpA5QFh9I25HgAOPfnZApuk2bm3CZt8bi6uuqM1x63yVTcJs24TdrjEYvFJl1YevUCkKmxZGVlQfn/SU0XL3cdS1iXxPNlPIIgIDs7W6/3zFzvjTku/BkTi6aLf+bYptWrV6tNSyQSrcnVV+V/JMjatWtVSTxjaNsmHx8fbNy4EWFhYXjy5Inq9X/++adA4tnUz8nLywsbNmwosK5Dhw5hzpw5BtWlbZsCAwPVpjMyMjTGXtjr+evV9R3yaiz5y+V9B2mzYMECq1wQ7tq1K+rXr69XWUPagVgsxvjx4zF69GjVa5GRkZg3b55B9aSnp6tNv3qTna2+IyxZD7fJsrFwmzTjNlk2Fm6TZtwm3bGIxeICcUmlUr0S3Np+PznyqH9OmXhu27Yt6tevj7Nnz2LhwoUYO3YsKlasaOuwnFpwsPpzrh49emRwHfmXMTVZTLp17twZkyZNglwJXEtUGpRoyE+ACErp/+7OEcsyIYLxd5xfT1RCrgQEAJ06dTK6HiJrEgRB7YKTVCo1W+8WIkvi8YCIiMzJ2c6JZDLZ/45kdvZ8OpHkZTwCoBrmmYxz4sQJ3Lp1S+21I0eO4MiRI0bV9+TJE0RGRqJLly4AzHexNI+XlxeGDh2K7777TvXa7du3zVa/tddVrlw5tWljritZ04IFC3D//n2Lr6dkyZJ6J54N1bhxY7Xp/Pu/Ph4+fKg2HRoaqjYtCILaaATmbgdEjoDtgIo6toGixykTz2KxGGvXrkXjxo2RlJSEN954A/v27UOlSpVsHZrTqlatmtp0XFycwXU8ePBAbbp69eomxUS6hYaGwtvbG/LsZNxKMm1YMqXUE6ml//ejxTf+lEk94G4lCZALInh7exf4AUpkr2QyGeLj41XTpUuXNkvvISJL4/GAyDxcXV1Ru3ZtKJVKtRGAPD09eTygIsXZzomkUilUl8aUSsAMvbrNRVC8HBZaBDj0e2wPVq5caZE633jjDQCWuQEj/zOkLdkD19Lryn/NLv81IjI/X19ftenc3FwolUqDeoLlTzzn7/jjbDciERmD7YCKOraBoscpE88AUKVKFfzzzz/o0qUL7ty5gzp16mDq1KkYPXo0SpUqZevwnE6VKlUgFotVz0JKTk7Gs2fP9O61nJ2dXSBZnT+ZTZZRsWJF3LxyAf89VyItV4CPq+2/9FNzBPz3XECuUowqHK2AiMgqeDwgMp2npyd69uyJ3Nxcp0q6ERV1Xl5eEP//xTF5Rjakvl46lrAeRWY2gJc9Rwx5vjapy8zMxF9//aX22kcffQQPDw+D6/r6669VF1f37NmDFy9eoFixYmaJM7/8F20DAgIssh5rrCsiIkJtOiYmxqz1m9v7779vlaG2LdXbGQCeP3+uNl28eHGDks7p6elqvb4rVKhg0X2QiIiIHIPTJp6HDBkCAKhVqxYeP36M9PR0zJw5EzNnzkSVKlVQqVIl+Pn5GfWsnbznR9P/uLu7o0aNGrhy5YrqtZMnT6JHjx56Lf/vv/+q3fVSvnx5tefCkOWMHDkSk99/H1lyJSLvKNC/uu2/FiLvKpAlB3KUEowcOdLW4RARFQk8HhARERUuLCwMUrEEIgCZD5/Cr4b93AyV+fApRACkYgkqV65s63Ac1pYtW5CamqqajoiIwLx584yq69y5c9i9ezeAlz1IN27ciPHjx5slzvyuXbumNl2mTBmLrMca6woLC0NAQACSkpIAAJcvX4YgCHbbI+r999+3dQgmyz+MvKHfIVeuXFEbOrVhw4bmCIuIiIgcnO2vKFrI2rVrNc67efMmbt68aXTdTDwXrmvXrmqJ58OHD+udeM5/stutWzdzhkZajBkzBp9//jmyMpOw+54C/apJbPrDThAE7LmrQJZCBG8fH4wZM8ZmsRARFSU8HhARERWuZs2aAF4mdzPinthV4jkj7gmk4pc31OfFSYZbtWqV2vTAgQONrmvQoEGqxDMArFmzptDEc0xMDEqXLm10D9GsrCysX79e7bX27dsXWtaa6zKWSCRCq1atsG3bNgBAWloarl69yv1ai9TUVKSkpCAkJMTgZbOysrBw4UK117p3725QHSdPnlSbbtu2rcFxEBERkfPRf/wUIh3yJ5nXrVun1otZE0EQsHr1arXXevbsac7QSAsXFxd0794dWUoJ4tOBf+4rbBrPwfsKxGcAWUoJevToARcXp70/hojIrvB4QEREVDh/f3+ULVsWrhIXZD54Yutw1GQ+eAJXiQtCQkLg5+dn63Ac0v3793H48GHVtEgkQv/+/Y2ur3v37vDy+t9w7BcuXFC7ST/PgQMHUKFCBcyZMwcpKSkGrUOhUGDs2LFqjywTiUQaE+bWXJcpOnfurDZ96NAhs6/DmSQmJqJ27doFhonXRSaTYejQobh3757qNR8fHwwbNsygel5tNwDQqVMng5YnIiIi58TEM5lNo0aNEB4erpp+/vw5Vq5cqXO5rVu34s6dO6rpKlWqoGXLlhaJkQr3xRdfQCJ1Q4ZchIUXFUjMFnQvZAGJ2QIWXVQgQy6CROqGWbNm2SQOIqKiiscDIiKiwtWpUwduEhek3oiFPCvb1uEAAOSZ2Ui9EQs3iQtq165t63Ac1urVq9WGC27WrBnKlStndH1eXl4Feo7+8ccfhZZNSUnB559/jlKlSmHYsGE4ePAgsrKyNNYtCAKioqLQsmXLAnWOHj0aderU0bisNddlrO7du6vdbLhv3z6zr8PZJCUloV+/fmjQoAE2b96MjIwMjWXlcjl27tyJunXrYsuWLWrzvv76a5QqVUrv9ebk5ODo0aOq6YYNG6Js2bKGbwARERE5HaftOpL/2TNkuB9//BHPnj1TTXfo0AGtW7fWWF4kEmHOnDlqP7CmTZuGN954A+XLly90mWfPnmHixIlqr33xxRdGPXubjFeuXDlMnjwZ386bh8RsGRacleHL5q5Wj2P+WRkSs4EMhQumTf3ApB/7RERkOB4PiIiICterVy/s3r0bSRmZeHEmBsEt69k6JLw4cwWQKeDp5YpevXrZOhyHVNgIbOboyTtw4EC1oak3bNiA7777Dq6uhZ9XZWVlYc2aNVizZg2kUinq1q2LypUro3jx4vD29kZ6ejpiY2Px77//4tGjRwWWb9++PRYsWKBXbNZcl6GCgoLQsWNH1VDlBw8eRGpqKnx9fS2yPmdy9uxZ9O3bF66urmjUqBEqVaqEoKAgiMVipKSk4NatWzh37pzqGdqv+vTTT/Hee+8ZtL79+/erPRd9xIgRpm4CEREROQmnTTxXq1bN1iE4vKVLl+LGjRuqaW9vb62JZ+Dls5nbtWuHgwcPAgCSk5Px+uuvY+PGjWjatKla2YsXL6Jfv36Ij49XvdakSROThrQi433xxRfYvn077t68hhPxCmy9KUfvKtb7ith6U47oeAGpcgmqVqvG3m1ERDbC4wEREVFBTZs2Rbly5ZByOwvPoy4gqEVdiEQim8UjCAKeH78Ab6kbQkNDC/zeJv0cO3YMd+/eVU27uLigb9++JtfbsWNHBAYGIjExEcDLm+737NmjdoNAnTp1EB4ejv/++09tWZlMhtOnT+P06dM61yOVSjFp0iTMnj0b7u7uGstZc12mGjVqlCrxnJOTgx07dmDo0KEWW58j8/f3x+uvv46oqCjVa7m5uYiKilJ7TZNSpUph/vz5Rl2H27Rpk+r/np6eGDBggMF1EBERkXNy2sSzs0pKSsK8efMKnZe/l/fdu3cxffr0Qsu2adMGb7zxhtnjA4A1a9agWbNmqh9vDx8+RLNmzVRDcYvFYsTExOD48eNqy5UtWxYbN2606Y/3okwsFmPz5s2oV68eUmVZWHRRAU8p0LGC5b8m9t6VY9FFBVJlYoik7vjrr78gFvNJAEREtsDjARERUUFisRgDBw7EvbnfIP5ZIpIu3kBghO1ueE+6cAPZz5IQ6OWHgQMH8nhppPyPB2vbti1KlChhcr1SqRS9e/fGsmXLVK+tXr1aLfHcqlUrXLp0CTdv3sSOHTtw+PBhHD9+HGlpaTrrr1ChAvr3749Ro0YhLCxMZ3lrrstU3bt3R1hYGG7fvg0A+P3335l41sDf3x/Hjh1DTEwM1q5di127dhX6PPFXSSQSNG7cGEOHDsWgQYPg4+Nj8HqTkpLUhuoeMWIEAgICDK6HiIiInBMTzw4mJSVFY+I5vwcPHmgs6+LiYrHEc8mSJXHkyBH07NkT58+fV72u7S7aWrVqYceOHQgJCbFITKSfqlWrYsWKFRg+fDhS5bn4/l8FMmWwaE+3rTf/P8kgFyNX5IrVK1agatWqFlsfERHpxuMBERFRQW+++SZ+++03pMtyEPfXAfiElYPUx9PqccjSMhG3+QB8Xd0R6B+A3r17Wz0GW2rXrp1aj9u6desaXVetWrUwc+ZM1XT79u1Niu1VEydORHBwsGraw8MDgiAUuNm+SpUqmDp1KqZOnQqlUol79+7h+vXrePToEVJSUpCTkwNvb2/4+vqiXLlyqF27ttHJcWuuy1hisRgffPABxo8fDwCIiorC1atXUaNGDa3LtWnTRu350IYkyV1dXdX2A2sk2M2pZs2a+Prrr/H1118jISEBFy9exN27d5GUlASlUgkfHx/4+/ujSpUqeO211+Dh4WHS+v744w/V88ElEgk++OADc2wGEREROQkmnskiQkJCcPr0aSxcuBALFixAbGxsoeXKli2LiRMnYtKkSRqfdUTW1a9fP6SlpeG9995DiiwXCy8qcOGZEpPrSxHobr7e6InZAuaflb0cTlX2MsmwaNEi9OvXz2zrICIi4/F4QGQ4QRCQk5OD3Nxc5Obmql7Pzs6GVCrlyD5EDs7Pzw+ff/45PpwyBVmZqYjbfBCV3upu9TjiNh+AKDMXfp6+mDFjBvz8/Kwegy21a9cO7dq1M0tdU6ZMMUs9halZsyY+//xz1bQ+xwGxWIxKlSqhUqVKFovLFusy1KhRozBv3jzcv38fAPDjjz+q9R4vTJs2bdCmTRuj1ufq6uo0j3cpXry42dpHYeRyOX766SfV9LBhw+xyHyIiIiLbYeLZwQQEBOCbb74xuZ5mzZrpLPPhhx/ixYsXqukWLVoYtA4XFxdMmjQJkyZNwpUrV3Dp0iU8e/YMgiCgRIkSCA8PR3h4OC/A2aFRo0bBx8cHI0eORGJuNo49VOBKQi7G15GgXajEpM9MEAQcvK/AoosKJGYDqXIXiKRuWL1iBZMMRER2hscDIsOkpKSoXYx91aRJk+Dv72/dgIjI7Lp27Yq///4bkfv34emF63hepRxKNKtjtfU/O34RSRduINjTF+3bt0eXLl2stm4ia8lLBL/11lsAXj7SbdasWShbtqyNI6P169fj3r17AAA3NzenSdgTERGR+YgEQRBsHQSRvYmJiUGtWrVU01euXEHNmjVtGJFt3LhxA3369MHNGzfgJZHDy0VAaW+gSwUJOleUwNftfwkHASIopf8bZk4sy4QI//t6Sc0REHlXgT13FYjPADLkImQoXFC1WjX89ddfHE6VnIIgCJDJZKpp9m4jZ8HjAZF+kpOTmXgmguHnRHK5HLdu3VJ7rXLlymrD5tqThIQEdOnSBY+eP0ViTiYqDOuKYvW1DwNsDi/OXsW9P3Yj0M0TZUoEY8+ePShevLjF10vGEQQBr15yE4lE/G1gAKVSiQYNGqge4TZu3DgsWrTIxlEVbXK5HDVr1sTNmzcBAB9//DG+/vprrcuwHRCxHRA5exuw1G8ZR85R2eevOCKyC1WrVsWlS5cwa9Ys/Pjjj8jKzUFGsgJL/lPgj6sKhJcQoXKACNUCxagWKIavMh0uIkAuAMk5wPVEJa4nKnErScB/zwVkyYEshQhZSgkkUjdMm/oBZs2aBbFYbOtNJTILkUjExwaQU+LxgIiIDOHs50TFixfH0qVLMXz4cChTBdz7YzeUObkW7fn87PhFxG3aD383DxT39cfSpUuZdLZzznZR1drEYjEWL16MJk2aQKlU4vfff8ekSZN4k6INLVmyRJV0Dg0NxWeffaZzGbYDIrYDIraBoschE8/Nmzcv8Nrx48d1ljGX/OsicmZisRhffvkl3n77bcycORM7d+5EQloa3OQKHHskIDpeCRexEoUdOgQAciUgF0TIVYqRo5TA28cHA3v0wKxZs1CuXDlrbw4RERmJxwMiIqL/qV27NpYsWYJ33nkH4lQR7m/cj9Sb91GuT3tIfTx1V6AnWVom4jYfQNKFGwh081QlnWvXrm22dRDZq4YNG2LFihWqoZ3v37/PxLMNiUQizJw5EwDQvn17eHqa77uOiIiInIdDJp5PnDhhljJEpL9y5cph5cqVkMvlWLp0KVasWIG7d+8iMT0dIgASkQARBIjwMsEgQASF8HJwVW9vb1SpWBEjR47EmDFj7HbIPCIi0o3HAyIiopcaNWqE1atXY8yYMZAmvkDipduIuRWHcn3aIyCiqkk9OwRBQNKFG4jbfACizFwEe/oiKLAYk85U5AwfPtzWIdD/Gz9+vK1DICIiIgfAq31EZBAXFxeMHz9e9YMjLi4Oe/fuxdmzZ5Geno6cnBy4ubnB29sb9evXR6dOndiTjYjICfF4QERE9LLn8549ezBz5kwcOHgQqblZuLtqJ9wjA1GieR0Ua1gLLp7uetcnz8zGizNX8Pz4BWQ/S4Kvqzv8PH3Rvn17zJo1i8NrExERERGRXRMJrz7V20EUdtdw/s2w5JjxDviWkYEc+cHttqJQKJCWlqaa9vHxgUQisWFERNbHdkDEdkBFm0KhwPPnz6FQKJCZmal63dPTEyVLlmRboCLD0GOBXC7HrVu31F6rXLmyQ42MIQgC9uzZgy+//BKJyUlIl+UgXZYDSCXwrVoeniEl4VWuJDzLBkPi6Q6RRAxBoYQiMxuZD58iI+4JMh88QeqNWECmgLfUDd5SNwT6B2DGjBno0qULn43nYARBgEKhUE1LJBJ+hlTksB0QsR0QOXsbsNRvGUfOUTnOr7hXyGQys5QhIvNRKBRITk5WTXt6evLiKhU5bAdEbAdUtEkkEpQsWRK5ubmIj49XvV6iRAm2AypSiuKxQCQSoWvXrnj99dexZcsWrF+/HnFxcciU5yLn+gMkxNzDY6UChd3GLgIgFUvgKnFBgMQNnl6uCA0NxcCBA9G7d2/4+flZe3PIDPJfZBWLxU51kZVIH2wHRGwHRGwDRY9DJp71uVPAke6MJiIiIiIiIiLH5+fnh5EjR2LEiBGIjo7Gtm3bcOnSJTx48AAAIFMqIAgCBLxMOItEIkjFL5PyISEhqF27Nnr16oWmTZtCLBbbbkOIiIiIiIiMwOwsEREREREREZEZicViNG/eHM2bNwcApKSkICYmBrdu3UJmZiZyc3Ph6uoKT09PVK5cGTVr1mTPZiIiIiIicnhMPBMRERERERERWZCfnx+aNm2Kpk2b2joUIiIiIiIii+G4TUREREREREREREREREREZBImnomIiIiIiIiIiIiIiIiIyCQcavsVBw4cwNq1axETE4OcnByEhISgS5cuGD58OLy8vGwdHhERERERERERERERERGRXXLKxHNaWhomTJigmi5Tpgy++uorjeXlcjlGjRqFP/74Q+31y5cvIzIyEvPnz8e2bdtQq1Yti8VMREREREREREREREREROSonDLxfOTIEaxevVo1vXTpUq3lZ86cWSDp/Krbt2+jQ4cO+Pfff1G6dGmzxUlERERERERERERERERE5AycMvF88uRJ1f+LFy+OESNGaCx7//59fPfddzrrjI+Px9SpU7F27VpzhEhERERERE4mOzsbp06dgkKhQFpamur1O3fuoFmzZnB3d7dhdEREREREREREliW2dQCWcOXKFdX/+/fvD6lUqrHswoULIZPJVNP169fHqVOnkJ6ejkuXLqFdu3aqeevXr0dsbKxFYiYiIiIiIseWnZ2No0eP4vjx47h06ZLq7/jx48jOzrZ1eEREREREREREFuWUief4+HjV/1u3bq217IYNG1T/9/X1xZ49e9CoUSN4eXkhPDwcO3fuRKVKlQAAgiColSei/xGJRJBKpao/kUhk65CIrI7tgIjtgIiIeCwgyiMSiVR/REUV2wER2wER20DR4pRDbScnJ6v+HxERobHc1atX8eDBA9X04MGDERQUpFbGw8MDEyZMwPvvvw9AfRhvIvofqVSKMmXK2DoMIptiOyBiOyAiIh4LiABALBbD1dXV1mEQ2RTbARHbARHbQNHjlD2ek5KSVP8vUaKExnLHjh1Tm+7du3eh5V7tNR0TE2NidEREREREREREREREREREzsUpE8+pqamq/3t6emos92rvZVdXV7z++uuFlqtQoYLq/y9evDBDhEREREREREREREREREREzsMph9p2dXWFXC4HAKSnp8PPz6/QckeOHFH9v379+nBzcyu03KvJ6/T0dPMFSkRERERETsPFxQWhoaEa5xEREREREREROTOnvPrh7++PzMxMAMCdO3dQt27dAmWuXbuGuLg41bSm3s4AkJaWpvq/th7URERERERUdHl7e2PEiBG2DoOIiIiIiIiIyCacMvFcrVo1xMfHAwAOHDhQaOJ5w4YNatOvPsc5v8TERNX/AwICzBQlkXORyWR49uyZajooKAhSqdSGERFZH9sBEdsBEcB2QMQ2QAQolUrVaHzAy5EvxGKnfOIdkUZsB0RsB0RsA0WPU366jRs3Vv3/559/VkscA0BSUhIWL16smvbw8EDz5s011nflyhXV/5l4JiqcIAiQyWSqP0EQbB0SkdWxHRCxHRABbAdEbANELwmCoPojKqrYDojYDojYBooWp0w8Dxw4UPX/+Ph4tGzZEtu2bcP169exc+dONG/eHM+fP1eV6d27N7y8vDTWd/78edX/ixcvbpmgiYiIiIiIiIiIiIiIiIgclFMOtV2rVi307t0bW7duBfCyx3Lv3r0LLSsWizFt2jSt9UVGRqr+X7NmTfMFSkRERERERERERERERETkBJyyxzMALFq0CGFhYTrLff7553jttdc0zn/06BHOnj2rmtZWloiIiIiIiIiIiIiIiIioKHLKHs8AEBwcjGPHjmH8+PHYvn17gfleXl6YMWMGPvroI631/P7772rjzoeHh5s7VCIiIiIiIiJyYikpKbhy5Qpu376NjIwMyGQySKVSeHl5ISwsDLVq1YKfn5+twyQymlwux/jx45GamgoAmDhxIpo2bWrjqIomhUKBsWPHIi0tDQAwYcIENGvWzMZRERERUVHhtIlnAChVqhS2bduGe/fu4eDBg4iPj4dEIkHlypXRvn17BAYG6qzj2bNn6NGjh2qaQ20TERERERERkTZKpRInTpzA9u3bcfHiRTx8+BAAIChkgFIJQAAgAsRiiCRSAEDZsmVRp04d9OrVC02bNoVY7LSD1Jlk/fr12LFjh8n1rFixAp6enjrLLV++HAcOHFB77b333kPz5s2NXvfx48fx66+/qqYrV66M2bNnm628LcyfPx+///47AKBixYqoX7++jSOyTyNGjEB2drbRy0+ZMkXneyuRSBASEoKZM2cCAC5fvoyLFy9CKpUavV4iIiIifTl14jlPhQoVMHr0aKOWXbRokZmjISIiIiIiIiJnlJKSgs2bN2PDhg2Ii4uDkJMBpSwbgjwHgjxX43IiF1fcT3+BB3duYPfu3ShXrhwGDhyIN998kz2h87l06RI2btxocj2//fabzsSzUqnEzJkz8ejRI7XXFQqFSYnn2NhYtW1o1KiR1kSyoeWt7dGjR/jyyy9V019//TVcXV1tGJH92rx5MzIyMoxefsCAAXol9T/88EMsXrwYT548wdWrV7FgwQJMnTrV6PUSERER6atIJJ6JiIiIiIiIiCxFEATs3r0bs2fPRkrSCyiz0qDMToebRIm6ZTxQpbgXqpYIRFgxV/i6SyARAQoBSM1W4PaLXNx4noObCTk4/ygBOemJiM1Mxrxv7uG3337DjBkz0KVLF4hEIltvZpGzf//+AklnANi1axcSExP1GkmvKJgxYwbS09MBANWrV0ffvn1tHBF5eXnhww8/VCWbv/rqK4waNYr7LBEREVkcE89ERERERERmkJKSgsWLFwN42Usuj1gsxrhx49hrkchJJSQkYObMmTh48ACUmSlQZCajnJ8U3ev4o31lb3i7SQpdzkUEBHq6oKGnCxqGvOx5m56jwIFb6dh5NRVxiclIyk7Dhx9+gL179+KLL75A8eLFrblpDqF169YYM2aMwct5eXnpLLNy5cpCX8/JycG6devw3nvvGbxeZ3P9+nWsXr1aNT19+nQOE6+n0qVL44cffjBomQYNGuhdduzYsfjmm2+QmJiIlJQUzJ07F99++62hYRIREREZhIlnIiIiIiIiMxAEATk5ORrnEZHzuXTpEsaMGYOkhGdQpD2Hn1SBiW2D0KKCl1E9lL3dJOhVyw89a/ri2L0M/HwiASlJGTjwdyTOnj2LpUuXonbt2hbYEsdVsWJFDBgwwOz1JiUlqT1LOiAgAElJSarpVatWMfGMlz1pFQoFACA4OBgDBw60cUSOw8fHxyL7bh5vb2+MHj0a8+bNAwAsXLgQ06ZNQ7FixSy2TiIiIiLegkhEREREREREZKDTp09j+PDhSHzyAPLkeLQMdcWKviFoWdHb5GGxRSIRWlb0xvI+IWhRzhXy5HgkPnmA4cOH48yZM2baAtJm/fr1ajcTvf3222q9Tc+dO4crV67YIjS78fDhQ7VnT48cORJSqdSGEVF+Y8aMUX0fZWZmYtGiRTaOiIiIiJwdE89EZBYSiQT+/v6qP4mk8OHkiJwZ2wER2wERERWNY8HFixfxzjvvICPxKZTpLzC5eXF83jYYfu7m3VZ/DwlmtAvG5ObFoUx/gYzEpxgzZgwuXbpk1vVQQatWrVKbHjJkCAYPHqz2mqahuIGXNw9IJBLVnzM+o3vhwoWQyWSq6VGjRtkwGipMxYoV0apVK9V0/s/M0opCOyDShe2Aijq2gaKnyAy1ffHiRURGRuLUqVO4e/cukpOTkZqaqvbsNX2lp6dbIEIix5Z3cYmoKGM7IGI7ICIi5z8WJCQkvEw6Jz2DMiMRH7cOQtswb4uus2t1X3hIRfjm8HNkiEQYM2YM9uzZw2c+W8jVq1fx77//qqZr1aqF8PBwBAcH48MPP1QNLb127VrMmzcPLi4FL6+JRKJCX3cWCoUCf/zxh2o6IiIClSpVMkvdDx48wM6dO3H//n08ffoUvr6+CA0NRfv27TnUvBH69euHw4cPAwCePn2KyMhI9OjRwyrrdvZ2QKQPtgMq6tgGih6n/7QvXLiASZMmISoqytahEBEREREREZEDEwQBM2fOfPlM5//v6WzppHOetmE+yJIJmH88AUkJbpg5cyZ+/fVX9hqxgPw9mfN6OgcHB6Ndu3bYt28fgJdJvL1796Jbt25Wj9HW/vnnH8THx6um33zzTb2XXbBgAU6dOqWa/uWXX1CiRAlcvXoVU6ZMwb59+zR2FGnevDl+/fVXJqAN0Lt3b4wfPx6CIAAAVq9ebbXEMxERERU9Tp14XrNmDUaPHq32TB4iIiIiIiJL8PLyQv/+/TXOIyLHt3v3bhw8eACKtOdoVdELXav7WnX9Xav74vyjLByLe46DBw9iz5496Nq1q1VjcHZyuRx//vmnalokEmHQoEGq6SFDhqgSz8DLJHVRTDxv375dbbpdu3Z6L3v8+HFs2bJFNT137lxER0dj0KBByMzM1Lns66+/jt27d6NFixYGxVxUBQUFITw8XDVE/759+5CdnQ13d3cbR0ZERETOyGkTz1FRURg1alSB55YUL14cwcHBiImJUb1Wu3ZtpKenIyEhASkpKQXqql69OlxdXS0eMxEREREROS6pVIpq1arZOgwispCUlBTMnj0byswU+EkVmNDUNsNcT2xWHJceP0BaZjK+/PJLvP766/Dz87NJLM7o77//xpMnT1TTr7/+OsqVK6ea7tWrF7y8vJCRkQHg5c0ICQkJRW7Y88jISNX/fXx8UK9ePaPrOnr0KMaMGYPc3FxIpVJ06NABLVq0QFBQENLT03Hq1Cn89ddfqo4laWlp6N+/P65evYqAgACTt6UoaNOmjSrxnJmZiSNHjqBjx442joqIiIickVMmngVBwLhx49SSzn369MGMGTPw2muvAYDaUFQXL15U/f/27dvYs2cPfvnlF9y5cwcA4OnpiR07dqBMmTLW2QAiByQIglqbk0qlHPKNihy2AyK2AyKA7YDIWdvAli1bkJL0AorMZExsGwR/D4lN4vD3kGBC0+KYfegZUpJ8sHXrVrz11ls2icUZaRpmO4+Xlxd69OiBdevWAQBkMhnWrl2LSZMmqZUTBEE1tDHw8jqUM7QDAIiN/T/27ju+qfL9//g7adOWQmmBUqRMZQ8ZMgUURFEUBwgIyBJxoSLOjygKjo8fF+ICRFwMEXEhyhQVEAREoIACggiyCpTRRSlt2ub3B1/Or6eDJk3aNMnr+Xj0wblPzrnPdbe5Gpor933+1f79+41269at3bp34wMPPKDMzEx16NBBs2bNUsOGDfM9Pm7cOF177bU6ePCgJOno0aN69dVX9corrxT7ut5mt9v1448/6vfff9fhw4eVnJysyMhIVa5cWc2bN1fnzp099l5k+/btTe0VK1aUSuHZn/MAcBZ5gEBHDgQevyw8L1myxDSj+fHHH9frr7/u1Ln169fXmDFjdN9992ncuHF64403tGnTJl1xxRXasGFDwH2CFXCW3W433d8pNjaWlQIQcMgDgDwAJPIA8MccyMnJ0dy5c5WTnqrakTZdebF3l8/vekl5zdhkU/zZVM2dO1fDhw+X1Wr1akz+4OTJk1q4cKHRDgkJUf/+/fMdN3jwYKPwLEkzZswosPDsjx/AkKTff//d1G7RooVb/aWlpalNmzb66aefCr01RePGjTVv3jx16tTJ2Ddz5ky99NJLCgoq/EMgdrtdQ4cOdSs+Z40fP15NmzZ1+vi9e/eqR48eFzzm8ssv12OPPaZbb73VredP3p/Rhg0bit2XK/w5DwBnkQcIdORA4PHLwvPSpUuN7ebNm+vll192uY/Q0FBNnDhRFSpU0PPPP699+/bp7rvv1vz58z0ZKgAAAAAAKOPWrl2rAwcOKOfsad3cKsrrb5ZZLBbd3LSipq5P0v79+7V27Vp16dLFqzF5y4oVKzRw4ECnj2/atKnGjx9f4GNz5sxRZmam0b7++usLXMr52muvVdWqVXX8+HFJ51bS27p1q1q2bOli9L4p98qBktSoUSO3+rNarZo9e3ahRefzLr/8cl155ZX65ZdfJJ2b9bx9+/YLFr6zs7M1b948t+Jz1n333edS4dkZ69atU79+/dSzZ0/NnDlTMTExxeqnYcOGslqtysnJkZT/ZwgAAOApfll4Xrt2rbF93333ubXcz/jx4/X9999r8+bN+vbbb7V9+3Y1a9bME2ECAAAAAAAfMH/+fDky0hQalKMeDSp4OxxJUo8GFfTR76eUlZGm+fPnB2zhee/evdq7d6/Tx3ft2rXQwnPeZbaHDBlS4HHBwcEaMGCAJk+ebDr3rbfecjoOX7Zv3z5Tu2bNmm71d8MNN6hJkyZOHduzZ0+j8CxJcXFxbs+49pbzy2k3adJElStXVnp6uuLj47V+/Xr9/PPPptlhS5cuVceOHbV27VpddNFFLl8rODhY1apV05EjRyRJSUlJSkpKUlRUlKeGAwAAIMlPC8+HDx82trt161bk8ZmZmYUu+2W1WvXggw/qzjvvlCTNnTtX//3vfz0SJwAAAAAAKPu2bNmiHPtZXVajnCqEeufeznlFhAbpshrltP7YWW3dutXb4fi8rVu3mmaBVqxYUTfeeGOhxw8ZMsRUeP7ss8/0+uuvy2azlWSYZcKBAwdM7djYWLf6u+GGG5w+Nu+M4vOF1MKEhIRo7ty5xYrLVc7Mdg4NDdWwYcN0//33q3nz5oUed/jwYT3zzDOaMWOGsW/fvn3q1auXfvvtt2JNsomNjTV9vw4cOEDhGQAAeJxfFp4TExON7dq1axd4THBwsLKysiRJ6enpF7zf1FVXXWVs//TTTxSeAQAAAAAIEElJSTp06JAcWRlqGO3dezvn1TA6VOsOp+ngwYNKTk5WZGSkt0MqdVdddZXuuecep48vbKnivLOd+/btq7CwsEL76dChg+rXr689e/ZIko4fP66FCxeqT58+Tsfiq1JSUkztopbILoorS5RXrlzZ1E5OTr7g8Var1aWl2Eva1q1bnZohXqNGDX3yySdq1aqVHn74YWP/5s2bNX36dN1///0uXzvvzynvzxEAAMAT/LLwnPteS+XKlSvwmIiICKNAffTo0Qv+cVa9enVjOz4+3kNRAgAAAACAsm779u2SJEdWphpVrVzE0aWrUdVQObLOvbexfft2derUycsRlb5LLrnE7cKi3W7XZ599Zto3ePDgIs8bPHiwnn/+eaM9Y8aMgCg8nzlzxtQu7L03Z1WpUsXpY/Ne6+zZs25du7S5uiz5mDFjtHnzZs2aNcvY98orrxSr8BweHm5qp6WludwHAABAUazeDqAk5P70Y2Gf3qtUqZKxXdS9gDIyMoztY8eOuRkdAAAAAADwFXv27JEj+9y9VutXKXy1NG84H48j266///7by9H4roULF+r48eNGOzY21rT6XWHyFqcXL16shIQEj8dX1gQFmZebz87Odqu/0NDQYp/rcDjcurYv+O9//2uaZHPw4MFiLa9/fuXH8/L+HAEAADzBLwvPDRs2NLYLu9dLo0aNjO3169dfsL8//vjD2HbnP8MAAAAA/FdmZqa2bNmibdu2ac+ePcbXtm3blJmZ6e3wABRTWlqalJMjSaoYVrYKNRHn7zftyMk3CxXOy30fXUkaNGiQrNai3zJr0KCB2rdvb7SzsrL06aefejq8MifvzNn09HQvRRIYatWqpVatWpn2/fbbby73k/fn5O4S6QAAAAXxy6W227Ztq5UrV0qS4uLi1KxZs3zHtGrVSkuWLJEkffbZZ5owYUKhf1R88MEHxnZsbKznAwYAAADg886cOaMFCxYU+Fj9+vUVElK2ZkoCcI7dbpd0blZlkOXCx5a24PNvYzgcfMClmBISErR48WLTvt9++83p5bvz3mN4xowZevTRRz0WX1kUHR1taiclJXknECfY7XYNHTq0VK41fvx4NW3atET6btSokeLi4ox2cVZkzPtcdWWJcwAAAGf5ZeG5Z8+emjhxoiRp1apVGjJkSL5jbrnlFr388suSzi2b9eSTT+q1114zLV0jSTNnztTMmTONdpcuXUowcgAAAAAAUJbYbDZJ594ryHZIwWWo+JyV838bFgsfbimmTz/9NN8SxGvWrCl2f3/88Yc2b96cb4aqP6lTp46pfejQIS9FUrTs7GzNmzevVK513333lVjhOSIiwtQ+ffq0y33k/jlZLJZ8P0cAAABP8Multrt27Wp8au+bb775v08nm3Xo0EGXXnqp0Z44caI6duyoiRMnau7cuZoyZYquv/563XHHHabzhg8fXqKxAwAAAACAsqN8+fLS/62QlnLWvXvZelpqxv/FY7HmW/4Yzsm7zLYnfPLJJx7vsyxp0KCBqX3gwAEvRRI4Tpw4YWq7Olv59OnTppnpNWvW5HaCAACgRPjljOfg4GCNGDFCEydO1KlTpzR37lwNGzYs33FvvfWWrr76aqO9YcMGbdiwodB+e/fuzYxnAAAAAAACSP369WUJskmS9pzMVPvwsvNWyp6T55bXtgTZ8hUDUbRNmzbpjz/+MNpVqlTR5MmTXe4nOTlZ9913n9GeO3euXn/99Xyr6vmLyy67zNT+888/vRRJ0UJCQjR37txSuVZJzXaWpI0bN5raNWrUcOn83M9z6dxtCgEAAEpC2flrycMeeOABrVu3TpK0cuXKAgvP3bt311tvvaWHH364yP7at2/v959YBdxhs9lM90A/txwdEFjIA4A8AAD432tBs2bNJEmW4BDtOp6h9rXKzsziXcczZAk+t8T2+TjhvLzv8/Tr18/pezvnNX36dG3evFmSdPLkSX3//ffq27ev8bg/FaHbtWsni8Uih+Pcvc+3bdvm5YgKZ7Vai/0zLStWrVqlgwcPmvZdddVVLvWxdetWU7tDhw5ux+UMi8Vieg3wpzwAnEUeINCRA4HHL5falqS6detqzZo1WrNmjT7++ONCjxszZox++OEH07LbuZUvX15jx47VypUrFRUVVULRAr7P8n/3FDv/xQsIAhF5AJAHCGxWq1WRkZEFflmtfvunF5CPv70WREVFqWbNmrIEh2r3iQxvh2Oy+0SGLMGhqlWrliIjI70djk/JzMzMNxN20KBBxe7v9ttvN7Vnzpwpq9VqfPl6HuQWFRVlmjG7fft20zLO8JyzZ8/q0UcfNe3r0KGD6cM9zvj1119N7R49ergdmzMsFovf5gHgLPIAgY4cCDx+O+PZFT169NC2bdu0a9cuxcXF6fjx4woLC9PFF1+sLl26KCwszNshAgAAACjjKlas6NRqSgB8T6tWrXTwn13afPiETmdkq0JokLdDUmpGtjYfTpe1XLRatmzp7XB8zoIFC3Tq1CmjXaNGDV1xxRXF7m/gwIH6z3/+o5ycHEnS0qVLdfToUV100UVux1oW9erVS7///rskKScnR6tWrdItt9zi5ajKrt27d2v37t3q1auX02+4p6amavDgwcZM+vNeeukll6+/YsUKY7t69epq3bq1y30AAAA4g4/d59KoUSMNHDhQo0eP1t13361rrrmGojMAAAAAAAGuT58+soSWV0a2Vcv/Pu3tcCRJy/8+rYxsqyyh5dWnTx9vh+NzZsyYYWoPGDDArdUp8haus7OzNXv27GL3V9b169fP1F6yZImXIvEN8fHxuummm9S8eXO99NJL+vPPP40PKeSVlpamjz76SC1bttT3339veuzuu+/W1Vdf7dK1//jjDx0+fNho9+vXj9lmAACgxPjljOelS5dKkhwOh0JDQ9W9e3cvRwQAAAAAAHxVp06dVLt2bf2blqjvdqSod7OKXi3cOBwOfbcjRdZyEapTp446derktVh80ZEjR7Rs2TLTPneW2c7dx6pVq4z2jBkz9MQTT7jdb1579uwp9n2L27Vrp8cee8ztGJo1a6Y2bdpo06ZNkqT58+drypQpCgry/moAZdmOHTv0zDPP6JlnnlG5cuXUpEkTRUdHKyIiQunp6Tpy5Ij++OMPZWVl5Tt34MCBeu+991y+5pdffmlq33HHHcUNHwAAoEh+WXi+/vrrje1nn32WwjNQCrKzs5Wammq0IyIi+IMTAYc8AMgDQCIPAH/MAavVqkGDBunVl/fqwKlk/bIvTV0vqeC1eFbtTdPBZLtslSM0aNAg7iPvotmzZys7O9to169f33TP4uLq37+/Ro8eLbvdLulckXHt2rVq3769goKCPPZhhZMnT2revHnFOvfs2bMeKTxL52bfni88JyQkaMWKFbrmmms80ncgSE9Pz7eMdkGioqL0xhtv6M477yzWdXI/V1q3bq3LLrusWP0Uh8PhMOWaJ/MA8BXkAQIdORB4/PIvk9xP2v79+3sxEiBwZGdnKykpyfjK/WICBAryACAPAIk8APw1B/r27avISlUUFB6ld349oaR074wrKT1b7649oaDwKEVWqqxbb73VK3H4srzLbHtitrMkVa5cWddee61p38yZM5WdnS2Hw+GRa5Qlw4YNU9WqVY329OnTvRhN2dakSRP997//Vf/+/dWoUaMiPywSERGhq666StOnT9ehQ4eKXXReuXKldu/ebbQ99aEDZ50vNpz/8sc8AIpCHiDQkQOBxy9nPEdHR+v48eOSpHr16nk5GgAAAAAA4OsiIyP17LPP6vHHH1NyYpreXXtCz15drdTjeOfXE0q2Bym4UpTGjx+vyMjIUo/Bm26//Xa1atXKaLv6vs/Zs2c1fvx4075u3bp5ILJzXn/9dQ0ZMkQ5OTnKzs4u8OdzxRVXaO7cuUY7Ojr6gn3mPd4dNWvW9Eg/klSuXDk9+OCDmjBhgiTp22+/1bFjx1St2oXz4pFHHjHdIzp38boo9erVM30vGjVq5GLU3lGtWjWNGzfOaKenpys+Pt74gE5ycrKCg4MVFRWlatWqqUGDBh5ZyWDatGnGdp06dTRgwAC3+wQAALgQvyw8V69e3Sg8Z2RkKDw83MsRAQAAAAAAX3fjjTdq6dKlWr50sVbujVfr2BTd2KRiqV3/+x0pWrUvTcFRserRo4d69epVatcuK1q0aKEWLVoU+/ywsLBi3x/ZGU2aNFGTJk2Uk5NjLLmdV506dVSnTh2n+3T1+NL08MMP691339WJEydkt9v11ltv6eWXX77gOZ07dy729SpVqlSiP7/SUq5cuRKfLLNv3z59/fXXRnvChAkKDvbLt4IBAEAZ4pdLbef+5OuuXbu8FwgAAAAAAPAbFotFzz//vCpFxyioQhW9teaEftpzulSu/dOeVL396wkFVaiiStExeu6557g/HryuYsWKeuqpp4z21KlTlZyc7MWIcN6rr76qrKwsSec+EDFs2DAvRwQAAAKBXxaec3/y8YsvvvBiJAAAAAAAwJ9ER0dr+vTpKl8pRtbylfXyigQt3JlSotf8fkeKXl5xXNbylVW+UoymT59e5PLMQGl58MEH1bBhQ0lSSkqKXn/9dS9HhL179+qTTz4x2m+99ZaCgoK8GBEAAAgUfll47tGjh/Ef3qlTp2rnzp1ejggAAAAAAPiLli1b6v3331f5ytVkrVBFb645oRd+PKak9GyPXicpPVsv/HhMb/16QtYKVVS+cjVNnz5dLVu29Oh1AHeEhIRoypQpRvvNN9/U4cOHvRgRnnrqKWVmZkqS+vXrp2uvvdbLEQEAgEDhlzf2CA4O1qxZs9StWzedPXtWPXr00KJFi/jDDAAAAECJSU1N1Zw5c+RwOEz39bTZbBoyZIgiIiK8GB0AT+vQoYNmzpype+65R4knQvXLgePaeuSgRneKVtdLyru1DLbD4dCqvWl6d+0JJduDFBwVq0rRMRSdUWZdc801WrhwoVJTUyVJaWlpXo4ocGVnZ+vWW29Vnz59JEndu3f3ckQAACCQ+GXhWTr3B+CiRYvUv39/HT58WG3bttVdd92lO++8U23btuU+SAAAAAA8Kjs7W8eOHSv0MQD+p2XLllq0aJEmTJigH39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\n", 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import html\n", + "from IPython.display import display, HTML, Markdown, Image, JSON\n", + "\n", + "def table(rows, columns=None):\n", + " \"\"\"An HTML table from a list of dicts (no pandas: the kernel's packages are not used).\"\"\"\n", + " columns = columns or list(dict.fromkeys(k for row in rows for k in row))\n", + " head = ''.join(f'{html.escape(str(c))}' for c in columns)\n", + " body = ''.join('' + ''.join(f'{html.escape(str(row.get(c, \"\")))}'\n", + " for c in columns) + '' for row in rows)\n", + " display(HTML(f'{head}{body}
'))\n", + "\n", + "run_dir = DEMO_ROOT / f'runs/{MODEL}/{DATASET}.{RUN_TAG}'\n", + "read_json = lambda p: json.loads(p.read_text())\n", + "baseline = read_json(run_dir / 'baseline.json')\n", + "summary = read_json(run_dir / 'summary.json')\n", + "stages = {stage: read_json(run_dir / 'logs' / stage / 'log.json')\n", + " for stage in ['M1', 'M2', 'M3', 'M4', 'M5']\n", + " if (run_dir / 'logs' / stage / 'log.json').exists()}\n", + "fix = (stages.get('M5', {}).get('fix') or [{}])[-1]\n", + "attempts = fix.get('attempted', [])\n", + "print(f\"Baseline: {baseline['n_pass']}/{baseline['n']} ({baseline['accuracy']:.1%})\")\n", + "print(f\"Verified hypotheses: {summary['n_verified']}\")\n", + "print('Frozen candidate:', fix.get('selected_on_explore') or 'none')\n", + "if attempts:\n", + " final = attempts[0]\n", + " table([{\n", + " 'split': 'CONFIRM', 'n': final['n_pairs'],\n", + " 'baseline_correct': final['n_baseline_correct'],\n", + " 'candidate_correct': final['n_candidate_correct'],\n", + " 'repaired_cases': final['n_fixed'], 'regressed_cases': final['n_broken'],\n", + " 'e_value': final['e_value'], 'required_e_value': final['e_threshold'],\n", + " 'verdict': final['verdict'], 'validated_fix': final['fixed'],\n", + " }])\n", + " display(JSON(final['payload']))\n", + " print('Accuracy repair validated.' if final['fixed'] else 'No statistically confirmed accuracy repair.')\n", + "else:\n", + " print('No candidate selected for CONFIRM; no validated accuracy repair.')\n", + "if fix.get('selection_attempted'):\n", + " display(Markdown('### EXPLORE candidates (selection only)'))\n", + " table(fix['selection_attempted'])\n", + "for image_file in sorted((run_dir / 'logs/M2/artifacts').glob('*.png')):\n", + " display(Image(filename=str(image_file)))" + ] + }, + { + "cell_type": "markdown", + "id": "hJkz0DarRXIh", + "metadata": { + "id": "hJkz0DarRXIh" + }, + "source": [ + "## 7. Every answer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "qkA56a95RXIh", + "metadata": { + "colab": { + "background_save": true + }, + "id": "qkA56a95RXIh", + "outputId": "fb228799-b949-4285-a752-2d99200c3cd9" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
idquestionexpectedobservedlabel
bbh_word_sorting-0000Sort the following words alphabetically: List: alleviate duopoly mattress gland benelux townsmen buoyant klaxon hardbound tomography felice gunk\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.alleviate benelux buoyant duopoly felice gland gunk hardbound klaxon mattress tomography townsmenHere are the words to sort alphabetically: alleviate, duopoly, mattress, gland, benelux, townsmen, buoyant, klaxon, hardbound, tomography, felice, gunk.\n", + "\n", + "1. **alleviate**\n", + "2. **benelux**\n", + "3. **buoyant**\n", + "4. **duopoly**\n", + "5. **felice**\n", + "6. **gland**\n", + "7. **hardbound**\n", + "8. **klaxon**\n", + "9. **mattress**\n", + "10. **tomography**\n", + "11. **townsmen**\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **a**lleviate\n", + "* **b**enelux, **b**uoyant (benelux before buoyant)\n", + "* **d**uopoly\n", + "* **f**elice\n", + "* **g**land, **g**unk (gland before gunk)\n", + "* **h**ardbound\n", + "* **k**laxon\n", + "* **m**attress\n", + "* **t**omography, **t**ownsmen (tomography before townsmen)\n", + "\n", + "**Final Sorted List:**\n", + "1. alleviate\n", + "2. benelux\n", + "3. buoyant\n", + "4. duopoly\n", + "5. felice\n", + "6. gland\n", + "7. gunk\n", + "8. hardbound\n", + "9. klaxon\n", + "10. mattress\n", + "11. tomography\n", + "12. townsmen\n", + "\n", + "Wait, let's re-check the 'g' words: gland, gunk. Gland comes before Gunk.\n", + "\n", + "Let's re-check the 'b' words: benelux, buoyant. Benelux comes before Buoyant.\n", + "\n", + "Let's re-check the 't' words: tomography, townsmen. Tomography comes before Townsmen.\n", + "\n", + "**Final Alphabetical Order:**\n", + "1. alleviate\n", + "2. benelux\n", + "3. buoyant\n", + "4. duopoly\n", + "5. felice\n", + "6. gland\n", + "7. gunk\n", + "8. hardbound\n", + "9. klaxon\n", + "10. mattress\n", + "11. tomography\n", + "12. townsmen\n", + "\n", + "Answer: alleviate, benelux, buoyant, duopoly, felice, gland, gunk, hardbound, klaxon, mattress, tomography, townsmenpass
bbh_word_sorting-0001Sort the following words alphabetically: List: yuh edt cheddar oblivion from poignant pang\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.cheddar edt from oblivion pang poignant yuhThe given list of words is: yuh, edt, cheddar, oblivion, from, poignant, pang.\n", + "\n", + "We need to sort these words alphabetically.\n", + "\n", + "1. **Compare the first letters:**\n", + " - yuh (y)\n", + " - edt (e)\n", + " - cheddar (c)\n", + " - oblivion (o)\n", + " - from (f)\n", + " - poignant (p)\n", + " - pang (p)\n", + "\n", + "2. **Order based on the first letter:**\n", + " - c (cheddar)\n", + " - e (edt)\n", + " - f (from)\n", + " - o (oblivion)\n", + " - p (poignant, pang)\n", + " - y (yuh)\n", + "\n", + "3. **Sort the words starting with 'p':**\n", + " - poignant\n", + " - pang\n", + " (Comparing the second letters: 'o' in poignant vs 'a' in pang. 'a' comes before 'o'.)\n", + " So, the order is: pang, poignant.\n", + "\n", + "4. **Assemble the final sorted list:**\n", + " - cheddar (c)\n", + " - edt (e)\n", + " - from (f)\n", + " - oblivion (o)\n", + " - pang (p)\n", + " - poignant (p)\n", + " - yuh (y)\n", + "\n", + "Let's re-check the 'p' words:\n", + "pang (p-a-n-g)\n", + "poignant (p-o-i-g-n-a-n-t)\n", + "'a' comes before 'o', so pang comes before poignant.\n", + "\n", + "Final sorted order: cheddar, edt, from, oblivion, pang, poignant, yuh.\n", + "\n", + "Answer: cheddar, edt, from, oblivion, pang, poignant, yuhpass
bbh_word_sorting-0002Sort the following words alphabetically: List: intercept tamp geopolitic loci newsmen umber credit atmospheric lunge siren swart chess\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.atmospheric chess credit geopolitic intercept loci lunge newsmen siren swart tamp umberHere are the words to sort: intercept, tamp, geopolitic, loci, newsmen, umber, credit, atmospheric, lunge, siren, swart, chess.\n", + "\n", + "Let's sort them alphabetically:\n", + "\n", + "1. atmospheric\n", + "2. chess\n", + "3. credit\n", + "4. geopolitic\n", + "5. intercept\n", + "6. loci\n", + "7. lunge\n", + "8. newsmen\n", + "9. siren\n", + "10. swart\n", + "11. tamp\n", + "12. umber\n", + "\n", + "Let's re-check the sorting carefully:\n", + "\n", + "* **a**tmospheric\n", + "* **c**hess\n", + "* **c**redit\n", + "* **g**eopolitic\n", + "* **i**ntercept\n", + "* **l**oci\n", + "* **l**unge\n", + "* **n**ewsmens\n", + "* **s**iren\n", + "* **s**wart\n", + "* **t**amp\n", + "* **u**mber\n", + "\n", + "Comparing 'loci' and 'lunge': 'loci' comes before 'lunge'.\n", + "\n", + "Comparing 'siren' and 'swart': 'siren' comes before 'swart'.\n", + "\n", + "The correct alphabetical order is:\n", + "1. atmospheric\n", + "2. chess\n", + "3. credit\n", + "4. geopolitic\n", + "5. intercept\n", + "6. loci\n", + "7. lunge\n", + "8. newsmen\n", + "9. siren\n", + "10. swart\n", + "11. tamp\n", + "12. umber\n", + "\n", + "Answer: atmospheric, chess, credit, geopolitic, intercept, loci, lunge, newsmen, siren, swart, tamp, umberpass
bbh_word_sorting-0003Sort the following words alphabetically: List: above prometheus broken coexist big dominate olive screw thirdhand irk\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.above big broken coexist dominate irk olive prometheus screw thirdhandHere are the words to sort: above, prometheus, broken, coexist, big, dominate, olive, screw, thirdhand, irk\n", + "\n", + "We will sort them alphabetically:\n", + "\n", + "1. **above**\n", + "2. **big**\n", + "3. **broken**\n", + "4. **coexist**\n", + "5. **dominate**\n", + "6. **irk**\n", + "7. **olive**\n", + "8. **prometheus**\n", + "9. **screw**\n", + "10. **thirdhand**\n", + "\n", + "Let's re-check the sorting carefully:\n", + "\n", + "* **a**bove\n", + "* **b**ig\n", + "* **b**roken (b-r comes after b-i)\n", + "* **c**oexist\n", + "* **d**ominate\n", + "* **i**rk\n", + "* **o**live\n", + "* **p**rometheus\n", + "* **s**crew\n", + "* **t**hirdhand\n", + "\n", + "Correct alphabetical order:\n", + "1. above\n", + "2. big\n", + "3. broken\n", + "4. coexist\n", + "5. dominate\n", + "6. irk\n", + "7. olive\n", + "8. prometheus\n", + "9. screw\n", + "10. thirdhand\n", + "\n", + "Answer: above, big, broken, coexist, dominate, irk, olive, prometheus, screw, thirdhand\n", + "\n", + "Answer: above, big, broken, coexist, dominate, irk, olive, prometheus, screw, thirdhandpass
bbh_word_sorting-0004Sort the following words alphabetically: List: hodges deoxyribose troupe motorcade devisee stratify auerbach decor incommensurable dianne\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.auerbach decor deoxyribose devisee dianne hodges incommensurable motorcade stratify troupeHere are the words to sort alphabetically:\n", + "hodges, deoxyribose, troupe, motorcade, devisee, stratify, auerbach, decor, incommensurable, dianne\n", + "\n", + "Let's sort them:\n", + "\n", + "1. **auerbach** (Starts with 'a')\n", + "2. **decor** (Starts with 'd', comes before 'dianne')\n", + "3. **devisee** (Starts with 'd', comes after 'decor')\n", + "4. **dianne** (Starts with 'd', comes after 'devisee')\n", + "5. **deoxyribose** (Starts with 'd', comes after 'dianne')\n", + "6. **hodges** (Starts with 'h')\n", + "7. **incommensurable** (Starts with 'i')\n", + "8. **motorcade** (Starts with 'm')\n", + "9. **stratify** (Starts with 's')\n", + "10. **troupe** (Starts with 't')\n", + "\n", + "Let's re-check the 'd' words:\n", + "decor\n", + "devisee\n", + "dianne\n", + "deoxyribose\n", + "\n", + "Alphabetical order for 'd' words:\n", + "decor\n", + "devisee (deci...)\n", + "dianne (dia...)\n", + "deoxyribose (deox...)\n", + "Wait, comparing 'devisee' and 'dianne':\n", + "d i a n n e\n", + "d e v i s e e\n", + "'i' comes before 'e' in the second position if we compare the full words:\n", + "d i a n n e\n", + "d e v i s e e\n", + "'i' comes before 'e' is incorrect. Let's compare character by character:\n", + "d i a n n e\n", + "d e v i s e e\n", + "'i' vs 'e'. 'e' comes before 'i'. So 'devisee' comes before 'dianne'.\n", + "\n", + "Corrected 'd' order:\n", + "1. decor\n", + "2. devisee\n", + "3. dianne\n", + "4. deoxyribose\n", + "\n", + "Let's re-sort the whole list:\n", + "\n", + "1. auerbach\n", + "2. decor\n", + "3. devisee\n", + "4. dianne\n", + "5. deoxyribose\n", + "6. hodges\n", + "7. incommensurable\n", + "8. motorcade\n", + "9. stratify\n", + "10. troupe\n", + "\n", + "Final check:\n", + "a - auerbach\n", + "d - decor, devisee, diannefail
bbh_word_sorting-0005Sort the following words alphabetically: List: seashore haughty\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.haughty seashoreThe given words are: seashore, haughty.\n", + "\n", + "To sort them alphabetically, we compare them letter by letter from the beginning.\n", + "\n", + "1. **s**eashore\n", + "2. **h**aughty\n", + "\n", + "Since 'h' comes before 's' in the alphabet, "haughty" comes before "seashore".\n", + "\n", + "The alphabetical order is: haughty, seashore.\n", + "\n", + "Answer: haughty, seashore\n", + "Answer: haughty, seashorepass
bbh_word_sorting-0006Sort the following words alphabetically: List: torpedo phosphorescent pristine decadent shrunk dey administer gradate littleneck thrown jacky coachman aeneid verdict tasting sinh delhi systemwide grim\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.administer aeneid coachman decadent delhi dey gradate grim jacky littleneck phosphorescent pristine shrunk sinh systemwide tasting thrown torpedo verdictHere are the words sorted alphabetically:\n", + "\n", + "1. administer\n", + "2. aeneid\n", + "3. decadent\n", + "4. coachman\n", + "5. delhi\n", + "6. gradate\n", + "7. grim\n", + "8. jacky\n", + "9. littleneck\n", + "10. phosphorescent\n", + "11. pristine\n", + "12. sinh\n", + "13. systemwide\n", + "14. tasting\n", + "15. torpedo\n", + "16. thrown\n", + "17. verdict\n", + "18. dey\n", + "19. shrunk\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* administer\n", + "* aeneid\n", + "* decadent\n", + "* coachman\n", + "* delhi\n", + "* grim\n", + "* gradate\n", + "* jacky\n", + "* littleneck\n", + "* phosphorescent\n", + "* pristine\n", + "* sinh\n", + "* systemwide\n", + "* tasting\n", + "* torpedo\n", + "* thrown\n", + "* verdict\n", + "* dey\n", + "* shrunk\n", + "\n", + "**Alphabetical Sorting:**\n", + "\n", + "1. administer\n", + "2. aeneid\n", + "3. decadent\n", + "4. coachman\n", + "5. delhi\n", + "6. dey\n", + "7. grim\n", + "8. gradate\n", + "9. jacky\n", + "10. littleneck\n", + "11. phosphorescent\n", + "12. pristine\n", + "13. sinh\n", + "14. shrunk\n", + "15. systemwide\n", + "16. tasting\n", + "17. torpedo\n", + "18. thrown\n", + "19. verdict\n", + "\n", + "Answer: administer, aeneid, decadent, coachman, delhi, dey, grim, gradate, jacky, littleneck, phosphorescent, pristine, sinh, shrunk, systemwide, tasting, torpedo, thrown, verdictfail
bbh_word_sorting-0007Sort the following words alphabetically: List: boletus ulysses debugging passband lucretia necktie deportee calypso revving conklin omnipotent\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.boletus calypso conklin debugging deportee lucretia necktie omnipotent passband revving ulyssesHere are the words to sort alphabetically:\n", + "boletus, ulysses, debugging, passband, lucretia, necktie, deportee, calypso, revving, conklin, omnipotent\n", + "\n", + "Let's sort them:\n", + "\n", + "1. **boletus**\n", + "2. **calypso**\n", + "3. **conklin**\n", + "4. **deportee**\n", + "5. **debugging**\n", + "6. **lucretia**\n", + "7. **necktie**\n", + "8. **omnipotent**\n", + "9. **passband**\n", + "10. **revving**\n", + "11. **ulysses**\n", + "\n", + "Let's re-check the sorting carefully:\n", + "\n", + "* b: boletus\n", + "* c: calypso, conklin\n", + "* d: debugging\n", + "* l: lucretia\n", + "* n: necktie\n", + "* o: omnipotent\n", + "* p: passband\n", + "* r: revving\n", + "* u: ulysses\n", + "\n", + "Sorting the 'c' words: calypso comes before conklin.\n", + "\n", + "Correct alphabetical order:\n", + "1. boletus\n", + "2. calypso\n", + "3. conklin\n", + "4. debugging\n", + "5. lucretia\n", + "6. necktie\n", + "7. omnipotent\n", + "8. passband\n", + "9. revving\n", + "10. ulysses\n", + "11. deportee (Wait, 'deportee' starts with 'd'. Let's re-sort based on the first letter.)\n", + "\n", + "Words and first letters:\n", + "* boletus (B)\n", + "* ulysses (U)\n", + "* debugging (D)\n", + "* passband (P)\n", + "* lucretia (L)\n", + "* necktie (N)\n", + "* deportee (D)\n", + "* calypso (C)\n", + "* revving (R)\n", + "* conklin (C)\n", + "* omnipotent (O)\n", + "\n", + "Sorting by first letter: B, C, D, L, N, O, P, R, U\n", + "\n", + "**B:** boletus\n", + "**C:** calypso, conklin (calypso before conklin)\n", + "**D:** debugging, deportee (debugging before deportee)\n", + "**L:** lucretia\n", + "**N:** necktie\n", + "**O:** omnipotent\n", + "**P:** passband\n", + "**R:** revving\n", + "**U:** ulysses\n", + "\n", + "Final sorted list:fail
bbh_word_sorting-0008Sort the following words alphabetically: List: plebiscite colombo inviolate quagmire garry satanic magnanimity aeneas notoriety nevada foothold glycerine lucre tennyson type scription pompey softball spleenwort fox\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.aeneas colombo foothold fox garry glycerine inviolate lucre magnanimity nevada notoriety plebiscite pompey quagmire satanic scription softball spleenwort tennyson typeHere are the words sorted alphabetically:\n", + "\n", + "1. aeneas\n", + "2. foothold\n", + "3. fox\n", + "4. glycerine\n", + "5. garry\n", + "6. inflaton (Assuming 'inviolate' was intended, but using the provided word 'inviolate') - Let's stick to the provided list: inviolate\n", + "7. lucre\n", + "8. magnanimity\n", + "9. nevada\n", + "10. pompey\n", + "11. plebiscite\n", + "12. quagmire\n", + "13. satanic\n", + "14. scription (Assuming 'scription' is a typo for 'scriptio' or similar, but sorting as written)\n", + "15. softball\n", + "16. spleenwort\n", + "17. tennyson\n", + "18. type\n", + "\n", + "Let's re-sort carefully:\n", + "\n", + "* aeneas\n", + "* foothold\n", + "* fox\n", + "* glycerine\n", + "* garry\n", + "* inviolate\n", + "* lucre\n", + "* magnanimity\n", + "* nevada\n", + "* pompey\n", + "* plebiscite\n", + "* quagmire\n", + "* satanic\n", + "* scription\n", + "* softball\n", + "* spleenwort\n", + "* tennyson\n", + "* type\n", + "\n", + "**Alphabetical Order Check:**\n", + "aeneas (A)\n", + "foothold (F)\n", + "fox (F) - fox comes before foothold\n", + "glycerine (G)\n", + "garry (G) - garry comes after glycerine\n", + "inviolate (I)\n", + "lucre (L)\n", + "magnanimity (M)\n", + "nevada (N)\n", + "pompey (P)\n", + "plebiscite (P) - pompey comes before plebiscite\n", + "quagmire (Q)\n", + "satanic (S)\n", + "scription (S) - satanic comes before scription\n", + "softball (S) - scription comes before softball\n", + "spleenwort (S) - softball comes before spleenwort\n", + "tennyson (T)\n", + "type (T) - tennyson comes before type\n", + "\n", + "**Refined Sorting:**\n", + "\n", + "1. aeneas\n", + "2. fox\n", + "3. foothold\n", + "4. glycerine\n", + "5. garry\n", + "6. inviolate\n", + "7. lucre\n", + "8. magnanimity\n", + "9. nevada\n", + "10. pompey\n", + "11. plebiscite\n", + "12. quagmire\n", + "13. satanic\n", + "14. scription\n", + "15. softball\n", + "16. spleenwort\n", + "17. tennyson\n", + "18.fail
bbh_word_sorting-0009Sort the following words alphabetically: List: scrumptious sidereal thermal yakima siena gorky saxon scottish figural hydroxyl seventeen neapolitan rampage nerve grapple fate plainfield stooge knives allotted\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.allotted fate figural gorky grapple hydroxyl knives neapolitan nerve plainfield rampage saxon scottish scrumptious seventeen sidereal siena stooge thermal yakimaHere are the words sorted alphabetically:\n", + "\n", + "1. allotted\n", + "2. fate\n", + "3. figural\n", + "4. gorky\n", + "5. grapple\n", + "6. hydroxyl\n", + "7. knives\n", + "8. neapolitan\n", + "9. plainfield\n", + "10. rampage\n", + "11. scrumptious\n", + "12. saxon\n", + "13. siena\n", + "14. scottish\n", + "15. sidereal\n", + "16. stooge\n", + "17. seventeen\n", + "18. thermal\n", + "19. yakima\n", + "20. nerve\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* allotted\n", + "* fate\n", + "* figural\n", + "* gorky\n", + "* grapple\n", + "* hydroxyl\n", + "* knives\n", + "* neapolitan\n", + "* plainfield\n", + "* rampage\n", + "* scrumptious\n", + "* saxon\n", + "* siena\n", + "* scottish\n", + "* sidereal\n", + "* stooge\n", + "* seventeen\n", + "* thermal\n", + "* yakima\n", + "* nerve\n", + "\n", + "Checking the order again:\n", + "A: allotted\n", + "F: fate, figural\n", + "G: gorky, grapple\n", + "H: hydroxyl\n", + "K: knives\n", + "N: neapolitan, nerve\n", + "P: plainfield\n", + "R: rampage\n", + "S: scrumptious, saxon, siena, scottish, sidereal\n", + "S: stooge (S comes before T)\n", + "S: seventeen\n", + "T: thermal\n", + "Y: yakima\n", + "\n", + "Correct alphabetical order:\n", + "1. allotted\n", + "2. fate\n", + "3. figural\n", + "4. gorky\n", + "5. grapple\n", + "6. hydroxyl\n", + "7. knives\n", + "8. neapolitan\n", + "9. nerve\n", + "10. plainfield\n", + "11. rampage\n", + "12. scrumptious\n", + "13. saxon\n", + "14. siena\n", + "15. scottish\n", + "16. sidereal\n", + "17. stooge\n", + "18. seventeen\n", + "19. thermal\n", + "20. yakima\n", + "\n", + "Answer: allotted, fate, figural, gorky, grapple, hydroxyl, knives, neapolitan, nerve, plainfield, rampage, scrumptious, saxon, siena, scottish, sidereal, stooge, seventeen, thermal, yakimafail
bbh_word_sorting-0010Sort the following words alphabetically: List: foamflower lawful cayenne chandigarh only excursion declassify equanimity aseptic inclement october debauch kruger groupoid scorch dingy alterate\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.alterate aseptic cayenne chandigarh debauch declassify dingy equanimity excursion foamflower groupoid inclement kruger lawful october only scorchHere are the words sorted alphabetically:\n", + "\n", + "1. alterate\n", + "2. aseptic\n", + "3. cayenne\n", + "4. chandigarh\n", + "5. declassify\n", + "6. debauch\n", + "7. dingy\n", + "8. excursion\n", + "9. equanimity\n", + "10. foamflower\n", + "11. lawful\n", + "12. october\n", + "13. only\n", + "14. groupoid\n", + "15. kruger\n", + "16. scorch\n", + "17. alterate (Wait, I listed alterate twice in my thought process, let's re-check the original list: foamflower lawful cayenne chandigarh only excursion declassify equanimity aseptic inclement october debauch kruger groupoid scorch dingy alterate)\n", + "\n", + "Let's re-sort carefully:\n", + "\n", + "Original List: foamflower, lawful, cayenne, chandigarh, only, excursion, declassify, equanimity, aseptic, inclement, october, debauch, kruger, groupoid, scorch, dingy, alterate\n", + "\n", + "1. alterate\n", + "2. aseptic\n", + "3. cayenne\n", + "4. chandigarh\n", + "5. declassify\n", + "6. debauch\n", + "7. dingy\n", + "8. excursion\n", + "9. equanimity\n", + "10. foamflower\n", + "11. inclement\n", + "12. lawful\n", + "13. october\n", + "14. only\n", + "15. groupoid\n", + "16. kruger\n", + "17. scorch\n", + "\n", + "Let's re-sort the remaining words based on the full list:\n", + "\n", + "A: alterate, aseptic\n", + "C: cayenne, chandigarh\n", + "D: declassify, debauch, dingy\n", + "E: excursion, equanimity\n", + "F: foamflower\n", + "I: inclement\n", + "L: lawful\n", + "O: october, only\n", + "G: groupoid\n", + "K: kruger\n", + "S: scorch\n", + "\n", + "Correct Alphabetical Order:\n", + "1. alterate\n", + "2. aseptic\n", + "3. cayenne\n", + "4. chandigarh\n", + "5. declassify\n", + "6. debauch\n", + "7. dingy\n", + "8. excursion\n", + "9. equanimity\n", + "10. foamflower\n", + "11. inclement\n", + "12. lawful\n", + "13. october\n", + "14. only\n", + "15. groupoid\n", + "16. kruger\n", + "17. scorch\n", + "\n", + "Answer: alterate, aseptic, cayenne, chandigarh, declassify, debauch, dingy, excursion, equanimity, foamflower, inclement, lawful, october, only, groupoid, kruger, scorchfail
bbh_word_sorting-0011Sort the following words alphabetically: List: lethe bilk perturb tactual\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.bilk lethe perturb tactualThe given list of words is: lethe, bilk, perturb, tactual.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter from the beginning.\n", + "\n", + "1. **lethe** (starts with 'l')\n", + "2. **bilk** (starts with 'b')\n", + "3. **perturb** (starts with 'p')\n", + "4. **tactual** (starts with 't')\n", + "\n", + "Comparing the first letters: 'b', 'l', 'p', 't'.\n", + "In alphabetical order: 'b', 'l', 'p', 't'.\n", + "\n", + "1. **bilk** (b)\n", + "2. **lethe** (l)\n", + "3. **perturb** (p)\n", + "4. **tactual** (t)\n", + "\n", + "The sorted list is: bilk, lethe, perturb, tactual.\n", + "\n", + "Answer: bilk, lethe, perturb, tactual\n", + "\n", + "Answer: bilk, lethe, perturb, tactualpass
bbh_word_sorting-0012Sort the following words alphabetically: List: portsmouth impalpable portrait gouda brainy succumb fatuous hoof lisbon erudite malaria enigma isaacson cony stronghold servomechanism\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.brainy cony enigma erudite fatuous gouda hoof impalpable isaacson lisbon malaria portrait portsmouth servomechanism stronghold succumbHere are the words sorted alphabetically:\n", + "\n", + "1. brainy\n", + "2. enigma\n", + "3. erudite\n", + "4. fatuous\n", + "5. gouda\n", + "6. impalpable\n", + "7. isaacson\n", + "8. lisbon\n", + "9. malaria\n", + "10. portsmouth\n", + "11. portrait\n", + "12. servomechanism\n", + "13. stronghold\n", + "14. succumb\n", + "15. cony\n", + "16. hoof\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* brainy\n", + "* cony\n", + "* erudite\n", + "* enigma\n", + "* fatuous\n", + "* gouda\n", + "* impalpable\n", + "* isaacson\n", + "* hoof\n", + "* lisbon\n", + "* malaria\n", + "* portsmouth\n", + "* portrait\n", + "* servomechanism\n", + "* stronghold\n", + "* succumb\n", + "\n", + "Let's check the order again:\n", + "B (brainy)\n", + "C (cony)\n", + "E (erudite, enigma) -> erudite before enigma\n", + "F (fatuous)\n", + "G (gouda)\n", + "I (impalpable, isaacson) -> impalpable before isaacson\n", + "H (hoof)\n", + "L (lisbon)\n", + "M (malaria)\n", + "P (portsmouth, portrait) -> portsmouth before portrait\n", + "S (servomechanism, succumb, stronghold) -> servomechanism, succumb, stronghold (S-e-r, S-u, S-t)\n", + "\n", + "Correct alphabetical order:\n", + "1. brainy\n", + "2. cony\n", + "3. erudite\n", + "4. enigma\n", + "5. fatuous\n", + "6. gouda\n", + "7. impalpable\n", + "8. isaacson\n", + "9. hoof\n", + "10. lisbon\n", + "11. malaria\n", + "12. portsmouth\n", + "13. portrait\n", + "14. servomechanism\n", + "15. stronghold\n", + "16. succumb\n", + "\n", + "Answer: brainy, cony, erudite, enigma, fatuous, gouda, impalpable, isaacson, hoof, lisbon, malaria, portsmouth, portrait, servomechanism, stronghold, succumbfail
bbh_word_sorting-0013Sort the following words alphabetically: List: logarithm robertson inanimate uganda micron incurring wilcox lumber sammy souvenir newman profuse maladapt cone butch airlift homeowner\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.airlift butch cone homeowner inanimate incurring logarithm lumber maladapt micron newman profuse robertson sammy souvenir uganda wilcoxHere are the words sorted alphabetically:\n", + "\n", + "1. airlift\n", + "2. maladapt\n", + "3. cone\n", + "4. butch\n", + "5. homeowner\n", + "6. incurring\n", + "7. logarithm\n", + "8. lumber\n", + "9. micron\n", + "10. newman\n", + "11. inanimate\n", + "12. profuse\n", + "13. robertson\n", + "14. sammy\n", + "15. souvenir\n", + "16. uganda\n", + "17. wilcox\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* airlift\n", + "* maladapt\n", + "* cone\n", + "* butch\n", + "* homeowner\n", + "* incurring\n", + "* logarithm\n", + "* lumber\n", + "* micron\n", + "* newman\n", + "* inanimate\n", + "* profuse\n", + "* robertson\n", + "* sammy\n", + "* souvenir\n", + "* uganda\n", + "* wilcox\n", + "\n", + "Comparing the initial list: logarithm, robertson, inanimate, uganda, micron, incurring, wilcox, lumber, sammy, souvenir, newman, profuse, maladapt, cone, butch, airlift, homeowner.\n", + "\n", + "Alphabetical order:\n", + "1. airlift\n", + "2. maladapt\n", + "3. cone\n", + "4. butch\n", + "5. homeowner\n", + "6. incurring\n", + "7. logarithm\n", + "8. lumber\n", + "9. micron\n", + "10. newman\n", + "11. inanimate\n", + "12. profuse\n", + "13. robertson\n", + "14. sammy\n", + "15. souvenir\n", + "16. uganda\n", + "17. wilcox\n", + "\n", + "Answer: airlift, maladapt, cone, butch, homeowner, incurring, logarithm, lumber, micron, newman, inanimate, profuse, robertson, sammy, souvenir, uganda, wilcoxfail
bbh_word_sorting-0014Sort the following words alphabetically: List: erudite marriageable stahl coach inadvisable vicksburg virgo brownian eosine magnesium flax\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.brownian coach eosine erudite flax inadvisable magnesium marriageable stahl vicksburg virgoHere are the words to sort alphabetically:\n", + "erudite, marriageable, stahl, coach, inadvisable, vicksburg, virgo, brownian, eosine, magnesium, flax\n", + "\n", + "Let's sort them:\n", + "\n", + "1. **brownian**\n", + "2. **coach**\n", + "3. **erudite**\n", + "4. **eosine**\n", + "5. **flax**\n", + "6. **inadvisable**\n", + "7. **magnesium**\n", + "8. **marriageable**\n", + "9. **stahl**\n", + "10. **vicksburg**\n", + "11. **virgo**\n", + "\n", + "Let's re-check the sorting carefully, especially for words starting with similar letters:\n", + "\n", + "* **b**rownian\n", + "* **c**oach\n", + "* **e**rudite, **e**osine (erudite comes before eosine)\n", + "* **f**lax\n", + "* **i**nadvisable\n", + "* **m**agnesium, **m**arriageable (magnesium comes before marriageable)\n", + "* **s**tahl\n", + "* **v**icksburg, **v**irgo (vicksburg comes before virgo)\n", + "\n", + "Corrected alphabetical order:\n", + "\n", + "1. brownian\n", + "2. coach\n", + "3. erudite\n", + "4. eosine\n", + "5. flax\n", + "6. inadvisable\n", + "7. magnesium\n", + "8. marriageable\n", + "9. stahl\n", + "10. vicksburg\n", + "11. virgo\n", + "\n", + "Answer: brownian, coach, erudite, eosine, flax, inadvisable, magnesium, marriageable, stahl, vicksburg, virgofail
bbh_word_sorting-0015Sort the following words alphabetically: List: jacob sleight globule consonant musician\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.consonant globule jacob musician sleightThe given list of words is: jacob, sleight, globule, consonant, musician.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter:\n", + "\n", + "1. **j**acob\n", + "2. **s**leight\n", + "3. **g**lobule\n", + "4. **c**onsonant\n", + "5. **m**usician\n", + "\n", + "Comparing the first letters: J, S, G, C, M.\n", + "In alphabetical order: C, G, J, M, S.\n", + "\n", + "1. **consonant** (starts with C)\n", + "2. **globule** (starts with G)\n", + "3. **jacob** (starts with J)\n", + "4. **musician** (starts with M)\n", + "5. **sleight** (starts with S)\n", + "\n", + "The sorted list is: consonant, globule, jacob, musician, sleight.\n", + "\n", + "Answer: consonant, globule, jacob, musician, sleight\n", + "Answer: consonant, globule, jacob, musician, sleightpass
bbh_word_sorting-0016Sort the following words alphabetically: List: inscribe upraise perceptive tragedian peculate cunard posterior crude danubian\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.crude cunard danubian inscribe peculate perceptive posterior tragedian upraiseThe given list of words is: inscribe, upraise, perceptive, tragedian, peculate, cunard, posterior, crude, danubian.\n", + "\n", + "Here is the alphabetical sorting process:\n", + "\n", + "1. **Compare the first letter:**\n", + " * inscribe (i)\n", + " * upraise (u)\n", + " * perceptive (p)\n", + " * tragedian (t)\n", + " * peculate (p)\n", + " * cunard (c)\n", + " * posterior (p)\n", + " * crude (c)\n", + " * danubian (d)\n", + "\n", + " Order based on the first letter: c, c, d, i, p, p, p, t, u.\n", + "\n", + "2. **Sort words starting with 'c':**\n", + " * cunard\n", + " * crude\n", + " * Alphabetical order: crude, cunard (cr comes before cu)\n", + "\n", + "3. **Sort words starting with 'd':**\n", + " * danubian\n", + "\n", + "4. **Sort words starting with 'i':**\n", + " * inscribe\n", + "\n", + "5. **Sort words starting with 'p':**\n", + " * perceptive\n", + " * peculate\n", + " * posterior\n", + " * Alphabetical order: perceptive, peculate, posterior (per comes before pec, pec comes before pos)\n", + "\n", + "6. **Sort words starting with 't':**\n", + " * tragedian\n", + "\n", + "7. **Sort words starting with 'u':**\n", + " * upraise\n", + "\n", + "**Combining the sorted groups:**\n", + "1. crude\n", + "2. cunard\n", + "3. danubian\n", + "4. inscribe\n", + "5. perceptive\n", + "6. peculate\n", + "7. posterior\n", + "8. tragedian\n", + "9. upraise\n", + "\n", + "Let's re-verify the 'c' words:\n", + "crude\n", + "cunard (crude comes before cunard)\n", + "\n", + "Let's re-verify the 'p' words:\n", + "perceptive (pe...)\n", + "peculate (pe...)\n", + "posterior (po...)\n", + "perceptive vs peculate: 'r' vs 'c'. 'c' comes before 'r'.\n", + "Correct order for 'p' words: peculate, perceptive, posterior. (pec, per, pos)\n", + "\n", + "Let's re-verify the full list based on standard alphabetical order:\n", + "\n", + "* crude (cr)\n", + "* cunard (cu)\n", + "* danubian (d)\n", + "* inscribe (i)\n", + "* peculate (pe-c)\n", + "* perceptive (pefail
bbh_word_sorting-0017Sort the following words alphabetically: List: tiresome sweepstake introvert carport firewood\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.carport firewood introvert sweepstake tiresomeThe given list of words is: tiresome, sweepstake, introvert, carport, firewood.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter:\n", + "\n", + "1. **carport** (Starts with 'c')\n", + "2. **firewood** (Starts with 'f')\n", + "3. **introvert** (Starts with 'i')\n", + "4. **sweepstake** (Starts with 's')\n", + "5. **tiresome** (Starts with 't')\n", + "\n", + "Let's verify the order:\n", + "C comes before F, I, S, T.\n", + "F comes before I, S, T.\n", + "I comes before S, T.\n", + "S comes before T.\n", + "\n", + "The alphabetical order is: carport, firewood, introvert, sweepstake, tiresome.\n", + "\n", + "Answer: carport, firewood, introvert, sweepstake, tiresomepass
bbh_word_sorting-0018Sort the following words alphabetically: List: thrill splutter panicking scorch same dot prod obstetric malton onus drumhead delmarva barn embezzle it&t damp guru subsist entirety greene\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.barn damp delmarva dot drumhead embezzle entirety greene guru it&t malton obstetric onus panicking prod same scorch splutter subsist thrillHere are the words sorted alphabetically:\n", + "\n", + "1. barn\n", + "2. embezzle\n", + "3. delmarva\n", + "4. dot\n", + "5. drumhead\n", + "6. entirety\n", + "7. guru\n", + "8. it&t\n", + "9. malton\n", + "10. panicking\n", + "11. prod\n", + "12. scorch\n", + "13. same\n", + "14. splutter\n", + "15. subsist\n", + "16. thrill\n", + "\n", + "(Note: "onus" is missing from the provided list, but if it were included, it would come before "obstetric" and after "onus" would be before "prod" if we assume standard alphabetical order for the provided list. Let's re-check the provided list: thrill, splutter, panicking, scorch, same, dot, prod, obstetric, malton, onus, drumhead, delmarva, barn, embezzle, it&t, damp, guru, subsist, entirety, greene.)\n", + "\n", + "Let's re-sort the complete list:\n", + "thrill, splutter, panicking, scorch, same, dot, prod, obstetric, malton, onus, drumhead, delmarva, barn, embezzle, it&t, damp, guru, subsist, entirety, greene\n", + "\n", + "1. barn\n", + "2. damp\n", + "3. delmarva\n", + "4. dot\n", + "5. drumhead\n", + "6. embezzle\n", + "7. entirety\n", + "8. greene\n", + "9. guru\n", + "10. it&t\n", + "11. malton\n", + "12. obstetric\n", + "13. onus\n", + "14. panicking\n", + "15. prod\n", + "16. scorch\n", + "17. same\n", + "18. splutter\n", + "19. subsist\n", + "20. thrill\n", + "\n", + "Answer: barn, damp, delmarva, dot, drumhead, embezzle, entirety, greene, guru, it&t, malton, obstetric, onus, panicking, prod, scorch, same, splutter, subsist, thrillfail
bbh_word_sorting-0019Sort the following words alphabetically: List: pewter gross gujarati cortland diffusible nomograph stag broadcast galvanometer transition incestuous sketchbook scout larynx\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.broadcast cortland diffusible galvanometer gross gujarati incestuous larynx nomograph pewter scout sketchbook stag transitionHere are the words sorted alphabetically:\n", + "\n", + "1. broadcast\n", + "2. cortland\n", + "3. diffusible\n", + "4. galvanometer\n", + "5. gross\n", + "6. gujarati\n", + "7. incestuous\n", + "8. larynx\n", + "9. nomograph\n", + "10. pewter\n", + "11. scout\n", + "12. sketchbook\n", + "13. stag\n", + "14. transition\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **b**roadcast\n", + "* **c**ortland\n", + "* **d**iffusible\n", + "* **g**alvometer\n", + "* **g**ross\n", + "* **g**ujarati\n", + "* **i**ncestuous\n", + "* **l**arynx\n", + "* **n**omograph\n", + "* **p**ewter\n", + "* **s**cout\n", + "* **s**teag (Wait, 'stag' comes before 'sketchbook' if we look at the second letter, but alphabetically 'stag' comes before 'sketchbook' if we compare 't' vs 'k'. Let's re-examine the list.)\n", + "\n", + "Original List: pewter gross gujarati cortland diffusible nomograph stag broadcast galvanometer transition incestuous sketchbook scout larynx\n", + "\n", + "1. broadcast\n", + "2. cortland\n", + "3. diffusible\n", + "4. galvanometer\n", + "5. gross\n", + "6. gujarati\n", + "7. incestuous\n", + "8. larynx\n", + "9. nomograph\n", + "10. pewter\n", + "11. scout\n", + "12. sketchbook\n", + "13. stag\n", + "14. transition\n", + "\n", + "Let's perform a strict alphabetical sort:\n", + "\n", + "1. broadcast (b)\n", + "2. cortland (c)\n", + "3. diffusible (d)\n", + "4. galvanometer (g)\n", + "5. gross (g)\n", + "6. gujarati (g)\n", + "7. incestuous (i)\n", + "8. larynx (l)\n", + "9. nomograph (n)\n", + "10. pewter (p)\n", + "11. scout (s)\n", + "12. sketchbook (s)\n", + "13. stag (s)\n", + "14. transition (t)\n", + "\n", + "Sorting the 'g' words: galvanometer, gross, gujarati.\n", + "* galvanometer\n", + "* gross\n", + "* gujarati\n", + "\n", + "Sorting the 's' words: scout, sketchbook, stag.\n", + "* sketchbook (sk)\n", + "* scout (sc)\n", + "* stag (st)\n", + "* Therefore: sketchbook, scout, stag\n", + "\n", + "Final sorted list:\n", + "1. broadcast\n", + "2. cortland\n", + "fail
bbh_word_sorting-0020Sort the following words alphabetically: List: bust midshipmen musicale urinary chalk rocket resiny horror tangential sapient dumb serf pathway urea goucher fatty masonry dentistry cowboy roadrunner\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.bust chalk cowboy dentistry dumb fatty goucher horror masonry midshipmen musicale pathway resiny roadrunner rocket sapient serf tangential urea urinaryHere are the words sorted alphabetically:\n", + "\n", + "1. bust\n", + "2. chalk\n", + "3. cowboy\n", + "4. dentistry\n", + "5. dumb\n", + "6. fatty\n", + "7. goucher\n", + "8. horror\n", + "9. masonry\n", + "10. midshipmen\n", + "11. musical\n", + "12. pathway\n", + "13. resiny\n", + "14. roadrunner\n", + "15. rocket\n", + "16. serf\n", + "17. sapient\n", + "18. tangential\n", + "19. urinary\n", + "20. urea\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* bust\n", + "* chalk\n", + "* cowboy\n", + "* dentistry\n", + "* dumb\n", + "* fatty\n", + "* goucher\n", + "* horror\n", + "* masonry\n", + "* midshipmen\n", + "* musical\n", + "* pathway\n", + "* resiny\n", + "* roadrunner\n", + "* rocket\n", + "* serf\n", + "* sapient\n", + "* tangential\n", + "* urinary\n", + "* urea\n", + "\n", + "Comparing the words:\n", + "B (bust)\n", + "C (chalk)\n", + "C (cowboy) -> chalk before cowboy\n", + "D (dentistry, dumb) -> dentistry before dumb\n", + "F (fatty)\n", + "G (goucher)\n", + "H (horror)\n", + "M (masonry, midshipmen) -> masonry before midshipmen\n", + "M (musical)\n", + "P (pathway)\n", + "R (resiny, roadrunner, rocket) -> resiny, roadrunner, rocket\n", + "S (serf, sapient) -> serf before sapient\n", + "T (tangential)\n", + "U (urinary, urea) -> urinary before urea\n", + "\n", + "Let's do a full alphabetical sort:\n", + "\n", + "1. bust\n", + "2. chalk\n", + "3. cowboy\n", + "4. dentistry\n", + "5. dumb\n", + "6. fatty\n", + "7. goucher\n", + "8. horror\n", + "9. masonry\n", + "10. midshipmen\n", + "11. musical\n", + "12. pathway\n", + "13. resiny\n", + "14. roadrunner\n", + "15. rocket\n", + "16. serf\n", + "17. sapient\n", + "18. tangential\n", + "19. urinary\n", + "20. urea\n", + "\n", + "Wait, let's re-check the 'M' words and 'R' words.\n", + "\n", + "M: masonry, midshipmen, musical\n", + "M: masonry, midshipmen, musical (Correct order: masonry, midshipmen, musical)\n", + "\n", + "R: resiny, roadrunner, rocket\n", + "R: resiny, roadrunner, rocket (Correct order: resiny, roadrunner, rocket)\n", + "\n", + "S: serffail
bbh_word_sorting-0021Sort the following words alphabetically: List: confidential ursa fiction heterozygous faery overture horehound\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.confidential faery fiction heterozygous horehound overture ursaThe given list of words is: confidential, ursa, fiction, heterozygous, faery, overture, horehound.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter:\n", + "\n", + "1. **confidential** (Starts with 'c')\n", + "2. **faery** (Starts with 'f')\n", + "3. **heterozygous** (Starts with 'h')\n", + "4. **fiction** (Starts with 'f')\n", + "5. **horehound** (Starts with 'h')\n", + "6. **overture** (Starts with 'o')\n", + "7. **ursa** (Starts with 'u')\n", + "\n", + "Let's sort the words starting with 'f':\n", + "* faery\n", + "* fiction\n", + "Comparing 'faery' and 'fiction': 'fai' vs 'fic'. 'a' comes before 'i', so 'faery' comes before 'fiction'.\n", + "\n", + "Let's sort the words starting with 'h':\n", + "* heterozygous\n", + "* horehound\n", + "Comparing 'heterozygous' and 'horehound': 'he' vs 'ho'. 'e' comes before 'o', so 'heterozygous' comes before 'horehound'.\n", + "\n", + "The full alphabetical order is:\n", + "1. confidential (c)\n", + "2. faery (f)\n", + "3. fiction (f)\n", + "4. heterozygous (h)\n", + "5. horehound (h)\n", + "6. overture (o)\n", + "7. ursa (u)\n", + "\n", + "Let's re-check the 'f' words:\n", + "faery\n", + "fiction\n", + "f-a-e-r-y\n", + "f-i-c-t-i-o-n\n", + "'a' comes before 'i', so faery comes before fiction.\n", + "\n", + "Let's re-check the 'h' words:\n", + "heterozygous\n", + "horehound\n", + "h-e-t-e-r-o-z-o-u-s\n", + "h-o-r-e-h-o-u-n-d\n", + "'e' comes before 'o', so heterozygous comes before horehound.\n", + "\n", + "Final sorted list:\n", + "1. confidential\n", + "2. faery\n", + "3. fiction\n", + "4. heterozygous\n", + "5. horehound\n", + "6. overture\n", + "7. ursa\n", + "\n", + "Answer: confidential, faery, fiction, heterozygous, horehound, overture, ursapass
bbh_word_sorting-0022Sort the following words alphabetically: List: sweatband murray\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.murray sweatbandThe given words are: sweatband, murray.\n", + "\n", + "To sort them alphabetically, we compare them letter by letter:\n", + "\n", + "1. **s**weatband\n", + "2. **m**urray\n", + "\n", + "Since 'm' comes before 's' in the alphabet, "murray" comes before "sweatband".\n", + "\n", + "The alphabetical order is: murray, sweatband.\n", + "\n", + "Answer: murray, sweatband\n", + "Answer: murray, sweatbandpass
bbh_word_sorting-0023Sort the following words alphabetically: List: sound consternate tariff blest proximity quizzes xerxes buxton\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.blest buxton consternate proximity quizzes sound tariff xerxesThe given list of words is: sound, consternate, tariff, blest, proximity, quizzes, xerxes, buxton.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter:\n", + "\n", + "1. **blest** (b)\n", + "2. **buxton** (b) - 'l' comes before 'u'\n", + "3. **consternate** (c)\n", + "4. **proximity** (p)\n", + "5. **quizzes** (q)\n", + "6. **sound** (s)\n", + "7. **tariff** (t)\n", + "8. **xerxes** (x)\n", + "\n", + "Let's re-sort carefully:\n", + "\n", + "* **b**: blest, buxton (blest comes before buxton)\n", + "* **c**: consternate\n", + "* **p**: proximity\n", + "* **q**: quizzes\n", + "* **s**: sound\n", + "* **t**: tariff\n", + "* **x**: xerxes\n", + "\n", + "Comparing the 'b' words:\n", + "* blest\n", + "* buxton\n", + "'l' comes before 'u', so **blest** comes before **buxton**.\n", + "\n", + "The sorted order is:\n", + "1. blest\n", + "2. buxton\n", + "3. consternate\n", + "4. proximity\n", + "5. quizzes\n", + "6. sound\n", + "7. tariff\n", + "8. xerxes\n", + "\n", + "Answer: blest, buxton, consternate, proximity, quizzes, sound, tariff, xerxes\n", + "\n", + "Answer: blest, buxton, consternate, proximity, quizzes, sound, tariff, xerxespass
bbh_word_sorting-0024Sort the following words alphabetically: List: wv habitation panicking schoolmate aerodynamic giacomo nebulous offset vector roosevelt jimmy botanist suburbia padre\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.aerodynamic botanist giacomo habitation jimmy nebulous offset padre panicking roosevelt schoolmate suburbia vector wvHere are the words to sort alphabetically:\n", + "wv, habitation, panicking, schoolmate, aerodynamic, giacomo, nebulous, offset, vector, roosevelt, jimmy, botanist, suburbia, padre\n", + "\n", + "Let's sort them:\n", + "\n", + "1. **aerodynamic**\n", + "2. **botanist**\n", + "3. **habitation**\n", + "4. **giacomo**\n", + "5. **jimmy**\n", + "6. **nebulous**\n", + "7. **offset**\n", + "8. **panicking**\n", + "9. **padre**\n", + "10. **roosevelt**\n", + "11. **schoolmate**\n", + "12. **suburbia**\n", + "13. **vector**\n", + "14. **wv** (Assuming 'wv' is treated as a word, or placed based on its initial letters if it's an abbreviation. In standard alphabetical sorting, it comes before 'habitation' if we consider 'w' before 'h', but usually, if it's an initialism, it's treated as is. If we treat it strictly alphabetically based on the first letter 'w', it would be near the end.)\n", + "\n", + "Let's re-evaluate based on standard alphabetical order (case-insensitive, treating 'wv' as starting with 'w'):\n", + "\n", + "* aerodynamic (a)\n", + "* botanist (b)\n", + "* habitation (h)\n", + "* giacomo (g)\n", + "* jimmy (j)\n", + "* nebulous (n)\n", + "* offset (o)\n", + "* panicking (p)\n", + "* padre (p)\n", + "* roosevelt (r)\n", + "* schoolmate (s)\n", + "* suburbia (s)\n", + "* vector (v)\n", + "* wv (w)\n", + "\n", + "Sorting the 'p' words: panicking, padre. (panicking comes before padre)\n", + "Sorting the 's' words: schoolmate, suburbia. (schoolmate comes before suburbia)\n", + "\n", + "Final sorted list:\n", + "1. aerodynamic\n", + "2. botanist\n", + "3. habitation\n", + "4. giacomo\n", + "5. jimmy\n", + "6. nebulous\n", + "7. offset\n", + "8. panicking\n", + "9. padre\n", + "10. roosevelt\n", + "11. schoolmate\n", + "12. suburbia\n", + "13. vector\n", + "14. wv\n", + "\n", + "Answer: aerodynamic, botanist, habitation, giacomo, jimmy, nebulous, offset, panicking, padre, roosevelt, schoolmate, suburbiafail
bbh_word_sorting-0025Sort the following words alphabetically: List: linchpin arrear eavesdropping fasciculate shin postdoctoral maritime ibn henry hermaphrodite sticky jane brookside incorrigible vehicular herodotus\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.arrear brookside eavesdropping fasciculate henry hermaphrodite herodotus ibn incorrigible jane linchpin maritime postdoctoral shin sticky vehicularHere are the words sorted alphabetically:\n", + "\n", + "1. arrear\n", + "2. eavesdropping\n", + "3. fasciculate\n", + "4. herodotus\n", + "5. hermaphrodite\n", + "6. ibn henry\n", + "7. incorrigible\n", + "8. jane\n", + "9. linchpin\n", + "10. maritime\n", + "11. postdoctoral\n", + "12. shin\n", + "13. sticky\n", + "14. brookside\n", + "15. vehicular\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **a**rrear\n", + "* **e**avesdropping\n", + "* **f**asciculate\n", + "* **h**erodotus\n", + "* **h**ermaphrodite (H before I)\n", + "* **i**bn henry\n", + "* **i**ncorrigible (I before L)\n", + "* **j**ane\n", + "* **l**inchpin\n", + "* **m**aritime\n", + "* **p**ostdoctoral\n", + "* **s**hin\n", + "* **s**ticky\n", + "* **b**rookside (B comes before S)\n", + "* **v**ehicular\n", + "\n", + "Let's do a full alphabetical sort:\n", + "\n", + "1. arrear\n", + "2. brookside\n", + "3. eavesdropping\n", + "4. fasciculate\n", + "5. herodotus\n", + "6. hermaphrodite\n", + "7. ibn henry\n", + "8. incorrigible\n", + "9. jane\n", + "10. linchpin\n", + "11. maritime\n", + "12. postdoctoral\n", + "13. shin\n", + "14. sticky\n", + "15. vehicular\n", + "\n", + "Wait, let's check the 'H' words: hermaphrodite and herodotus.\n", + "H-e-r-m-a-p-h-r-o-d-i-t-e\n", + "H-e-r-o-d-t-u-s\n", + "'herm' comes before 'hero'. So, hermaphrodite comes before herodotus.\n", + "\n", + "Let's re-verify the full list:\n", + "arrear\n", + "brookside\n", + "eavesdropping\n", + "fasciculate\n", + "hermaphrodite\n", + "herodotus\n", + "ibn henry\n", + "incorrigible\n", + "jane\n", + "linchpin\n", + "maritime\n", + "postdoctoral\n", + "shin\n", + "sticky\n", + "vehicular\n", + "\n", + "Final check on the order:\n", + "A: arrear\n", + "B: brookside\n", + "E: eavesdropping\n", + "F: fasciculate\n", + "H: hermaphrodite, herodotus (herm before hero)\n", + "I: ibn henry, incorrigible (ibn before incorrigible)\n", + "J:fail
bbh_word_sorting-0026Sort the following words alphabetically: List: tipoff fructose coleus hurricane cite tularemia irreducible wier improbable whitetail vesper whereabout\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.cite coleus fructose hurricane improbable irreducible tipoff tularemia vesper whereabout whitetail wierHere are the words sorted alphabetically:\n", + "\n", + "1. cite\n", + "2. coleus\n", + "3. fructose\n", + "4. hurricane\n", + "5. irreducible\n", + "6. improbable\n", + "7. tipoff\n", + "8. tularemia\n", + "9. vesper\n", + "10. whereabout\n", + "11. whitetail\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **c**ite\n", + "* **c**oleus\n", + "* **f**ructose\n", + "* **h**urricane\n", + "* **i**rreducible\n", + "* **i**mprobable\n", + "* **t**ipoff\n", + "* **t**ularemia\n", + "* **v**esper\n", + "* **w**hereabout\n", + "* **w**hitetail\n", + "\n", + "Comparing 'cite' and 'coleus': 'cite' comes before 'coleus'.\n", + "Comparing 'irreducible' and 'improbable': 'irreducible' comes before 'improbable'.\n", + "Comparing 'tipoff' and 'tularemia': 'tipoff' comes before 'tularemia'.\n", + "Comparing 'whereabout' and 'whitetail': 'whereabout' comes before 'whitetail'.\n", + "\n", + "Correct alphabetical order:\n", + "1. cite\n", + "2. coleus\n", + "3. fructose\n", + "4. hurricane\n", + "5. irreducible\n", + "6. improbable\n", + "7. tipoff\n", + "8. tularemia\n", + "9. vesper\n", + "10. whereabout\n", + "11. whitetail\n", + "\n", + "Wait, let's re-check the 'i' words: irreducible, improbable.\n", + "i-r-r... vs i-m-p... $\\rightarrow$ irreducible comes before improbable. (Correct)\n", + "\n", + "Let's re-check the 't' words: tipoff, tularemia.\n", + "t-i-p... vs t-u-l... $\\rightarrow$ tipoff comes before tularemia. (Correct)\n", + "\n", + "Final sorted list:\n", + "1. cite\n", + "2. coleus\n", + "3. fructose\n", + "4. hurricane\n", + "5. irreducible\n", + "6. improbable\n", + "7. tipoff\n", + "8. tularemia\n", + "9. vesper\n", + "10. whereabout\n", + "11. whitetail\n", + "\n", + "Answer: cite, coleus, fructose, hurricane, irreducible, improbable, tipoff, tularemia, vesper, whereabout, whitetailfail
bbh_word_sorting-0027Sort the following words alphabetically: List: testate orchestra permafrost leasehold shiva\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.leasehold orchestra permafrost shiva testateThe given list of words is: testate, orchestra, permafrost, leasehold, shiva.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter:\n", + "\n", + "1. **leasehold** (Starts with 'l')\n", + "2. ** orchestra** (Starts with 'o')\n", + "3. **permafrost** (Starts with 'p')\n", + "4. **shiva** (Starts with 's')\n", + "5. **testate** (Starts with 't')\n", + "\n", + "Let's re-sort based on the first letter: L, O, P, S, T.\n", + "\n", + "1. **leasehold** (L)\n", + "2. **orchestra** (O)\n", + "3. **permafrost** (P)\n", + "4. **shiva** (S)\n", + "5. **testate** (T)\n", + "\n", + "The alphabetical order is: leasehold, orchestra, permafrost, shiva, testate.\n", + "\n", + "Answer: leasehold, orchestra, permafrost, shiva, testate\n", + "Answer: leasehold, orchestra, permafrost, shiva, testatepass
bbh_word_sorting-0028Sort the following words alphabetically: List: butterscotch veterinarian kochab molybdate extoller amos farrell academia glance predatory beautiful ford fiducial metzler europium circuitous diatom monomer\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.academia amos beautiful butterscotch circuitous diatom europium extoller farrell fiducial ford glance kochab metzler molybdate monomer predatory veterinarianHere are the words sorted alphabetically:\n", + "\n", + "1. academia\n", + "2. amos\n", + "3. beautiful\n", + "4. butterscotch\n", + "5. circuitous\n", + "6. diatom\n", + "7. extoller\n", + "8. farrell\n", + "9. fiducial\n", + "10. glance\n", + "11. kochab\n", + "12. ladmoner (Wait, the list has 'monomer'. Let's re-check the list.)\n", + "\n", + "Original List: butterscotch veterinarian kochab molybdate extoller amos farrell academia glance predatory beautiful ford fiducial metzler europium circuitous diatom monomer\n", + "\n", + "Let's sort them systematically:\n", + "\n", + "1. academia\n", + "2. amos\n", + "3. beautiful\n", + "4. butterscotch\n", + "5. circuitous\n", + "6. diatom\n", + "7. extoller\n", + "8. farrell\n", + "9. fiducial\n", + "10. glance\n", + "11. kochab\n", + "12. metzler\n", + "13. molybdate\n", + "14. monomer\n", + "15. predatory\n", + "16. europium\n", + "17. veterinarian\n", + "18. ford\n", + "\n", + "Let's re-sort carefully:\n", + "\n", + "A: academia, amos\n", + "B: beautiful, butterscotch\n", + "C: circuitous\n", + "D: diatom\n", + "E: extoller, europium\n", + "F: farrell, fiducial, ford\n", + "G: glance\n", + "K: kochab\n", + "M: molybdate, monomer, metzler\n", + "P: predatory\n", + "V: veterinarian\n", + "\n", + "Sorting within groups:\n", + "\n", + "A: academia, amos\n", + "B: beautiful, butterscotch\n", + "C: circuitous\n", + "D: diatom\n", + "E: extoller, europium\n", + "F: farrell, fiducial, ford\n", + "G: glance\n", + "K: kochab\n", + "M: molybdate, monomer, metzler (molybdate, monomer, metzler)\n", + "P: predatory\n", + "V: veterinarian\n", + "\n", + "Final Alphabetical Order:\n", + "1. academia\n", + "2. amos\n", + "3. beautiful\n", + "4. butterscotch\n", + "5. circuitous\n", + "6. diatom\n", + "7. extoller\n", + "8. europium\n", + "9. farrell\n", + "10. fiducial\n", + "11. ford\n", + "12. glance\n", + "13. kochab\n", + "14. molybdate\n", + "15. monomer\n", + "16. metzler\n", + "17. predatory\n", + "18. veterinarian\n", + "\n", + "Answer: academia, amos, beautiful, butterscotch, circuitous, diatom, extoller, europium, farrell, fiducial, fordfail
bbh_word_sorting-0029Sort the following words alphabetically: List: bituminous ami decadent knickerbocker exeter\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.ami bituminous decadent exeter knickerbockerThe given list of words is: bituminous, ami, decadent, knickerbocker, exeter.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter:\n", + "\n", + "1. **ami**: Starts with 'a'\n", + "2. **bituminous**: Starts with 'b'\n", + "3. **decadent**: Starts with 'd'\n", + "4. **exeter**: Starts with 'e'\n", + "5. **knickerbocker**: Starts with 'k'\n", + "\n", + "Comparing the first letters: a, b, d, e, k.\n", + "In alphabetical order: a, b, d, e, k.\n", + "\n", + "1. **ami** (a)\n", + "2. **bituminous** (b)\n", + "3. **decadent** (d)\n", + "4. **exeter** (e)\n", + "5. **knickerbocker** (k)\n", + "\n", + "The sorted list is: ami, bituminous, decadent, exeter, knickerbocker.\n", + "\n", + "Answer: ami, bituminous, decadent, exeter, knickerbockerpass
bbh_word_sorting-0030Sort the following words alphabetically: List: woebegone nimble complementary afternoon immaculate laughlin kind hesitate loire septuagenarian sandia toggle dixie horsepower sierra shuffleboard mechanism\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.afternoon complementary dixie hesitate horsepower immaculate kind laughlin loire mechanism nimble sandia septuagenarian shuffleboard sierra toggle woebegoneHere are the words sorted alphabetically:\n", + "\n", + "1. afternoon\n", + "2. complementary\n", + "3. dixie\n", + "4. horsepower\n", + "5. immaculate\n", + "6. kind\n", + "7. laughlin\n", + "8. loire\n", + "9. mechanism\n", + "10. nimble\n", + "11. sandia\n", + "12. septuagenarian\n", + "13. shuffleboard\n", + "14. sierra\n", + "15. toggle\n", + "16. woebegone\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **a**fternoon\n", + "* **c**omplementary\n", + "* **d**ixie\n", + "* **h**orsepower\n", + "* **i**mmaculate\n", + "* **k**ind\n", + "* **l**aughlin\n", + "* **l**oire (L-A vs L-O: afternoon comes before loire)\n", + "* **m**echanism\n", + "* **n**imble\n", + "* **s**andia\n", + "* **s**eptuagenarian\n", + "* **s**huffleboard\n", + "* **s**ierra\n", + "* **t**oggle\n", + "* **w**oebegone\n", + "\n", + "Let's do a strict alphabetical sort:\n", + "\n", + "1. afternoon\n", + "2. complementary\n", + "3. dixie\n", + "4. horsepower\n", + "5. immaculate\n", + "6. kind\n", + "7. laughlin\n", + "8. loire\n", + "9. mechanism\n", + "10. nimble\n", + "11. sandia\n", + "12. septuagenarian\n", + "13. shuffleboard\n", + "14. sierra\n", + "15. toggle\n", + "16. woebegone\n", + "\n", + "Wait, let's check 'laughlin' vs 'loire' and 'mechanism' vs 'nimble'.\n", + "\n", + "L words: laughlin, loire\n", + "* laughlin\n", + "* loire\n", + "\n", + "M words: mechanism\n", + "N words: nimble\n", + "\n", + "S words: sandia, septuagenarian, shuffleboard, sierra\n", + "* sandia\n", + "* septuagenarian\n", + "* shuffleboard\n", + "* sierra\n", + "\n", + "Final check of the full list:\n", + "afternoon\n", + "complementary\n", + "dixie\n", + "horsepower\n", + "immaculate\n", + "kind\n", + "laughlin\n", + "loire\n", + "mechanism\n", + "nimble\n", + "sandia\n", + "septuagenarian\n", + "shuffleboard\n", + "sierra\n", + "toggle\n", + "woebegone\n", + "\n", + "Answer: afternoon, complementary, dixie, horsepower, immaculate, kind, laughlin, loire, mechanism, nimble, sandia, septuagenarian, shuffleboard, sierra, toggle, woebegonefail
bbh_word_sorting-0031Sort the following words alphabetically: List: thereupon wireman crag scrawny electrolytic wife cutover dickson clytemnestra took diocletian lipton inhuman stalk workplace marginal\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.clytemnestra crag cutover dickson diocletian electrolytic inhuman lipton marginal scrawny stalk thereupon took wife wireman workplaceHere are the words sorted alphabetically:\n", + "\n", + "1. clytemnestra\n", + "2. crag\n", + "3. cutover\n", + "4. dickson\n", + "5. diocletian\n", + "6. electrolytic\n", + "7. inhuman\n", + "8. lipton\n", + "9. marginal\n", + "10. stalk\n", + "11. thereupon\n", + "12. workplace\n", + "13. wireman\n", + "14. wife\n", + "15. took\n", + "16. scrawny\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* clytemnestra\n", + "* crag\n", + "* cutover\n", + "* dickson\n", + "* diocletian\n", + "* electrolytic\n", + "* inhuman\n", + "* lipton\n", + "* marginal\n", + "* stalk\n", + "* thereupon\n", + "* workplace\n", + "* wireman\n", + "* wife\n", + "* took\n", + "* scrawny\n", + "\n", + "Comparing the initial list: thereupon, wireman, crag, scrawny, electrolytic, wife, cutover, dickson, clytemnestra, took, diocletian, lipton, inhuman, stalk, workplace, marginal.\n", + "\n", + "Alphabetical order:\n", + "1. clytemnestra\n", + "2. crag\n", + "3. cutover\n", + "4. dickson\n", + "5. diocletian\n", + "6. electrolytic\n", + "7. inhuman\n", + "8. lipton\n", + "9. marginal\n", + "10. stalk\n", + "11. thereupon\n", + "12. workplace\n", + "13. wireman\n", + "14. wife\n", + "15. took\n", + "16. scrawny\n", + "\n", + "Answer: clytemnestra, crag, cutover, dickson, diocletian, electrolytic, inhuman, lipton, marginal, stalk, thereupon, workplace, wireman, wife, took, scrawnyfail
bbh_word_sorting-0032Sort the following words alphabetically: List: yves butt scribe seaworthy manumitted clapeyron lift blackguard reconnaissance agile woodcut lightfooted sawbelly meager geophysical wiseacre mathieu cognoscenti purposive flamboyant\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.agile blackguard butt clapeyron cognoscenti flamboyant geophysical lift lightfooted manumitted mathieu meager purposive reconnaissance sawbelly scribe seaworthy wiseacre woodcut yvesHere are the words sorted alphabetically:\n", + "\n", + "1. agile\n", + "2. blackguard\n", + "3. clapeyron\n", + "4. cognoscenti\n", + "5. geophysical\n", + "6. flamboyant\n", + "7. lift\n", + "8. lightfooted\n", + "9. manumitted\n", + "10. meager\n", + "11. mathieu\n", + "12. purposive\n", + "13. reconnaissance\n", + "14. seaworthy\n", + "15. scribe\n", + "16. sawbelly\n", + "17. wiseacre\n", + "18. woodcut\n", + "19. yves\n", + "20. butt\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **a**gile\n", + "* **b**lackguard, butt\n", + "* **c**lapeyron, cognoscenti\n", + "* **g**eophysical\n", + "* **l**ift, lightfooted\n", + "* **m**anumitted, meager, mathieu\n", + "* **p**urposeive\n", + "* **r**econnaissance\n", + "* **s**eaworthy, scribe, sawbelly\n", + "* **w**iseacre, woodcut, yves\n", + "\n", + "Detailed sorting:\n", + "\n", + "1. agile\n", + "2. blackguard\n", + "3. butt\n", + "4. clapeyron\n", + "5. cognoscenti\n", + "6. geophysical\n", + "7. flamboyant\n", + "8. lift\n", + "9. lightfooted\n", + "10. manumitted\n", + "11. meager\n", + "12. mathieu\n", + "13. purposive\n", + "14. reconnaissance\n", + "15. seaworthy\n", + "16. scribe\n", + "17. sawbelly\n", + "18. wiseacre\n", + "19. woodcut\n", + "20. yves\n", + "\n", + "Answer: agile, blackguard, butt, clapeyron, cognoscenti, geophysical, flamboyant, lift, lightfooted, manumitted, meager, mathieu, purposive, reconnaissance, seaworthy, scribe, sawbelly, wiseacre, woodcut, yvesfail
bbh_word_sorting-0033Sort the following words alphabetically: List: holocene muriel r&d pathetic talon mare staircase bradshaw aperture sigh\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.aperture bradshaw holocene mare muriel pathetic r&d sigh staircase talonThe given list of words is: holocene, muriel, r&d, pathetic, talon, mare, staircase, bradshaw, aperture, sigh.\n", + "\n", + "To sort them alphabetically, we compare them letter by letter:\n", + "\n", + "1. **aperture** (starts with 'a')\n", + "2. **bradshaw** (starts with 'b')\n", + "3. **holocene** (starts with 'h')\n", + "4. **mare** (starts with 'm')\n", + "5. **muriel** (starts with 'm', comes after 'mare' because 'u' comes after 'a')\n", + "6. **pathetic** (starts with 'p')\n", + "7. **r&d** (starts with 'r')\n", + "8. **sigh** (starts with 's')\n", + "9. **staircase** (starts with 's', comes after 'sigh' because 't' comes after 'i')\n", + "10. **talon** (starts with 't', comes after 'staircase' because 'a' comes before 's' in the second letter, wait, let's recheck 'staircase' and 'talon'. 'st' vs 'ta'. 's' comes before 't', so 'staircase' comes before 'talon'.)\n", + "\n", + "Let's re-sort systematically:\n", + "\n", + "Words: holocene, muriel, r&d, pathetic, talon, mare, staircase, bradshaw, aperture, sigh\n", + "\n", + "1. **a**perture\n", + "2. **b**radshaw\n", + "3. **h**olocene\n", + "4. **m**are\n", + "5. **m**uriel (mare vs muriel: mare ends, muriel continues. 'mare' comes before 'muriel')\n", + "6. **p**athetic\n", + "7. **r**&d\n", + "8. **s**igh\n", + "9. **s**taircase\n", + "10. **t**alon\n", + "\n", + "Comparing 'sigh' and 'staircase':\n", + "sigh (s-i)\n", + "staircase (s-t)\n", + "'i' comes before 't', so 'sigh' comes before 'staircase'.\n", + "\n", + "Comparing 'talon' and 'staircase':\n", + "talon (t-a)\n", + "staircase (s-t)\n", + "'s' comes before 't', so 'staircase' comes before 'talon'.\n", + "\n", + "Final sorted order:\n", + "fail
bbh_word_sorting-0034Sort the following words alphabetically: List: spontaneity smooth execrable epaulet bobbin calumny hun anastasia omnipotent award hostelry macedon roughshod burrow align apache putty adsorption anastomotic\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.adsorption align anastasia anastomotic apache award bobbin burrow calumny epaulet execrable hostelry hun macedon omnipotent putty roughshod smooth spontaneityHere are the words sorted alphabetically:\n", + "\n", + "1. apache\n", + "2. adsorption\n", + "3. align\n", + "4. anastomotic\n", + "5. anastasia\n", + "6. award\n", + "7. bobbin\n", + "8. burrow\n", + "9. calumny\n", + "10. hun\n", + "11. hostelry\n", + "12. macedon\n", + "13. omnipotent\n", + "14. epaulet\n", + "15. putty\n", + "16. roughshod\n", + "17. spontaneity\n", + "18. smooth\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **a**pache\n", + "* **a**dsorption\n", + "* **a**lign\n", + "* **a**nastomotic\n", + "* **a**nastasia\n", + "* **a**ward\n", + "* **b**obbin\n", + "* **b**urrow\n", + "* **c**alumny\n", + "* **h**on (assuming 'hun' is a typo or should be sorted as 'hun')\n", + "* **h**ostelry\n", + "* **m**acedon\n", + "* **o**mnipotent\n", + "* **e**paulet\n", + "* **p**utty\n", + "* **r**oughshod\n", + "* **s**pontaneity\n", + "* **s**mooth\n", + "\n", + "Let's re-examine the list provided: spontaneity, smooth, execrable, epaulet, bobbin, calumny, hun, anastasia, omnipotent, award, hostelry, macedon, roughshod, burrow, align, apache, putty, adsorption, anastomotic.\n", + "\n", + "**Alphabetical Sorting:**\n", + "\n", + "1. apache\n", + "2. adsorption\n", + "3. align\n", + "4. anastomotic\n", + "5. anastasia\n", + "6. award\n", + "7. bobbin\n", + "8. burrow\n", + "9. calumny\n", + "10. execrable\n", + "11. epaulet\n", + "12. hun\n", + "13. hostelry\n", + "14. macedon\n", + "15. omnipotent\n", + "16. putty\n", + "17. roughshod\n", + "18. spontaneity\n", + "19. smooth\n", + "\n", + "(Note: 'execrable' comes before 'epaulet' if we strictly follow the standard alphabet, but 'epaulet' starts with 'e' and 'execrable' starts with 'e'. E-P comes before E-X.)\n", + "\n", + "Let's re-check the 'E' words: execrable, epaulet.\n", + "E-P-A-U-L-E-fail
bbh_word_sorting-0035Sort the following words alphabetically: List: covenant thence davenport sonorant scoreboard densitometer noisy\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.covenant davenport densitometer noisy scoreboard sonorant thenceThe given list of words is: covenant, thence, davenport, sonorant, scoreboard, densitometer, noisy.\n", + "\n", + "We need to sort these words alphabetically.\n", + "\n", + "1. **covenant**\n", + "2. **davenport**\n", + "3. **densitometer**\n", + "4. **noisy**\n", + "5. **scoreboard**\n", + "6. **sonorant**\n", + "7. **thence**\n", + "\n", + "Let's compare them letter by letter:\n", + "\n", + "* **c**ovenant\n", + "* **d**avenport\n", + "* **d**ensitometer (davenport comes before densitometer because 'a' comes before 'e')\n", + "* **n**oisy\n", + "* **s**coreboard\n", + "* **s**onorant (scoreboard comes before sonorant because 'c' comes before 'o')\n", + "* **t**hence\n", + "\n", + "Let's re-sort carefully:\n", + "\n", + "1. **covenant** (Starts with C)\n", + "2. **davenport** (Starts with D, 'a')\n", + "3. **densitometer** (Starts with D, 'e') $\\rightarrow$ davenport comes before densitometer.\n", + "4. **noisy** (Starts with N)\n", + "5. **scoreboard** (Starts with S, 'c')\n", + "6. **sonorant** (Starts with S, 'o') $\\rightarrow$ scoreboard comes before sonorant.\n", + "7. **thence** (Starts with T)\n", + "\n", + "The correct alphabetical order is:\n", + "1. covenant\n", + "2. davenport\n", + "3. densitometer\n", + "4. noisy\n", + "5. scoreboard\n", + "6. sonorant\n", + "7. thence\n", + "\n", + "Let's verify the D words:\n", + "davenport\n", + "densitometer\n", + "'a' comes before 'e', so davenport comes before densitometer.\n", + "\n", + "Let's verify the S words:\n", + "scoreboard\n", + "sonorant\n", + "'c' comes before 'o', so scoreboard comes before sonorant.\n", + "\n", + "Final sorted list: covenant, davenport, densitometer, noisy, scoreboard, sonorant, thence.\n", + "\n", + "Answer: covenant, davenport, densitometer, noisy, scoreboard, sonorant, thencepass
bbh_word_sorting-0036Sort the following words alphabetically: List: sinter cortex kangaroo pillage threshold insane mcleod roundabout stipulate marionette trammel incident\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.cortex incident insane kangaroo marionette mcleod pillage roundabout sinter stipulate threshold trammelHere are the words to sort alphabetically:\n", + "sinter, cortex, kangaroo, pillage, threshold, insane, mcleod, roundabout, stipulate, marionette, trammel, incident\n", + "\n", + "Let's sort them:\n", + "\n", + "1. **cortex**\n", + "2. **incident**\n", + "3. **insane**\n", + "4. **kangaroo**\n", + "5. **mcleod**\n", + "6. **marionette**\n", + "7. **pillage**\n", + "8. **roundabout**\n", + "9. **sinter**\n", + "10. **stipulate**\n", + "11. **threshold**\n", + "12. **trammel**\n", + "\n", + "Let's re-check the sorting carefully, especially for words starting with similar letters:\n", + "\n", + "* **c**ortex\n", + "* **i**ncident, **i**nsane (incident before insane)\n", + "* **k**angaroo\n", + "* **m**cleod, **m**arionette (mcleod before marionette)\n", + "* **p**illage\n", + "* **r**oundabout\n", + "* **s**inter, **s**tipulate (sinter before stipulate)\n", + "* **t**hreshold, **t**rammel\n", + "\n", + "Corrected alphabetical order:\n", + "\n", + "1. cortex\n", + "2. incident\n", + "3. insane\n", + "4. kangaroo\n", + "5. mcleod\n", + "6. marionette\n", + "7. pillage\n", + "8. roundabout\n", + "9. sinter\n", + "10. stipulate\n", + "11. threshold\n", + "12. trammel\n", + "\n", + "Answer: cortex, incident, insane, kangaroo, mcleod, marionette, pillage, roundabout, sinter, stipulate, threshold, trammel\n", + "\n", + "Answer: cortex, incident, insane, kangaroo, mcleod, marionette, pillage, roundabout, sinter, stipulate, threshold, trammelfail
bbh_word_sorting-0037Sort the following words alphabetically: List: them abdominal address fomalhaut pay optimal hanoverian yang berry tattoo islamabad swathe jordan bounty stearic stigmata involve effusive tornado\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.abdominal address berry bounty effusive fomalhaut hanoverian involve islamabad jordan optimal pay stearic stigmata swathe tattoo them tornado yangHere are the words sorted alphabetically:\n", + "\n", + "1. abdominal\n", + "2. address\n", + "3. bounty\n", + "4. berry\n", + "5. effusive\n", + "6. fomalhaut\n", + "7. hanoverian\n", + "8. islamabad\n", + "9. involve\n", + "10. pay\n", + "11. stigmata\n", + "12. stearic\n", + "13. swathe\n", + "14. tattoo\n", + "15. them\n", + "16. tornado\n", + "17. optimal\n", + "18. yang\n", + "19. jordan\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **a**bdominal\n", + "* **a**ddress\n", + "* **b**ounty\n", + "* **b**erry\n", + "* **e**ffusive\n", + "* **f**omalhaut\n", + "* **h**anoverian\n", + "* **i**slamabad\n", + "* **i**nvolve\n", + "* **p**ay\n", + "* **s**tigmata\n", + "* **s**tearic\n", + "* **s**wathe\n", + "* **t**attoo\n", + "* **t**hem\n", + "* **t**ornado\n", + "* **o**ptimal\n", + "* **y**ang\n", + "* **j**ordan (Wait, J comes before O and Y)\n", + "\n", + "Let's re-sort based on the first letter:\n", + "\n", + "A: abdominal, address\n", + "B: bounty, berry\n", + "E: effusive\n", + "F: fomalhaut\n", + "H: hanoverian\n", + "I: islamabad, involve\n", + "J: jordan\n", + "O: optimal\n", + "S: stigmata, stearic, swathe\n", + "T: tattoo, them, tornado\n", + "Y: yang\n", + "\n", + "**Detailed Sorting:**\n", + "\n", + "1. abdominal\n", + "2. address\n", + "3. bounty\n", + "4. berry\n", + "5. effusive\n", + "6. fomalhaut\n", + "7. hanoverian\n", + "8. islamabad\n", + "9. involve\n", + "10. jordan\n", + "11. optimal\n", + "12. stigmata\n", + "13. stearic\n", + "14. swathe\n", + "15. tattoo\n", + "16. them\n", + "17. tornado\n", + "18. yang\n", + "\n", + "Answer: abdominal, address, bounty, berry, effusive, fomalhaut, hanoverian, islamabad, involve, jordan, optimal, stigmata, stearic, swathe, tattoo, them, tornado, yangfail
bbh_word_sorting-0038Sort the following words alphabetically: List: coypu anharmonic inflammation beauteous\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.anharmonic beauteous coypu inflammationThe given list of words is: coypu, anharmonic, inflammation, beauteous.\n", + "\n", + "We need to sort these words alphabetically.\n", + "\n", + "1. **Compare the first letters:**\n", + " - coypu starts with 'c'\n", + " - anharmonic starts with 'a'\n", + " - inflammation starts with 'i'\n", + " - beauteous starts with 'b'\n", + "\n", + " The alphabetical order of the starting letters is: a, b, c, i.\n", + "\n", + "2. **Order the words based on the starting letters:**\n", + " - 'a': anharmonic\n", + " - 'b': beauteous\n", + " - 'c': coypu\n", + " - 'i': inflammation\n", + "\n", + "3. **The sorted list is:** anharmonic, beauteous, coypu, inflammation.\n", + "\n", + "Answer: anharmonic, beauteous, coypu, inflammationpass
bbh_word_sorting-0039Sort the following words alphabetically: List: thirteenth tinfoil thimble snuff ernie pow celebrity abuilding indiscoverable chigger burgher synchronous yap medlar exorcism newsboy\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.abuilding burgher celebrity chigger ernie exorcism indiscoverable medlar newsboy pow snuff synchronous thimble thirteenth tinfoil yapHere are the words sorted alphabetically:\n", + "\n", + "1. abuilding\n", + "2. celebrity\n", + "3. burgher\n", + "4. chigger\n", + "5. indiscoverable\n", + "6. medlar\n", + "7. newsboy\n", + "8. exorcism\n", + "9. pow\n", + "10. snuff\n", + "11. synchronous\n", + "12. thirteenth\n", + "13. tinfoil\n", + "14. thimble\n", + "15. ernie\n", + "16. yap\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **a**building\n", + "* **c**elebrity\n", + "* **b**urgher\n", + "* **c**higger\n", + "* **i**ndiscoverable\n", + "* **m**edlar\n", + "* **n**ewsboy\n", + "* **e**xorcism (comes before pow)\n", + "* **p**ow\n", + "* **s**nuff\n", + "* **s**ynchronous\n", + "* **t**hirteenth\n", + "* **t**infoil\n", + "* **t**himble\n", + "* **e**rnie (comes before thimble)\n", + "* **y**ap\n", + "\n", + "Let's do a strict alphabetical sort:\n", + "\n", + "1. abuilding\n", + "2. celebrity\n", + "3. burgher\n", + "4. chigger\n", + "5. ernie\n", + "6. indiscoverable\n", + "7. medlar\n", + "8. newsboy\n", + "9. exorcism\n", + "10. pow\n", + "11. snuff\n", + "12. synchronous\n", + "13. thirteenth\n", + "14. tinfoil\n", + "15. thimble\n", + "16. yap\n", + "\n", + "Wait, let's check the 't' words: thirteenth, tinfoil, thimble.\n", + "thirteenth (thir...)\n", + "tinfoil (tinf...)\n", + "thimble (thim...)\n", + "thimble comes before thirteenth.\n", + "tinfoil comes before thimble.\n", + "thirteenth comes after tinfoil.\n", + "\n", + "Corrected 't' order: tinfoil, thimble, thirteenth.\n", + "\n", + "Let's re-sort the whole list:\n", + "\n", + "1. abuilding\n", + "2. celebrity\n", + "3. burgher\n", + "4. chigger\n", + "5. ernie\n", + "6. indiscoverable\n", + "7. medlar\n", + "8. newsboy\n", + "9. exorcism\n", + "10. pow\n", + "11. snuff\n", + "12. synchronous\n", + "13. tinfoil\n", + "14. thimble\n", + "15. thirteenth\n", + "16. yap\n", + "\n", + "Answer: abuilding, celebrity, burgher,fail
bbh_word_sorting-0040Sort the following words alphabetically: List: avoid whimsey grassland elegy pitfall error gaillardia hostile caramel conclave stepchild accept carbuncle compressor utopia drib spectra rosa embower\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.accept avoid caramel carbuncle compressor conclave drib elegy embower error gaillardia grassland hostile pitfall rosa spectra stepchild utopia whimseyHere are the words sorted alphabetically:\n", + "\n", + "1. accept\n", + "2. avoid\n", + "3. carbuncle\n", + "4. caramel\n", + "5. conclave\n", + "6. compressor\n", + "7. drib\n", + "8. elegy\n", + "9. embower\n", + "10. error\n", + "11. gaillardia\n", + "12. hostile\n", + "13. pitfall\n", + "14. rosa\n", + "15. spectra\n", + "16. stepchild\n", + "17. utopia\n", + "18. whimsey\n", + "19. grassland\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **a**: accept, avoid\n", + "* **c**: carbuncle, caramel, conclave, compressor\n", + "* **d**: drib\n", + "* **e**: elegy, error, embower\n", + "* **g**: gaillardia, grassland\n", + "* **h**: hostile\n", + "* **p**: pitfall\n", + "* **r**: rosa\n", + "* **s**: spectra, stepchild\n", + "* **u**: utopia\n", + "* **w**: whimsey\n", + "\n", + "**Detailed Sorting:**\n", + "\n", + "1. accept\n", + "2. avoid\n", + "3. carbuncle\n", + "4. caramel\n", + "5. conclave\n", + "6. compressor\n", + "7. drib\n", + "8. elegy\n", + "9. embower\n", + "10. error\n", + "11. gaillardia\n", + "12. grassland\n", + "13. hostile\n", + "14. pitfall\n", + "15. rosa\n", + "16. spectra\n", + "17. stepchild\n", + "18. utopia\n", + "19. whimsey\n", + "\n", + "(Checking 'gaillardia' vs 'grassland': g-a-i vs g-r-a. 'gaillardia' comes before 'grassland'.)\n", + "(Checking 'error' vs 'embower': e-r vs e-m. 'embower' comes before 'error'.)\n", + "\n", + "**Final Sorted List:**\n", + "accept, avoid, carbuncle, caramel, conclave, compressor, drib, elegy, embower, error, gaillardia, grassland, hostile, pitfall, rosa, spectra, stepchild, utopia, whimsey\n", + "\n", + "Answer: accept, avoid, carbuncle, caramel, conclave, compressor, drib, elegy, embower, error, gaillardia, grassland, hostile, pitfall, rosa, spectra, stepchild, utopia, whimseyfail
bbh_word_sorting-0041Sort the following words alphabetically: List: sociolinguistic nigeria odysseus demystify proton voltaire penny contaminate bighorn stirrup\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.bighorn contaminate demystify nigeria odysseus penny proton sociolinguistic stirrup voltaireHere are the words sorted alphabetically:\n", + "\n", + "1. bighorn\n", + "2. contaminate\n", + "3. demystify\n", + "4. odysseus\n", + "5. penny\n", + "6. proton\n", + "7. sociolinguistic\n", + "8. stirrup\n", + "9. voltaire\n", + "10. nigeria\n", + "11. **voltaire** (Wait, I listed voltaire twice in my mental check, let's re-sort carefully)\n", + "\n", + "Let's list them and sort them:\n", + "sociolinguistic, nigeria, odysseus, demystify, proton, voltaire, penny, contaminate, bighorn, stirrup\n", + "\n", + "1. bighorn\n", + "2. contaminate\n", + "3. demystify\n", + "4. nigeria\n", + "5. odysseus\n", + "6. penny\n", + "7. proton\n", + "8. sociolinguistic\n", + "9. stirrup\n", + "10. voltaire\n", + "\n", + "Let's re-check the sorting based on the initial list: sociolinguistic, nigeria, odysseus, demystify, proton, voltaire, penny, contaminate, bighorn, stirrup\n", + "\n", + "* Bighorn (B)\n", + "* Contaminate (C)\n", + "* Demystify (D)\n", + "* Nigeria (N)\n", + "* Odysseus (O)\n", + "* Penny (P)\n", + "* Proton (P) - P-e comes before P-e-n, so Penny before Proton.\n", + "* Sociolinguistic (S)\n", + "* Stirrup (S) - Stirrup comes after Sociolinguistic (S-t vs S-o).\n", + "* Voltaire (V)\n", + "\n", + "Correct alphabetical order:\n", + "1. bighorn\n", + "2. contaminate\n", + "3. demystify\n", + "4. nigeria\n", + "5. odysseus\n", + "6. penny\n", + "7. proton\n", + "8. sociolinguistic\n", + "9. stirrup\n", + "10. voltaire\n", + "\n", + "Wait, let's check S words: sociolinguistic, stirrup.\n", + "S-o-c... vs S-t-i... $\\rightarrow$ sociolinguistic comes before stirrup.\n", + "\n", + "Final check:\n", + "Bighorn\n", + "Contaminate\n", + "Demystify\n", + "Nigeria\n", + "Odysseus\n", + "Penny\n", + "Proton\n", + "Sociolinguistic\n", + "Stirrup\n", + "Voltaire\n", + "\n", + "Answer: bighorn, contaminate, demystify, nigeria, odysseus, penny, proton, sociolinguistic, stirrup, voltaire\n", + "\n", + "Answer: bighorn, contaminate, demystify, nigeria, odysseus, penny, proton, sociolinguistic, stirfail
bbh_word_sorting-0042Sort the following words alphabetically: List: falter tuscarora invigorate porridge wetland libya residuary wrapup ludlow landlady kamikaze christiana jot antler mallow\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.antler christiana falter invigorate jot kamikaze landlady libya ludlow mallow porridge residuary tuscarora wetland wrapupHere are the words sorted alphabetically:\n", + "\n", + "1. antler\n", + "2. christiana\n", + "3. falter\n", + "4. landlady\n", + "5. libya\n", + "6. ludlow\n", + "7. mallow\n", + "8. porridge\n", + "9. residuary\n", + "10. tuscarora\n", + "11. wrapup\n", + "12. invigorate\n", + "13. jot\n", + "14. kamikaze\n", + "15. wetland\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **a**ntler\n", + "* **c**hristiana\n", + "* **f**alter\n", + "* **i**nvigorate\n", + "* **j**ot\n", + "* **k**amikaze\n", + "* **l**andlady\n", + "* **l**ibya\n", + "* **l**udlow\n", + "* **m**allow\n", + "* **p**orridge\n", + "* **r**esiduary\n", + "* **t**uscarora\n", + "* **w**etland\n", + "* **w**rapup\n", + "\n", + "Sorting the 'L' words: landlady, libya, ludlow.\n", + "* landlady\n", + "* libya\n", + "* ludlow\n", + "\n", + "Sorting the 'W' words: wetland, wrapup.\n", + "* wetland\n", + "* wrapup\n", + "\n", + "The final sorted list:\n", + "1. antler\n", + "2. christiana\n", + "3. falter\n", + "4. invigorate\n", + "5. jot\n", + "6. kamikaze\n", + "7. landlady\n", + "8. libya\n", + "9. ludlow\n", + "10. mallow\n", + "11. porridge\n", + "12. residuary\n", + "13. tuscarora\n", + "14. wetland\n", + "15. wrapup\n", + "\n", + "Answer: antler, christiana, falter, invigorate, jot, kamikaze, landlady, libya, ludlow, mallow, porridge, residuary, tuscarora, wetland, wrapuppass
bbh_word_sorting-0043Sort the following words alphabetically: List: condescend rampant percolate coltish date rochester placid significant\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.coltish condescend date percolate placid rampant rochester significantThe given list of words is: condescend, rampant, percolate, coltish, date, rochester, placid, significant.\n", + "\n", + "Here is the alphabetical sorting process:\n", + "\n", + "1. **Compare the first letter:**\n", + " * c: condescend\n", + " * r: rampant, rochester\n", + " * p: percolate, placid\n", + " * c: coltish\n", + " * d: date\n", + " * r: (already listed)\n", + " * p: (already listed)\n", + " * s: significant\n", + "\n", + "2. **Sort words starting with 'c':**\n", + " * condescend\n", + " * coltish\n", + " * (Order: condescend, coltish)\n", + "\n", + "3. **Sort words starting with 'd':**\n", + " * date\n", + "\n", + "4. **Sort words starting with 'p':**\n", + " * percolate\n", + " * placid\n", + " * (Comparing 'percolate' and 'placid': 'p' is the same. 'e' in percolate comes before 'l' in placid. Wait, 'l' comes before 'e' in the alphabet? No. 'e' comes before 'l'. Let's recheck: p-e-r-c-o-l-a-t-e vs p-l-a-c-i-d. 'e' comes before 'l'. So, percolate comes before placid.)\n", + " * percolate\n", + " * placid\n", + "\n", + "5. **Sort words starting with 'r':**\n", + " * rampant\n", + " * rochester\n", + " * (Comparing 'rampant' and 'rochester': 'a' in rampant comes before 'o' in rochester.)\n", + " * rampant\n", + " * rochester\n", + "\n", + "6. **Sort words starting with 's':**\n", + " * significant\n", + "\n", + "**Combining the sorted groups:**\n", + "\n", + "1. condescend\n", + "2. coltish\n", + "3. date\n", + "4. percolate\n", + "5. placid\n", + "6. rampant\n", + "7. rochester\n", + "8. significant\n", + "\n", + "Let's re-verify the full alphabetical order:\n", + "* condescend (c-o...)\n", + "* coltish (c-o...) -> condescend vs coltish: 'n' vs 'l'. 'l' comes before 'n'. So, coltish comes before condescend.\n", + " * Corrected 'c' order: coltish, condescend\n", + "* date (d)\n", + "* percolate (p-efail
bbh_word_sorting-0044Sort the following words alphabetically: List: almost delphic semitic unbeknownst astute deadlock libertarian antic levitate execution sanction scathe storehouse dandelion affable sweeney fortunate leverage horntail apache\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.affable almost antic apache astute dandelion deadlock delphic execution fortunate horntail leverage levitate libertarian sanction scathe semitic storehouse sweeney unbeknownstHere are the words sorted alphabetically:\n", + "\n", + "1. almost\n", + "2. apache\n", + "3. affable\n", + "4. antic\n", + "5. astute\n", + "6. delphic\n", + "7. deadlock\n", + "8. execution\n", + "9. fortunate\n", + "10. leverage\n", + "11. horntail\n", + "12. libertarian\n", + "13. levitate\n", + "14. scathe\n", + "15. semitic\n", + "16. sweeney\n", + "17. sanction\n", + "18. storehouse\n", + "19. unbeknownst\n", + "20. dandelion\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **a**lmost\n", + "* **a**pache\n", + "* **a**ffable\n", + "* **a**ntic\n", + "* **a**stute\n", + "* **d**elphic\n", + "* **d**eadlock\n", + "* **e**xecution\n", + "* **f**ortunate\n", + "* **l**everage\n", + "* **l**ebitate\n", + "* **l**ibertarian\n", + "* **h**orntail\n", + "* **s**cache\n", + "* **s**emitic\n", + "* **s**weeney\n", + "* **s**anction\n", + "* **s**torehouse\n", + "* **u**nbeknownst\n", + "* **d**andelion (Wait, 'd' comes before 'e' and 'f'. Let's re-check the full list.)\n", + "\n", + "Original List: almost, delphic, semitic, unbeknownst, astute, deadlock, libertarian, antic, levitate, execution, sanction, scathe, storehouse, dandelion, affable, sweeney, fortunate, leverage, horntail, apache\n", + "\n", + "Alphabetical Sorting:\n", + "\n", + "1. almost\n", + "2. apache\n", + "3. affable\n", + "4. antic\n", + "5. astute\n", + "6. dandelion\n", + "7. delphic\n", + "8. deadlock\n", + "9. execution\n", + "10. fortunate\n", + "11. leverage\n", + "12. horntail\n", + "13. levitate\n", + "14. libertarian\n", + "15. scathe\n", + "16. sanction\n", + "17. semitic\n", + "18. sweeney\n", + "19. storehouse\n", + "20. unbeknownst\n", + "\n", + "Answer: almost, apache, affable, antic, astute, dandelion, delphic, deadlock, execution, fortunate, leverage, horntail, levitate, libertarian, scathe, sanction, semitic, sweeney, storehouse, unbeknownstfail
bbh_word_sorting-0045Sort the following words alphabetically: List: spongy separate gabriel jackson quail hendrickson washy backslide shelter calvert anastomosis variety commando pizzicato sticktight syndicate hollister\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.anastomosis backslide calvert commando gabriel hendrickson hollister jackson pizzicato quail separate shelter spongy sticktight syndicate variety washyHere are the words sorted alphabetically:\n", + "\n", + "1. backslide\n", + "2. calvert\n", + "3. gabriel\n", + "4. hendrickson\n", + "5. hollister\n", + "6. jackson\n", + "7. pizzicato\n", + "8. quail\n", + "9. separate\n", + "10. shelter\n", + "11. sticktight\n", + "12. spongy\n", + "13. syndicate\n", + "14. variety\n", + "15. washy\n", + "16. commando\n", + "17. anastomosis\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* backslide\n", + "* calvert\n", + "* gabriel\n", + "* hendrickson\n", + "* hollister\n", + "* jackson\n", + "* pizzicato\n", + "* quail\n", + "* separate\n", + "* shelter\n", + "* sticktight\n", + "* spongy\n", + "* syndicate\n", + "* variety\n", + "* washy\n", + "* commando\n", + "* anastomosis\n", + "\n", + "Comparing the words:\n", + "B: backslide\n", + "C: calvert, commando\n", + "G: gabriel\n", + "H: hendrickson, hollister\n", + "J: jackson\n", + "P: pizzicato\n", + "Q: quail\n", + "S: separate, shelter, spongy, sticktight, syndicate\n", + "V: variety\n", + "W: washy\n", + "A: anastomosis\n", + "\n", + "Detailed alphabetical sort:\n", + "1. backslide\n", + "2. calvert\n", + "3. commando\n", + "4. gabriel\n", + "5. hendrickson\n", + "6. hollister\n", + "7. jackson\n", + "8. anastomosis\n", + "9. pizzicato\n", + "10. quail\n", + "11. separate\n", + "12. shelter\n", + "13. sticktight\n", + "14. spongy\n", + "15. syndicate\n", + "16. variety\n", + "17. washy\n", + "\n", + "Answer: backslide, calvert, commando, gabriel, hendrickson, hollister, jackson, anastomosis, pizzicato, quail, separate, shelter, sticktight, spongy, syndicate, variety, washyfail
bbh_word_sorting-0046Sort the following words alphabetically: List: syndrome therefrom\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.syndrome therefromThe words to be sorted are: syndrome, therefrom.\n", + "\n", + "To sort them alphabetically, we compare them letter by letter:\n", + "\n", + "1. **syndrome**: Starts with 's', followed by 'y', 'n', 'd', 'r', 'o', 'm', 'e'.\n", + "2. **therefrom**: Starts with 't', followed by 'h', 'e', 'r', 'e', 'f', 'r', 'o', 'm'.\n", + "\n", + "Comparing the first letters: 's' comes before 't' in the alphabet.\n", + "\n", + "Therefore, "syndrome" comes before "therefrom".\n", + "\n", + "The alphabetical order is: syndrome, therefrom.\n", + "\n", + "Answer: syndrome, therefrompass
bbh_word_sorting-0047Sort the following words alphabetically: List: odessa dance formulae dietetic mantle cost huddle resplendent bolshevism proust warmth deadline progeny hesitant palace rackety judson foster belize thirdhand\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.belize bolshevism cost dance deadline dietetic formulae foster hesitant huddle judson mantle odessa palace progeny proust rackety resplendent thirdhand warmthHere are the words sorted alphabetically:\n", + "\n", + "1. bolshevism\n", + "2. belize\n", + "3. dance\n", + "4. deadline\n", + "5. dietetic\n", + "6. foster\n", + "7. formulae\n", + "8. hesitant\n", + "9. huddle\n", + "10. judson\n", + "11. mantle\n", + "12. odessa\n", + "13. palace\n", + "14. proust\n", + "15. rackety\n", + "16. resplendent\n", + "17. thirdhand\n", + "18. warmth\n", + "19. cost\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **b**olshevism, **b**elize (bolshevism before belize)\n", + "* **d**ance, **d**eadline, **d**ietetic\n", + "* **f**oster, **f**ormulae\n", + "* **h**esitant, **h**uddle\n", + "* **j**udson\n", + "* **m**antle\n", + "* **o**dessa\n", + "* **p**alace, **p**roust\n", + "* **r**ackety, **r**esplendent\n", + "* **t**hirdhand\n", + "* **w**armth\n", + "* **c**ost\n", + "\n", + "**Alphabetical Order:**\n", + "\n", + "1. bolshevism\n", + "2. belize\n", + "3. dance\n", + "4. deadline\n", + "5. dietetic\n", + "6. foster\n", + "7. formulae\n", + "8. hesitant\n", + "9. huddle\n", + "10. judson\n", + "11. mantle\n", + "12. odessa\n", + "13. palace\n", + "14. proust\n", + "15. rackety\n", + "16. resplendent\n", + "17. thirdhand\n", + "18. warmth\n", + "19. cost\n", + "\n", + "Wait, let's check the 'b' words again: bolshevism, belize. 'b'e comes before 'b'o.\n", + "\n", + "Let's re-sort the full list:\n", + "odessa, dance, formulae, dietetic, mantle, cost, huddle, resplendent, bolshevism, proust, warmth, deadline, progeny, hesitant, palace, rackety, judson, foster, belize, thirdhand\n", + "\n", + "(Note: I missed 'progeny' in my initial count, let's include it.)\n", + "\n", + "Full List: odessa, dance, formulae, dietetic, mantle, cost, huddle, resplendent, bolshevism, proust, warmth, deadline, progeny, hesitant, palace, rackety, judfail
bbh_word_sorting-0048Sort the following words alphabetically: List: automate abner shampoo gurgle none exquisite abramson amity fruitful shorten waterproof\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.abner abramson amity automate exquisite fruitful gurgle none shampoo shorten waterproofHere are the words to sort: automate, abner, shampoo, gurgle, none, exquisite, abramson, amity, fruitful, shorten, waterproof.\n", + "\n", + "Let's sort them alphabetically:\n", + "\n", + "1. **abner**\n", + "2. **abramson**\n", + "3. **automate**\n", + "4. **amity**\n", + "5. **fruitful**\n", + "6. **gurgle**\n", + "7. **none**\n", + "8. **shorten**\n", + "9. **shampoo**\n", + "10. **waterproof**\n", + "11. **exquisite**\n", + "\n", + "Let's re-sort carefully, paying attention to the starting letters:\n", + "\n", + "* **a**: automate, abner, abramson, amity\n", + " * abner\n", + " * abramson\n", + " * amity\n", + " * automate\n", + "* **f**: fruitful\n", + "* **g**: gurgle\n", + "* **n**: none\n", + "* **s**: shampoo, shorten\n", + "* **w**: waterproof\n", + "* **e**: exquisite\n", + "\n", + "Correct alphabetical order:\n", + "\n", + "1. abner\n", + "2. abramson\n", + "3. amity\n", + "4. automate\n", + "5. exquisite\n", + "6. fruitful\n", + "7. gurgle\n", + "8. none\n", + "9. shorten\n", + "10. shampoo\n", + "11. waterproof\n", + "\n", + "Let's check the 'a' words again:\n", + "abner\n", + "abramson\n", + "amity\n", + "automate\n", + "(a, b, a, a) -> abner, abramson, amity, automate. This order is correct.\n", + "\n", + "Let's check the 'e' word:\n", + "exquisite (comes after automate)\n", + "\n", + "Let's check 'f' and 'g':\n", + "fruitful, gurgle (f comes before g)\n", + "\n", + "Let's check 'n' and 's':\n", + "none, shorten, shampoo (n comes before s)\n", + "\n", + "Let's check 'w':\n", + "waterproof (w is last among these)\n", + "\n", + "Final sorted list:\n", + "1. abner\n", + "2. abramson\n", + "3. amity\n", + "4. automate\n", + "5. exquisite\n", + "6. fruitful\n", + "7. gurgle\n", + "8. none\n", + "9. shorten\n", + "10. shampoo\n", + "11. waterproof\n", + "\n", + "Answer: abner, abramson, amity, automate, exquisite, fruitful, gurgle, none, shorten, shampoo, waterprooffail
bbh_word_sorting-0049Sort the following words alphabetically: List: crystallography survey bindle rundown shipshape roadside strange chiang dent mambo savannah spew won't ram\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.bindle chiang crystallography dent mambo ram roadside rundown savannah shipshape spew strange survey won'tHere are the words to sort alphabetically:\n", + "crystallography, survey, bindle, rundown, shipshape, roadside, strange, chiang, dent, mambo, savannah, spew, won't, ram\n", + "\n", + "Let's sort them:\n", + "\n", + "1. bindle\n", + "2. chiang\n", + "3. crystallography\n", + "4. dent\n", + "5. mambo\n", + "6. ram\n", + "7. roadside\n", + "8. rundown\n", + "9. survey\n", + "10. shipshape\n", + "11. savannah\n", + "12. strange\n", + "13. spew\n", + "14. won't\n", + "\n", + "Let's re-sort carefully, paying attention to the first letter:\n", + "\n", + "* **b**: bindle\n", + "* **c**: crystallography, chiang\n", + "* **d**: dent\n", + "* **m**: mambo\n", + "* **r**: ram, roadside, rundown\n", + "* **s**: survey, shipshape, savannah, strange, spew\n", + "* **w**: won't\n", + "\n", + "**Sorting within groups:**\n", + "\n", + "**C:** crystallography, chiang (cryst... comes before chiang)\n", + " * crystallography\n", + " * chiang\n", + "**R:** ram, roadside, rundown (ram, roadside, rundown)\n", + " * ram\n", + " * roadside\n", + " * rundown\n", + "**S:** survey, shipshape, savannah, strange, spew (savannah, shipshape, spew, survey, strange)\n", + " * savannah\n", + " * shipshape\n", + " * spew\n", + " * survey\n", + " * strange\n", + "**W:** won't\n", + "\n", + "**Final Alphabetical Order:**\n", + "\n", + "1. bindle\n", + "2. crystallography\n", + "3. chiang\n", + "4. dent\n", + "5. mambo\n", + "6. ram\n", + "7. roadside\n", + "8. rundown\n", + "9. savannah\n", + "10. shipshape\n", + "11. spew\n", + "12. survey\n", + "13. strange\n", + "14. won't\n", + "\n", + "Let's re-check the 'R' words: ram, roadside, rundown.\n", + "ram (ra...)\n", + "roadside (ro...)\n", + "rundown (ru...)\n", + "Order: ram, roadside, rundown. (Correct)\n", + "\n", + "Let's re-check the 'S' words: savannah, shipshape, spew, survey, strange.\n", + "savannah (sa...)\n", + "shipshape (sh...)\n", + "spew (sp...)\n", + "survey (su...)\n", + "strange (st...)\n", + "Order: savannah, shipshape, spew, survey, strange. (Correct)\n", + "\n", + "Thefail
bbh_word_sorting-0050Sort the following words alphabetically: List: griffith common nanosecond loosestrife decoy swabby aerospace denmark yates frill spitz enviable screechy allure sow spermatozoa saute jibe exclusive\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.aerospace allure common decoy denmark enviable exclusive frill griffith jibe loosestrife nanosecond saute screechy sow spermatozoa spitz swabby yatesHere are the words sorted alphabetically:\n", + "\n", + "1. aerospace\n", + "2. allure\n", + "3. decoy\n", + "4. denmark\n", + "5. enviable\n", + "6. exclusive\n", + "7. frill\n", + "8. griffith\n", + "9. jibe\n", + "10. loosestrife\n", + "11. nanosecond\n", + "12. sow\n", + "13. spitz\n", + "14. screechy\n", + "15. spermatozoa\n", + "16. saute\n", + "17. swabby\n", + "18. yates\n", + "19. common\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **a**erospace\n", + "* **a**llure\n", + "* **c**ommon\n", + "* **d**enmark\n", + "* **d**ecoy\n", + "* **e**nable\n", + "* **e**xclusive\n", + "* **f**rill\n", + "* **g**riffith\n", + "* **j**ibe\n", + "* **l**oosestrife\n", + "* **n**anosecond\n", + "* **s**ow\n", + "* **s**pitz\n", + "* **s**creechy\n", + "* **s** spermatozoa\n", + "* **s**aute\n", + "* **s**wabby\n", + "* **y**ates\n", + "\n", + "**Detailed Alphabetical Sorting:**\n", + "\n", + "1. aerospace\n", + "2. allure\n", + "3. common\n", + "4. denmark\n", + "5. decoy\n", + "6. enviable\n", + "7. exclusive\n", + "8. frill\n", + "9. griffith\n", + "10. jibe\n", + "11. loosestrife\n", + "12. nanosecond\n", + "13. sow\n", + "14. spitz\n", + "15. screechy\n", + "16. spermatozoa\n", + "17. saute\n", + "18. swabby\n", + "19. yates\n", + "\n", + "(Checking 's' words: spermatozoa, saute, screechy, sow, spitz, swabby. Order: spermatozoa, saute, screechy, sow, spitz, swabby)\n", + "\n", + "Let's re-verify the 's' words:\n", + "* spermatozoa\n", + "* saute\n", + "* screechy\n", + "* sow\n", + "* spitz\n", + "* swabby\n", + "\n", + "Correct order for 's' words:\n", + "1. spermatozoa\n", + "2. saute\n", + "3. screechy\n", + "4. sow\n", + "5. spitz\n", + "6. swabby\n", + "\n", + "Final Sorted List:\n", + "1. aerospace\n", + "2. allure\n", + "3. common\n", + "4. denmark\n", + "5. decoy\n", + "6. enviable\n", + "7. exclusive\n", + "8. frill\n", + "9. griffith\n", + "10. jibe\n", + "11.fail
bbh_word_sorting-0051Sort the following words alphabetically: List: dynastic inflammable prick tristan vitiate tackle stagnate conglomerate nebulae phosphide\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.conglomerate dynastic inflammable nebulae phosphide prick stagnate tackle tristan vitiateHere are the words to sort: dynastic, inflammable, prick, tristan, vitiate, tackle, stagnate, conglomerate, nebulae, phosphide.\n", + "\n", + "We will sort them alphabetically:\n", + "\n", + "1. **conglomerate**\n", + "2. **dynastic**\n", + "3. **inflammable**\n", + "4. **nebulae**\n", + "5. **phosphide**\n", + "6. **prick**\n", + "7. **stagnate**\n", + "8. **tackle**\n", + "9. **tristan**\n", + "10. **vitiate**\n", + "\n", + "Let's re-check the sorting carefully:\n", + "\n", + "* **c**onglomerate\n", + "* **d**ynastic\n", + "* **i**nflammable\n", + "* **n**ebulae\n", + "* **p**hosphide\n", + "* **p**rick (P-h comes before P-r)\n", + "* **s**tagnate\n", + "* **t**ackle\n", + "* **t**ristan (T-a comes before T-r)\n", + "* **v**itiate\n", + "\n", + "Let's re-sort the 'P' words: phosphide, prick.\n", + "P-h-o...\n", + "P-r-i...\n", + "'h' comes before 'r', so phosphide comes before prick.\n", + "\n", + "Corrected order:\n", + "1. conglomerate\n", + "2. dynastic\n", + "3. inflammable\n", + "4. nebulae\n", + "5. phosphide\n", + "6. prick\n", + "7. stagnate\n", + "8. tackle\n", + "9. tristan\n", + "10. vitiate\n", + "\n", + "Let's check the 'T' words: tackle, tristan.\n", + "tackle (ta...)\n", + "tristan (tr...)\n", + "'a' comes before 'r', so tackle comes before tristan.\n", + "\n", + "Final sorted list:\n", + "1. conglomerate\n", + "2. dynastic\n", + "3. inflammable\n", + "4. nebulae\n", + "5. phosphide\n", + "6. prick\n", + "7. stagnate\n", + "8. tackle\n", + "9. tristan\n", + "10. vitiate\n", + "\n", + "Answer: conglomerate, dynastic, inflammable, nebulae, phosphide, prick, stagnate, tackle, tristan, vitiatepass
bbh_word_sorting-0052Sort the following words alphabetically: List: din spruce nate pomade jordan gaillardia putt inroad jest kill jackboot whelan mirth amerada craftsmen shortcoming eclipse\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.amerada craftsmen din eclipse gaillardia inroad jackboot jest jordan kill mirth nate pomade putt shortcoming spruce whelanHere are the words sorted alphabetically:\n", + "\n", + "1. amerada\n", + "2. craftsmen\n", + "3. eclipse\n", + "4. din\n", + "5. gaillardia\n", + "6. inroad\n", + "7. jest\n", + "8. jackboot\n", + "9. jordan\n", + "10. kill\n", + "11. mirth\n", + "12. nate\n", + "13. pomade\n", + "14. putt\n", + "15. shortcoming\n", + "16. spruce\n", + "17. whelan\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* amerada\n", + "* craftsmen\n", + "* din\n", + "* eclipse\n", + "* gaillardia\n", + "* inroad\n", + "* jest\n", + "* jackboot\n", + "* jordan\n", + "* kill\n", + "* mirth\n", + "* nate\n", + "* pomade\n", + "* putt\n", + "* shortcoming\n", + "* spruce\n", + "* whelan\n", + "\n", + "Comparing the initial list: din spruce nate pomade jordan gaillardia putt inroad jest kill jackboot whelan mirth amerada craftsmen shortcoming eclipse\n", + "\n", + "1. amerada\n", + "2. craftsmen\n", + "3. din\n", + "4. eclipse\n", + "5. gaillardia\n", + "6. inroad\n", + "7. jest\n", + "8. jackboot\n", + "9. jordan\n", + "10. kill\n", + "11. mirth\n", + "12. nate\n", + "13. pomade\n", + "14. putt\n", + "15. shortcoming\n", + "16. spruce\n", + "17. whelan\n", + "\n", + "Answer: amerada, craftsmen, din, eclipse, gaillardia, inroad, jest, jackboot, jordan, kill, mirth, nate, pomade, putt, shortcoming, spruce, whelanfail
bbh_word_sorting-0053Sort the following words alphabetically: List: fallen juvenile bermuda diagram ezra screechy aristocratic musty absorption slack quartile nod nigeria testicle congo cheerful eucre cesium\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.absorption aristocratic bermuda cesium cheerful congo diagram eucre ezra fallen juvenile musty nigeria nod quartile screechy slack testicleHere are the words sorted alphabetically:\n", + "\n", + "1. absorption\n", + "2. aristocratic\n", + "3. bermuda\n", + "4. cheerful\n", + "5. congo\n", + "6. cesium\n", + "7. diagram\n", + "8. fallen\n", + "9. eucre\n", + "10. ezra\n", + "11. junitile (Assuming "juvenile" was a typo or meant to be sorted as is, but standard sorting applies) - Let's stick to the provided list: juvenile\n", + "12. kotlinx (Assuming "screechy" is a typo or meant to be sorted as is) - Let's stick to the provided list: screechy\n", + "13. musty\n", + "14. nod\n", + "15. nigeria\n", + "16. quartile\n", + "17. slack\n", + "18. testicle\n", + "\n", + "Let's re-sort carefully:\n", + "\n", + "* absorption\n", + "* aristocratic\n", + "* bermuda\n", + "* cheerful\n", + "* congo\n", + "* cesium\n", + "* diagram\n", + "* fallen\n", + "* eucre\n", + "* ezra\n", + "* juvenile\n", + "* musty\n", + "* nod\n", + "* nigeria\n", + "* quartile\n", + "* screechy\n", + "* slack\n", + "* testicle\n", + "\n", + "Comparing 'ezra' and 'eucre': 'ezra' comes before 'eucre'.\n", + "Comparing 'juvenile' and 'musty': 'juvenile' comes before 'musty'.\n", + "\n", + "Final sorted list:\n", + "1. absorption\n", + "2. aristocratic\n", + "3. bermuda\n", + "4. cheerful\n", + "5. congo\n", + "6. cesium\n", + "7. diagram\n", + "8. fallen\n", + "9. eucre\n", + "10. ezra\n", + "11. juvenile\n", + "12. musty\n", + "13. nod\n", + "14. nigeria\n", + "15. quartile\n", + "16. screechy\n", + "17. slack\n", + "18. testicle\n", + "\n", + "Answer: absorption, aristocratic, bermuda, cheerful, congo, cesium, diagram, fallen, eucre, ezra, juvenile, musty, nod, nigeria, quartile, screechy, slack, testiclefail
bbh_word_sorting-0054Sort the following words alphabetically: List: saul fresco teen chopstick footwork smalley anaplasmosis palisade orthography storey bumble pilate clue ingot fiesta\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.anaplasmosis bumble chopstick clue fiesta footwork fresco ingot orthography palisade pilate saul smalley storey teenHere are the words sorted alphabetically:\n", + "\n", + "1. anaplasmosis\n", + "2. bumble\n", + "3. chopstick\n", + "4. clue\n", + "5. fresco\n", + "6. footwork\n", + "7. fiesta\n", + "8. ingot\n", + "9. orthography\n", + "10. palisade\n", + "11. pilate\n", + "12. saul\n", + "13. smalley\n", + "14. storey\n", + "15. teen\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* anaplasmosis (A)\n", + "* bumble (B)\n", + "* chopstick (C)\n", + "* clue (C) - clue comes before chopstick\n", + "* fresco (F)\n", + "* footwork (F) - footwork comes after fresco\n", + "* fiesta (F) - fiesta comes after footwork\n", + "* ingot (I)\n", + "* orthography (O)\n", + "* palisade (P)\n", + "* pilate (P) - pilate comes after palisade\n", + "* saul (S)\n", + "* smalley (S) - saul comes before smalley\n", + "* storey (S) - storey comes after smalley\n", + "* teen (T)\n", + "\n", + "Let's do a strict alphabetical sort:\n", + "\n", + "1. anaplasmosis\n", + "2. bumble\n", + "3. chopstick\n", + "4. clue\n", + "5. fresco\n", + "6. footwork\n", + "7. fiesta\n", + "8. ingot\n", + "9. orthography\n", + "10. palisade\n", + "11. pilate\n", + "12. saul\n", + "13. smalley\n", + "14. storey\n", + "15. teen\n", + "\n", + "Wait, let's re-check the 'S' words: saul, smalley, storey.\n", + "S-a-u-l\n", + "S-m-a-l-l-e-y\n", + "S-t-o-r-e-y\n", + "'a' comes before 'm' and 't'. So, saul comes first.\n", + "'m' comes before 't'. So, smalley comes before storey.\n", + "Order for S words: saul, smalley, storey.\n", + "\n", + "Let's re-check the 'F' words: fresco, footwork, fiesta.\n", + "F-r-e-s-c-o\n", + "F-o-o-t-w-o-r-k\n", + "F-i-e-s-t-a\n", + "Order: fresco, footwork, fiesta. (e < o < i is false. e < i <fail
bbh_word_sorting-0055Sort the following words alphabetically: List: emission upon labour whim fairfax ride crepe prig accomplice az doff clatter circumcircle tea wheelbase pleura incantation choral viaduct lorry\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.accomplice az choral circumcircle clatter crepe doff emission fairfax incantation labour lorry pleura prig ride tea upon viaduct wheelbase whimHere are the words sorted alphabetically:\n", + "\n", + "1. accomplice\n", + "2. choral\n", + "3. crepe\n", + "4. circumcircle\n", + "5. clatter\n", + "6. doff\n", + "7. emission\n", + "8. fairfax\n", + "9. incantation\n", + "10. labour\n", + "11. lorry\n", + "12. pleura\n", + "13. prig\n", + "14. ride\n", + "15. tea\n", + "16. viaduct\n", + "17. wheelbase\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* accomplice (A)\n", + "* choral (C)\n", + "* crepe (C)\n", + "* circumcircle (C)\n", + "* clatter (C)\n", + "* doff (D)\n", + "* emission (E)\n", + "* fairfax (F)\n", + "* incantation (I)\n", + "* labour (L)\n", + "* lorry (L)\n", + "* pleura (P)\n", + "* prig (P)\n", + "* ride (R)\n", + "* tea (T)\n", + "* viaduct (V)\n", + "* wheelbase (W)\n", + "\n", + "**Detailed Alphabetical Sorting:**\n", + "\n", + "1. accomplice\n", + "2. choral\n", + "3. crepe\n", + "4. circumcircle\n", + "5. clatter\n", + "6. doff\n", + "7. emission\n", + "8. fairfax\n", + "9. incantation\n", + "10. labour\n", + "11. lorry\n", + "12. pleura\n", + "13. prig\n", + "14. ride\n", + "15. tea\n", + "16. viaduct\n", + "17. wheelbase\n", + "\n", + "(Checking C words: choral, crepe, circumcircle, clatter. C-h comes before C-l, C-r, C-i. Wait, 'ch' comes before 'l', 'r', 'i'. Let's re-sort the C words: choral, clatter, crepe, circumcircle.)\n", + "\n", + "**Corrected Sorting:**\n", + "\n", + "1. accomplice (A)\n", + "2. choral (C)\n", + "3. clatter (C)\n", + "4. crepe (C)\n", + "5. circumcircle (C)\n", + "6. doff (D)\n", + "7. emission (E)\n", + "8. fairfax (F)\n", + "9. incantation (I)\n", + "10. labour (L)\n", + "11. lorry (L)\n", + "12. pleura (P)\n", + "13. prig (P)\n", + "14. ride (R)\n", + "15. tea (T)\n", + "16. viaduct (V)\n", + "17. wheelbase (W)fail
bbh_word_sorting-0056Sort the following words alphabetically: List: deadwood macedonia foxtail block hexadecimal guaranty rubaiyat victoria custodian\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.block custodian deadwood foxtail guaranty hexadecimal macedonia rubaiyat victoriaHere are the words to sort: deadwood, macedonia, foxtail, block, hexadecimal, guaranty, rubaiyat, victoria, custodian.\n", + "\n", + "We will sort them alphabetically:\n", + "\n", + "1. **block**\n", + "2. **custodian**\n", + "3. **deadwood**\n", + "4. **foxtail**\n", + "5. **guaranty**\n", + "6. **hexadecimal**\n", + "7. **macedonia**\n", + "8. **rubaiyat**\n", + "9. **victoria**\n", + "\n", + "Let's re-check the sorting carefully:\n", + "\n", + "* **b**lock\n", + "* **c**ustodian\n", + "* **d**eadwood\n", + "* **f**oxtail\n", + "* **g**uaranty\n", + "* **h**exadecimal\n", + "* **m**acedonia\n", + "* **r**ubaiyat\n", + "* **v**ictoria\n", + "\n", + "The order seems correct based on the first letter.\n", + "\n", + "Answer: block, custodian, deadwood, foxtail, guaranty, hexadecimal, macedonia, rubaiyat, victoria\n", + "\n", + "Answer: block, custodian, deadwood, foxtail, guaranty, hexadecimal, macedonia, rubaiyat, victoriapass
bbh_word_sorting-0057Sort the following words alphabetically: List: matte game aldebaran inkling fiberboard pulsate lakeside shape mcgee watchworks entrepreneurial boxwood peruse backyard cabbage polyhedra lightface rowley rae invincible\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.aldebaran backyard boxwood cabbage entrepreneurial fiberboard game inkling invincible lakeside lightface matte mcgee peruse polyhedra pulsate rae rowley shape watchworksHere are the words sorted alphabetically:\n", + "\n", + "1. aldebaran\n", + "2. backyard\n", + "3. cabbage\n", + "4. entrepreneurial\n", + "5. fiberboard\n", + "6. game\n", + "7. inkling\n", + "8. invincible\n", + "9. lake\n", + "10. lightface\n", + "11. matte\n", + "12. mcgee\n", + "13. peruse\n", + "14. pulsate\n", + "15. polyhedra\n", + "16. rae\n", + "17. rowley\n", + "18. shape\n", + "19. watchworks\n", + "20. boxwood\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **a**: aldebaran, backyard\n", + "* **b**: cabbage, boxwood\n", + "* **e**: entrepreneurial\n", + "* **f**: fiberboard\n", + "* **g**: game\n", + "* **i**: inkling, invincible\n", + "* **l**: lakeside, lightface\n", + "* **m**: matte, mcgee\n", + "* **p**: pulsate, polyhedra, peruse\n", + "* **r**: rae, rowley\n", + "* **s**: shape\n", + "* **w**: watchworks\n", + "\n", + "Let's do a full alphabetical sort:\n", + "\n", + "1. aldebaran\n", + "2. backyard\n", + "3. boxwood\n", + "4. cabbage\n", + "5. entrepreneurial\n", + "6. fiberboard\n", + "7. game\n", + "8. inkling\n", + "9. invincible\n", + "10. lakeside (Assuming 'lakeside' is the intended word, as 'lakes' is not in the list, but 'lakeside' is often implied or a common word. Checking the list again: "lakeside" is not in the list. The list contains "lakeside" if we assume "lakeside" is present. Let's re-examine the provided list: matte game aldebaran inkling fiberboard pulsate lakeside shape mcgee watchworks entrepreneurial boxwood peruse backyard cabbage polyhedra lightface rowley rae invincible)\n", + " * *Correction: The word is "lakeside".*\n", + "11. lightface\n", + "12. matte\n", + "13. mcgee\n", + "14. peruse\n", + "15. pulsate\n", + "16. polyhedra\n", + "17. rae\n", + "18. rowley\n", + "19. shape\n", + "20. watchworks\n", + "\n", + "Let's re-sort based on the exact list provided:\n", + "matte, game, aldebaran, inkling, fiberboard, pulsate, lakeside, shape, mcgee, watchworks, entrepreneurial, boxwood, peruse, backyard, cabbagefail
bbh_word_sorting-0058Sort the following words alphabetically: List: pea yonkers darkle quadrant moldboard instant olsen hookup erudite lip\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.darkle erudite hookup instant lip moldboard olsen pea quadrant yonkersThe given list of words is: pea, yonkers, darkle, quadrant, moldboard, instant, olsen, hookup, erudite, lip.\n", + "\n", + "Here is the alphabetical sorting process:\n", + "\n", + "1. **Compare the first letter:**\n", + " * darkle (d)\n", + " * erudite (e)\n", + " * hookup (h)\n", + " * instant (i)\n", + " * lip (l)\n", + " * moldboard (m)\n", + " * olsen (o)\n", + " * pea (p)\n", + " * quadrant (q)\n", + " * yonkers (y)\n", + "\n", + "2. **Sort based on the first letter:**\n", + " * darkle (d)\n", + " * erudite (e)\n", + " * hookup (h)\n", + " * instant (i)\n", + " * lip (l)\n", + " * moldboard (m)\n", + " * olsen (o)\n", + " * pea (p)\n", + " * quadrant (q)\n", + " * yonkers (y)\n", + "\n", + "3. **Final sorted list:**\n", + " * darkle\n", + " * erudite\n", + " * hookup\n", + " * instant\n", + " * lip\n", + " * moldboard\n", + " * olsen\n", + " * pea\n", + " * quadrant\n", + " * yonkers\n", + "\n", + "Answer: darkle, erudite, hookup, instant, lip, moldboard, olsen, pea, quadrant, yonkerspass
bbh_word_sorting-0059Sort the following words alphabetically: List: paginate amicable they've histology extreme indoeuropean sonority tramway turvy coo deerstalker browne henchman pelvis calumny\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.amicable browne calumny coo deerstalker extreme henchman histology indoeuropean paginate pelvis sonority they've tramway turvyHere are the words sorted alphabetically:\n", + "\n", + "1. amicable\n", + "2. browne\n", + "3. calumny\n", + "4. coo\n", + "5. deerstalker\n", + "6. eminence (Wait, "extreme" is in the list, let's re-check)\n", + "7. henchman\n", + "8. histology\n", + "9. indoeuropean\n", + "10. paginate\n", + "11. pelvis\n", + "12. sonority\n", + "13. tramway\n", + "14. they've\n", + "15. turvy\n", + "\n", + "Let's re-sort carefully:\n", + "\n", + "* amicable (A)\n", + "* browne (B)\n", + "* calumny (C)\n", + "* coo (C) - coo comes before calumny if we look at the second letter, but alphabetically, 'cal' comes before 'coo'. Let's stick to standard alphabetical order.\n", + " * amicable\n", + " * browne\n", + " * calumny\n", + " * coo\n", + "* deerstalker (D)\n", + "* eminence (Not in list, using extreme)\n", + "* extreme (E)\n", + "* henchman (H)\n", + "* histology (H) - henchman comes before histology.\n", + "* indoeuropean (I)\n", + "* paginate (P)\n", + "* pelvis (P) - paginate comes before pelvis.\n", + "* sonority (S)\n", + "* tramway (T)\n", + "* they've (T) - tramway comes before they've.\n", + "* turvy (T) - turvy comes after tramway.\n", + "\n", + "Let's perform a strict alphabetical sort:\n", + "\n", + "1. amicable\n", + "2. browne\n", + "3. calumny\n", + "4. coo\n", + "5. deerstalker\n", + "6. extreme\n", + "7. henchman\n", + "8. histology\n", + "9. indoeuropean\n", + "10. paginate\n", + "11. pelvis\n", + "12. sonority\n", + "13. tramway\n", + "14. they've\n", + "15. turvy\n", + "\n", + "Checking the 'H' words: henchman, histology. H-e-n vs H-i-s. henchman comes first.\n", + "Checking the 'P' words: paginate, pelvis. P-a vs P-e. paginate comes first.\n", + "Checking the 'T' words: tramway, they've, turvy. T-r, T-h, T-u. tramway, they've, turvy.\n", + " * tramway (trfail
bbh_word_sorting-0060Sort the following words alphabetically: List: statutory feed spavin hecatomb pestle plume figural pasty giveth incense undulate middle blackstone tel obstinacy toothpaste hunt sinkhole\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.blackstone feed figural giveth hecatomb hunt incense middle obstinacy pasty pestle plume sinkhole spavin statutory tel toothpaste undulateHere are the words sorted alphabetically:\n", + "\n", + "1. blackstone\n", + "2. feed\n", + "3. figural\n", + "4. giveth\n", + "5. hecatomb\n", + "6. hunt\n", + "7. incense\n", + "8. middle\n", + "9. obstinacy\n", + "10. pasty\n", + "11. pestle\n", + "12. plume\n", + "13. sinkhole\n", + "14. statutory\n", + "15. tel\n", + "16. toothpaste\n", + "17. undulate\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **b**lackstone\n", + "* **f**eed\n", + "* **f**igural\n", + "* **g**iveth\n", + "* **h**ecatomb\n", + "* **h**unt\n", + "* **i**ncense\n", + "* **m**iddle\n", + "* **o**bstinacy\n", + "* **p**asty\n", + "* **p**estle\n", + "* **p**lume\n", + "* **s**inkhole\n", + "* **s**tatutory\n", + "* **t**el\n", + "* **t**oothpaste (toothpaste)\n", + "* **u**ndulate\n", + "\n", + "Comparing words starting with 'f': feed, figural. (feed comes before figural)\n", + "Comparing words starting with 'p': pasty, pestle, plume. (plume, pasty, pestle) - Wait, P-L, P-A, P-E. Plume, Pasty, Pestle.\n", + "P-l-u-m-e\n", + "P-a-s-t-y\n", + "P-e-s-t-l-e\n", + "Alphabetical order for P words: plume, pasty, pestle.\n", + "\n", + "Let's re-sort the full list:\n", + "blackstone (B)\n", + "feed (F)\n", + "figural (F)\n", + "giveth (G)\n", + "hecatomb (H)\n", + "hunt (H)\n", + "incense (I)\n", + "middle (M)\n", + "obstinacy (O)\n", + "pasty (P)\n", + "pestle (P)\n", + "plume (P)\n", + "sinkhole (S)\n", + "statutory (S)\n", + "tel (T)\n", + "toothpaste (T)\n", + "undulate (U)\n", + "\n", + "**Detailed Alphabetical Sort:**\n", + "\n", + "1. blackstone\n", + "2. feed\n", + "3. figural\n", + "4. giveth\n", + "5. hecatomb\n", + "6. hunt\n", + "7. incense\n", + "8. middle\n", + "9. obstinacy\n", + "10. pasty\n", + "11.fail
bbh_word_sorting-0061Sort the following words alphabetically: List: slurp raytheon gloucester\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.gloucester raytheon slurpThe given list of words is: slurp, raytheon, gloucester.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter from the beginning.\n", + "\n", + "1. **s**lurp\n", + "2. **r**aytheon\n", + "3. **g**looucester\n", + "\n", + "Comparing the first letters: G comes before R, and R comes before S.\n", + "So, 'gloucester' comes first.\n", + "\n", + "Now we compare 'slurp' and 'raytheon'.\n", + "'s' comes before 'r' is false. 'r' comes before 's'.\n", + "So, 'raytheon' comes before 'slurp'.\n", + "\n", + "The alphabetical order is:\n", + "1. gloucester\n", + "2. raytheon\n", + "3. slurp\n", + "\n", + "Answer: gloucester, raytheon, slurp\n", + "\n", + "Answer: gloucester, raytheon, slurppass
bbh_word_sorting-0062Sort the following words alphabetically: List: gloriana milord rendezvous schnabel allegoric loge lollipop salle collate prolix mast euphony\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.allegoric collate euphony gloriana loge lollipop mast milord prolix rendezvous salle schnabelHere are the words to sort alphabetically:\n", + "gloriana, milord, rendezvous, schnabel, allegoric, loge, lollipop, salle, collate, prolix, mast, euphony\n", + "\n", + "Let's sort them letter by letter:\n", + "\n", + "1. **allegoric** (A)\n", + "2. **collate** (C)\n", + "3. **gloriana** (G)\n", + "4. **loge** (L) - *Comparing loge and lollipop, 'loge' comes before 'lollipop'*\n", + "5. **lollipop** (L)\n", + "6. **milord** (M)\n", + "7. **mast** (M) - *Comparing milord and mast, 'mast' comes before 'milord'*\n", + "8. **prolix** (P)\n", + "9. **rendezvous** (R)\n", + "10. **salle** (S)\n", + "11. **schnabel** (S) - *Comparing salle and schnabel, 'salle' comes before 'schnabel'*\n", + "12. **euphony** (E) - *Wait, 'euphony' starts with E, so it should come before 'gloriana' (G).*\n", + "\n", + "Let's re-sort based on the first letter:\n", + "\n", + "E: euphony\n", + "A: allegoric\n", + "C: collate\n", + "G: gloriana\n", + "L: loge, lollipop\n", + "M: mast, milord\n", + "P: prolix\n", + "R: rendezvous\n", + "S: salle, schnabel\n", + "\n", + "**Sorting within groups:**\n", + "\n", + "* **L:** loge, lollipop (loge comes before lollipop)\n", + "* **M:** mast, milord (mast comes before milord)\n", + "* **S:** salle, schnabel (salle comes before schnabel)\n", + "\n", + "**Final Alphabetical Order:**\n", + "\n", + "1. euphony\n", + "2. allegoric\n", + "3. collate\n", + "4. gloriana\n", + "5. loge\n", + "6. lollipop\n", + "7. mast\n", + "8. milord\n", + "9. prolix\n", + "10. rendezvous\n", + "11. salle\n", + "12. schnabel\n", + "\n", + "Answer: euphony, allegoric, collate, gloriana, loge, lollipop, mast, milord, prolix, rendezvous, salle, schnabelfail
bbh_word_sorting-0063Sort the following words alphabetically: List: vegetate artillery harm fda doris prosody bainite incongruous monkey vivian\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.artillery bainite doris fda harm incongruous monkey prosody vegetate vivianThe given list of words is: vegetate, artillery, harm, fda, doris, prosody, bainite, incongruous, monkey, vivian.\n", + "\n", + "Here is the alphabetical sorting process:\n", + "\n", + "1. **Compare the first letter:**\n", + " * bainite (b)\n", + " * doris (d)\n", + " * fda (f)\n", + " * harm (h)\n", + " * incongruous (i)\n", + " * monkey (m)\n", + " * prosody (p)\n", + " * artillery (a)\n", + " * vegetate (v)\n", + " * vivian (v)\n", + "\n", + "2. **Sort based on the first letter:**\n", + " * **a:** artillery\n", + " * **b:** bainite\n", + " * **d:** doris\n", + " * **f:** fda\n", + " * **h:** harm\n", + " * **i:** incongruous\n", + " * **m:** monkey\n", + " * **p:** prosody\n", + " * **v:** vegetate, vivian\n", + "\n", + "3. **Sort the 'v' words:**\n", + " * vegetate\n", + " * vivian\n", + " * Comparing the second letter: 'e' in vegetate, 'i' in vivian. 'e' comes before 'i'.\n", + " * So, vegetate comes before vivian.\n", + "\n", + "4. **Final sorted order:**\n", + " 1. artillery\n", + " 2. bainite\n", + " 3. doris\n", + " 4. fda\n", + " 5. harm\n", + " 6. incongruous\n", + " 7. monkey\n", + " 8. prosody\n", + " 9. vegetate\n", + " 10. vivian\n", + "\n", + "Answer: artillery, bainite, doris, fda, harm, incongruous, monkey, prosody, vegetate, vivianpass
bbh_word_sorting-0064Sort the following words alphabetically: List: hyperboloid buckle liquidate alkali nagasaki breach falsetto parmesan mirth\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.alkali breach buckle falsetto hyperboloid liquidate mirth nagasaki parmesanThe given list of words is: hyperboloid, buckle, liquidate, alkali, nagasaki, breach, falsetto, parmesan, mirth.\n", + "\n", + "Here is the alphabetical sorting process:\n", + "\n", + "1. **Compare the first letter:**\n", + " * b (buckle, breach)\n", + " * f (falsetto)\n", + " * h (hyperboloid)\n", + " * l (liquidate)\n", + " * m (mirth)\n", + " * n (nagasaki)\n", + " * p (parmesan)\n", + " * a (alkali)\n", + "\n", + " Sorting by the first letter: a, b, f, h, l, m, n, p.\n", + "\n", + "2. **Sort words starting with 'a':**\n", + " * alkali\n", + "\n", + "3. **Sort words starting with 'b':**\n", + " * buckle\n", + " * breach\n", + " * (b**r**each comes before b**u**ckle)\n", + " * breach, buckle\n", + "\n", + "4. **Sort words starting with 'f':**\n", + " * falsetto\n", + "\n", + "5. **Sort words starting with 'h':**\n", + " * hyperboloid\n", + "\n", + "6. **Sort words starting with 'l':**\n", + " * liquidate\n", + "\n", + "7. **Sort words starting with 'm':**\n", + " * mirth\n", + "\n", + "8. **Sort words starting with 'n':**\n", + " * nagasaki\n", + "\n", + "9. **Sort words starting with 'p':**\n", + " * parmesan\n", + "\n", + "**Combining the sorted lists:**\n", + "\n", + "1. alkali\n", + "2. breach\n", + "3. buckle\n", + "4. falsetto\n", + "5. hyperboloid\n", + "6. liquidate\n", + "7. mirth\n", + "8. nagasaki\n", + "9. parmesan\n", + "\n", + "Let's re-verify the order carefully:\n", + "\n", + "* **a**: alkali\n", + "* **b**: breach, buckle (breach before buckle)\n", + "* **f**: falsetto\n", + "* **h**: hyperboloid\n", + "* **l**: liquidate\n", + "* **m**: mirth\n", + "* **n**: nagasaki\n", + "* **p**: parmesan\n", + "\n", + "Final sorted list: alkali, breach, buckle, falsetto, hyperboloid, liquidate, mirth, nagasaki, parmesan.\n", + "\n", + "Answer: alkali, breach, buckle, falsetto, hyperboloid, liquidate, mirth, nagasaki, parmesanpass
bbh_word_sorting-0065Sort the following words alphabetically: List: comet pincer snippet manley injun spokesperson hydrate heusen cocksure\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.cocksure comet heusen hydrate injun manley pincer snippet spokespersonThe given list of words is: comet, pincer, snippet, manley, injun, spokesperson, hydrate, heusen, cocksure.\n", + "\n", + "Here is the alphabetical sorting process:\n", + "\n", + "1. **Compare the first letter:**\n", + " * comet (c)\n", + " * pincer (p)\n", + " * snippet (s)\n", + " * manley (m)\n", + " * injun (i)\n", + " * spokesperson (s)\n", + " * hydrate (h)\n", + " * heusen (h)\n", + " * cocksure (c)\n", + "\n", + " Words starting with 'c': comet, cocksure\n", + " Words starting with 'h': hydrate, heusen\n", + " Words starting with 'i': injun\n", + " Words starting with 'm': manley\n", + " Words starting with 'p': pincer\n", + " Words starting with 's': snippet, spokesperson\n", + "\n", + "2. **Sort words starting with 'c':**\n", + " * comet\n", + " * cocksure\n", + " (comet comes before cocksure)\n", + "\n", + "3. **Sort words starting with 'h':**\n", + " * hydrate\n", + " * heusen\n", + "(hydrate comes before heusen)\n", + "\n", + "4. **Sort words starting with 'i':**\n", + " * injun\n", + "\n", + "5. **Sort words starting with 'm':**\n", + " * manley\n", + "\n", + "6. **Sort words starting with 'p':**\n", + " * pincer\n", + "\n", + "7. **Sort words starting with 's':**\n", + " * snippet\n", + " * spokesperson\n", + "(snippet comes before spokesperson)\n", + "\n", + "8. **Combine the sorted groups:**\n", + " * comet\n", + " * cocksure\n", + " * hydrate\n", + " * heusen\n", + " * injun\n", + " * manley\n", + " * pincer\n", + " * snippet\n", + " * spokesperson\n", + "\n", + "Let's re-check the full alphabetical order:\n", + "C: comet, cocksure\n", + "H: hydrate, heusen\n", + "I: injun\n", + "M: manley\n", + "P: pincer\n", + "S: snippet, spokesperson\n", + "\n", + "Comparing C words: comet vs cocksure. 'o' comes before 'o' (wait, 'm' vs 'o'). comet vs cocksure. 'o' vs 'o'. 'm' vs 'c'. 'm' comes before 'c' in the second letter? No, 'o' vs 'o'.\n", + "comet (co...)\n", + "cocksure (co...)\n", + "comet (co**mfail
bbh_word_sorting-0066Sort the following words alphabetically: List: oakland seaborg jacobi membrane trapezoidal allis marmot toggle anthology\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.allis anthology jacobi marmot membrane oakland seaborg toggle trapezoidalHere are the words to sort alphabetically:\n", + "oakland, seaborg, jacobi, membrane, trapezoidal, allis, marmot, toggle, anthology\n", + "\n", + "Let's sort them letter by letter:\n", + "\n", + "1. **allis** (A)\n", + "2. **anthology** (A) - 'allis' comes before 'anthology' because 'l' comes before 'n'.\n", + "3. **jacobi** (J)\n", + "4. **marmot** (M)\n", + "5. **membrane** (M) - 'marmot' comes before 'membrane' because 'a' comes before 'e'.\n", + "6. **oakland** (O)\n", + "7. **seaborg** (S)\n", + "8. **trapezoidal** (T)\n", + "9. **toggle** (T) - 'trapezoidal' comes before 'toggle' because 'r' comes before 'o'.\n", + "\n", + "Let's re-check the 'M' words:\n", + "marmot\n", + "membrane\n", + "'marmot' vs 'membrane': m-a-r vs m-e-m. 'a' comes before 'e', so marmot comes before membrane.\n", + "\n", + "Let's re-check the 'T' words:\n", + "trapezoidal\n", + "toggle\n", + "'trapezoidal' vs 'toggle': t-r-a vs t-o-g. 'r' comes before 'o', so trapezoidal comes before toggle.\n", + "\n", + "Final sorted order:\n", + "1. allis\n", + "2. anthology\n", + "3. jacobi\n", + "4. marmot\n", + "5. membrane\n", + "6. oakland\n", + "7. seaborg\n", + "8. trapezoidal\n", + "9. toggle\n", + "\n", + "Answer: allis, anthology, jacobi, marmot, membrane, oakland, seaborg, trapezoidal, togglefail
bbh_word_sorting-0067Sort the following words alphabetically: List: cornerstone green brindle prowl nonce ballard scrappy credulous des pitney difluoride toward predilect nostalgic rape mixup horseplay jew curio\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.ballard brindle cornerstone credulous curio des difluoride green horseplay jew mixup nonce nostalgic pitney predilect prowl rape scrappy towardHere are the words sorted alphabetically:\n", + "\n", + "1. ballard\n", + "2. brindle\n", + "3. cornerstone\n", + "4. credulous\n", + "5. curio\n", + "6. des\n", + "7. difluoride\n", + "8. green\n", + "9. horseplay\n", + "10. jew\n", + "11. mixup\n", + "12. nonce\n", + "13. nostalgic\n", + "14. pitney\n", + "15. predilect\n", + "16. prowl\n", + "17. rape\n", + "18. scrappy\n", + "19. toward\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "Original list: cornerstone, green, brindle, prowl, nonce, ballard, scrappy, credulous, des, pitney, difluoride, toward, predilect, nostalgic, rape, mixup, horseplay, jew, curio\n", + "\n", + "Sorted list:\n", + "1. ballard\n", + "2. brindle\n", + "3. cornerstone\n", + "4. credulous\n", + "5. curio\n", + "6. des\n", + "7. difluoride\n", + "8. green\n", + "9. horseplay\n", + "10. jew\n", + "11. mixup\n", + "12. nonce\n", + "13. nostalgic\n", + "14. pitney\n", + "15. predilect\n", + "16. prowl\n", + "17. rape\n", + "18. scrappy\n", + "19. toward\n", + "\n", + "Answer: ballard, brindle, cornerstone, credulous, curio, des, difluoride, green, horseplay, jew, mixup, nonce, nostalgic, pitney, predilect, prowl, rape, scrappy, towardpass
bbh_word_sorting-0068Sort the following words alphabetically: List: strove hendrickson apostasy jacm indonesia vibrate afloat lanthanide grosbeak scavenge quark chattel conner bechtel melancholy ferment\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.afloat apostasy bechtel chattel conner ferment grosbeak hendrickson indonesia jacm lanthanide melancholy quark scavenge strove vibrateHere are the words to sort alphabetically:\n", + "strove, hendrickson, apostasy, jacm, indonesia, vibrate, afloat, lanthanide, grosbeak, scavenge, chattel, conner, bechtel, melancholy, ferment\n", + "\n", + "Let's sort them:\n", + "\n", + "1. **afloat**\n", + "2. **apostasy**\n", + "3. **bechtel**\n", + "4. **chattel**\n", + "5. **conner**\n", + "6. **ferment**\n", + "7. **grosbeak**\n", + "8. **hendrickson**\n", + "9. **indonesia**\n", + "10. **jacm**\n", + "11. **lanthanide**\n", + "12. **melancholy**\n", + "13. **scavenge**\n", + "14. **strove**\n", + "15. **vibrate**\n", + "\n", + "Let's re-check the sorting, especially for words starting with similar letters:\n", + "\n", + "* **a**: afloat, apostasy\n", + "* **b**: bechtel\n", + "* **c**: chattel, conner\n", + "* **f**: ferment\n", + "* **g**: grosbeak\n", + "* **h**: hendrickson\n", + "* **i**: indonesia\n", + "* **j**: jacm\n", + "* **l**: lanthanide\n", + "* **m**: melancholy\n", + "* **s**: scavenge, strove\n", + "* **v**: vibrate\n", + "\n", + "Detailed comparison:\n", + "* afloat (a-f)\n", + "* apostasy (a-p) $\\rightarrow$ afloat comes before apostasy.\n", + "* bechtel (b)\n", + "* chattel (c-h)\n", + "* conner (c-o) $\\rightarrow$ chattel comes before conner.\n", + "* ferment (f)\n", + "* grosbeak (g)\n", + "* hendrickson (h)\n", + "* indonesia (i)\n", + "* jacm (j)\n", + "* lanthanide (l)\n", + "* melancholy (m)\n", + "* scavenge (s-c)\n", + "* strove (s-t) $\\rightarrow$ scavenge comes before strove.\n", + "* vibrate (v)\n", + "\n", + "Corrected order:\n", + "1. afloat\n", + "2. apostasy\n", + "3. bechtel\n", + "4. chattel\n", + "5. conner\n", + "6. ferment\n", + "7. grosbeak\n", + "8. hendrickson\n", + "9. indonesia\n", + "10. jacm\n", + "11. lanthanide\n", + "12. melancholy\n", + "13. scavenge\n", + "14. strove\n", + "15. vibrate\n", + "\n", + "Answer: afloatfail
bbh_word_sorting-0069Sort the following words alphabetically: List: quench autocracy lodge exploitation custody ventricle annuity bruno thunderclap militant somatic dare acquisitive\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.acquisitive annuity autocracy bruno custody dare exploitation lodge militant quench somatic thunderclap ventricleHere are the words sorted alphabetically:\n", + "\n", + "1. acquisitive\n", + "2. annuity\n", + "3. autocracy\n", + "4. bruno\n", + "5. custody\n", + "6. dare\n", + "7. exploitation\n", + "8. lodge\n", + "9. militant\n", + "10. quench\n", + "11. somatic\n", + "12. thunderclap\n", + "13. ventricle\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **a**cquisitive, **a**nnuity, **a**utocracy\n", + "* **b**runo\n", + "* **c**ustody\n", + "* **d**are\n", + "* **e**xploitation (Wait, 'e' comes before 'l', 'm', 'q', 's', 't', 'v')\n", + "* **l**odge\n", + "* **m**ilitant\n", + "* **q**uench\n", + "* **s**omatic\n", + "* **t**hunderclap\n", + "* **v**entricle\n", + "\n", + "Let's list them and compare:\n", + "1. acquisitive\n", + "2. annuity\n", + "3. autocracy\n", + "4. bruno\n", + "5. custody\n", + "6. dare\n", + "7. exploitation\n", + "8. lodge\n", + "9. militant\n", + "10. quench\n", + "11. somatic\n", + "12. thunderclap\n", + "13. ventricle\n", + "\n", + "Checking the order:\n", + "acquisitive (a-c)\n", + "annuity (a-n)\n", + "autocracy (a-u)\n", + "bruno (b)\n", + "custody (c-u)\n", + "dare (d)\n", + "exploitation (e)\n", + "lodge (l)\n", + "militant (m)\n", + "quench (q)\n", + "somatic (s)\n", + "thunderclap (t)\n", + "ventricle (v)\n", + "\n", + "The correct alphabetical order is:\n", + "1. acquisitive\n", + "2. annuity\n", + "3. autocracy\n", + "4. bruno\n", + "5. custody\n", + "6. dare\n", + "7. exploitation\n", + "8. lodge\n", + "9. militant\n", + "10. quench\n", + "11. somatic\n", + "12. thunderclap\n", + "13. ventricle\n", + "\n", + "Answer: acquisitive, annuity, autocracy, bruno, custody, dare, exploitation, lodge, militant, quench, somatic, thunderclap, ventriclepass
bbh_word_sorting-0070Sort the following words alphabetically: List: reedy bully multiple edt sell referring lexicographer tit flogging perceptive minor bosporus rancorous forfeit pizza pungent sedition cork\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.bosporus bully cork edt flogging forfeit lexicographer minor multiple perceptive pizza pungent rancorous reedy referring sedition sell titHere are the words to sort alphabetically:\n", + "reedy, bully, multiple, edt, sell, referring, lexicographer, tit, flogging, perceptive, minor, bosporus, rancorous, forfeit, pizza, pungent, sedition, cork\n", + "\n", + "Let's sort them:\n", + "\n", + "1. **bosporus**\n", + "2. **bully**\n", + "3. **cork**\n", + "4. **edt**\n", + "5. **forfeit**\n", + "6. **flogging**\n", + "7. **lexicographer**\n", + "8. **multiple**\n", + "9. **minor**\n", + "10. **perceptive**\n", + "11. **pizza**\n", + "12. **pungent**\n", + "13. **rancorous**\n", + "14. **reedy**\n", + "15. **referring**\n", + "16. **sell**\n", + "17. **sedition**\n", + "18. **tit**\n", + "\n", + "Let's re-check the sorting, especially for words starting with similar letters:\n", + "\n", + "* **b**: bully, bosporus\n", + " * bosporus (bo...)\n", + " * bully (bu...) $\\rightarrow$ bosporus, bully\n", + "* **c**: cork\n", + "* **e**: edt\n", + "* **f**: forfeit, flogging\n", + " * forfeit (for...)\n", + " * flogging (flo...) $\\rightarrow$ flogging, forfeit\n", + "* **l**: lexicographer\n", + "* **m**: multiple, minor\n", + " * multiple (mul...)\n", + " * minor (min...) $\\rightarrow$ minor, multiple\n", + "* **p**: perceptive, pizza, pungent\n", + " * perceptive (per...)\n", + " * pizza (pi...)\n", + " * pungent (pun...) $\\rightarrow$ perceptive, pizza, pungent\n", + "* **r**: reedy, referring, rancorous\n", + " * reedy (ree...)\n", + " * referring (ref...)\n", + " * rancorous (ran...) $\\rightarrow$ rancorous, reedy, referring\n", + "* **s**: sell, sedition\n", + " * sell (sel...)\n", + " * sedition (sed...) $\\rightarrow$ sell, sedition\n", + "* **t**: tit\n", + "\n", + "Let's perform a strict alphabetical sort:\n", + "\n", + "1. bosporus (B)\n", + "2. bully (B)\n", + "3. cork (C)\n", + "4. edt (E)\n", + "5. flogging (F)\n", + "6. forfeit (F)\n", + "7. lexicographer (L)\n", + "8. minor (M)\n", + "fail
bbh_word_sorting-0071Sort the following words alphabetically: List: behold oxalic maybe hew steel termcap pray stiffen dissipate misogyny format dew\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.behold dew dissipate format hew maybe misogyny oxalic pray steel stiffen termcapThe given list of words is: behold, oxalic, maybe, hew, steel, termcap, pray, stiffen, dissipate, misogyny, format, dew.\n", + "\n", + "Here is the alphabetical sorting process:\n", + "\n", + "1. **behold**\n", + "2. **dew**\n", + "3. **dissipate**\n", + "4. **format**\n", + "5. **hew**\n", + "6. **maybe**\n", + "7. **misogyny**\n", + "8. **oxalic**\n", + "9. **pray**\n", + "10. **steel**\n", + "11. **stiffen**\n", + "12. **termcap**\n", + "\n", + "Let's re-sort carefully:\n", + "\n", + "* **b**: behold\n", + "* **d**: dew, dissipate\n", + " * dew comes before dissipate\n", + "* **f**: format\n", + "* **h**: hew\n", + "* **m**: maybe, misogyny\n", + " * maybe comes before misogyny\n", + "* **o**: oxalic\n", + "* **p**: pray\n", + "* **s**: steel, stiffen\n", + " * steel comes before stiffen\n", + "* **t**: termcap\n", + "\n", + "Let's re-evaluate the order based on the first letter:\n", + "B: behold\n", + "D: dew, dissipate\n", + "F: format\n", + "H: hew\n", + "M: maybe, misogyny\n", + "O: oxalic\n", + "P: pray\n", + "S: steel, stiffen\n", + "T: termcap\n", + "\n", + "Sorting within groups:\n", + "D: dew, dissipate (dew before dissipate)\n", + "M: maybe, misogyny (maybe before misogyny)\n", + "S: steel, stiffen (steel before stiffen)\n", + "\n", + "Final sorted list:\n", + "1. behold\n", + "2. dew\n", + "3. dissipate\n", + "4. format\n", + "5. hew\n", + "6. maybe\n", + "7. misogyny\n", + "8. oxalic\n", + "9. pray\n", + "10. steel\n", + "11. stiffen\n", + "12. termcap\n", + "\n", + "Answer: behold, dew, dissipate, format, hew, maybe, misogyny, oxalic, pray, steel, stiffen, termcappass
bbh_word_sorting-0072Sort the following words alphabetically: List: impede appliance pulitzer superior\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.appliance impede pulitzer superiorThe given list of words is: impede, appliance, pulitzer, superior.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter:\n", + "\n", + "1. **impede** (starts with 'i')\n", + "2. **appliance** (starts with 'a')\n", + "3. **pulitzer** (starts with 'p')\n", + "4. **superior** (starts with 's')\n", + "\n", + "Comparing the first letters: 'a', 'i', 'p', 's'.\n", + "Alphabetical order of the first letters: 'a', 'i', 'p', 's'.\n", + "\n", + "1. **appliance** (starts with 'a')\n", + "2. **impede** (starts with 'i')\n", + "3. **pulitzer** (starts with 'p')\n", + "4. **superior** (starts with 's')\n", + "\n", + "The sorted list is: appliance, impede, pulitzer, superior.\n", + "\n", + "Answer: appliance, impede, pulitzer, superior\n", + "Answer: appliance, impede, pulitzer, superiorpass
bbh_word_sorting-0073Sort the following words alphabetically: List: plural dose allstate stalin dyad multitudinous powderpuff\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.allstate dose dyad multitudinous plural powderpuff stalinThe given list of words is: plural, dose, allstate, stalin, dyad, multitudinous, powderpuff.\n", + "\n", + "Here is the alphabetical sorting process:\n", + "\n", + "1. **Compare the first letter:**\n", + " * plural (p)\n", + " * dose (d)\n", + " * allstate (a)\n", + " * stalin (s)\n", + " * dyad (d)\n", + " * multitudinous (m)\n", + " * powderpuff (p)\n", + "\n", + " Words starting with 'a': allstate\n", + " Words starting with 'd': dose, dyad\n", + " Words starting with 'm': multitudinous\n", + " Words starting with 'p': plural, powderpuff\n", + " Words starting with 's': stalin\n", + "\n", + "2. **Sort words starting with 'd':**\n", + " * dose\n", + " * dyad\n", + " (dose comes before dyad)\n", + "\n", + "3. **Sort words starting with 'p':**\n", + " * plural\n", + " * powderpuff\n", + " (plural comes before powderpuff)\n", + "\n", + "4. **Assemble the sorted list based on the first letter order (A, D, M, P, S):**\n", + " * allstate (A)\n", + " * dose (D)\n", + " * dyad (D)\n", + " * multitudinous (M)\n", + " * plural (P)\n", + " * powderpuff (P)\n", + " * stalin (S)\n", + "\n", + "5. **Refine the order within groups:**\n", + " * D group: dose, dyad (dose before dyad)\n", + " * P group: plural, powderpuff (plural before powderpuff)\n", + "\n", + "The final alphabetical order is: allstate, dose, dyad, multitudinous, plural, powderpuff, stalin.\n", + "\n", + "Answer: allstate, dose, dyad, multitudinous, plural, powderpuff, stalinpass
bbh_word_sorting-0074Sort the following words alphabetically: List: maraud downtrodden hurst propane morphine parsonage gadgetry inertial gamin\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.downtrodden gadgetry gamin hurst inertial maraud morphine parsonage propaneHere are the words to sort: maraud, downtrodden, hurst, propane, morphine, parsonage, gadgetry, inertial, gamin\n", + "\n", + "Let's sort them alphabetically:\n", + "\n", + "1. **gadgetry** (g)\n", + "2. **gamin** (g)\n", + "3. **hurst** (h)\n", + "4. **inertial** (i)\n", + "5. **maraud** (m)\n", + "6. **morphine** (m)\n", + "7. **parsonage** (p)\n", + "8. **propane** (p)\n", + "9. **downtrodden** (d)\n", + "\n", + "Let's re-sort carefully:\n", + "\n", + "* **d**owntrodden\n", + "* **g**adgetry\n", + "* **g**amin\n", + "* **h**urst\n", + "* **i**nertial\n", + "* **m**araud\n", + "* **m**orphine\n", + "* **p**arsonage\n", + "* **p**ropane\n", + "\n", + "Comparing 'gadgetry' and 'gamin':\n", + "gadgetry (g-a-d...)\n", + "gamin (g-a-m...)\n", + "'d' comes before 'm', so gadgetry comes before gamin.\n", + "\n", + "Comparing 'maraud' and 'morphine':\n", + "maraud (m-a-r...)\n", + "morphine (m-o-r...)\n", + "'a' comes before 'o', so maraud comes before morphine.\n", + "\n", + "Comparing 'parsonage' and 'propane':\n", + "parsonage (p-a-r-s...)\n", + "propane (p-r-o-p...)\n", + "'a' comes before 'r', so parsonage comes before propane.\n", + "\n", + "The correct alphabetical order is:\n", + "1. downtrodden\n", + "2. gadgetry\n", + "3. gamin\n", + "4. hurst\n", + "5. inertial\n", + "6. maraud\n", + "7. morphine\n", + "8. parsonage\n", + "9. propane\n", + "\n", + "Answer: downtrodden, gadgetry, gamin, hurst, inertial, maraud, morphine, parsonage, propanepass
bbh_word_sorting-0075Sort the following words alphabetically: List: confrontation quantitative proserpine proofread daddy hirsute\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.confrontation daddy hirsute proofread proserpine quantitativeThe given list of words is: confrontation, quantitative, proserpine, proofread, daddy, hirsute.\n", + "\n", + "To sort them alphabetically, we compare them letter by letter:\n", + "\n", + "1. **confrontation** (C)\n", + "2. **quantitative** (Q)\n", + "3. **proserpine** (P)\n", + "4. **proofread** (P)\n", + "5. **daddy** (D)\n", + "6. **hirsute** (H)\n", + "\n", + "Let's sort them based on the first letter:\n", + "D: daddy\n", + "C: confrontation\n", + "H: hirsute\n", + "P: proserpine, proofread\n", + "Q: quantitative\n", + "\n", + "Now, let's sort the words starting with 'P':\n", + "proserpine\n", + "proofread\n", + "Comparing 'proserpine' and 'proofread':\n", + "proserpine (p r o s e r p i n e)\n", + "proofread (p r o o f r e a d)\n", + "The third letter: 'o' vs 'o'.\n", + "The fourth letter: 's' vs 'o'. Since 'o' comes before 's', 'proofread' comes before 'proserpine'.\n", + "Wait, let's recheck:\n", + "p r o **s** e r p i n e\n", + "p r o **o** f r e a d\n", + "'o' comes before 's'. So, 'proofread' comes before 'proserpine'.\n", + "\n", + "Correct order for P words: proofread, proserpine.\n", + "\n", + "The complete alphabetical order is:\n", + "1. daddy (D)\n", + "2. confrontation (C) - Wait, C comes before D. Let's re-sort by the first letter:\n", + "\n", + "D: daddy\n", + "C: confrontation\n", + "H: hirsute\n", + "P: proofread, proserpine\n", + "Q: quantitative\n", + "\n", + "Alphabetical order of first letters: C, D, H, P, Q.\n", + "\n", + "1. **confrontation** (C)\n", + "2. **daddy** (D)\n", + "3. **hirsute** (H)\n", + "4. **proofread** (P)\n", + "5. **proserpine** (P)\n", + "6. **quantitative** (Q)\n", + "\n", + "Now, sorting the P words:\n", + "proofread vs proserpine\n", + "p r o **o** f r e a d\n", + "p r o **s** e r p i n e\n", + "'o' comes before 's', so proofread comesfail
bbh_word_sorting-0076Sort the following words alphabetically: List: whisper eyesight titmice mirage fletch cry biennial creedal miltonic fraudulent j\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.biennial creedal cry eyesight fletch fraudulent j miltonic mirage titmice whisperThe given list of words is: whisper, eyesight, titmice, mirage, fletch, cry, biennial, creedal, miltonic, fraudulent, j.\n", + "\n", + "Here is the alphabetical sorting process:\n", + "\n", + "1. **Compare the first letter:**\n", + " * fletch (f)\n", + " * fraudulent (f)\n", + " * j (j)\n", + " * miltonic (m)\n", + " * mirage (m)\n", + " * creedal (c)\n", + " * biennial (b)\n", + " * cry (c)\n", + " * whisper (w)\n", + " * eyesight (e)\n", + " * titmice (t)\n", + "\n", + "2. **Sort by the first letter:**\n", + " * **b:** biennial\n", + " * **c:** creedal, cry\n", + " * **e:** eyesight\n", + " * **f:** fletch, fraudulent\n", + " * **j:** j\n", + " * **m:** miltonic, mirage\n", + " * **t:** titmice\n", + " * **w:** whisper\n", + "\n", + "3. **Sort within groups:**\n", + "\n", + " * **c:** creedal, cry (creedal comes before cry)\n", + " * **f:** fletch, fraudulent (fletch comes before fraudulent)\n", + " * **m:** miltonic, mirage (miltonic comes before mirage)\n", + "\n", + "4. **Final ordered list:**\n", + " * biennial (b)\n", + " * creedal (c)\n", + " * cry (c)\n", + " * eyesight (e)\n", + " * fletch (f)\n", + " * fraudulent (f)\n", + " * j (j)\n", + " * miltonic (m)\n", + " * mirage (m)\n", + " * titmice (t)\n", + " * whisper (w)\n", + "\n", + "Let's re-verify the order based on standard alphabetical rules:\n", + "\n", + "1. biennial (b)\n", + "2. creedal (c)\n", + "3. cry (c)\n", + "4. eyesight (e)\n", + "5. fletch (f)\n", + "6. fraudulent (f)\n", + "7. j (j)\n", + "8. miltonic (m)\n", + "9. mirage (m)\n", + "10. titmice (t)\n", + "11. whisper (w)\n", + "\n", + "(Note on 'j': In alphabetical sorting, 'j' comes after 'f' and before 'm'.)\n", + "\n", + "The correct alphabetical order is: biennial, creedal, cry, eyesight, fletch,fail
bbh_word_sorting-0077Sort the following words alphabetically: List: mater rica sigmund dean eosine inebriate gibson vassar mulligatawny formula\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.dean eosine formula gibson inebriate mater mulligatawny rica sigmund vassarHere are the words sorted alphabetically:\n", + "\n", + "1. dean\n", + "2. eosine\n", + "3. formula\n", + "4. gibson\n", + "5. inebriate\n", + "6. mater\n", + "7. mulligatawny\n", + "8. rica\n", + "9. sigmund\n", + "10. vassar\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **d**ean\n", + "* **e**osine\n", + "* **f**ormula\n", + "* **g**ibson\n", + "* **i**nebriate\n", + "* **m**ater\n", + "* **m**ulligatawny (mulligatawny comes after mater because 'u' comes after 'a')\n", + "* **r**ica\n", + "* **s**igmund\n", + "* **v**assar\n", + "\n", + "Let's check the 'm' words again: mater, mulligatawny.\n", + "mater\n", + "mulligatawny (mulligatawny comes after mater)\n", + "\n", + "Correct alphabetical order:\n", + "1. dean\n", + "2. eosine\n", + "3. formula\n", + "4. gibson\n", + "5. inebriate\n", + "6. mater\n", + "7. mulligatawny\n", + "8. rica\n", + "9. sigmund\n", + "10. vassar\n", + "\n", + "Answer: dean, eosine, formula, gibson, inebriate, mater, mulligatawny, rica, sigmund, vassarpass
bbh_word_sorting-0078Sort the following words alphabetically: List: cast palazzi inhibit arragon tabletop danbury abbe adposition emplace gavin income falsetto onerous\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.abbe adposition arragon cast danbury emplace falsetto gavin income inhibit onerous palazzi tabletopHere are the words to sort alphabetically:\n", + "cast, palazzi, inhibit, arragon, tabletop, danbury, abbe, adposition, emplace, gavin, income, falsetto, onerous\n", + "\n", + "Let's sort them:\n", + "\n", + "1. **abbe**\n", + "2. **adposition**\n", + "3. **arragon**\n", + "4. **cast**\n", + "5. **danbury**\n", + "6. **emplace**\n", + "7. **falsetto**\n", + "8. **gavin**\n", + "9. **inhibit**\n", + "10. **income**\n", + "11. ** onerous**\n", + "12. **palazzi**\n", + "13. **tabletop**\n", + "\n", + "Let's re-sort carefully, focusing on the first letter and subsequent letters:\n", + "\n", + "* **a**: abbe, adposition, arragon\n", + " * abbe\n", + " * adposition (a-d)\n", + " * arragon (a-r)\n", + "* **c**: cast\n", + "* **d**: danbury\n", + "* **e**: emplace\n", + "* **f**: falsetto\n", + "* **g**: gavin\n", + "* **i**: inhibit, income\n", + " * inhibit (i-n-h)\n", + " * income (i-n-c) $\\rightarrow$ inhibit comes before income\n", + "* **o**: onerous\n", + "* **p**: palazzi\n", + "* **t**: tabletop\n", + "\n", + "Let's re-evaluate the 'a' words:\n", + "1. abbe\n", + "2. adposition\n", + "3. arragon\n", + "\n", + "Let's check the order of the remaining words: cast, danbury, emplace, falsetto, gavin, inhibit, income, onerous, palazzi, tabletop.\n", + "\n", + "1. abbe\n", + "2. adposition\n", + "3. arragon\n", + "4. cast\n", + "5. danbury\n", + "6. emplace\n", + "7. falsetto\n", + "8. gavin\n", + "9. inhibit\n", + "10. income\n", + "11. onerous\n", + "12. palazzi\n", + "13. tabletop\n", + "\n", + "Final check:\n", + "a-b-b-e\n", + "a-d-p-o-s-i-t-i-o-n\n", + "a-r-r-a-g-o-n\n", + "c-a-s-t\n", + "d-a-n-b-u-r-y\n", + "e-m-p-l-a-c-e\n", + "f-a-l-s-efail
bbh_word_sorting-0079Sort the following words alphabetically: List: twelvefold o'connell mainstream malformed umbilical bivalve vinegar mortify sleuth paunchy\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.bivalve mainstream malformed mortify o'connell paunchy sleuth twelvefold umbilical vinegarHere are the words sorted alphabetically:\n", + "\n", + "1. bivalve\n", + "2. formaldehyde (Wait, the list has "malformed", not "formaldehyde". Let's stick to the provided list.)\n", + "3. mortify\n", + "4. mainstream\n", + "5. malformed\n", + "6. o'connell\n", + "7. paunchy\n", + "8. sleuth\n", + "9. twelvefold\n", + "10. umbilical\n", + "11. vinegar\n", + "\n", + "Let's re-sort the provided list: twelvefold, o'connell, mainstream, malformed, umbilical, bivalve, vinegar, mortify, sleuth, paunchy.\n", + "\n", + "1. bivalve\n", + "2. mainstream\n", + "3. malformed\n", + "4. mortify\n", + "5. o'connell\n", + "6. paunchy\n", + "7. sleuth\n", + "8. twelvefold\n", + "9. umbilical\n", + "10. vinegar\n", + "\n", + "Let's check the sorting carefully:\n", + "B: bivalve\n", + "M: mainstream, malformed, mortify\n", + "O: o'connell\n", + "P: paunchy\n", + "S: sleuth\n", + "T: twelvefold\n", + "U: umbilical\n", + "V: vinegar\n", + "\n", + "Comparing M words: mainstream, malformed, mortify\n", + "* mainstream\n", + "* malformed (m-a-l)\n", + "* mortify (m-o-r)\n", + "So: mainstream, malformed, mortify\n", + "\n", + "Final sorted list:\n", + "1. bivalve\n", + "2. mainstream\n", + "3. malformed\n", + "4. mortify\n", + "5. o'connell\n", + "6. paunchy\n", + "7. sleuth\n", + "8. twelvefold\n", + "9. umbilical\n", + "10. vinegar\n", + "\n", + "Answer: bivalve, mainstream, malformed, mortify, o'connell, paunchy, sleuth, twelvefold, umbilical, vinegarpass
bbh_word_sorting-0080Sort the following words alphabetically: List: rowley midshipman saline furl geld locomote exhumation anarchic bstj gradual riddance j liniment pantheist elution profess\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.anarchic bstj elution exhumation furl geld gradual j liniment locomote midshipman pantheist profess riddance rowley salineHere are the words to sort alphabetically:\n", + "rowley, midshipman, saline, furl, geld, locomote, exhumation, anarchic, bstj, gradual, riddance, j, liniment, pantheist, elution, profess\n", + "\n", + "Let's sort them:\n", + "\n", + "1. **anarchic**\n", + "2. **bstj**\n", + "3. **elution**\n", + "4. **furl**\n", + "5. **geld**\n", + "6. **gradual**\n", + "7. **j**\n", + "8. **locomote**\n", + "9. **liniment**\n", + "10. **midshipman**\n", + "11. **pantheist**\n", + "12. **profess**\n", + "13. **riddance**\n", + "14. **rowley**\n", + "15. **saline**\n", + "16. **exhumation**\n", + "\n", + "Let's re-check the sorting, especially for words starting with similar letters:\n", + "\n", + "* **a**: anarchic\n", + "* **b**: bstj\n", + "* **e**: elution, exhumation\n", + " * elution comes before exhumation (l before x)\n", + "* **f**: furl\n", + "* **g**: geld, gradual\n", + " * geld comes before gradual (e before r)\n", + "* **j**: j\n", + "* **l**: locomote, liniment\n", + " * locomote comes before liniment (o before i is false, l comes before o) - Wait, L comes before M.\n", + " * locomote\n", + " * liniment\n", + "* **m**: midshipman\n", + "* **p**: pantheist, profess\n", + " * pantheist comes before profess (a before r)\n", + "* **r**: riddance, rowley\n", + " * riddance comes before rowley (i before o)\n", + "* **s**: saline\n", + "* **(remaining)**\n", + "\n", + "Let's perform a strict alphabetical sort:\n", + "\n", + "1. anarchic\n", + "2. bstj\n", + "3. elution\n", + "4. exhumation\n", + "5. furl\n", + "6. geld\n", + "7. gradual\n", + "8. j\n", + "9. liniment\n", + "10. locomote\n", + "11. midshipman\n", + "12. pantheist\n", + "13. profess\n", + "14. riddance\n", + "15. rowley\n", + "16. saline\n", + "\n", + "Let's re-verify the L words: liniment, locomote. L-I vs L-O. I comes before Ofail
bbh_word_sorting-0081Sort the following words alphabetically: List: ulcerate ballroom sparse intrepid arcing prorogue dodge benefit coupon ladle buggy shagging bassoon hypothermia decide affirmative airframe junior nineveh schmitt\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.affirmative airframe arcing ballroom bassoon benefit buggy coupon decide dodge hypothermia intrepid junior ladle nineveh prorogue schmitt shagging sparse ulcerateHere are the words sorted alphabetically:\n", + "\n", + "1. airframe\n", + "2. arcing\n", + "3. affirmative\n", + "4. ballroom\n", + "5. benefit\n", + "6. buggy\n", + "7. bassoon\n", + "8. decide\n", + "9. dodge\n", + "10. hyphenmia (hypothermia)\n", + "11. intrepid\n", + "12. junior\n", + "13. ladle\n", + "14. nineveh\n", + "15. prorogue\n", + "16. schmitt\n", + "17. shagging\n", + "18. sparse\n", + "19. ulcerate\n", + "20. coupon\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **a**: airframe, arcing, affirmative\n", + "* **b**: ballroom, benefit, buggy, bassoon\n", + "* **c**: coupon\n", + "* **d**: decide, dodge\n", + "* **h**: hyphenmia (hypothermia), shagging\n", + "* **i**: intrepid\n", + "* **j**: junior\n", + "* **l**: ladle\n", + "* **n**: nineveh\n", + "* **p**: prorogue\n", + "* **s**: schmitt, sparse\n", + "* **u**: ulcerate\n", + "\n", + "**Detailed Alphabetical Sort:**\n", + "\n", + "1. airframe\n", + "2. arcing\n", + "3. affirmative\n", + "4. ballroom\n", + "5. benefit\n", + "6. buggy\n", + "7. bassoon\n", + "8. coupon\n", + "9. decide\n", + "10. dodge\n", + "11. hypothermia (or hyphenmia, assuming 'hypothermia' is the intended word)\n", + "12. intrepid\n", + "13. junior\n", + "14. ladle\n", + "15. nineveh\n", + "16. prorogue\n", + "17. schmitt\n", + "18. shagging\n", + "19. sparse\n", + "20. ulcerate\n", + "\n", + "(Note: I treated "hypothermia" as the intended word from "hypothermia" and sorted it under H. If "hyphenmia" was intended, it would be placed before "hypothermia".)\n", + "\n", + "Answer: airframe, arcing, affirmative, ballroom, benefit, buggy, bassoon, coupon, decide, dodge, hypothermia, intrepid, junior, ladle, nineveh, prorogue, schmitt, shagging, sparse, ulceratefail
bbh_word_sorting-0082Sort the following words alphabetically: List: chrysalis wallaby\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.chrysalis wallabyThe given words are: chrysalis, wallaby.\n", + "\n", + "To sort them alphabetically, we compare them letter by letter from the beginning.\n", + "\n", + "1. **chrysalis** starts with 'c'.\n", + "2. **wallaby** starts with 'w'.\n", + "\n", + "Since 'c' comes before 'w' in the alphabet, "chrysalis" comes before "wallaby".\n", + "\n", + "The alphabetical order is: chrysalis, wallaby.\n", + "\n", + "Answer: chrysalis, wallabypass
bbh_word_sorting-0083Sort the following words alphabetically: List: yang relinquish merry maladjust olefin canaan ranch batavia\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.batavia canaan maladjust merry olefin ranch relinquish yangThe given list of words is: yang, relinquish, merry, maladjust, olefin, canaan, ranch, batavia.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter:\n", + "\n", + "1. **batavia** (Starts with 'b')\n", + "2. **canaan** (Starts with 'c')\n", + "3. **maladjust** (Starts with 'm')\n", + "4. **merry** (Starts with 'm') - Compare 'maladjust' and 'merry'. 'a' comes before 'e', so 'maladjust' comes before 'merry'.\n", + "5. **olefin** (Starts with 'o')\n", + "6. **ranch** (Starts with 'r')\n", + "7. **relinquish** (Starts with 'r') - Compare 'ranch' and 'relinquish'. 'a' comes before 'e', so 'ranch' comes before 'relinquish'.\n", + "8. **yang** (Starts with 'y')\n", + "\n", + "Let's re-sort carefully:\n", + "\n", + "Words starting with 'b':\n", + "* batavia\n", + "\n", + "Words starting with 'c':\n", + "* canaan\n", + "\n", + "Words starting with 'm':\n", + "* maladjust\n", + "* merry\n", + " * Comparing 'maladjust' and 'merry': m-a vs m-e. 'a' comes before 'e'. So, maladjust, merry.\n", + "\n", + "Words starting with 'o':\n", + "* olefin\n", + "\n", + "Words starting with 'r':\n", + "* ranch\n", + "* relinquish\n", + " * Comparing 'ranch' and 'relinquish': r-a vs r-e. 'a' comes before 'e'. So, ranch, relinquish.\n", + "\n", + "Words starting with 'y':\n", + "* yang\n", + "\n", + "The sorted order is:\n", + "1. batavia\n", + "2. canaan\n", + "3. maladjust\n", + "4. merry\n", + "5. olefin\n", + "6. ranch\n", + "7. relinquish\n", + "8. yang\n", + "\n", + "Answer: batavia, canaan, maladjust, merry, olefin, ranch, relinquish, yang\n", + "\n", + "Answer: batavia, canaan, maladjust, merry, olefin, ranch, relinquish, yangpass
bbh_word_sorting-0084Sort the following words alphabetically: List: bruckner admiration technion bandgap heron postprocessor maidenhair fungus allusion gambit accessory proscenium cruise\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.accessory admiration allusion bandgap bruckner cruise fungus gambit heron maidenhair postprocessor proscenium technionHere are the words sorted alphabetically:\n", + "\n", + "1. admiration\n", + "2. accessory\n", + "3. allusion\n", + "4. bandgap\n", + "5. bruckner\n", + "6. cruise\n", + "7. fungus\n", + "8. gambit\n", + "9. heron\n", + "10. maidenhair\n", + "11. postprocessor\n", + "12. proscenium\n", + "13. technion\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **a**dmiration\n", + "* **a**ccessory\n", + "* **a**llusion\n", + "* **b**andgap\n", + "* **b**ruckner\n", + "* **c**ruise\n", + "* **f**ungus\n", + "* **g**ambit\n", + "* **h**eron\n", + "* **m**aidenhair\n", + "* **p**ostprocessor\n", + "* **p**roscenium\n", + "* **t**echnion\n", + "\n", + "Comparing 'admiration' and 'accessory': 'admiration' comes before 'accessory'.\n", + "Comparing 'accessory' and 'allusion': 'accessory' comes before 'allusion'. (Wait, 'a'llusion vs 'a'ccessory. 'll' vs 'cc'. 'll' comes before 'cc' in standard alphabetical order if we look at the third letter, but let's compare them directly: a-l-l-u-s-i-o-n vs a-c-c-e-s-s-o-r. 'c' comes before 'l', so 'accessory' comes before 'allusion'.)\n", + "\n", + "Let's re-sort the 'a' words:\n", + "1. admiration\n", + "2. accessory\n", + "3. allusion\n", + "\n", + "Let's re-sort the rest:\n", + "* bandgap\n", + "* bruckner\n", + "* cruise\n", + "* fungus\n", + "* gambit\n", + "* heron\n", + "* maidenhair\n", + "* postprocessor\n", + "* proscenium\n", + "* technion\n", + "\n", + "Final sorted list:\n", + "1. admiration\n", + "2. accessory\n", + "3. allusion\n", + "4. bandgap\n", + "5. bruckner\n", + "6. cruise\n", + "7. fungus\n", + "8. gambit\n", + "9. heron\n", + "10. maidenhair\n", + "11. postprocessor\n", + "12. proscenium\n", + "13. technion\n", + "\n", + "Answer: admiration, accessory, allusion, bandgap, bruckner, cruise, fungus, gambit, heron, maidenhair, postprocessor, proscenium, technionfail
bbh_word_sorting-0085Sort the following words alphabetically: List: underclassmen pegging dale bartok goat martinson whoop badinage brunswick fraternity allele dar yourselves dun anthropocentric banish starkey morphemic monomer desolater\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.allele anthropocentric badinage banish bartok brunswick dale dar desolater dun fraternity goat martinson monomer morphemic pegging starkey underclassmen whoop yourselvesHere are the words sorted alphabetically:\n", + "\n", + "1. allele\n", + "2. anthropocentric\n", + "3. banish\n", + "4. bartok\n", + "5. badinage\n", + "6. brunswick\n", + "7. desolater\n", + "8. dale\n", + "9. dar\n", + "10. fraternity\n", + "11. goat\n", + "12. martinson\n", + "13. morphemic\n", + "14. monomer\n", + "15. pegging\n", + "16. starkey\n", + "17. yourselves\n", + "18. underclassmen\n", + "19. whoop\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **a**llele\n", + "* **a**nthropocentric\n", + "* **b**adinage\n", + "* **b**artok\n", + "* **b**runswick\n", + "* **d**ale\n", + "* **d**ar\n", + "* **f**raternity\n", + "* **g**oat\n", + "* **m**artinson\n", + "* **m**orphemic\n", + "* **m**onomer\n", + "* **p**egging\n", + "* **s**tarkey\n", + "* **u**nderclassmen\n", + "* **w**hoop\n", + "\n", + "Wait, I missed some words in the initial list check. Let's list all the words provided:\n", + "underclassmen, pegging, dale, bartok, goat, martinson, whoop, badinage, brunswick, fraternity, allele, dar, yourselves, dun, anthropocentric, banish, starkey, morphemic, monomer, desolater\n", + "\n", + "Let's sort them systematically:\n", + "\n", + "1. allele\n", + "2. anthropocentric\n", + "3. banish\n", + "4. badinage\n", + "5. bartok\n", + "6. brunswick\n", + "7. desolater\n", + "8. dale\n", + "9. dar\n", + "10. fraternity\n", + "11. goat\n", + "12. martinson\n", + "13. morphemic\n", + "14. monomer\n", + "15. pegging\n", + "16. starkey\n", + "17. yourselves\n", + "18. underclassmen\n", + "19. whoop\n", + "20. dun (This word was missed in the previous manual check, let's place it)\n", + "\n", + "Sorting 'dun': 'd' comes before 'd'ale, 'd'ar, etc.\n", + "\n", + "Corrected alphabetical order:\n", + "\n", + "1. allele\n", + "2. anthropocentric\n", + "3. banish\n", + "4. badinage\n", + "5. bartok\n", + "6. brunswick\n", + "7. desolater\n", + "8. dalefail
bbh_word_sorting-0086Sort the following words alphabetically: List: regret uptake satisfy behold fumarole motif forth alcohol escutcheon pease hackberry walkie\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.alcohol behold escutcheon forth fumarole hackberry motif pease regret satisfy uptake walkieHere are the words to sort alphabetically:\n", + "regret, uptake, satisfy, behold, fumarole, motif, forth, alcohol, escutcheon, pease, hackberry, walkie\n", + "\n", + "Let's sort them:\n", + "\n", + "1. alcohol\n", + "2. behold\n", + "3. escutcheon\n", + "4. fumarole\n", + "5. forth\n", + "6. hackberry\n", + "7. motif\n", + "8. pease\n", + "9. regret\n", + "10. satisfy\n", + "11. uptake\n", + "12. walkie\n", + "\n", + "Detailed comparison:\n", + "* **a**lcohol\n", + "* **b**ehold\n", + "* **e**scutcheon\n", + "* **f**umarole\n", + "* **f**orth (fumarole comes before forth if we compare the third letter: u vs o. Wait, F-O-R-T-H vs F-U-M-A-R-O-L-E. O comes before U.)\n", + " * Re-evaluating F words: fumarole (fu...), forth (fo...) -> forth comes before fumarole.\n", + " * Let's re-sort F words: forth, fumarole.\n", + "* **h**ackberry\n", + "* **m**otif\n", + "* **p**ease\n", + "* **r**egret\n", + "* **s**atisfy\n", + "* **u**ptake\n", + "* **w**alkie\n", + "\n", + "Let's perform a full alphabetical sort:\n", + "\n", + "1. alcohol (A)\n", + "2. behold (B)\n", + "3. escutcheon (E)\n", + "4. forth (F)\n", + "5. fumarole (F) (f-o vs f-u)\n", + "6. hackberry (H)\n", + "7. motif (M)\n", + "8. pease (P)\n", + "9. regret (R)\n", + "10. satisfy (S)\n", + "11. uptake (U)\n", + "12. walkie (W)\n", + "\n", + "Checking F words again:\n", + "forth: f-o-r-t-h\n", + "fumarole: f-u-m-a-r-o-l-e\n", + "'o' comes before 'u', so **forth** comes before **fumarole**.\n", + "\n", + "Final sorted list:\n", + "1. alcohol\n", + "2. behold\n", + "3. escutcheon\n", + "4. forth\n", + "5. fumarole\n", + "6. hackberry\n", + "7. motif\n", + "8. pease\n", + "9. regret\n", + "10. satisfy\n", + "11. uptake\n", + "1fail
bbh_word_sorting-0087Sort the following words alphabetically: List: catawba almagest transfusable milt photolysis limpid gnome archenemy sagging councilwomen decrement jungian\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.almagest archenemy catawba councilwomen decrement gnome jungian limpid milt photolysis sagging transfusableHere are the words to sort alphabetically:\n", + "catawba, almagest, transfusable, milt, photolysis, limpid, gnome, archenemy, sagging, councilwomen, decrement, jungian\n", + "\n", + "Let's sort them:\n", + "\n", + "1. **almagest** (Starts with 'a', followed by 'l')\n", + "2. **achenemy** (Starts with 'a', followed by 'c')\n", + "3. **catawba** (Starts with 'c', followed by 'a')\n", + "4. **decrement** (Starts with 'd')\n", + "5. **gnome** (Starts with 'g')\n", + "6. **jungian** (Starts with 'j')\n", + "7. **limpid** (Starts with 'l')\n", + "8. **milt** (Starts with 'm')\n", + "9. **photolysis** (Starts with 'p')\n", + "10. **sagging** (Starts with 's')\n", + "11. **councilwomen** (Starts with 'c', followed by 'o')\n", + "12. **transfusable** (Starts with 't')\n", + "\n", + "Let's re-sort carefully, paying attention to the first letter and subsequent letters:\n", + "\n", + "Words starting with A:\n", + "* almagest\n", + "* archenemy\n", + "\n", + "Comparing 'almagest' and 'achenemy':\n", + "almagest (a-l)\n", + "achenemy (a-c)\n", + "'c' comes before 'l'. So, archenemy comes before almagest.\n", + "1. archenemy\n", + "2. almagest\n", + "\n", + "Words starting with C:\n", + "* catawba\n", + "* councilwomen\n", + "\n", + "Comparing 'catawba' and 'councilwomen':\n", + "catawba (c-a)\n", + "councilwomen (c-o)\n", + "'a' comes before 'o'. So, catawba comes before councilwomen.\n", + "3. catawba\n", + "4. councilwomen\n", + "\n", + "Words starting with D:\n", + "5. decrement\n", + "\n", + "Words starting with G:\n", + "6. gnome\n", + "\n", + "Words starting with J:\n", + "7. jungian\n", + "\n", + "Words starting with L:\n", + "8. limpid\n", + "\n", + "Words starting with M:\n", + "9. milt\n", + "\n", + "Words starting with P:\n", + "10. photolysis\n", + "\n", + "Words starting with S:\n", + "11. sagging\n", + "\n", + "Words starting with T:\n", + "12. transfusable\n", + "\n", + "Final sorted list:\n", + "1. archenemy\n", + "2. almagest\n", + "3fail
bbh_word_sorting-0088Sort the following words alphabetically: List: wink envious scotia planetaria pooh emancipate army\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.army emancipate envious planetaria pooh scotia winkThe given list of words is: wink, envious, scotia, planetaria, pooh, emancipate, army.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter:\n", + "\n", + "1. **emancipate** (Starts with 'e')\n", + "2. **envious** (Starts with 'e', 'm' comes before 'n')\n", + "3. **army** (Starts with 'a')\n", + "4. **planetaria** (Starts with 'p')\n", + "5. **pooh** (Starts with 'p', 'o' comes before 'p' in planetaria if we consider the second letter, but 'p' vs 'p' is the same. 'o' vs 'l' in planetaria. Let's compare 'planetaria' and 'pooh'. 'p' is the same. 'l' in planetaria vs 'o' in pooh. 'l' comes before 'o'.)\n", + " * Re-evaluating 'planetaria' and 'pooh':\n", + " * p l a n e t a r i a\n", + " * p o o h\n", + " * 'l' comes before 'o', so 'planetaria' comes before 'pooh'.\n", + "6. **scotia** (Starts with 's')\n", + "7. **wink** (Starts with 'w')\n", + "\n", + "Let's re-sort systematically:\n", + "\n", + "Words starting with 'a':\n", + "* army\n", + "\n", + "Words starting with 'e':\n", + "* emancipate\n", + "* envious\n", + " * Comparing 'emancipate' and 'envious': e-m vs e-n. 'm' comes before 'n'.\n", + " * Order: emancipate, envious\n", + "\n", + "Words starting with 'p':\n", + "* planetaria\n", + "* pooh\n", + " * Comparing 'planetaria' and 'pooh': p-l vs p-o. 'l' comes before 'o'.\n", + " * Order: planetaria, pooh\n", + "\n", + "Words starting with 's':\n", + "* scotia\n", + "\n", + "Words starting with 'w':\n", + "* wink\n", + "\n", + "Combining the sorted groups:\n", + "1. army\n", + "2. emancipate\n", + "3. envious\n", + "4. planetaria\n", + "5. pooh\n", + "6. scotia\n", + "7. wink\n", + "\n", + "Final alphabetical order: army, emancipate, envious, planetaria, pooh, scotia, wink.\n", + "\n", + "Answer: army, emancipate, envious, planetaria, pooh, scotia, winkpass
bbh_word_sorting-0089Sort the following words alphabetically: List: delmarva sawfly aroma nod carcinogen parochial facetious designate syllabus rally\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.aroma carcinogen delmarva designate facetious nod parochial rally sawfly syllabusThe given list of words is: delmarva, sawfly, aroma, nod, carcinogen, parochial, facetious, designate, syllabus, rally.\n", + "\n", + "Here is the alphabetical sorting process:\n", + "\n", + "1. **Compare the first letter:**\n", + " * a: aroma\n", + " * d: delmarva, designate\n", + " * f: facetious\n", + " * n: nod\n", + " * p: parochial\n", + " * r: rally\n", + " * s: sawfly, syllabus\n", + "\n", + "2. **Sort words starting with 'a':**\n", + " * aroma\n", + "\n", + "3. **Sort words starting with 'd':**\n", + " * delmarva\n", + " * designate (d-e-l vs d-e-s) $\\rightarrow$ delmarva, designate\n", + "\n", + "4. **Sort words starting with 'f':**\n", + " * facetious\n", + "\n", + "5. **Sort words starting with 'n':**\n", + " * nod\n", + "\n", + "6. **Sort words starting with 'p':**\n", + " * parochial\n", + "\n", + "7. **Sort words starting with 'r':**\n", + " * rally\n", + "\n", + "8. **Sort words starting with 's':**\n", + " * sawfly\n", + " * syllabus (s-a-w vs s-y-l) $\\rightarrow$ syllabus, sawfly (Wait, 'a' comes before 'y') $\\rightarrow$ syllabus, sawfly\n", + "\n", + "Let's re-verify the 's' words:\n", + "* syllabus (s-y-l-l-a-b-u-s)\n", + "* sawfly (s-a-w-f-l-y)\n", + "'a' comes before 'y', so 'sawfly' comes before 'syllabus'.\n", + "* sawfly\n", + "* syllabus\n", + "\n", + "Let's re-verify the 'd' words:\n", + "* delmarva (d-e-l-m...)\n", + "* designate (d-e-g-n...)\n", + "'g' comes before 'l', so designate comes before delmarva.\n", + "* designate\n", + "* delmarva\n", + "\n", + "**Final Sorted Order:**\n", + "1. aroma\n", + "2. designate\n", + "3. delmarva\n", + "4. facetious\n", + "5. nod\n", + "6. parochial\n", + "7. rally\n", + "8. sawfly\n", + "9. syllabus\n", + "\n", + "Let's check the full list again:\n", + "aroma\n", + "designate\n", + "delmarva\n", + "facetious\n", + "nod\n", + "parochial\n", + "rally\n", + "sawfly\n", + "syllabus\n", + "\n", + "Answer: aroma, designate, delmarfail
bbh_word_sorting-0090Sort the following words alphabetically: List: calyx emphases vii elgin perquisite implacable blackbird afro jura mayapple whit blame\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.afro blackbird blame calyx elgin emphases implacable jura mayapple perquisite vii whitHere are the words to sort alphabetically:\n", + "calyx, emphases, vii, elgin, perquisite, implacable, blackbird, afro, jura, mayapple, whit, blame\n", + "\n", + "Let's sort them:\n", + "\n", + "1. **afro**\n", + "2. **blame**\n", + "3. **blackbird**\n", + "4. **calyx**\n", + "5. **emphases**\n", + "6. **elgin**\n", + "7. **implacable**\n", + "8. **jura**\n", + "9. **mayapple**\n", + "10. **perquisite**\n", + "11. **vii** (Numbers usually come before letters, or are sorted based on their character code)\n", + "12. **whit**\n", + "\n", + "Let's re-evaluate the sorting, paying attention to capitalization (assuming standard alphabetical sorting, treating all as lowercase for comparison unless specified):\n", + "\n", + "* afro\n", + "* blame\n", + "* blackbird\n", + "* calyx\n", + "* emphases\n", + "* elgin\n", + "* implacable\n", + "* jura\n", + "* mayapple\n", + "* perquisite\n", + "* vii\n", + "* whit\n", + "\n", + "Comparing 'vii' with other words: 'v' comes after 's' in 'emphases', 'elgin', etc. If we treat 'vii' as the sequence of letters 'v', 'i', 'i', it would be sorted based on the first letter 'v'.\n", + "\n", + "Standard alphabetical order:\n", + "1. afro\n", + "2. blame\n", + "3. blackbird\n", + "4. calyx\n", + "5. emphases\n", + "6. elgin\n", + "7. implacable\n", + "8. jura\n", + "9. mayapple\n", + "10. perquisite\n", + "11. vii (Starts with 'v')\n", + "12. whit (Starts with 'w')\n", + "\n", + "Let's check the order of 'vii' and 'whit': 'v' comes before 'w'.\n", + "\n", + "Final sorted list:\n", + "1. afro\n", + "2. blame\n", + "3. blackbird\n", + "4. calyx\n", + "5. emphases\n", + "6. elgin\n", + "7. implacable\n", + "8. jura\n", + "9. mayapple\n", + "10. perquisite\n", + "11. vii\n", + "12. whit\n", + "\n", + "Answer: afro, blame, blackbird, calyx, emphases, elgin, implacable, jura, mayapple, perquisite, vii, whitfail
bbh_word_sorting-0091Sort the following words alphabetically: List: solstice roadway loquacious niece planet steed abutted hondo loosen agamemnon islamic capacity tibet hitachi essex casualty guinea aquatic suspicion\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.abutted agamemnon aquatic capacity casualty essex guinea hitachi hondo islamic loosen loquacious niece planet roadway solstice steed suspicion tibetHere are the words sorted alphabetically:\n", + "\n", + "1. abutted\n", + "2. agamemnon\n", + "3. aquatic\n", + "4. casualty\n", + "5. capacity\n", + "6. hondo\n", + "7. hitachi\n", + "8. islamic\n", + "9. loosen\n", + "10. loquacious\n", + "11. niece\n", + "12. planet\n", + "13. roadway\n", + "14. solstice\n", + "15. steed\n", + "16. suspicion\n", + "17. tibet\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* abutted\n", + "* agamemnon\n", + "* aquatic\n", + "* casualty\n", + "* capacity\n", + "* hondo\n", + "* hitachi\n", + "* islamic\n", + "* loosen\n", + "* loquacious\n", + "* niece\n", + "* planet\n", + "* roadway\n", + "* solstice\n", + "* steed\n", + "* suspicion\n", + "* tibet\n", + "* essex (Wait, I missed 'essex' in the initial list check. Let's re-examine the original list: solstice roadway loquacious niece planet steed abutted hondo loosen agamemnon islamic capacity tibet hitachi essex casualty guinea aquatic suspicion)\n", + "\n", + "Let's re-sort the full list:\n", + "solstice, roadway, loquacious, niece, planet, steed, abutted, hondo, loosen, agamemnon, islamic, capacity, tibet, hitachi, essex, casualty, guinea, aquatic, suspicion\n", + "\n", + "1. abutted\n", + "2. agamemnon\n", + "3. aquatic\n", + "4. casualty\n", + "5. capacity\n", + "6. essex\n", + "7. guinea\n", + "8. hondo\n", + "9. hitachi\n", + "10. islamic\n", + "11. loosen\n", + "12. loquacious\n", + "13. niece\n", + "14. planet\n", + "15. roadway\n", + "16. solstice\n", + "17. steed\n", + "18. suspicion\n", + "19. tibet\n", + "\n", + "(Checking 'guinea' placement: G comes after E, before H. Correct.)\n", + "\n", + "Final sorted list:\n", + "abutted, agamemnon, aquatic, casualty, capacity, essex, guinea, hondo, hitachi, islamic, loosen, loquacious, niece, planet, roadway, solstice, steed, suspicion, tibet\n", + "\n", + "Answer: abutted, agamemnon, aquatic, casualty, capacity, essex, guinea, hondo, hitachi, islamic, loosen, loquacious, niece, planet, roadway, solstice, steed, suspicion, tibetfail
bbh_word_sorting-0092Sort the following words alphabetically: List: corpulent diagnose code scalp bombproof fluorine blythe swipe honeybee damn pore maharaja cytolysis solicit\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.blythe bombproof code corpulent cytolysis damn diagnose fluorine honeybee maharaja pore scalp solicit swipeHere are the words sorted alphabetically:\n", + "\n", + "1. bombproof\n", + "2. bpythe (Assuming "blythe" is a typo for "bpythe" or sorting based on the provided spelling) - If we strictly use "blythe":\n", + "3. corpulent\n", + "4. code\n", + "5. cytolysis\n", + "6. damn\n", + "7. diagnose\n", + "8. fluorine\n", + "9. honeybee\n", + "10. maharaja\n", + "11. pore\n", + "12. solicit\n", + "13. scalp\n", + "14. swipe\n", + "\n", + "Let's re-sort carefully using the exact words provided: corpulent, diagnose, code, scalp, bombproof, fluorine, blythe, swipe, honeybee, damn, pore, maharaja, cytolysis, solicit.\n", + "\n", + "1. bombproof\n", + "2. bpythe (blythe)\n", + "3. code\n", + "4. corpulent\n", + "5. cytolysis\n", + "6. damn\n", + "7. diagnose\n", + "8. fluorine\n", + "9. honeybee\n", + "10. maharaja\n", + "11. pore\n", + "12. solicit\n", + "13. scalp\n", + "14. swipe\n", + "\n", + "Sorting "blythe" (b-l-y-t-h-e) relative to others:\n", + "* bombproof (b-o-m-b-p-r-o-o-f)\n", + "* blythe (b-l-y-t-h-e)\n", + "* code (c-o-d-e)\n", + "* corpulent (c-o-r-p-u-l-e-n-t)\n", + "* cytolysis (c-y-t-o-l-y-s-i-s)\n", + "* damn (d-a-m-n)\n", + "* diagnose (d-i-a-g-n-o-s-e)\n", + "* fluorine (f-l-u-o-r-i-n-e)\n", + "* honeybee (h-o-n-e-y-b-e-e)\n", + "* maharaja (m-a-h-a-r-a-j-a)\n", + "* pore (p-o-r-e)\n", + "* solicit (s-o-l-i-c-i-t)\n", + "* scalp (s-c-a-l-p)\n", + "* swipe (s-w-i-p-e)\n", + "\n", + "Comparing 'bombproof' and 'blythe': 'b' is the same. 'fail
bbh_word_sorting-0093Sort the following words alphabetically: List: windowsill appoint biharmonic moustache baneberry wiry dyne pirate\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.appoint baneberry biharmonic dyne moustache pirate windowsill wiryThe given list of words is: windowsill, appoint, biharmonic, moustache, baneberry, wiry, dyne, pirate.\n", + "\n", + "Here is the alphabetical sorting process:\n", + "\n", + "1. **Compare the first letter:**\n", + " * windowsill (w)\n", + " * appoint (a)\n", + " * biharmonic (b)\n", + " * moustache (m)\n", + " * baneberry (b)\n", + " * wiry (w)\n", + " * dyne (d)\n", + " * pirate (p)\n", + "\n", + " Order based on the first letter: a, b, b, d, m, p, w, w\n", + "\n", + "2. **Sort words starting with 'a':**\n", + " * appoint\n", + "\n", + "3. **Sort words starting with 'b':**\n", + " * biharmonic\n", + " * baneberry\n", + " * (Comparing 'biharmonic' and 'baneberry': 'bi' comes before 'ba' if we consider the second letter, but standard alphabetical order compares character by character. 'b' is the same. 'i' in biharmonic vs 'a' in baneberry. 'a' comes before 'i'.)\n", + " * baneberry\n", + " * biharmonic\n", + "\n", + "4. **Sort words starting with 'd':**\n", + " * dyne\n", + "\n", + "5. **Sort words starting with 'm':**\n", + " * moustache\n", + "\n", + "6. **Sort words starting with 'p':**\n", + " * pirate\n", + "\n", + "7. **Sort words starting with 'w':**\n", + " * windowsill\n", + " * wiry\n", + " * (Comparing 'windowsill' and 'wiry': 'in' comes before 'ir'.)\n", + " * windowsill\n", + " * wiry\n", + "\n", + "**Combining the sorted lists:**\n", + "1. appoint\n", + "2. baneberry\n", + "3. biharmonic\n", + "4. dyne\n", + "5. moustache\n", + "6. pirate\n", + "7. windowsill\n", + "8. wiry\n", + "\n", + "Let's re-verify the 'b' words:\n", + "baneberry (ba...)\n", + "biharmonic (bi...)\n", + "'a' comes before 'i', so baneberry comes before biharmonic.\n", + "\n", + "Let's re-verify the 'w' words:\n", + "windowsill (win...)\n", + "wiry (wir...)\n", + "'in' comes before 'ir', so windowsill comes before wiry.\n", + "\n", + "**Final Sorted Order:**\n", + "1. appoint\n", + "2. baneberry\n", + "3. biharmonic\n", + "4. dyne\n", + "5. moustache\n", + "6. pirate\n", + "7. windowsill\n", + "8.fail
bbh_word_sorting-0094Sort the following words alphabetically: List: crossbill bushland capacitive meager ricochet battery enigma wysiwyg wallet lipton jane wacke contingent\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.battery bushland capacitive contingent crossbill enigma jane lipton meager ricochet wacke wallet wysiwygHere are the words sorted alphabetically:\n", + "\n", + "1. battery\n", + "2. bushland\n", + "3. capacitive\n", + "4. contingent\n", + "5. crossbill\n", + "6. enigma\n", + "7. jane\n", + "8. lipton\n", + "9. meager\n", + "10. ricochet\n", + "11. wallet\n", + "12. wacke\n", + "13. wysiwyg\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **b**attery\n", + "* **b**ushland\n", + "* **c**apacitive\n", + "* **c**ontingent\n", + "* **c**rossbill\n", + "* **e**nigma\n", + "* **j**ane\n", + "* **l**ipton\n", + "* **m**eager\n", + "* **r**icochet\n", + "* **w**allet\n", + "* **w**acke\n", + "* **w**ysiwyg\n", + "\n", + "Comparing the 'c' words:\n", + "* capacitive\n", + "* contingent\n", + "* crossbill\n", + "Alphabetical order: capacitive, contingent, crossbill.\n", + "\n", + "Comparing the 'w' words:\n", + "* wallet\n", + "* wacke\n", + "* wysiwyg\n", + "Alphabetical order: wallet, wacke, wysiwyg.\n", + "\n", + "The final sorted list is:\n", + "1. battery\n", + "2. bushland\n", + "3. capacitive\n", + "4. contingent\n", + "5. crossbill\n", + "6. enigma\n", + "7. jane\n", + "8. lipton\n", + "9. meager\n", + "10. ricochet\n", + "11. wallet\n", + "12. wacke\n", + "13. wysiwyg\n", + "\n", + "Answer: battery, bushland, capacitive, contingent, crossbill, enigma, jane, lipton, meager, ricochet, wallet, wacke, wysiwygfail
bbh_word_sorting-0095Sort the following words alphabetically: List: frontage gunky indeterminable smokestack taint assure ostentatious sanderson contend paradigmatic lustrous butterball deaf dinosaur bully rhyme sashimi bye cornet\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.assure bully butterball bye contend cornet deaf dinosaur frontage gunky indeterminable lustrous ostentatious paradigmatic rhyme sanderson sashimi smokestack taintHere are the words sorted alphabetically:\n", + "\n", + "1. butterball\n", + "2. bye\n", + "3. bully\n", + "4. cornet\n", + "5. contend\n", + "6. dinosaur\n", + "7. deaf\n", + "8. frontage\n", + "9. gunky\n", + "10. indeterminable\n", + "11. lustrous\n", + "12. rhyme\n", + "13. sanderson\n", + "14. sashimi\n", + "15. smokestack\n", + "16. taint\n", + "17. assure\n", + "18. ostentatious\n", + "19. paradigmatic\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **b**utterball\n", + "* **b**ye\n", + "* **b**ully\n", + "* **c**ornet\n", + "* **c**ontend\n", + "* **d**inosaur\n", + "* **d**eaf\n", + "* **f**rontage\n", + "* **g**unky\n", + "* **i**ndeterminable\n", + "* **l**ustrous\n", + "* **r**hyme\n", + "* **s**anderson\n", + "* **s** sashimi\n", + "* **s**mokestack\n", + "* **t**aint\n", + "* **a**ssure\n", + "* **o**stentatious\n", + "* **p**aradigmatic\n", + "\n", + "Correct alphabetical order:\n", + "\n", + "1. assure\n", + "2. butterball\n", + "3. bye\n", + "4. bully\n", + "5. cornet\n", + "6. contend\n", + "7. dinosaur\n", + "8. deaf\n", + "9. frontage\n", + "10. gunky\n", + "11. indeterminable\n", + "12. lustrous\n", + "13. paradigmatic\n", + "14. rhyme\n", + "15. sanderson\n", + "16. sashimi\n", + "17. smokestack\n", + "18. taint\n", + "19. ostentatious\n", + "\n", + "Let's re-check the 'a' words and 's' words:\n", + "Assure (A)\n", + "Butterball (B)\n", + "Bye (B)\n", + "Bully (B)\n", + "Cornet (C)\n", + "Contend (C)\n", + "Dinosaur (D)\n", + "Deaf (D)\n", + "Frontage (F)\n", + "Gunky (G)\n", + "Indeterminable (I)\n", + "Lustrous (L)\n", + "Paradigmatic (P)\n", + "Rhyme (R)\n", + "Sanderson (S)\n", + "Sashimi (S)\n", + "Smokestack (S)\n", + "Taint (T)\n", + "Ostentatious (O)\n", + "\n", + "Sorting the S words: Sanderson, Sashimi, Smokestack.\n", + "Sashimi (Sa)\n", + "Sanderson (fail
bbh_word_sorting-0096Sort the following words alphabetically: List: livre substitution ix frisian panoramic buckley\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.buckley frisian ix livre panoramic substitutionThe given list of words is: livre, substitution, ix, frisian, panoramic, buckley.\n", + "\n", + "We need to sort these words alphabetically. We will compare them letter by letter.\n", + "\n", + "1. **buckley** (Starts with 'b')\n", + "2. **frisian** (Starts with 'f')\n", + "3. **ix** (Starts with 'i')\n", + "4. **livre** (Starts with 'l')\n", + "5. **panoramic** (Starts with 'p')\n", + "6. **substitution** (Starts with 's')\n", + "\n", + "Let's re-sort based on the first letter:\n", + "B: buckley\n", + "F: frisian\n", + "I: ix\n", + "L: livre\n", + "P: panoramic\n", + "S: substitution\n", + "\n", + "Alphabetical order of the first letters: B, F, I, L, P, S.\n", + "\n", + "The sorted list is:\n", + "1. buckley\n", + "2. frisian\n", + "3. ix\n", + "4. livre\n", + "5. panoramic\n", + "6. substitution\n", + "\n", + "Answer: buckley, frisian, ix, livre, panoramic, substitution\n", + "\n", + "Answer: buckley, frisian, ix, livre, panoramic, substitutionpass
bbh_word_sorting-0097Sort the following words alphabetically: List: fugue chicanery mountain\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.chicanery fugue mountainThe given list of words is: fugue, chicanery, mountain.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter:\n", + "\n", + "1. **f**ugue\n", + "2. **c**hicanery\n", + "3. **m**ountain\n", + "\n", + "Comparing the first letters: 'c' comes before 'f', and 'f' comes before 'm'.\n", + "\n", + "So the order based on the first letter is:\n", + "'c' (chicanery)\n", + "'f' (fugue)\n", + "'m' (mountain)\n", + "\n", + "The alphabetical order is: chicanery, fugue, mountain.\n", + "\n", + "Answer: chicanery, fugue, mountain\n", + "\n", + "Answer: chicanery, fugue, mountainpass
bbh_word_sorting-0098Sort the following words alphabetically: List: murk pvc singlet yeats muslim coachmen clergymen companion gazpacho embark pristine granular hobble fatten allot too niggle chauncey threefold coddington\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.allot chauncey clergymen coachmen coddington companion embark fatten gazpacho granular hobble murk muslim niggle pristine pvc singlet threefold too yeatsHere are the words sorted alphabetically:\n", + "\n", + "1. chauncey\n", + "2. coddington\n", + "3. companion\n", + "4. coachmen\n", + "5. clergymen\n", + "6. embark\n", + "7. fatten\n", + "8. threefold\n", + "9. granular\n", + "10. gazpacho\n", + "11. hobble\n", + "12. murk\n", + "13. muslim\n", + "14. niggle\n", + "15. pristine\n", + "16. pvc\n", + "17. singlet\n", + "18. too\n", + "19. yeats\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **c**hauncey\n", + "* **c**oddington\n", + "* **c**ompanion\n", + "* **c**oachmen\n", + "* **c**lergymen\n", + "* **e**mbark\n", + "* **f**atten\n", + "* **f**reedom (Wait, 'threefold' comes before 'fatten' if we look at the 't' sound, but alphabetically 'fatten' comes before 'threefold'.)\n", + "* **g**azpacho\n", + "* **g**ranular\n", + "* **h**obble\n", + "* **m**urk\n", + "* **m**uslim\n", + "* **n**iggle\n", + "* **p**ristine\n", + "* **p**vc\n", + "* **s**inglet\n", + "* **t**oo\n", + "* **y**eats\n", + "\n", + "Let's do a strict alphabetical sort:\n", + "\n", + "1. chauncey\n", + "2. coddington\n", + "3. companion\n", + "4. coachmen\n", + "5. clergymen\n", + "6. embark\n", + "7. fatten\n", + "8. threefold\n", + "9. granular\n", + "10. gazpacho\n", + "11. hobble\n", + "12. murk\n", + "13. muslim\n", + "14. niggle\n", + "15. pristine\n", + "16. pvc\n", + "17. singlet\n", + "18. too\n", + "19. yeats\n", + "\n", + "(Checking 'fatten' vs 'threefold': F-A-T-T-E-N vs T-H-R-E-E-F-O-L-D. 'fatten' comes first.)\n", + "(Checking 'granular' vs 'gazpacho': G-R vs G-A. 'gazpacho' comes before 'granular'.)\n", + "\n", + "Corrected final sort:\n", + "\n", + "1. chauncey\n", + "2. coddington\n", + "3. companion\n", + "4. coachmen\n", + "5. clergymen\n", + "6. embark\n", + "7. fatten\n", + "8. threefoldfail
bbh_word_sorting-0099Sort the following words alphabetically: List: hornblower dissipate amanita canticle annoy besiege straight notre propylene sepia california pasture encephalitis boggle crocodilian dexter snipe amatory dizzy psychiatric\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.amanita amatory annoy besiege boggle california canticle crocodilian dexter dissipate dizzy encephalitis hornblower notre pasture propylene psychiatric sepia snipe straightHere are the words sorted alphabetically:\n", + "\n", + "1. amanita\n", + "2. annoy\n", + "3. besiege\n", + "4. boggle\n", + "5. california\n", + "6. canticle\n", + "7. encephalitis\n", + "8. dissipate\n", + "9. dizzy\n", + "10. dexter\n", + "11. hornblower\n", + "12. notre\n", + "13. pasture\n", + "14. propylene\n", + "15. psychiatric\n", + "16. sepia\n", + "17. snipe\n", + "18. straight\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* amanita\n", + "* annoy\n", + "* besiege\n", + "* boggle\n", + "* california\n", + "* canticle\n", + "* encephalitis\n", + "* dissipate\n", + "* dizzy\n", + "* dexter\n", + "* hornblower\n", + "* notre\n", + "* pasture\n", + "* propylene\n", + "* psychiatric\n", + "* sepia\n", + "* snipe\n", + "* straight\n", + "\n", + "Comparing the initial list: hornblower, dissipate, amanita, canticle, annoy, besiege, straight, notre, propylene, sepia, california, pasture, encephalitis, boggle, crocodilian, dexter, snipe, amatory, dizzy, psychiatric.\n", + "\n", + "Wait, I missed "crocodilian" and "amatory" from the initial list provided in the prompt. Let's use the exact list provided:\n", + "List: hornblower dissipate amanita canticle annoy besiege straight notre propylene sepia california pasture encephalitis boggle crocodilian dexter snipe amatory dizzy psychiatric\n", + "\n", + "Sorting the 20 words:\n", + "\n", + "1. amanita\n", + "2. amatory\n", + "3. annoy\n", + "4. besiege\n", + "5. boggle\n", + "6. california\n", + "7. canticle\n", + "8. crocodilian\n", + "9. dissipate\n", + "10. dizzy\n", + "11. dexter\n", + "12. encephalitis\n", + "13. hornblower\n", + "14. notre\n", + "15. pasture\n", + "16. propylene\n", + "17. psychiatric\n", + "18. sepia\n", + "19. snipe\n", + "20. straight\n", + "\n", + "Let's re-verify the order, especially around 'a' and 'b':\n", + "A: amanita, amatory\n", + "B: besiege, boggle\n", + "C: california, canticle, crocodilian\n", + "D: dissipate, dizzy, dexter\n", + "E: encephalitis\n", + "H: hornblower\n", + "N: notre\n", + "P: pasture, propylene, psychiatric\n", + "S: sepia, snipe, straight\n", + "\n", + "Correct alphabetical order:\n", + "1. amanita\n", + "2. amatory\n", + "3. annoy\n", + "4. besiege\n", + "5. boggle\n", + "fail
bbh_word_sorting-0100Sort the following words alphabetically: List: stanhope sisyphus fungi purpose midwinter mendelevium studious driveway symmetry despond trample wring strip clearheaded encapsulate vs gob christen\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.christen clearheaded despond driveway encapsulate fungi gob mendelevium midwinter purpose sisyphus stanhope strip studious symmetry trample vs wringHere are the words sorted alphabetically:\n", + "\n", + "1. clearheaded\n", + "2. christen\n", + "3. despond\n", + "4. driveway\n", + "5. encapsulate\n", + "6. fungi\n", + "7. mendelevium\n", + "8. midwinter\n", + "9. purpose\n", + "10. stanhope\n", + "11. strip\n", + "12. studious\n", + "13. symmetry\n", + "14. trample\n", + "15. vs\n", + "16. wring\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* clearheaded\n", + "* christen\n", + "* despond\n", + "* driveway\n", + "* encapsulate\n", + "* fungi\n", + "* mendelevium\n", + "* midwinter\n", + "* purpose\n", + "* stanhope\n", + "* strip\n", + "* studious\n", + "* symmetry\n", + "* trample\n", + "* vs\n", + "* wring\n", + "\n", + "(Checking the initial list again: stanhope, sisyphus, fungi, purpose, midwinter, mendelevium, studious, driveway, symmetry, despond, trample, wring, strip, clearheaded, encapsulate, vs, gob, christen)\n", + "\n", + "Wait, I missed "sisyphus" and "gob". Let's re-list and sort:\n", + "\n", + "Original List: stanhope, sisyphus, fungi, purpose, midwinter, mendelevium, studious, driveway, symmetry, despond, trample, wring, strip, clearheaded, encapsulate, vs, gob, christen\n", + "\n", + "Sorted List:\n", + "1. clearheaded\n", + "2. christen\n", + "3. despond\n", + "4. driveway\n", + "5. encapsulate\n", + "6. fungi\n", + "7. gob\n", + "8. mendelevium\n", + "9. midwinter\n", + "10. purpose\n", + "11. stanhope\n", + "12. sisyphus\n", + "13. strip\n", + "14. studious\n", + "15. symmetry\n", + "16. trample\n", + "17. vs\n", + "18. wring\n", + "\n", + "Answer: clearheaded, christen, despond, driveway, encapsulate, fungi, gob, mendelevium, midwinter, purpose, stanhope, sisyphus, strip, studious, symmetry, trample, vs, wringfail
bbh_word_sorting-0101Sort the following words alphabetically: List: regret starlight wallboard cotyledon more pepperoni\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.cotyledon more pepperoni regret starlight wallboardThe given list of words is: regret, starlight, wallboard, cotyledon, more, pepperoni.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter:\n", + "\n", + "1. **cotyledon** (Starts with 'c')\n", + "2. **more** (Starts with 'm')\n", + "3. **pepperoni** (Starts with 'p')\n", + "4. **regret** (Starts with 'r')\n", + "5. **starlight** (Starts with 's')\n", + "6. **wallboard** (Starts with 'w')\n", + "\n", + "Let's verify the order:\n", + "* c (cotyledon)\n", + "* m (more)\n", + "* p (pepperoni)\n", + "* r (regret)\n", + "* s (starlight)\n", + "* w (wallboard)\n", + "\n", + "The alphabetical order is: cotyledon, more, pepperoni, regret, starlight, wallboard.\n", + "\n", + "Answer: cotyledon, more, pepperoni, regret, starlight, wallboard\n", + "\n", + "Answer: cotyledon, more, pepperoni, regret, starlight, wallboardpass
bbh_word_sorting-0102Sort the following words alphabetically: List: bottle kenney chapati okinawa boon bedtime\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.bedtime boon bottle chapati kenney okinawaThe given list of words is: bottle, kenney, chapati, okinawa, boon, bedtime.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter:\n", + "\n", + "1. **b**ottle\n", + "2. **b**oon\n", + "3. **b**edtime\n", + "4. **c**hapati\n", + "5. **k**enney\n", + "6. **o**kinawa\n", + "\n", + "Comparing the words starting with 'b':\n", + "* bottle\n", + "* boon\n", + "* bedtime\n", + "\n", + "Comparing the second letter:\n", + "* bo**t**tle\n", + "* bo**o**n\n", + "* be**d**time\n", + "\n", + "'b' comes before 'd' and 't'.\n", + "'boon' comes before 'bottle' because 'o' comes before 't'.\n", + "So the order for 'b' words is: boon, bottle, bedtime.\n", + "\n", + "Now we place the rest:\n", + "* chapati (starts with 'c')\n", + "* kenney (starts with 'k')\n", + "* okinawa (starts with 'o')\n", + "\n", + "The alphabetical order of the starting letters is: b, c, k, o.\n", + "\n", + "Combining the sorted groups:\n", + "1. boon\n", + "2. bottle\n", + "3. bedtime\n", + "4. chapati\n", + "5. kenney\n", + "6. okinawa\n", + "\n", + "Let's re-check the 'b' words:\n", + "boon\n", + "bottle\n", + "bedtime\n", + "'boon' vs 'bottle': 'o' vs 't'. 'o' comes before 't'. So, boon comes before bottle.\n", + "'boon' vs 'bedtime': 'o' vs 'e'. 'e' comes before 'o'. So, bedtime comes before boon.\n", + "\n", + "Let's re-sort the 'b' words:\n", + "bedtime (b-e)\n", + "boon (b-o-o)\n", + "bottle (b-o-t)\n", + "'e' comes before 'o'. So, bedtime is first.\n", + "'o' comes before 't'. So, boon is before bottle.\n", + "Order: bedtime, boon, bottle.\n", + "\n", + "The full sorted list:\n", + "1. bedtime\n", + "2. boon\n", + "3. bottle\n", + "4. chapati\n", + "5. kenney\n", + "6. okinawa\n", + "\n", + "Final check:\n", + "bedtime\n", + "boon\n", + "bottle\n", + "chapati\n", + "kenney\n", + "okinawa\n", + "\n", + "Answer: bedtime, boon, bottle, chapati, kenney, okinawa\n", + "\n", + "Answer: bedtime, boon, bottle, chapati, kenneyfail
bbh_word_sorting-0103Sort the following words alphabetically: List: anarchic liven shortcut total diatom guelph usage acoustic yarmulke phenomenology neo bureaucracy trammel leftward immovable fabricate syndrome suggestive provide\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.acoustic anarchic bureaucracy diatom fabricate guelph immovable leftward liven neo phenomenology provide shortcut suggestive syndrome total trammel usage yarmulkeHere are the words sorted alphabetically:\n", + "\n", + "1. acoustic\n", + "2. anarchic\n", + "3. diatom\n", + "4. fabricate\n", + "5. phenomenology\n", + "6. leftward\n", + "7. liven\n", + "8. neo\n", + "9. immovable\n", + "10. provide\n", + "11. shortcut\n", + "12. syndrome\n", + "13. total\n", + "14. trammel\n", + "15. usage\n", + "16. yarmulke\n", + "17. bureaucracy\n", + "18. guelph\n", + "19. suggestive\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **a**narchic, **a**coustic\n", + "* **b**ureaucracy\n", + "* **d**iatom\n", + "* **f**abricate\n", + "* **g**uelph\n", + "* **i**mmovable\n", + "* **l**eftward, **l**iven\n", + "* **n**eo\n", + "* **p**henomenology, **p**rovide\n", + "* **s**hortcut, **s**yndrome, **s**uggestive\n", + "* **t**otal, **t**rammel\n", + "* **u**sage\n", + "* **y**armulke\n", + "\n", + "**Detailed Alphabetical Sort:**\n", + "\n", + "1. acoustic\n", + "2. anarchic\n", + "3. bureaucracy\n", + "4. diatom\n", + "5. fabricate\n", + "6. guelph\n", + "7. immovable\n", + "8. leftward\n", + "9. liven\n", + "10. neo\n", + "11. phenomenology\n", + "12. provide\n", + "13. shortcut\n", + "14. syndrome\n", + "15. total\n", + "16. trammel\n", + "17. usage\n", + "18. yarmulke\n", + "19. suggestive\n", + "\n", + "(Checking L words: leftward, liven. L-e-f-t vs L-i-v-e. Leftward comes before liven.)\n", + "(Checking P words: phenomenology, provide. Phenomenology comes before provide.)\n", + "(Checking S words: shortcut, syndrome, suggestive. Shortcut, syndrome, suggestive.)\n", + "\n", + "Final check of the full list:\n", + "anarchic, acoustic, bureaucracy, diatom, fabricate, guelph, immovable, leftward, liven, neo, phenomenology, provide, shortcut, syndrome, total, trammel, usage, yarmulke, suggestive.\n", + "\n", + "Answer: acoustic, anarchic, bureaucracy, diatom, fabricate, guelph, immovable, leftward, liven, neo, phenomenology, provide, shortcut, syndrome, total, trammel,fail
bbh_word_sorting-0104Sort the following words alphabetically: List: berniece bremsstrahlung hoe avalanche showroom goff dactylic befriend hurry housekeep lanka gilbertson posterior flick bong hereafter metazoan\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.avalanche befriend berniece bong bremsstrahlung dactylic flick gilbertson goff hereafter hoe housekeep hurry lanka metazoan posterior showroomHere are the words to sort alphabetically:\n", + "berniece, bremsstrahlung, hoe, avalanche, showroom, goff, dactylic, befriend, hurry, housekeep, lanka, gilbertson, posterior, flick, bong, hereafter, metazoan\n", + "\n", + "Let's sort them:\n", + "\n", + "1. **ache** (Wait, "ache" is not in the list. Let's re-examine the list.)\n", + "\n", + "Words: berniece, bremsstrahlung, hoe, avalanche, showroom, goff, dactylic, befriend, hurry, housekeep, lanka, gilbertson, posterior, flick, bong, hereafter, metazoan\n", + "\n", + "Sorting by the first letter:\n", + "A: avalanche\n", + "B: berniece, bremsstrahlung, befriend\n", + "D: dactylic\n", + "F: flick\n", + "G: goff, gilbertson\n", + "H: hoe, hurry, housekeep\n", + "L: lanka\n", + "M: metazoan\n", + "P: posterior\n", + "S: showroom\n", + "H: hereafter (already listed under H, but we need to check order)\n", + "\n", + "Let's sort systematically:\n", + "\n", + "1. **ache** (None)\n", + "2. **avalanche**\n", + "3. **befriend**\n", + "4. **berniece**\n", + "5. **bremsstrahlung**\n", + "6. **bong**\n", + "7. **dactylic**\n", + "8. **flick**\n", + "9. **goff**\n", + "10. **gilbertson**\n", + "11. **hereafter**\n", + "12. **hoe**\n", + "13. **hurry**\n", + "14. **housekeep**\n", + "15. **lanka**\n", + "16. **metazoan**\n", + "17. **posterior**\n", + "18. **showroom**\n", + "\n", + "Let's refine the sorting, especially for words starting with the same letter:\n", + "\n", + "**B words:** befriend, berniece, bremsstrahlung (befriend, berniece, bremsstrahlung)\n", + "**H words:** hereafter, hoe, hurry, housekeep (hereafter, hoe, hurry, housekeep)\n", + "**G words:** goff, gilbertson (goff, gilbertson)\n", + "\n", + "**Alphabetical Order:**\n", + "\n", + "1. avalanche (A)\n", + "2. befriend (B)\n", + "3. berniece (B)\n", + "4. bremsstrahlung (B)\n", + "5. bong (B - Wait, 'bong' starts with B, it should come before 'berniece' if we sort strictly by the second letter, butfail
bbh_word_sorting-0105Sort the following words alphabetically: List: blutwurst horizon uranyl arraign portentous faber rancho peste glacier convenient inconspicuous\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.arraign blutwurst convenient faber glacier horizon inconspicuous peste portentous rancho uranylHere are the words to sort: blutwurst, horizon, uranyl, arraign, portentous, faber, rancho, peste, glacier, convenient, inconspicuous.\n", + "\n", + "Let's sort them alphabetically:\n", + "\n", + "1. arraign\n", + "2. blutwurst\n", + "3. convenient\n", + "4. faber\n", + "5. glacier\n", + "6. horizon\n", + "7. inconspicuous\n", + "8. portentous\n", + "9. peste\n", + "10. rancho\n", + "11. uranyl\n", + "\n", + "Let's re-check the sorting carefully, especially for words starting with similar letters:\n", + "\n", + "* **a**rraign\n", + "* **b**lutwurst\n", + "* **c**onvenient\n", + "* **f**aber\n", + "* **g**lacier\n", + "* **h**orizon\n", + "* **i**nconspicuous\n", + "* **p**ortentous\n", + "* **p**este (Peste comes before Portentous because 'e' comes before 'o')\n", + "* **r**ancho\n", + "* **u**ranyl\n", + "\n", + "Let's re-sort the 'P' words:\n", + "* portentous\n", + "* peste\n", + "'peste' comes before 'portentous'.\n", + "\n", + "Corrected order:\n", + "1. arraign\n", + "2. blutwurst\n", + "3. convenient\n", + "4. faber\n", + "5. glacier\n", + "6. horizon\n", + "7. inconspicuous\n", + "8. peste\n", + "9. portentous\n", + "10. rancho\n", + "11. uranyl\n", + "\n", + "Answer: arraign, blutwurst, convenient, faber, glacier, horizon, inconspicuous, peste, portentous, rancho, uranyl\n", + "\n", + "Answer: arraign, blutwurst, convenient, faber, glacier, horizon, inconspicuous, peste, portentous, rancho, uranylpass
bbh_word_sorting-0106Sort the following words alphabetically: List: solvate nat usable kajar downey vision detest aitken barycentric\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.aitken barycentric detest downey kajar nat solvate usable visionThe given list of words is: solvate, nat, usable, kajar, downey, vision, detest, aitken, barycentric.\n", + "\n", + "Here is the alphabetical sorting process:\n", + "\n", + "1. **Compare the first letter:**\n", + " * a: aitken\n", + " * b: barycentric\n", + " * d: downey, detest\n", + " * k: kajar\n", + " * n: nat\n", + " * s: solvate\n", + " * u: usable\n", + " * v: vision\n", + "\n", + "2. **Sort words starting with 'd':**\n", + " * downey\n", + " * detest (d-o comes before d-e, wait, 'd' is the same. 'o' in downey, 'e' in detest. 'e' comes before 'o'.)\n", + " * Correct order: detest, downey\n", + "\n", + "3. **Combine the sorted list:**\n", + " * aitken (a)\n", + " * barycentric (b)\n", + " * detest (d)\n", + " * downey (d)\n", + " * kajar (k)\n", + " * nat (n)\n", + " * solvate (s)\n", + " * usable (u)\n", + " * vision (v)\n", + "\n", + "4. **Final check of the order:**\n", + " * aitken\n", + " * barycentric\n", + " * detest\n", + " * downey\n", + " * kajar\n", + " * nat\n", + " * solvate\n", + " * usable\n", + " * vision\n", + "\n", + "Answer: aitken, barycentric, detest, downey, kajar, nat, solvate, usable, vision\n", + "Answer: aitken, barycentric, detest, downey, kajar, nat, solvate, usable, visionpass
bbh_word_sorting-0107Sort the following words alphabetically: List: wreak hidebound bandwidth\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.bandwidth hidebound wreakThe given words are: wreak, hidebound, bandwidth.\n", + "\n", + "To sort them alphabetically, we compare them letter by letter:\n", + "\n", + "1. **wreak**: Starts with 'w'\n", + "2. **hidebound**: Starts with 'h'\n", + "3. **bandwidth**: Starts with 'b'\n", + "\n", + "Comparing the first letters: 'b', 'h', 'w'.\n", + "In alphabetical order: 'b' comes before 'h', and 'h' comes before 'w'.\n", + "\n", + "So, 'bandwidth' comes first.\n", + "\n", + "Now compare 'hidebound' and 'wreak'.\n", + "'h' comes before 'w'.\n", + "So, 'hidebound' comes before 'wreak'.\n", + "\n", + "The alphabetical order is:\n", + "1. bandwidth\n", + "2. hidebound\n", + "3. wreak\n", + "\n", + "Answer: bandwidth, hidebound, wreak\n", + "\n", + "Answer: bandwidth, hidebound, wreakpass
bbh_word_sorting-0108Sort the following words alphabetically: List: mckinley pesticide school radiometer marque prig relish trianon inspiration artful cancelled castrate glaucous ear croon tioga rothschild excite endpoint citadel\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.artful cancelled castrate citadel croon ear endpoint excite glaucous inspiration marque mckinley pesticide prig radiometer relish rothschild school tioga trianonHere are the words sorted alphabetically:\n", + "\n", + "1. artful\n", + "2. cancelled\n", + "3. castrate\n", + "4. citadel\n", + "5. croon\n", + "6. endpoint\n", + "7. ear\n", + "8. excite\n", + "9. glaucous\n", + "10. inspiration\n", + "11. marque\n", + "12. mckinley\n", + "13. pesticide\n", + "14. prig\n", + "15. relish\n", + "16. radiometer\n", + "17. rothschild\n", + "18. school\n", + "19. tioga\n", + "20. trianon\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* artful\n", + "* cancelled\n", + "* castrate\n", + "* citadel\n", + "* croon\n", + "* endpoint\n", + "* ear\n", + "* excite\n", + "* glaucous\n", + "* inspiration\n", + "* marque\n", + "* mckinley\n", + "* pesticide\n", + "* prig\n", + "* relish\n", + "* radiometer\n", + "* rothschild\n", + "* school\n", + "* tioga\n", + "* trianon\n", + "\n", + "Checking the order again:\n", + "A: artful\n", + "C: cancelled, castrate, citadel, croon\n", + "E: endpoint, ear, excite\n", + "G: glaucous\n", + "I: inspiration\n", + "M: marque, mckinley\n", + "P: pesticide, prig\n", + "R: relish, radiometer, rothschild\n", + "S: school\n", + "T: tioga, trianon\n", + "\n", + "Detailed alphabetical sort:\n", + "1. artful\n", + "2. cancelled\n", + "3. castrate\n", + "4. citadel\n", + "5. croon\n", + "6. endpoint\n", + "7. ear\n", + "8. excite\n", + "9. glaucous\n", + "10. inspiration\n", + "11. marque\n", + "12. mckinley\n", + "13. pesticide\n", + "14. prig\n", + "15. relish\n", + "16. radiometer\n", + "17. rothschild\n", + "18. school\n", + "19. tioga\n", + "20. trianon\n", + "\n", + "Answer: artful, cancelled, castrate, citadel, croon, endpoint, ear, excite, glaucous, inspiration, marque, mckinley, pesticide, prig, relish, radiometer, rothschild, school, tioga, trianonfail
bbh_word_sorting-0109Sort the following words alphabetically: List: greasy lapidary mark\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.greasy lapidary markThe given list of words is: greasy, lapidary, mark.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter:\n", + "\n", + "1. **greasy** (starts with 'g')\n", + "2. **lapidary** (starts with 'l')\n", + "3. **mark** (starts with 'm')\n", + "\n", + "Comparing the first letters: 'g', 'l', 'm'.\n", + "In alphabetical order, 'g' comes before 'l', and 'l' comes before 'm'.\n", + "\n", + "Therefore, the alphabetical order is: greasy, lapidary, mark.\n", + "\n", + "Answer: greasy, lapidary, markpass
bbh_word_sorting-0110Sort the following words alphabetically: List: monotonous slick provocation functor hideaway holeable assimilable lola calypso nebuchadnezzar dogwood pacifism bivariate bought\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.assimilable bivariate bought calypso dogwood functor hideaway holeable lola monotonous nebuchadnezzar pacifism provocation slickHere are the words sorted alphabetically:\n", + "\n", + "1. assimilable\n", + "2. bivariate\n", + "3. bought\n", + "4. calypso\n", + "5. dogwood\n", + "6. functor\n", + "7. hideaway\n", + "8. holeable\n", + "9. lola\n", + "10. monotonous\n", + "11. nebuchadnezzar\n", + "12. pacifism\n", + "13. provocation\n", + "14. slick\n", + "\n", + "Answer: assimilable, bivariate, bought, calypso, dogwood, functor, hideaway, holeable, lola, monotonous, nebuchadnezzar, pacifism, provocation, slickpass
bbh_word_sorting-0111Sort the following words alphabetically: List: tuba synapse inaudible brewster tithing\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.brewster inaudible synapse tithing tubaThe given list of words is: tuba, synapse, inaudible, brewster, tithing.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter:\n", + "\n", + "1. **brewster** (Starts with 'b')\n", + "2. **inaudible** (Starts with 'i')\n", + "3. **tithing** (Starts with 't')\n", + "4. **tuba** (Starts with 't') - Compare 'tithing' and 'tuba'. 'i' comes before 'u', so 'tithing' comes before 'tuba'.\n", + "5. **synapse** (Starts with 's')\n", + "\n", + "Let's re-sort based on the first letter:\n", + "B: brewster\n", + "I: inaudible\n", + "S: synapse\n", + "T: tithing, tuba\n", + "\n", + "Now compare the words starting with 'T':\n", + "tithing\n", + "tuba\n", + "'i' comes before 'u', so 'tithing' comes before 'tuba'.\n", + "\n", + "The alphabetical order is:\n", + "1. brewster\n", + "2. inaudible\n", + "3. synapse\n", + "4. tithing\n", + "5. tuba\n", + "\n", + "Answer: brewster, inaudible, synapse, tithing, tuba\n", + "\n", + "Answer: brewster, inaudible, synapse, tithing, tubapass
bbh_word_sorting-0112Sort the following words alphabetically: List: kenya beth\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.beth kenyaThe given list of words is: kenya, beth.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter from the beginning.\n", + "\n", + "1. **Compare the first letter:**\n", + " - 'k' in "kenya"\n", + " - 'b' in "beth"\n", + " Since 'b' comes before 'k' in the alphabet, "beth" comes before "kenya".\n", + "\n", + "Therefore, the alphabetical order is: beth, kenya.\n", + "\n", + "Answer: beth, kenyapass
bbh_word_sorting-0113Sort the following words alphabetically: List: million boone chalcedony sinai satin genteel alternate charity olden\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.alternate boone chalcedony charity genteel million olden satin sinaiHere are the words to sort: million, boone, chalcedony, sinai, satin, genteel, alternate, charity, olden\n", + "\n", + "1. **alternate**\n", + "2. **boone**\n", + "3. **chalcedony**\n", + "4. **charity**\n", + "5. **genteel**\n", + "6. **million**\n", + "7. **olden**\n", + "8. **satin**\n", + "9. **sinai**\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **a**lternate\n", + "* **b**oone\n", + "* **c**halcedony\n", + "* **c**harity (comes after chalcedony because 'h' comes before 'r')\n", + "* **g**enteel\n", + "* **m**illion\n", + "* **o**lden\n", + "* **s**atin\n", + "* **s**inai (satin comes before sinai because 'a' comes before 'i')\n", + "\n", + "Let's re-examine the 'c' words: chalcedony, charity.\n", + "chalcedony (ch-a-l)\n", + "charity (ch-a-r)\n", + "'l' comes before 'r', so chalcedony comes before charity.\n", + "\n", + "Let's re-examine the 's' words: satin, sinai.\n", + "satin (s-a-t)\n", + "sinai (s-i-n)\n", + "'a' comes before 'i', so satin comes before sinai.\n", + "\n", + "Final alphabetical order:\n", + "1. alternate\n", + "2. boone\n", + "3. chalcedony\n", + "4. charity\n", + "5. genteel\n", + "6. million\n", + "7. olden\n", + "8. satin\n", + "9. sinai\n", + "\n", + "Answer: alternate, boone, chalcedony, charity, genteel, million, olden, satin, sinaipass
bbh_word_sorting-0114Sort the following words alphabetically: List: him correspond seashore herpes\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.correspond herpes him seashoreThe given list of words is: him, correspond, seashore, herpes.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter:\n", + "\n", + "1. **h**im\n", + "2. **h**erpes\n", + "3. **c**orrespond\n", + "4. **s**eashore\n", + "\n", + "Comparing the first letter: 'c' comes before 'h', and 'h' comes before 's'.\n", + "\n", + "So, 'correspond' comes first among the words starting with 'c', 'h', or 's'. Wait, let's re-examine the starting letters:\n", + "* him: h\n", + "* correspond: c\n", + "* seashore: s\n", + "* herpes: h\n", + "\n", + "Alphabetical order of starting letters: c, h, h, s.\n", + "\n", + "1. **c**orrespond (starts with c)\n", + "2. **h**im (starts with h)\n", + "3. **h**erpes (starts with h)\n", + "4. **s**eashore (starts with s)\n", + "\n", + "Now we compare the words starting with 'h': 'him' and 'herpes'.\n", + "* hi**m**\n", + "* he**r**pes\n", + "'i' comes before 'e' is false. 'e' comes before 'i'.\n", + "So, 'herpes' comes before 'him'.\n", + "\n", + "The sorted order is:\n", + "1. correspond\n", + "2. herpes\n", + "3. him\n", + "4. seashore\n", + "\n", + "Let's verify the comparison between 'herpes' and 'him':\n", + "h e r p e s\n", + "h i m\n", + "'e' comes before 'i', so 'herpes' comes before 'him'.\n", + "\n", + "Final sorted list: correspond, herpes, him, seashore.\n", + "\n", + "Answer: correspond, herpes, him, seashore\n", + "\n", + "Answer: correspond, herpes, him, seashorepass
bbh_word_sorting-0115Sort the following words alphabetically: List: catechism daddy\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.catechism daddyThe given list of words is: catechism, daddy.\n", + "\n", + "To sort them alphabetically, we compare them letter by letter:\n", + "\n", + "1. **catechism**: Starts with 'c'\n", + "2. **daddy**: Starts with 'd'\n", + "\n", + "Since 'c' comes before 'd' in the alphabet, "catechism" comes before "daddy".\n", + "\n", + "The alphabetical order is: catechism, daddy.\n", + "\n", + "Answer: catechism, daddypass
bbh_word_sorting-0116Sort the following words alphabetically: List: zinc coriolanus leather easternmost highwaymen scavenge musky cutlet synaptic creak heaven paula astigmat muscular godson boyish\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.astigmat boyish coriolanus creak cutlet easternmost godson heaven highwaymen leather muscular musky paula scavenge synaptic zincHere are the words sorted alphabetically:\n", + "\n", + "1. astigmat\n", + "2. boyish\n", + "3. creak\n", + "4. coriolanus\n", + "5. cutlet\n", + "6. easternmost\n", + "7. godson\n", + "8. heaven\n", + "9. highwaymen\n", + "10. leather\n", + "11. musky\n", + "12. muscular\n", + "13. paula\n", + "14. scavenge\n", + "15. synaptic\n", + "16. zinc\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **a**stigmat\n", + "* **b**oyish\n", + "* **c**reak, **c**oriolanus, **c**utlet (creak, coriolanus, cutlet)\n", + "* **e**asternmost\n", + "* **g**odson\n", + "* **h**aven, **h**ighwaymen\n", + "* **l**eather\n", + "* **m**usky, **m**uscular\n", + "* **p**aula\n", + "* **s**cavenge, **s**ynaptic\n", + "* **z**inc\n", + "\n", + "Detailed sorting:\n", + "\n", + "1. astigmat\n", + "2. boyish\n", + "3. creak\n", + "4. coriolanus\n", + "5. cutlet\n", + "6. easternmost\n", + "7. godson\n", + "8. heaven\n", + "9. highwaymen\n", + "10. leather\n", + "11. musky\n", + "12. muscular\n", + "13. paula\n", + "14. scavenge\n", + "15. synaptic\n", + "16. zinc\n", + "\n", + "(Comparing 'heaven' and 'highwaymen': H-e vs H-i. 'heaven' comes before 'highwaymen'.)\n", + "(Comparing 'musky' and 'muscular': 'musky' comes before 'muscular'.)\n", + "\n", + "Final sorted list:\n", + "astigmat, boyish, creak, coriolanus, cutlet, easternmost, godson, heaven, highwaymen, leather, musky, muscular, paula, scavenge, synaptic, zinc\n", + "\n", + "Answer: astigmat, boyish, creak, coriolanus, cutlet, easternmost, godson, heaven, highwaymen, leather, musky, muscular, paula, scavenge, synaptic, zincfail
bbh_word_sorting-0117Sort the following words alphabetically: List: wagging cabdriver astronomic pivot loch coherent\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.astronomic cabdriver coherent loch pivot waggingThe given list of words is: wagging, cabdriver, astronomic, pivot, loch, coherent.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter:\n", + "\n", + "1. **astronomic** (Starts with 'a')\n", + "2. **cabdriver** (Starts with 'c')\n", + "3. **coherent** (Starts with 'c')\n", + "4. **loch** (Starts with 'l')\n", + "5. **pivot** (Starts with 'p')\n", + "6. **wagging** (Starts with 'w')\n", + "\n", + "Now, let's compare the words starting with 'c':\n", + "* cabdriver\n", + "* coherent\n", + "\n", + "Comparing 'cabdriver' and 'coherent':\n", + "* c-a-b...\n", + "* c-o-h...\n", + "'a' comes before 'o', so 'cabdriver' comes before 'coherent'.\n", + "\n", + "The sorted order for the 'c' words is: cabdriver, coherent.\n", + "\n", + "Now, let's put all the words in order:\n", + "1. astronomic (a)\n", + "2. cabdriver (c)\n", + "3. coherent (c)\n", + "4. loch (l)\n", + "5. pivot (p)\n", + "6. wagging (w)\n", + "\n", + "The final alphabetical order is: astronomic, cabdriver, coherent, loch, pivot, wagging.\n", + "\n", + "Answer: astronomic, cabdriver, coherent, loch, pivot, waggingpass
bbh_word_sorting-0118Sort the following words alphabetically: List: no spokane cartilaginous that'd science\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.cartilaginous no science spokane that'dThe given list of words is: no, spokane, cartilaginous, that'd, science.\n", + "\n", + "To sort them alphabetically, we compare them letter by letter:\n", + "\n", + "1. **cartilaginous** (Starts with 'c')\n", + "2. **no** (Starts with 'n')\n", + "3. **science** (Starts with 's')\n", + "4. **spokane** (Starts with 's', but 'sp' comes after 'sc')\n", + "5. **that'd** (Starts with 't')\n", + "\n", + "Let's compare the words starting with 's': 'science' and 'spokane'.\n", + "'science' comes before 'spokane' because 'ci' comes before 'po'.\n", + "\n", + "Now let's order the words starting with 'n' and 't':\n", + "'no' comes before 'that'd'.\n", + "\n", + "The complete alphabetical order is:\n", + "1. cartilaginous\n", + "2. no\n", + "3. science\n", + "4. spokane\n", + "5. that'd\n", + "\n", + "Answer: cartilaginous, no, science, spokane, that'd\n", + "\n", + "Answer: cartilaginous, no, science, spokane, that'dpass
bbh_word_sorting-0119Sort the following words alphabetically: List: gentle boletus galveston aniline eddy fontainebleau wile scandalous skat sportsmen\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.aniline boletus eddy fontainebleau galveston gentle scandalous skat sportsmen wileThe given list of words is: gentle, boletus, galveston, aniline, eddy, fontainebleau, wile, scandalous, skat, sportsmen.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter:\n", + "\n", + "1. **aniline** (a)\n", + "2. **boletus** (b)\n", + "3. **eddy** (e)\n", + "4. **fontainebleau** (f)\n", + "5. **gentle** (g)\n", + "6. **galveston** (g) - 'galveston' comes after 'gentle' because 'a' comes before 'e' in the second position of 'galveston' compared to 'gentle' (if we consider the first letter, 'g' is the same, then 'a' vs 'e'). Wait, let's re-evaluate 'gentle' and 'galveston'.\n", + " - g e n t l e\n", + " - g a l v e s t o n\n", + " 'a' comes before 'e', so **galveston** comes before **gentle**.\n", + "\n", + "Let's re-sort based on the first letter:\n", + "A: aniline\n", + "B: boletus\n", + "E: eddy\n", + "F: fontainebleau\n", + "G: gentle, galveston\n", + "S: scandalous, skat, sportsmen, wile\n", + "\n", + "Sorting the 'G' words:\n", + "gentle (g e n t l e)\n", + "galveston (g a l v e s t o n)\n", + "'a' comes before 'e', so **galveston** comes before **gentle**.\n", + "\n", + "Sorting the 'S' words:\n", + "scandalous (s c a n d a l o u s)\n", + "skat (s k a t)\n", + "sportsmen (s p o r t s m e n)\n", + "wile (w i l e) - Wait, 'wile' starts with 'w', so it comes after 's' words.\n", + "\n", + "Let's list all words and sort them:\n", + "1. aniline\n", + "2. boletus\n", + "3. eddy\n", + "4. fontainebleau\n", + "5. galveston (G, a)\n", + "6. gentle (G, e)\n", + "7. scandalous (S, c)\n", + "8. skat (S, k)\n", + "9. sportsmen (S, p)\n", + "10. wile (W)\n", + "\n", + "Comparing the 'S' words:\n", + "scandalous (s c...)\n", + "skat (fail
bbh_word_sorting-0120Sort the following words alphabetically: List: labile crunchy highlight silage judaism allocable vale phenol dissipate bertram necessity champlain boutique hydrology facto often\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.allocable bertram boutique champlain crunchy dissipate facto highlight hydrology judaism labile necessity often phenol silage valeHere are the words sorted alphabetically:\n", + "\n", + "1. allocable\n", + "2. bertram\n", + "3. boutique\n", + "4. champlain\n", + "5. crunchy\n", + "6. dissipate\n", + "7. facet (facto)\n", + "8. highlight\n", + "9. hydrology\n", + "10. labile\n", + "11. necessity\n", + "12. often\n", + "13. phenol\n", + "14. silage\n", + "15. vale\n", + "16. judaism\n", + "\n", + "Let's re-sort carefully:\n", + "\n", + "* allocable\n", + "* bertram\n", + "* boutique\n", + "* champlain\n", + "* crunchy\n", + "* dissipate\n", + "* facet (facto) - Assuming 'facto' is a typo for 'fact' or sorting by the first letter 'f'. If we treat 'facto' as is:\n", + "* highlight\n", + "* hydrology\n", + "* labile\n", + "* necessity\n", + "* often\n", + "* phenol\n", + "* silage\n", + "* vale\n", + "* judaism\n", + "\n", + "Comparing 'facto' and 'highlight': 'f' comes before 'h'.\n", + "\n", + "Let's list them and sort:\n", + "allocable (A)\n", + "bertram (B)\n", + "boutique (B) - bertram before boutique\n", + "champlain (C)\n", + "crunchy (C) - champlain before crunchy\n", + "dissipate (D)\n", + "facto (F)\n", + "highlight (H)\n", + "hydrology (H) - facto before hydrology\n", + "labile (L)\n", + "necessity (N)\n", + "often (O)\n", + "phenol (P)\n", + "silage (S)\n", + "vale (V)\n", + "judaism (J) - J comes before L\n", + "\n", + "Correct alphabetical order:\n", + "1. allocable\n", + "2. bertram\n", + "3. boutique\n", + "4. champlain\n", + "5. crunchy\n", + "6. dissipate\n", + "7. facto\n", + "8. highlight\n", + "9. hydrology\n", + "10. labile\n", + "11. judaism\n", + "12. necessity\n", + "13. often\n", + "14. phenol\n", + "15. silage\n", + "16. vale\n", + "\n", + "Wait, 'judaism' starts with J, which comes before L. Let's re-check the full list:\n", + "\n", + "labile (L)\n", + "judaism (J) -> J comes before L.\n", + "\n", + "Corrected order:\n", + "1. allocable\n", + "2. bertram\n", + "3. boutique\n", + "4. champlain\n", + "5. crunchy\n", + "6. dissipate\n", + "7. facto\n", + "8. highlight\n", + "9. hydrology\n", + "10. judaism\n", + "11. labile\n", + "12. necessity\n", + "13fail
bbh_word_sorting-0121Sort the following words alphabetically: List: hyperboloidal borough\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.borough hyperboloidalThe given words are: hyperboloidal, borough.\n", + "\n", + "To sort them alphabetically, we compare them letter by letter:\n", + "\n", + "1. **h**yperboloidal\n", + "2. **b**orough\n", + "\n", + "Since 'b' comes before 'h' in the alphabet, "borough" comes before "hyperboloidal".\n", + "\n", + "The alphabetical order is: borough, hyperboloidal.\n", + "\n", + "Answer: borough, hyperboloidal\n", + "Answer: borough, hyperboloidalpass
bbh_word_sorting-0122Sort the following words alphabetically: List: heterostructure bertrand careful wherewith libra eyelid feign southeastern paste snip\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.bertrand careful eyelid feign heterostructure libra paste snip southeastern wherewithHere are the words to sort: heterostructure, bertrand, careful, wherewith, libra, eyelid, feign, southeastern, paste, snip.\n", + "\n", + "Let's sort them alphabetically:\n", + "\n", + "1. careful\n", + "2. bertrand\n", + "3. eyelid\n", + "4. feign\n", + "5. heterostructure\n", + "6. libra\n", + "7. paste\n", + "8. southeastern\n", + "9. snip\n", + "10. wherewith\n", + "\n", + "Detailed comparison:\n", + "* **c**areful\n", + "* **b**ertrand\n", + "* **e**yelid\n", + "* **f**eign\n", + "* **h**eterostructure\n", + "* **l**ibra\n", + "* **p**aste\n", + "* **s**outheastern\n", + "* **s**nip (southeastern comes before snip because 'o' comes before 'n') - Wait, let's recheck 's' words.\n", + " * southeastern\n", + " * snip\n", + " * southeastern vs snip: southeastern comes before snip.\n", + "* **w**herewith\n", + "\n", + "Let's re-sort the 's' words:\n", + "* southeastern\n", + "* snip\n", + "\n", + "The final sorted list is:\n", + "1. careful\n", + "2. bertrand\n", + "3. eyelid\n", + "4. feign\n", + "5. heterostructure\n", + "6. libra\n", + "7. paste\n", + "8. southeastern\n", + "9. snip\n", + "10. wherewith\n", + "\n", + "Answer: careful, bertrand, eyelid, feign, heterostructure, libra, paste, southeastern, snip, wherewithfail
bbh_word_sorting-0123Sort the following words alphabetically: List: infantrymen knob yaounde aqueous deregulate lysergic gala\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.aqueous deregulate gala infantrymen knob lysergic yaoundeThe given list of words is: infantrymen, knob, yaounde, aqueous, deregulate, lysergic, gala.\n", + "\n", + "We need to sort these words alphabetically.\n", + "\n", + "Let's compare them letter by letter:\n", + "\n", + "1. **aqueous** (Starts with 'a')\n", + "2. **deregulate** (Starts with 'd')\n", + "3. **gala** (Starts with 'g')\n", + "4. **infantrymen** (Starts with 'i')\n", + "5. **knob** (Starts with 'k')\n", + "6. **lysergic** (Starts with 'l')\n", + "7. **yaounde** (Starts with 'y')\n", + "\n", + "Now let's sort the words starting with the same letter if any (none in this case, but we check the order of the first letters):\n", + "a, d, g, i, k, l, y\n", + "\n", + "Alphabetical order of the starting letters: a, d, g, i, k, l, y.\n", + "\n", + "1. **aqueous** (a)\n", + "2. **deregulate** (d)\n", + "3. **gala** (g)\n", + "4. **infantrymen** (i)\n", + "5. **knob** (k)\n", + "6. **lysergic** (l)\n", + "7. **yaounde** (y)\n", + "\n", + "The sorted list is: aqueous, deregulate, gala, infantrymen, knob, lysergic, yaounde.\n", + "\n", + "Answer: aqueous, deregulate, gala, infantrymen, knob, lysergic, yaounde\n", + "Answer: aqueous, deregulate, gala, infantrymen, knob, lysergic, yaoundepass
bbh_word_sorting-0124Sort the following words alphabetically: List: burley bela arapaho bacteria bock\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.arapaho bacteria bela bock burleyThe given list of words is: burley, bela, arapaho, bacteria, bock.\n", + "\n", + "We need to sort these words alphabetically. We compare them letter by letter:\n", + "\n", + "1. **bela** (Starts with 'b', second letter 'e')\n", + "2. **bacteria** (Starts with 'b', second letter 'a')\n", + "3. **bock** (Starts with 'b', second letter 'o')\n", + "4. **burley** (Starts with 'b', second letter 'u')\n", + "5. **arapaho** (Starts with 'a')\n", + "\n", + "Comparing the first letters: 'a' comes before 'b'.\n", + "So, **arapaho** is the first word.\n", + "\n", + "Now we compare the words starting with 'b': burley, bela, bacteria, bock.\n", + "\n", + "Comparing the second letters:\n", + "* bela: 'e'\n", + "* bacteria: 'a'\n", + "* bock: 'o'\n", + "* burley: 'u'\n", + "\n", + "'a' comes before 'e', 'o', and 'u'. So, **bacteria** comes next.\n", + "\n", + "Remaining words: burley, bela, bock.\n", + "\n", + "Comparing the second letters of the remaining words:\n", + "* bela: 'e'\n", + "* bock: 'o'\n", + "* burley: 'u'\n", + "\n", + "'e' comes before 'o' and 'u'. So, **bela** comes next.\n", + "\n", + "Remaining words: burley, bock.\n", + "\n", + "Comparing the second letters:\n", + "* bock: 'o'\n", + "* burley: 'u'\n", + "\n", + "'o' comes before 'u'. So, **bock** comes next.\n", + "\n", + "The last word is **burley**.\n", + "\n", + "The sorted order is: arapaho, bacteria, bela, bock, burley.\n", + "\n", + "Answer: arapaho, bacteria, bela, bock, burley\n", + "Answer: arapaho, bacteria, bela, bock, burleypass
bbh_word_sorting-0125Sort the following words alphabetically: List: betony bronzy charge cordial handicraft boar collet clemens bootleg possession mesh centaur contemporaneous macmahon underclassman monterey dorado contravariant regina\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.betony boar bootleg bronzy centaur charge clemens collet contemporaneous contravariant cordial dorado handicraft macmahon mesh monterey possession regina underclassmanHere are the words to be sorted alphabetically:\n", + "betony, bronzy, charge, cordial, handicraft, boar, collet, clemens, bootleg, possession, mesh, centaur, contemporaneous, macmahon, monterey, dorado, contravariant, regina, underclassman\n", + "\n", + "Let's sort them:\n", + "\n", + "1. **boar**\n", + "2. **betony**\n", + "3. **bootleg**\n", + "4. **bronzy**\n", + "5. **charge**\n", + "6. **centaur**\n", + "7. **clemens**\n", + "8. **collet**\n", + "9. **contravariant**\n", + "10. **cordial**\n", + "11. **danicraft** (handicraft)\n", + "12. **dorado**\n", + "13. **macmahon**\n", + "14. **mesh**\n", + "15. **monterey**\n", + "16. **possession**\n", + "17. **regina**\n", + "18. **underclassman**\n", + "19. **contemporaneous**\n", + "\n", + "Let's re-sort carefully, paying attention to the initial letters:\n", + "\n", + "B: betony, boar, bootleg, bronzy\n", + "C: charge, cordial, collet, clemens, centaur, contemporaneous, contravariant\n", + "D: dorado\n", + "H: handicraft\n", + "M: macmahon, mesh, monterey\n", + "P: possession\n", + "R: regina\n", + "U: underclassman\n", + "\n", + "**Step 1: Group by first letter**\n", + "\n", + "B: betony, boar, bootleg, bronzy\n", + "C: charge, cordial, collet, clemens, centaur, contemporaneous, contravariant\n", + "D: dorado\n", + "H: handicraft\n", + "M: macmahon, mesh, monterey\n", + "P: possession\n", + "R: regina\n", + "U: underclassman\n", + "\n", + "**Step 2: Sort within groups**\n", + "\n", + "B: betony, boar, bootleg, bronzy (betony, boar, bootleg, bronzy)\n", + " * betony\n", + " * boar\n", + " * bootleg\n", + " * bronzy\n", + "\n", + "C: charge, cordial, collet, clemens, centaur, contemporaneous, contravariant (charge, cordial, collet, clemens, centaur, contemporaneous, contravariant)\n", + " * charge\n", + " * cordial\n", + " * collet\n", + " * clemens\n", + " * centaur\n", + " * contemporaneous\n", + " * contravariant\n", + "\n", + "D: dorado\n", + "\n", + "H: handicfail
bbh_word_sorting-0126Sort the following words alphabetically: List: skinny sharon sudan earthy parentage hello inmate announce co clayton carp phonetic watson nimbus\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.announce carp clayton co earthy hello inmate nimbus parentage phonetic sharon skinny sudan watsonHere are the words sorted alphabetically:\n", + "\n", + "1. announce\n", + "2. carp\n", + "3. clayton\n", + "4. earthy\n", + "5. hello\n", + "6. inmate\n", + "7. nimbus\n", + "8. parentage\n", + "9. phonetic\n", + "10. skinny\n", + "11. sharon\n", + "12. sudan\n", + "13. watson\n", + "\n", + "Answer: announce, carp, clayton, earthy, hello, inmate, nimbus, parentage, phonetic, skinny, sharon, sudan, watsonfail
bbh_word_sorting-0127Sort the following words alphabetically: List: covalent spiderwort horowitz divisive spiritual cheshire affluent gideon quadrature julio peanut epsilon diagnostician grover folklore gothic salient\n", + "\n", + "Solve the problem. Put the final answer on its own last line as 'Answer: <answer>'.affluent cheshire covalent diagnostician divisive epsilon folklore gideon gothic grover horowitz julio peanut quadrature salient spiderwort spiritualHere are the words sorted alphabetically:\n", + "\n", + "1. affluent\n", + "2. cheshire\n", + "3. covalent\n", + "4. diagnostician\n", + "5. divisive\n", + "6. epsilon\n", + "7. folklore\n", + "8. gothic\n", + "9. gideon\n", + "10. grover\n", + "11. horowitz\n", + "12. julio\n", + "13. peanut\n", + "14. quadrature\n", + "15. salient\n", + "16. spiderwort\n", + "17. spiritual\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **a**ffluent\n", + "* **c**ovalent, **c**heshire\n", + "* **d**iagnostician, **d**ivisive\n", + "* **e**psilon\n", + "* **f**olklore, **g**othic, **g**ideon, **g**rover\n", + "* **h**orowitz\n", + "* **j**ulio\n", + "* **p**eanut\n", + "* **q**uadrature\n", + "* **s**alient, **s**piderwort, **s**piritual\n", + "\n", + "**Detailed Alphabetical Sort:**\n", + "\n", + "1. affluent\n", + "2. cheshire\n", + "3. covalent\n", + "4. diagnostician\n", + "5. divisive\n", + "6. epsilon\n", + "7. folklore\n", + "8. gothic\n", + "9. gideon\n", + "10. grover\n", + "11. horowitz\n", + "12. julio\n", + "13. peanut\n", + "14. quadrature\n", + "15. salient\n", + "16. spiderwort\n", + "17. spiritual\n", + "\n", + "Wait, let's check the 'G' words: gothic, gideon, grover.\n", + "Gothic (go...)\n", + "Gideon (gi...)\n", + "Grover (gr...)\n", + "Order: gothic, gideon, grover.\n", + "\n", + "Let's check the 'S' words: salient, spiderwort, spiritual.\n", + "Salient (sal...)\n", + "Spiderwort (spi...)\n", + "Spiritual (spi...)\n", + "Salient comes before Spiderwort and Spiritual.\n", + "Spiderwort (spi**d**)\n", + "Spiritual (spi**r**)\n", + "Order: salient, spiderwort, spiritual.\n", + "\n", + "**Final Sorted List:**\n", + "\n", + "1. affluent\n", + "2. cheshire\n", + "3. covalent\n", + "4. diagnostician\n", + "5. divisive\n", + "6. epsilon\n", + "7. folklore\n", + "8. gothic\n", + "9. gideon\n", + "10. grover\n", + "11. horowitz\n", + "12. julio\n", + "13. peanut\n", + "14. quadrature\n", + "15. salient\n", + "16. spiderwort\n", + "17. spiritual\n", + "\n", + "(Re-checkingfail
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idexpectedbaselinecandidaterepairedregressed
bbh_word_sorting-0047belize bolshevism cost dance deadline dietetic formulae foster hesitant huddle judson mantle odessa palace progeny proust rackety resplendent thirdhand warmthHere are the words sorted alphabetically:\n", + "\n", + "1. bolshevism\n", + "2. belize\n", + "3. dance\n", + "4. deadline\n", + "5. dietetic\n", + "6. foster\n", + "7. formulae\n", + "8. hesitant\n", + "9. huddle\n", + "10. judson\n", + "11. mantle\n", + "12. odessa\n", + "13. palace\n", + "14. proust\n", + "15. rackety\n", + "16. resplendent\n", + "17. thirdhand\n", + "18. warmth\n", + "19. cost\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **b**olshevism, **b**elize (bolshevism before belize)\n", + "* **d**ance, **d**eadline, **d**ietetic\n", + "* **f**oster, **f**ormulae\n", + "* **h**esitant, **h**uddle\n", + "* **j**udson\n", + "* **m**antle\n", + "* **o**dessa\n", + "* **p**alace, **p**roust\n", + "* **r**ackety, **r**esplendent\n", + "* **t**hirdhand\n", + "* **w**armth\n", + "* **c**ost\n", + "\n", + "**Alphabetical Order:**\n", + "\n", + "1. bolshevism\n", + "2. belize\n", + "3. dance\n", + "4. deadline\n", + "5. dietetic\n", + "6. foster\n", + "7. formulae\n", + "8. hesitant\n", + "9. huddle\n", + "10. judson\n", + "11. mantle\n", + "12. odessa\n", + "13. palace\n", + "14. proust\n", + "15. rackety\n", + "16. resplendent\n", + "17. thirdhand\n", + "18. warmth\n", + "19. cost\n", + "\n", + "Wait, let's check the 'b' words again: bolshevism, belize. 'b'e comes before 'b'o.\n", + "\n", + "Let's re-sort the full list:\n", + "odessa, dance, formulae, dietetic, mantle, cost, huddle, resplendent, bolshevism, proust, warmth, deadline, progeny, hesitant, palace, rackety, judson, foster, belize, thirdhand\n", + "\n", + "(Note: I missed 'progeny' in my initial count, let's include it.)\n", + "\n", + "Full List: odessa, dance, formulae, dietetic, mantle, cost, huddle, resplendent, bolshevism, proust, warmth, deadline, progeny, hesitant, palace, rackety, judAnswer: bolshevism, belize, cost, dance, deadline, dietetic, foster, formulae, hesitant, huddle, judson, mantle, odessa, palace, proust, progeny, rackety, resplendent, thirdhand, warmthFalseFalse
bbh_word_sorting-0019broadcast cortland diffusible galvanometer gross gujarati incestuous larynx nomograph pewter scout sketchbook stag transitionHere are the words sorted alphabetically:\n", + "\n", + "1. broadcast\n", + "2. cortland\n", + "3. diffusible\n", + "4. galvanometer\n", + "5. gross\n", + "6. gujarati\n", + "7. incestuous\n", + "8. larynx\n", + "9. nomograph\n", + "10. pewter\n", + "11. scout\n", + "12. sketchbook\n", + "13. stag\n", + "14. transition\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **b**roadcast\n", + "* **c**ortland\n", + "* **d**iffusible\n", + "* **g**alvometer\n", + "* **g**ross\n", + "* **g**ujarati\n", + "* **i**ncestuous\n", + "* **l**arynx\n", + "* **n**omograph\n", + "* **p**ewter\n", + "* **s**cout\n", + "* **s**teag (Wait, 'stag' comes before 'sketchbook' if we look at the second letter, but alphabetically 'stag' comes before 'sketchbook' if we compare 't' vs 'k'. Let's re-examine the list.)\n", + "\n", + "Original List: pewter gross gujarati cortland diffusible nomograph stag broadcast galvanometer transition incestuous sketchbook scout larynx\n", + "\n", + "1. broadcast\n", + "2. cortland\n", + "3. diffusible\n", + "4. galvanometer\n", + "5. gross\n", + "6. gujarati\n", + "7. incestuous\n", + "8. larynx\n", + "9. nomograph\n", + "10. pewter\n", + "11. scout\n", + "12. sketchbook\n", + "13. stag\n", + "14. transition\n", + "\n", + "Let's perform a strict alphabetical sort:\n", + "\n", + "1. broadcast (b)\n", + "2. cortland (c)\n", + "3. diffusible (d)\n", + "4. galvanometer (g)\n", + "5. gross (g)\n", + "6. gujarati (g)\n", + "7. incestuous (i)\n", + "8. larynx (l)\n", + "9. nomograph (n)\n", + "10. pewter (p)\n", + "11. scout (s)\n", + "12. sketchbook (s)\n", + "13. stag (s)\n", + "14. transition (t)\n", + "\n", + "Sorting the 'g' words: galvanometer, gross, gujarati.\n", + "* galvanometer\n", + "* gross\n", + "* gujarati\n", + "\n", + "Sorting the 's' words: scout, sketchbook, stag.\n", + "* sketchbook (sk)\n", + "* scout (sc)\n", + "* stag (st)\n", + "* Therefore: sketchbook, scout, stag\n", + "\n", + "Final sorted list:\n", + "1. broadcast\n", + "2. cortland\n", + "Answer: broadcast, cortland, diffusible, galvanometer, gross, gujarati, incestuous, larynx, nomograph, pewter, sketchbook, scout, stag, transitionFalseFalse
bbh_word_sorting-0005haughty seashoreThe given words are: seashore, haughty.\n", + "\n", + "To sort them alphabetically, we compare them letter by letter from the beginning.\n", + "\n", + "1. **s**eashore\n", + "2. **h**aughty\n", + "\n", + "Since 'h' comes before 's' in the alphabet, "haughty" comes before "seashore".\n", + "\n", + "The alphabetical order is: haughty, seashore.\n", + "\n", + "Answer: haughty, seashore\n", + "Answer: haughty, seashoreAnswer: haughty, seashoreFalseFalse
bbh_word_sorting-0024aerodynamic botanist giacomo habitation jimmy nebulous offset padre panicking roosevelt schoolmate suburbia vector wvHere are the words to sort alphabetically:\n", + "wv, habitation, panicking, schoolmate, aerodynamic, giacomo, nebulous, offset, vector, roosevelt, jimmy, botanist, suburbia, padre\n", + "\n", + "Let's sort them:\n", + "\n", + "1. **aerodynamic**\n", + "2. **botanist**\n", + "3. **habitation**\n", + "4. **giacomo**\n", + "5. **jimmy**\n", + "6. **nebulous**\n", + "7. **offset**\n", + "8. **panicking**\n", + "9. **padre**\n", + "10. **roosevelt**\n", + "11. **schoolmate**\n", + "12. **suburbia**\n", + "13. **vector**\n", + "14. **wv** (Assuming 'wv' is treated as a word, or placed based on its initial letters if it's an abbreviation. In standard alphabetical sorting, it comes before 'habitation' if we consider 'w' before 'h', but usually, if it's an initialism, it's treated as is. If we treat it strictly alphabetically based on the first letter 'w', it would be near the end.)\n", + "\n", + "Let's re-evaluate based on standard alphabetical order (case-insensitive, treating 'wv' as starting with 'w'):\n", + "\n", + "* aerodynamic (a)\n", + "* botanist (b)\n", + "* habitation (h)\n", + "* giacomo (g)\n", + "* jimmy (j)\n", + "* nebulous (n)\n", + "* offset (o)\n", + "* panicking (p)\n", + "* padre (p)\n", + "* roosevelt (r)\n", + "* schoolmate (s)\n", + "* suburbia (s)\n", + "* vector (v)\n", + "* wv (w)\n", + "\n", + "Sorting the 'p' words: panicking, padre. (panicking comes before padre)\n", + "Sorting the 's' words: schoolmate, suburbia. (schoolmate comes before suburbia)\n", + "\n", + "Final sorted list:\n", + "1. aerodynamic\n", + "2. botanist\n", + "3. habitation\n", + "4. giacomo\n", + "5. jimmy\n", + "6. nebulous\n", + "7. offset\n", + "8. panicking\n", + "9. padre\n", + "10. roosevelt\n", + "11. schoolmate\n", + "12. suburbia\n", + "13. vector\n", + "14. wv\n", + "\n", + "Answer: aerodynamic, botanist, habitation, giacomo, jimmy, nebulous, offset, panicking, padre, roosevelt, schoolmate, suburbiaAnswer: aerodynamic, botanist, giacomo, habitation, jimmy, nebulous, offset, panicking, padre, roosevelt, schoolmate, suburbia, vector, wvFalseFalse
bbh_word_sorting-0086alcohol behold escutcheon forth fumarole hackberry motif pease regret satisfy uptake walkieHere are the words to sort alphabetically:\n", + "regret, uptake, satisfy, behold, fumarole, motif, forth, alcohol, escutcheon, pease, hackberry, walkie\n", + "\n", + "Let's sort them:\n", + "\n", + "1. alcohol\n", + "2. behold\n", + "3. escutcheon\n", + "4. fumarole\n", + "5. forth\n", + "6. hackberry\n", + "7. motif\n", + "8. pease\n", + "9. regret\n", + "10. satisfy\n", + "11. uptake\n", + "12. walkie\n", + "\n", + "Detailed comparison:\n", + "* **a**lcohol\n", + "* **b**ehold\n", + "* **e**scutcheon\n", + "* **f**umarole\n", + "* **f**orth (fumarole comes before forth if we compare the third letter: u vs o. Wait, F-O-R-T-H vs F-U-M-A-R-O-L-E. O comes before U.)\n", + " * Re-evaluating F words: fumarole (fu...), forth (fo...) -> forth comes before fumarole.\n", + " * Let's re-sort F words: forth, fumarole.\n", + "* **h**ackberry\n", + "* **m**otif\n", + "* **p**ease\n", + "* **r**egret\n", + "* **s**atisfy\n", + "* **u**ptake\n", + "* **w**alkie\n", + "\n", + "Let's perform a full alphabetical sort:\n", + "\n", + "1. alcohol (A)\n", + "2. behold (B)\n", + "3. escutcheon (E)\n", + "4. forth (F)\n", + "5. fumarole (F) (f-o vs f-u)\n", + "6. hackberry (H)\n", + "7. motif (M)\n", + "8. pease (P)\n", + "9. regret (R)\n", + "10. satisfy (S)\n", + "11. uptake (U)\n", + "12. walkie (W)\n", + "\n", + "Checking F words again:\n", + "forth: f-o-r-t-h\n", + "fumarole: f-u-m-a-r-o-l-e\n", + "'o' comes before 'u', so **forth** comes before **fumarole**.\n", + "\n", + "Final sorted list:\n", + "1. alcohol\n", + "2. behold\n", + "3. escutcheon\n", + "4. forth\n", + "5. fumarole\n", + "6. hackberry\n", + "7. motif\n", + "8. pease\n", + "9. regret\n", + "10. satisfy\n", + "11. uptake\n", + "1Answer: alcohol, behold, escutcheon, forth, fumarole, hackberry, motif, pease, regret, satisfy, uptake, walkieTrueFalse
bbh_word_sorting-0100christen clearheaded despond driveway encapsulate fungi gob mendelevium midwinter purpose sisyphus stanhope strip studious symmetry trample vs wringHere are the words sorted alphabetically:\n", + "\n", + "1. clearheaded\n", + "2. christen\n", + "3. despond\n", + "4. driveway\n", + "5. encapsulate\n", + "6. fungi\n", + "7. mendelevium\n", + "8. midwinter\n", + "9. purpose\n", + "10. stanhope\n", + "11. strip\n", + "12. studious\n", + "13. symmetry\n", + "14. trample\n", + "15. vs\n", + "16. wring\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* clearheaded\n", + "* christen\n", + "* despond\n", + "* driveway\n", + "* encapsulate\n", + "* fungi\n", + "* mendelevium\n", + "* midwinter\n", + "* purpose\n", + "* stanhope\n", + "* strip\n", + "* studious\n", + "* symmetry\n", + "* trample\n", + "* vs\n", + "* wring\n", + "\n", + "(Checking the initial list again: stanhope, sisyphus, fungi, purpose, midwinter, mendelevium, studious, driveway, symmetry, despond, trample, wring, strip, clearheaded, encapsulate, vs, gob, christen)\n", + "\n", + "Wait, I missed "sisyphus" and "gob". Let's re-list and sort:\n", + "\n", + "Original List: stanhope, sisyphus, fungi, purpose, midwinter, mendelevium, studious, driveway, symmetry, despond, trample, wring, strip, clearheaded, encapsulate, vs, gob, christen\n", + "\n", + "Sorted List:\n", + "1. clearheaded\n", + "2. christen\n", + "3. despond\n", + "4. driveway\n", + "5. encapsulate\n", + "6. fungi\n", + "7. gob\n", + "8. mendelevium\n", + "9. midwinter\n", + "10. purpose\n", + "11. stanhope\n", + "12. sisyphus\n", + "13. strip\n", + "14. studious\n", + "15. symmetry\n", + "16. trample\n", + "17. vs\n", + "18. wring\n", + "\n", + "Answer: clearheaded, christen, despond, driveway, encapsulate, fungi, gob, mendelevium, midwinter, purpose, stanhope, sisyphus, strip, studious, symmetry, trample, vs, wringclearheaded, christen, despond, driveway, encapsulate, fungi, gob, mendelevium, midwinter, purpose, stanhope, sisyphus, strip, studious, symmetry, trample, vs, wring\n", + "\n", + "Answer: clearheaded, christen, despond, driveway, encapsulate, fungi, gob, mendelevium, midwinter, purpose, stanhope, sisyphus, strip, studious, symmetry, trample, vs, wringFalseFalse
bbh_word_sorting-0011bilk lethe perturb tactualThe given list of words is: lethe, bilk, perturb, tactual.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter from the beginning.\n", + "\n", + "1. **lethe** (starts with 'l')\n", + "2. **bilk** (starts with 'b')\n", + "3. **perturb** (starts with 'p')\n", + "4. **tactual** (starts with 't')\n", + "\n", + "Comparing the first letters: 'b', 'l', 'p', 't'.\n", + "In alphabetical order: 'b', 'l', 'p', 't'.\n", + "\n", + "1. **bilk** (b)\n", + "2. **lethe** (l)\n", + "3. **perturb** (p)\n", + "4. **tactual** (t)\n", + "\n", + "The sorted list is: bilk, lethe, perturb, tactual.\n", + "\n", + "Answer: bilk, lethe, perturb, tactual\n", + "\n", + "Answer: bilk, lethe, perturb, tactualThe given list of words is: lethe, bilk, perturb, tactual.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter from the beginning.\n", + "\n", + "The words are:\n", + "1. lethe\n", + "2. bilk\n", + "3. perturb\n", + "4. tactual (Corrected to tactical)\n", + "\n", + "Comparing the first letters: 'l', 'b', 'p', 't'.\n", + "In alphabetical order: 'b', 'l', 'p', 't'.\n", + "\n", + "1. **bilk** (b)\n", + "2. **lethe** (l)\n", + "3. **perturb** (p)\n", + "4. **tactical** (t)\n", + "\n", + "The sorted list is: bilk, lethe, perturb, tactical.\n", + "\n", + "Answer: bilk, lethe, perturb, tacticalFalseTrue
bbh_word_sorting-0037abdominal address berry bounty effusive fomalhaut hanoverian involve islamabad jordan optimal pay stearic stigmata swathe tattoo them tornado yangHere are the words sorted alphabetically:\n", + "\n", + "1. abdominal\n", + "2. address\n", + "3. bounty\n", + "4. berry\n", + "5. effusive\n", + "6. fomalhaut\n", + "7. hanoverian\n", + "8. islamabad\n", + "9. involve\n", + "10. pay\n", + "11. stigmata\n", + "12. stearic\n", + "13. swathe\n", + "14. tattoo\n", + "15. them\n", + "16. tornado\n", + "17. optimal\n", + "18. yang\n", + "19. jordan\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **a**bdominal\n", + "* **a**ddress\n", + "* **b**ounty\n", + "* **b**erry\n", + "* **e**ffusive\n", + "* **f**omalhaut\n", + "* **h**anoverian\n", + "* **i**slamabad\n", + "* **i**nvolve\n", + "* **p**ay\n", + "* **s**tigmata\n", + "* **s**tearic\n", + "* **s**wathe\n", + "* **t**attoo\n", + "* **t**hem\n", + "* **t**ornado\n", + "* **o**ptimal\n", + "* **y**ang\n", + "* **j**ordan (Wait, J comes before O and Y)\n", + "\n", + "Let's re-sort based on the first letter:\n", + "\n", + "A: abdominal, address\n", + "B: bounty, berry\n", + "E: effusive\n", + "F: fomalhaut\n", + "H: hanoverian\n", + "I: islamabad, involve\n", + "J: jordan\n", + "O: optimal\n", + "S: stigmata, stearic, swathe\n", + "T: tattoo, them, tornado\n", + "Y: yang\n", + "\n", + "**Detailed Sorting:**\n", + "\n", + "1. abdominal\n", + "2. address\n", + "3. bounty\n", + "4. berry\n", + "5. effusive\n", + "6. fomalhaut\n", + "7. hanoverian\n", + "8. islamabad\n", + "9. involve\n", + "10. jordan\n", + "11. optimal\n", + "12. stigmata\n", + "13. stearic\n", + "14. swathe\n", + "15. tattoo\n", + "16. them\n", + "17. tornado\n", + "18. yang\n", + "\n", + "Answer: abdominal, address, bounty, berry, effusive, fomalhaut, hanoverian, islamabad, involve, jordan, optimal, stigmata, stearic, swathe, tattoo, them, tornado, yangAnswer: abdominal, address, bounty, berry, effusive, fomalhaut, hanoverian, islamabad, involve, jordan, optimal, stigmata, stearic, swathe, tattoo, them, tornado, yangFalseFalse
bbh_word_sorting-0069acquisitive annuity autocracy bruno custody dare exploitation lodge militant quench somatic thunderclap ventricleHere are the words sorted alphabetically:\n", + "\n", + "1. acquisitive\n", + "2. annuity\n", + "3. autocracy\n", + "4. bruno\n", + "5. custody\n", + "6. dare\n", + "7. exploitation\n", + "8. lodge\n", + "9. militant\n", + "10. quench\n", + "11. somatic\n", + "12. thunderclap\n", + "13. ventricle\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **a**cquisitive, **a**nnuity, **a**utocracy\n", + "* **b**runo\n", + "* **c**ustody\n", + "* **d**are\n", + "* **e**xploitation (Wait, 'e' comes before 'l', 'm', 'q', 's', 't', 'v')\n", + "* **l**odge\n", + "* **m**ilitant\n", + "* **q**uench\n", + "* **s**omatic\n", + "* **t**hunderclap\n", + "* **v**entricle\n", + "\n", + "Let's list them and compare:\n", + "1. acquisitive\n", + "2. annuity\n", + "3. autocracy\n", + "4. bruno\n", + "5. custody\n", + "6. dare\n", + "7. exploitation\n", + "8. lodge\n", + "9. militant\n", + "10. quench\n", + "11. somatic\n", + "12. thunderclap\n", + "13. ventricle\n", + "\n", + "Checking the order:\n", + "acquisitive (a-c)\n", + "annuity (a-n)\n", + "autocracy (a-u)\n", + "bruno (b)\n", + "custody (c-u)\n", + "dare (d)\n", + "exploitation (e)\n", + "lodge (l)\n", + "militant (m)\n", + "quench (q)\n", + "somatic (s)\n", + "thunderclap (t)\n", + "ventricle (v)\n", + "\n", + "The correct alphabetical order is:\n", + "1. acquisitive\n", + "2. annuity\n", + "3. autocracy\n", + "4. bruno\n", + "5. custody\n", + "6. dare\n", + "7. exploitation\n", + "8. lodge\n", + "9. militant\n", + "10. quench\n", + "11. somatic\n", + "12. thunderclap\n", + "13. ventricle\n", + "\n", + "Answer: acquisitive, annuity, autocracy, bruno, custody, dare, exploitation, lodge, militant, quench, somatic, thunderclap, ventricleAnswer: acquisitive, annuity, autocracy, bruno, custody, dare, exploitation, lodge, militant, quench, somatic, thunderclap, ventricleFalseFalse
bbh_word_sorting-0021confidential faery fiction heterozygous horehound overture ursaThe given list of words is: confidential, ursa, fiction, heterozygous, faery, overture, horehound.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter:\n", + "\n", + "1. **confidential** (Starts with 'c')\n", + "2. **faery** (Starts with 'f')\n", + "3. **heterozygous** (Starts with 'h')\n", + "4. **fiction** (Starts with 'f')\n", + "5. **horehound** (Starts with 'h')\n", + "6. **overture** (Starts with 'o')\n", + "7. **ursa** (Starts with 'u')\n", + "\n", + "Let's sort the words starting with 'f':\n", + "* faery\n", + "* fiction\n", + "Comparing 'faery' and 'fiction': 'fai' vs 'fic'. 'a' comes before 'i', so 'faery' comes before 'fiction'.\n", + "\n", + "Let's sort the words starting with 'h':\n", + "* heterozygous\n", + "* horehound\n", + "Comparing 'heterozygous' and 'horehound': 'he' vs 'ho'. 'e' comes before 'o', so 'heterozygous' comes before 'horehound'.\n", + "\n", + "The full alphabetical order is:\n", + "1. confidential (c)\n", + "2. faery (f)\n", + "3. fiction (f)\n", + "4. heterozygous (h)\n", + "5. horehound (h)\n", + "6. overture (o)\n", + "7. ursa (u)\n", + "\n", + "Let's re-check the 'f' words:\n", + "faery\n", + "fiction\n", + "f-a-e-r-y\n", + "f-i-c-t-i-o-n\n", + "'a' comes before 'i', so faery comes before fiction.\n", + "\n", + "Let's re-check the 'h' words:\n", + "heterozygous\n", + "horehound\n", + "h-e-t-e-r-o-z-o-u-s\n", + "h-o-r-e-h-o-u-n-d\n", + "'e' comes before 'o', so heterozygous comes before horehound.\n", + "\n", + "Final sorted list:\n", + "1. confidential\n", + "2. faery\n", + "3. fiction\n", + "4. heterozygous\n", + "5. horehound\n", + "6. overture\n", + "7. ursa\n", + "\n", + "Answer: confidential, faery, fiction, heterozygous, horehound, overture, ursaAnswer: confidential, faery, fiction, heterozygous, horehound, overture, ursaFalseFalse
bbh_word_sorting-0041bighorn contaminate demystify nigeria odysseus penny proton sociolinguistic stirrup voltaireHere are the words sorted alphabetically:\n", + "\n", + "1. bighorn\n", + "2. contaminate\n", + "3. demystify\n", + "4. odysseus\n", + "5. penny\n", + "6. proton\n", + "7. sociolinguistic\n", + "8. stirrup\n", + "9. voltaire\n", + "10. nigeria\n", + "11. **voltaire** (Wait, I listed voltaire twice in my mental check, let's re-sort carefully)\n", + "\n", + "Let's list them and sort them:\n", + "sociolinguistic, nigeria, odysseus, demystify, proton, voltaire, penny, contaminate, bighorn, stirrup\n", + "\n", + "1. bighorn\n", + "2. contaminate\n", + "3. demystify\n", + "4. nigeria\n", + "5. odysseus\n", + "6. penny\n", + "7. proton\n", + "8. sociolinguistic\n", + "9. stirrup\n", + "10. voltaire\n", + "\n", + "Let's re-check the sorting based on the initial list: sociolinguistic, nigeria, odysseus, demystify, proton, voltaire, penny, contaminate, bighorn, stirrup\n", + "\n", + "* Bighorn (B)\n", + "* Contaminate (C)\n", + "* Demystify (D)\n", + "* Nigeria (N)\n", + "* Odysseus (O)\n", + "* Penny (P)\n", + "* Proton (P) - P-e comes before P-e-n, so Penny before Proton.\n", + "* Sociolinguistic (S)\n", + "* Stirrup (S) - Stirrup comes after Sociolinguistic (S-t vs S-o).\n", + "* Voltaire (V)\n", + "\n", + "Correct alphabetical order:\n", + "1. bighorn\n", + "2. contaminate\n", + "3. demystify\n", + "4. nigeria\n", + "5. odysseus\n", + "6. penny\n", + "7. proton\n", + "8. sociolinguistic\n", + "9. stirrup\n", + "10. voltaire\n", + "\n", + "Wait, let's check S words: sociolinguistic, stirrup.\n", + "S-o-c... vs S-t-i... $\\rightarrow$ sociolinguistic comes before stirrup.\n", + "\n", + "Final check:\n", + "Bighorn\n", + "Contaminate\n", + "Demystify\n", + "Nigeria\n", + "Odysseus\n", + "Penny\n", + "Proton\n", + "Sociolinguistic\n", + "Stirrup\n", + "Voltaire\n", + "\n", + "Answer: bighorn, contaminate, demystify, nigeria, odysseus, penny, proton, sociolinguistic, stirrup, voltaire\n", + "\n", + "Answer: bighorn, contaminate, demystify, nigeria, odysseus, penny, proton, sociolinguistic, stirAnswer: bighorn, contaminate, demystify, nigeria, odysseus, penny, proton, sociolinguistic, stirrup, voltaireTrueFalse
bbh_word_sorting-0080anarchic bstj elution exhumation furl geld gradual j liniment locomote midshipman pantheist profess riddance rowley salineHere are the words to sort alphabetically:\n", + "rowley, midshipman, saline, furl, geld, locomote, exhumation, anarchic, bstj, gradual, riddance, j, liniment, pantheist, elution, profess\n", + "\n", + "Let's sort them:\n", + "\n", + "1. **anarchic**\n", + "2. **bstj**\n", + "3. **elution**\n", + "4. **furl**\n", + "5. **geld**\n", + "6. **gradual**\n", + "7. **j**\n", + "8. **locomote**\n", + "9. **liniment**\n", + "10. **midshipman**\n", + "11. **pantheist**\n", + "12. **profess**\n", + "13. **riddance**\n", + "14. **rowley**\n", + "15. **saline**\n", + "16. **exhumation**\n", + "\n", + "Let's re-check the sorting, especially for words starting with similar letters:\n", + "\n", + "* **a**: anarchic\n", + "* **b**: bstj\n", + "* **e**: elution, exhumation\n", + " * elution comes before exhumation (l before x)\n", + "* **f**: furl\n", + "* **g**: geld, gradual\n", + " * geld comes before gradual (e before r)\n", + "* **j**: j\n", + "* **l**: locomote, liniment\n", + " * locomote comes before liniment (o before i is false, l comes before o) - Wait, L comes before M.\n", + " * locomote\n", + " * liniment\n", + "* **m**: midshipman\n", + "* **p**: pantheist, profess\n", + " * pantheist comes before profess (a before r)\n", + "* **r**: riddance, rowley\n", + " * riddance comes before rowley (i before o)\n", + "* **s**: saline\n", + "* **(remaining)**\n", + "\n", + "Let's perform a strict alphabetical sort:\n", + "\n", + "1. anarchic\n", + "2. bstj\n", + "3. elution\n", + "4. exhumation\n", + "5. furl\n", + "6. geld\n", + "7. gradual\n", + "8. j\n", + "9. liniment\n", + "10. locomote\n", + "11. midshipman\n", + "12. pantheist\n", + "13. profess\n", + "14. riddance\n", + "15. rowley\n", + "16. saline\n", + "\n", + "Let's re-verify the L words: liniment, locomote. L-I vs L-O. I comes before Oanarchic, bstj, elution, exhumation, furl, geld, gradual, j, liniment, locomote, midshipman, pantheist, profess, riddance, rowley, saline\n", + "\n", + "Answer: anarchic, bstj, elution, exhumation, furl, geld, gradual, j, liniment, locomote, midshipman, pantheist, profess, riddance, rowley, salineTrueFalse
bbh_word_sorting-0004auerbach decor deoxyribose devisee dianne hodges incommensurable motorcade stratify troupeHere are the words to sort alphabetically:\n", + "hodges, deoxyribose, troupe, motorcade, devisee, stratify, auerbach, decor, incommensurable, dianne\n", + "\n", + "Let's sort them:\n", + "\n", + "1. **auerbach** (Starts with 'a')\n", + "2. **decor** (Starts with 'd', comes before 'dianne')\n", + "3. **devisee** (Starts with 'd', comes after 'decor')\n", + "4. **dianne** (Starts with 'd', comes after 'devisee')\n", + "5. **deoxyribose** (Starts with 'd', comes after 'dianne')\n", + "6. **hodges** (Starts with 'h')\n", + "7. **incommensurable** (Starts with 'i')\n", + "8. **motorcade** (Starts with 'm')\n", + "9. **stratify** (Starts with 's')\n", + "10. **troupe** (Starts with 't')\n", + "\n", + "Let's re-check the 'd' words:\n", + "decor\n", + "devisee\n", + "dianne\n", + "deoxyribose\n", + "\n", + "Alphabetical order for 'd' words:\n", + "decor\n", + "devisee (deci...)\n", + "dianne (dia...)\n", + "deoxyribose (deox...)\n", + "Wait, comparing 'devisee' and 'dianne':\n", + "d i a n n e\n", + "d e v i s e e\n", + "'i' comes before 'e' in the second position if we compare the full words:\n", + "d i a n n e\n", + "d e v i s e e\n", + "'i' comes before 'e' is incorrect. Let's compare character by character:\n", + "d i a n n e\n", + "d e v i s e e\n", + "'i' vs 'e'. 'e' comes before 'i'. So 'devisee' comes before 'dianne'.\n", + "\n", + "Corrected 'd' order:\n", + "1. decor\n", + "2. devisee\n", + "3. dianne\n", + "4. deoxyribose\n", + "\n", + "Let's re-sort the whole list:\n", + "\n", + "1. auerbach\n", + "2. decor\n", + "3. devisee\n", + "4. dianne\n", + "5. deoxyribose\n", + "6. hodges\n", + "7. incommensurable\n", + "8. motorcade\n", + "9. stratify\n", + "10. troupe\n", + "\n", + "Final check:\n", + "a - auerbach\n", + "d - decor, devisee, dianneAnswer: auerbach, dianne, decor, deoxyribose, devisee, hodges, incommensurable, motorcade, stratify, troupeFalseFalse
bbh_word_sorting-0114correspond herpes him seashoreThe given list of words is: him, correspond, seashore, herpes.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter:\n", + "\n", + "1. **h**im\n", + "2. **h**erpes\n", + "3. **c**orrespond\n", + "4. **s**eashore\n", + "\n", + "Comparing the first letter: 'c' comes before 'h', and 'h' comes before 's'.\n", + "\n", + "So, 'correspond' comes first among the words starting with 'c', 'h', or 's'. Wait, let's re-examine the starting letters:\n", + "* him: h\n", + "* correspond: c\n", + "* seashore: s\n", + "* herpes: h\n", + "\n", + "Alphabetical order of starting letters: c, h, h, s.\n", + "\n", + "1. **c**orrespond (starts with c)\n", + "2. **h**im (starts with h)\n", + "3. **h**erpes (starts with h)\n", + "4. **s**eashore (starts with s)\n", + "\n", + "Now we compare the words starting with 'h': 'him' and 'herpes'.\n", + "* hi**m**\n", + "* he**r**pes\n", + "'i' comes before 'e' is false. 'e' comes before 'i'.\n", + "So, 'herpes' comes before 'him'.\n", + "\n", + "The sorted order is:\n", + "1. correspond\n", + "2. herpes\n", + "3. him\n", + "4. seashore\n", + "\n", + "Let's verify the comparison between 'herpes' and 'him':\n", + "h e r p e s\n", + "h i m\n", + "'e' comes before 'i', so 'herpes' comes before 'him'.\n", + "\n", + "Final sorted list: correspond, herpes, him, seashore.\n", + "\n", + "Answer: correspond, herpes, him, seashore\n", + "\n", + "Answer: correspond, herpes, him, seashoreAnswer: correspond, herpes, him, seashoreFalseFalse
bbh_word_sorting-0012brainy cony enigma erudite fatuous gouda hoof impalpable isaacson lisbon malaria portrait portsmouth servomechanism stronghold succumbHere are the words sorted alphabetically:\n", + "\n", + "1. brainy\n", + "2. enigma\n", + "3. erudite\n", + "4. fatuous\n", + "5. gouda\n", + "6. impalpable\n", + "7. isaacson\n", + "8. lisbon\n", + "9. malaria\n", + "10. portsmouth\n", + "11. portrait\n", + "12. servomechanism\n", + "13. stronghold\n", + "14. succumb\n", + "15. cony\n", + "16. hoof\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* brainy\n", + "* cony\n", + "* erudite\n", + "* enigma\n", + "* fatuous\n", + "* gouda\n", + "* impalpable\n", + "* isaacson\n", + "* hoof\n", + "* lisbon\n", + "* malaria\n", + "* portsmouth\n", + "* portrait\n", + "* servomechanism\n", + "* stronghold\n", + "* succumb\n", + "\n", + "Let's check the order again:\n", + "B (brainy)\n", + "C (cony)\n", + "E (erudite, enigma) -> erudite before enigma\n", + "F (fatuous)\n", + "G (gouda)\n", + "I (impalpable, isaacson) -> impalpable before isaacson\n", + "H (hoof)\n", + "L (lisbon)\n", + "M (malaria)\n", + "P (portsmouth, portrait) -> portsmouth before portrait\n", + "S (servomechanism, succumb, stronghold) -> servomechanism, succumb, stronghold (S-e-r, S-u, S-t)\n", + "\n", + "Correct alphabetical order:\n", + "1. brainy\n", + "2. cony\n", + "3. erudite\n", + "4. enigma\n", + "5. fatuous\n", + "6. gouda\n", + "7. impalpable\n", + "8. isaacson\n", + "9. hoof\n", + "10. lisbon\n", + "11. malaria\n", + "12. portsmouth\n", + "13. portrait\n", + "14. servomechanism\n", + "15. stronghold\n", + "16. succumb\n", + "\n", + "Answer: brainy, cony, erudite, enigma, fatuous, gouda, impalpable, isaacson, hoof, lisbon, malaria, portsmouth, portrait, servomechanism, stronghold, succumbAnswer: brainy, cony, erudite, enigma, fatuous, gouda, impalpable, isaacson, hoof, lisbon, malaria, portsmouth, portrait, servomechanism, stronghold, succumbFalseFalse
bbh_word_sorting-0032agile blackguard butt clapeyron cognoscenti flamboyant geophysical lift lightfooted manumitted mathieu meager purposive reconnaissance sawbelly scribe seaworthy wiseacre woodcut yvesHere are the words sorted alphabetically:\n", + "\n", + "1. agile\n", + "2. blackguard\n", + "3. clapeyron\n", + "4. cognoscenti\n", + "5. geophysical\n", + "6. flamboyant\n", + "7. lift\n", + "8. lightfooted\n", + "9. manumitted\n", + "10. meager\n", + "11. mathieu\n", + "12. purposive\n", + "13. reconnaissance\n", + "14. seaworthy\n", + "15. scribe\n", + "16. sawbelly\n", + "17. wiseacre\n", + "18. woodcut\n", + "19. yves\n", + "20. butt\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **a**gile\n", + "* **b**lackguard, butt\n", + "* **c**lapeyron, cognoscenti\n", + "* **g**eophysical\n", + "* **l**ift, lightfooted\n", + "* **m**anumitted, meager, mathieu\n", + "* **p**urposeive\n", + "* **r**econnaissance\n", + "* **s**eaworthy, scribe, sawbelly\n", + "* **w**iseacre, woodcut, yves\n", + "\n", + "Detailed sorting:\n", + "\n", + "1. agile\n", + "2. blackguard\n", + "3. butt\n", + "4. clapeyron\n", + "5. cognoscenti\n", + "6. geophysical\n", + "7. flamboyant\n", + "8. lift\n", + "9. lightfooted\n", + "10. manumitted\n", + "11. meager\n", + "12. mathieu\n", + "13. purposive\n", + "14. reconnaissance\n", + "15. seaworthy\n", + "16. scribe\n", + "17. sawbelly\n", + "18. wiseacre\n", + "19. woodcut\n", + "20. yves\n", + "\n", + "Answer: agile, blackguard, butt, clapeyron, cognoscenti, geophysical, flamboyant, lift, lightfooted, manumitted, meager, mathieu, purposive, reconnaissance, seaworthy, scribe, sawbelly, wiseacre, woodcut, yvesAnswer: agile, blackguard, butt, clapeyron, cognoscenti, geophysical, flamboyant, lift, lightfooted, manumitted, meager, mathieu, purposive, reconnaissance, seaworthy, scribe, sawbelly, wiseacre, woodcut, yvesFalseFalse
bbh_word_sorting-0112beth kenyaThe given list of words is: kenya, beth.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter from the beginning.\n", + "\n", + "1. **Compare the first letter:**\n", + " - 'k' in "kenya"\n", + " - 'b' in "beth"\n", + " Since 'b' comes before 'k' in the alphabet, "beth" comes before "kenya".\n", + "\n", + "Therefore, the alphabetical order is: beth, kenya.\n", + "\n", + "Answer: beth, kenyaAnswer: beth, kenyaFalseFalse
bbh_word_sorting-0001cheddar edt from oblivion pang poignant yuhThe given list of words is: yuh, edt, cheddar, oblivion, from, poignant, pang.\n", + "\n", + "We need to sort these words alphabetically.\n", + "\n", + "1. **Compare the first letters:**\n", + " - yuh (y)\n", + " - edt (e)\n", + " - cheddar (c)\n", + " - oblivion (o)\n", + " - from (f)\n", + " - poignant (p)\n", + " - pang (p)\n", + "\n", + "2. **Order based on the first letter:**\n", + " - c (cheddar)\n", + " - e (edt)\n", + " - f (from)\n", + " - o (oblivion)\n", + " - p (poignant, pang)\n", + " - y (yuh)\n", + "\n", + "3. **Sort the words starting with 'p':**\n", + " - poignant\n", + " - pang\n", + " (Comparing the second letters: 'o' in poignant vs 'a' in pang. 'a' comes before 'o'.)\n", + " So, the order is: pang, poignant.\n", + "\n", + "4. **Assemble the final sorted list:**\n", + " - cheddar (c)\n", + " - edt (e)\n", + " - from (f)\n", + " - oblivion (o)\n", + " - pang (p)\n", + " - poignant (p)\n", + " - yuh (y)\n", + "\n", + "Let's re-check the 'p' words:\n", + "pang (p-a-n-g)\n", + "poignant (p-o-i-g-n-a-n-t)\n", + "'a' comes before 'o', so pang comes before poignant.\n", + "\n", + "Final sorted order: cheddar, edt, from, oblivion, pang, poignant, yuh.\n", + "\n", + "Answer: cheddar, edt, from, oblivion, pang, poignant, yuhAnswer: cheddar, edt, from, oblivion, pang, poignant, yuhFalseFalse
bbh_word_sorting-0070bosporus bully cork edt flogging forfeit lexicographer minor multiple perceptive pizza pungent rancorous reedy referring sedition sell titHere are the words to sort alphabetically:\n", + "reedy, bully, multiple, edt, sell, referring, lexicographer, tit, flogging, perceptive, minor, bosporus, rancorous, forfeit, pizza, pungent, sedition, cork\n", + "\n", + "Let's sort them:\n", + "\n", + "1. **bosporus**\n", + "2. **bully**\n", + "3. **cork**\n", + "4. **edt**\n", + "5. **forfeit**\n", + "6. **flogging**\n", + "7. **lexicographer**\n", + "8. **multiple**\n", + "9. **minor**\n", + "10. **perceptive**\n", + "11. **pizza**\n", + "12. **pungent**\n", + "13. **rancorous**\n", + "14. **reedy**\n", + "15. **referring**\n", + "16. **sell**\n", + "17. **sedition**\n", + "18. **tit**\n", + "\n", + "Let's re-check the sorting, especially for words starting with similar letters:\n", + "\n", + "* **b**: bully, bosporus\n", + " * bosporus (bo...)\n", + " * bully (bu...) $\\rightarrow$ bosporus, bully\n", + "* **c**: cork\n", + "* **e**: edt\n", + "* **f**: forfeit, flogging\n", + " * forfeit (for...)\n", + " * flogging (flo...) $\\rightarrow$ flogging, forfeit\n", + "* **l**: lexicographer\n", + "* **m**: multiple, minor\n", + " * multiple (mul...)\n", + " * minor (min...) $\\rightarrow$ minor, multiple\n", + "* **p**: perceptive, pizza, pungent\n", + " * perceptive (per...)\n", + " * pizza (pi...)\n", + " * pungent (pun...) $\\rightarrow$ perceptive, pizza, pungent\n", + "* **r**: reedy, referring, rancorous\n", + " * reedy (ree...)\n", + " * referring (ref...)\n", + " * rancorous (ran...) $\\rightarrow$ rancorous, reedy, referring\n", + "* **s**: sell, sedition\n", + " * sell (sel...)\n", + " * sedition (sed...) $\\rightarrow$ sell, sedition\n", + "* **t**: tit\n", + "\n", + "Let's perform a strict alphabetical sort:\n", + "\n", + "1. bosporus (B)\n", + "2. bully (B)\n", + "3. cork (C)\n", + "4. edt (E)\n", + "5. flogging (F)\n", + "6. forfeit (F)\n", + "7. lexicographer (L)\n", + "8. minor (M)\n", + "Answer: bosporus, bully, cork, edt, flogging, forfeit, lexicographer, minor, multiple, perceptive, pizza, pungent, rancorous, reedy, referring, sell, sedition, titFalseFalse
bbh_word_sorting-0103acoustic anarchic bureaucracy diatom fabricate guelph immovable leftward liven neo phenomenology provide shortcut suggestive syndrome total trammel usage yarmulkeHere are the words sorted alphabetically:\n", + "\n", + "1. acoustic\n", + "2. anarchic\n", + "3. diatom\n", + "4. fabricate\n", + "5. phenomenology\n", + "6. leftward\n", + "7. liven\n", + "8. neo\n", + "9. immovable\n", + "10. provide\n", + "11. shortcut\n", + "12. syndrome\n", + "13. total\n", + "14. trammel\n", + "15. usage\n", + "16. yarmulke\n", + "17. bureaucracy\n", + "18. guelph\n", + "19. suggestive\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **a**narchic, **a**coustic\n", + "* **b**ureaucracy\n", + "* **d**iatom\n", + "* **f**abricate\n", + "* **g**uelph\n", + "* **i**mmovable\n", + "* **l**eftward, **l**iven\n", + "* **n**eo\n", + "* **p**henomenology, **p**rovide\n", + "* **s**hortcut, **s**yndrome, **s**uggestive\n", + "* **t**otal, **t**rammel\n", + "* **u**sage\n", + "* **y**armulke\n", + "\n", + "**Detailed Alphabetical Sort:**\n", + "\n", + "1. acoustic\n", + "2. anarchic\n", + "3. bureaucracy\n", + "4. diatom\n", + "5. fabricate\n", + "6. guelph\n", + "7. immovable\n", + "8. leftward\n", + "9. liven\n", + "10. neo\n", + "11. phenomenology\n", + "12. provide\n", + "13. shortcut\n", + "14. syndrome\n", + "15. total\n", + "16. trammel\n", + "17. usage\n", + "18. yarmulke\n", + "19. suggestive\n", + "\n", + "(Checking L words: leftward, liven. L-e-f-t vs L-i-v-e. Leftward comes before liven.)\n", + "(Checking P words: phenomenology, provide. Phenomenology comes before provide.)\n", + "(Checking S words: shortcut, syndrome, suggestive. Shortcut, syndrome, suggestive.)\n", + "\n", + "Final check of the full list:\n", + "anarchic, acoustic, bureaucracy, diatom, fabricate, guelph, immovable, leftward, liven, neo, phenomenology, provide, shortcut, syndrome, total, trammel, usage, yarmulke, suggestive.\n", + "\n", + "Answer: acoustic, anarchic, bureaucracy, diatom, fabricate, guelph, immovable, leftward, liven, neo, phenomenology, provide, shortcut, syndrome, total, trammel,Answer: acoustic, anarchic, bureaucracy, diatom, fabricate, guelph, immovable, leftward, liven, neo, phenomenology, provide, shortcut, syndrome, total, trammel, usage, yarmulke, suggestiveFalseFalse
bbh_word_sorting-0068afloat apostasy bechtel chattel conner ferment grosbeak hendrickson indonesia jacm lanthanide melancholy quark scavenge strove vibrateHere are the words to sort alphabetically:\n", + "strove, hendrickson, apostasy, jacm, indonesia, vibrate, afloat, lanthanide, grosbeak, scavenge, chattel, conner, bechtel, melancholy, ferment\n", + "\n", + "Let's sort them:\n", + "\n", + "1. **afloat**\n", + "2. **apostasy**\n", + "3. **bechtel**\n", + "4. **chattel**\n", + "5. **conner**\n", + "6. **ferment**\n", + "7. **grosbeak**\n", + "8. **hendrickson**\n", + "9. **indonesia**\n", + "10. **jacm**\n", + "11. **lanthanide**\n", + "12. **melancholy**\n", + "13. **scavenge**\n", + "14. **strove**\n", + "15. **vibrate**\n", + "\n", + "Let's re-check the sorting, especially for words starting with similar letters:\n", + "\n", + "* **a**: afloat, apostasy\n", + "* **b**: bechtel\n", + "* **c**: chattel, conner\n", + "* **f**: ferment\n", + "* **g**: grosbeak\n", + "* **h**: hendrickson\n", + "* **i**: indonesia\n", + "* **j**: jacm\n", + "* **l**: lanthanide\n", + "* **m**: melancholy\n", + "* **s**: scavenge, strove\n", + "* **v**: vibrate\n", + "\n", + "Detailed comparison:\n", + "* afloat (a-f)\n", + "* apostasy (a-p) $\\rightarrow$ afloat comes before apostasy.\n", + "* bechtel (b)\n", + "* chattel (c-h)\n", + "* conner (c-o) $\\rightarrow$ chattel comes before conner.\n", + "* ferment (f)\n", + "* grosbeak (g)\n", + "* hendrickson (h)\n", + "* indonesia (i)\n", + "* jacm (j)\n", + "* lanthanide (l)\n", + "* melancholy (m)\n", + "* scavenge (s-c)\n", + "* strove (s-t) $\\rightarrow$ scavenge comes before strove.\n", + "* vibrate (v)\n", + "\n", + "Corrected order:\n", + "1. afloat\n", + "2. apostasy\n", + "3. bechtel\n", + "4. chattel\n", + "5. conner\n", + "6. ferment\n", + "7. grosbeak\n", + "8. hendrickson\n", + "9. indonesia\n", + "10. jacm\n", + "11. lanthanide\n", + "12. melancholy\n", + "13. scavenge\n", + "14. strove\n", + "15. vibrate\n", + "\n", + "Answer: afloatAnswer: afloat, apostasy, bechtel, chattel, conner, ferment, grosbeak, hendrickson, indonesia, jacm, lanthanide, melancholy, scavenge, strove, vibrateFalseFalse
bbh_word_sorting-0125betony boar bootleg bronzy centaur charge clemens collet contemporaneous contravariant cordial dorado handicraft macmahon mesh monterey possession regina underclassmanHere are the words to be sorted alphabetically:\n", + "betony, bronzy, charge, cordial, handicraft, boar, collet, clemens, bootleg, possession, mesh, centaur, contemporaneous, macmahon, monterey, dorado, contravariant, regina, underclassman\n", + "\n", + "Let's sort them:\n", + "\n", + "1. **boar**\n", + "2. **betony**\n", + "3. **bootleg**\n", + "4. **bronzy**\n", + "5. **charge**\n", + "6. **centaur**\n", + "7. **clemens**\n", + "8. **collet**\n", + "9. **contravariant**\n", + "10. **cordial**\n", + "11. **danicraft** (handicraft)\n", + "12. **dorado**\n", + "13. **macmahon**\n", + "14. **mesh**\n", + "15. **monterey**\n", + "16. **possession**\n", + "17. **regina**\n", + "18. **underclassman**\n", + "19. **contemporaneous**\n", + "\n", + "Let's re-sort carefully, paying attention to the initial letters:\n", + "\n", + "B: betony, boar, bootleg, bronzy\n", + "C: charge, cordial, collet, clemens, centaur, contemporaneous, contravariant\n", + "D: dorado\n", + "H: handicraft\n", + "M: macmahon, mesh, monterey\n", + "P: possession\n", + "R: regina\n", + "U: underclassman\n", + "\n", + "**Step 1: Group by first letter**\n", + "\n", + "B: betony, boar, bootleg, bronzy\n", + "C: charge, cordial, collet, clemens, centaur, contemporaneous, contravariant\n", + "D: dorado\n", + "H: handicraft\n", + "M: macmahon, mesh, monterey\n", + "P: possession\n", + "R: regina\n", + "U: underclassman\n", + "\n", + "**Step 2: Sort within groups**\n", + "\n", + "B: betony, boar, bootleg, bronzy (betony, boar, bootleg, bronzy)\n", + " * betony\n", + " * boar\n", + " * bootleg\n", + " * bronzy\n", + "\n", + "C: charge, cordial, collet, clemens, centaur, contemporaneous, contravariant (charge, cordial, collet, clemens, centaur, contemporaneous, contravariant)\n", + " * charge\n", + " * cordial\n", + " * collet\n", + " * clemens\n", + " * centaur\n", + " * contemporaneous\n", + " * contravariant\n", + "\n", + "D: dorado\n", + "\n", + "H: handicAnswer: betony, boar, bootleg, bronzy, charge, cordial, collet, clemens, centaur, contemporaneous, contravariant, dorado, handicraft, macmahon, mesh, monterey, possession, regina, underclassmanFalseFalse
bbh_word_sorting-0010alterate aseptic cayenne chandigarh debauch declassify dingy equanimity excursion foamflower groupoid inclement kruger lawful october only scorchHere are the words sorted alphabetically:\n", + "\n", + "1. alterate\n", + "2. aseptic\n", + "3. cayenne\n", + "4. chandigarh\n", + "5. declassify\n", + "6. debauch\n", + "7. dingy\n", + "8. excursion\n", + "9. equanimity\n", + "10. foamflower\n", + "11. lawful\n", + "12. october\n", + "13. only\n", + "14. groupoid\n", + "15. kruger\n", + "16. scorch\n", + "17. alterate (Wait, I listed alterate twice in my thought process, let's re-check the original list: foamflower lawful cayenne chandigarh only excursion declassify equanimity aseptic inclement october debauch kruger groupoid scorch dingy alterate)\n", + "\n", + "Let's re-sort carefully:\n", + "\n", + "Original List: foamflower, lawful, cayenne, chandigarh, only, excursion, declassify, equanimity, aseptic, inclement, october, debauch, kruger, groupoid, scorch, dingy, alterate\n", + "\n", + "1. alterate\n", + "2. aseptic\n", + "3. cayenne\n", + "4. chandigarh\n", + "5. declassify\n", + "6. debauch\n", + "7. dingy\n", + "8. excursion\n", + "9. equanimity\n", + "10. foamflower\n", + "11. inclement\n", + "12. lawful\n", + "13. october\n", + "14. only\n", + "15. groupoid\n", + "16. kruger\n", + "17. scorch\n", + "\n", + "Let's re-sort the remaining words based on the full list:\n", + "\n", + "A: alterate, aseptic\n", + "C: cayenne, chandigarh\n", + "D: declassify, debauch, dingy\n", + "E: excursion, equanimity\n", + "F: foamflower\n", + "I: inclement\n", + "L: lawful\n", + "O: october, only\n", + "G: groupoid\n", + "K: kruger\n", + "S: scorch\n", + "\n", + "Correct Alphabetical Order:\n", + "1. alterate\n", + "2. aseptic\n", + "3. cayenne\n", + "4. chandigarh\n", + "5. declassify\n", + "6. debauch\n", + "7. dingy\n", + "8. excursion\n", + "9. equanimity\n", + "10. foamflower\n", + "11. inclement\n", + "12. lawful\n", + "13. october\n", + "14. only\n", + "15. groupoid\n", + "16. kruger\n", + "17. scorch\n", + "\n", + "Answer: alterate, aseptic, cayenne, chandigarh, declassify, debauch, dingy, excursion, equanimity, foamflower, inclement, lawful, october, only, groupoid, kruger, scorchalterate, aseptic, cayenne, chandigarh, declassify, debauch, dingy, excursion, equanimity, foamflower, inclement, lawful, october, only, groupoid, kruger, scorch\n", + "\n", + "Answer: alterate, aseptic, cayenne, chandigarh, declassify, debauch, dingy, excursion, equanimity, foamflower, inclement, lawful, october, only, groupoid, kruger, scorchFalseFalse
bbh_word_sorting-0052amerada craftsmen din eclipse gaillardia inroad jackboot jest jordan kill mirth nate pomade putt shortcoming spruce whelanHere are the words sorted alphabetically:\n", + "\n", + "1. amerada\n", + "2. craftsmen\n", + "3. eclipse\n", + "4. din\n", + "5. gaillardia\n", + "6. inroad\n", + "7. jest\n", + "8. jackboot\n", + "9. jordan\n", + "10. kill\n", + "11. mirth\n", + "12. nate\n", + "13. pomade\n", + "14. putt\n", + "15. shortcoming\n", + "16. spruce\n", + "17. whelan\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* amerada\n", + "* craftsmen\n", + "* din\n", + "* eclipse\n", + "* gaillardia\n", + "* inroad\n", + "* jest\n", + "* jackboot\n", + "* jordan\n", + "* kill\n", + "* mirth\n", + "* nate\n", + "* pomade\n", + "* putt\n", + "* shortcoming\n", + "* spruce\n", + "* whelan\n", + "\n", + "Comparing the initial list: din spruce nate pomade jordan gaillardia putt inroad jest kill jackboot whelan mirth amerada craftsmen shortcoming eclipse\n", + "\n", + "1. amerada\n", + "2. craftsmen\n", + "3. din\n", + "4. eclipse\n", + "5. gaillardia\n", + "6. inroad\n", + "7. jest\n", + "8. jackboot\n", + "9. jordan\n", + "10. kill\n", + "11. mirth\n", + "12. nate\n", + "13. pomade\n", + "14. putt\n", + "15. shortcoming\n", + "16. spruce\n", + "17. whelan\n", + "\n", + "Answer: amerada, craftsmen, din, eclipse, gaillardia, inroad, jest, jackboot, jordan, kill, mirth, nate, pomade, putt, shortcoming, spruce, whelanAnswer: amerada, craftsmen, din, eclipse, gaillardia, inroad, jest, jackboot, jordan, kill, mirth, nate, pomade, putt, shortcoming, spruce, whelanFalseFalse
bbh_word_sorting-0018barn damp delmarva dot drumhead embezzle entirety greene guru it&t malton obstetric onus panicking prod same scorch splutter subsist thrillHere are the words sorted alphabetically:\n", + "\n", + "1. barn\n", + "2. embezzle\n", + "3. delmarva\n", + "4. dot\n", + "5. drumhead\n", + "6. entirety\n", + "7. guru\n", + "8. it&t\n", + "9. malton\n", + "10. panicking\n", + "11. prod\n", + "12. scorch\n", + "13. same\n", + "14. splutter\n", + "15. subsist\n", + "16. thrill\n", + "\n", + "(Note: "onus" is missing from the provided list, but if it were included, it would come before "obstetric" and after "onus" would be before "prod" if we assume standard alphabetical order for the provided list. Let's re-check the provided list: thrill, splutter, panicking, scorch, same, dot, prod, obstetric, malton, onus, drumhead, delmarva, barn, embezzle, it&t, damp, guru, subsist, entirety, greene.)\n", + "\n", + "Let's re-sort the complete list:\n", + "thrill, splutter, panicking, scorch, same, dot, prod, obstetric, malton, onus, drumhead, delmarva, barn, embezzle, it&t, damp, guru, subsist, entirety, greene\n", + "\n", + "1. barn\n", + "2. damp\n", + "3. delmarva\n", + "4. dot\n", + "5. drumhead\n", + "6. embezzle\n", + "7. entirety\n", + "8. greene\n", + "9. guru\n", + "10. it&t\n", + "11. malton\n", + "12. obstetric\n", + "13. onus\n", + "14. panicking\n", + "15. prod\n", + "16. scorch\n", + "17. same\n", + "18. splutter\n", + "19. subsist\n", + "20. thrill\n", + "\n", + "Answer: barn, damp, delmarva, dot, drumhead, embezzle, entirety, greene, guru, it&t, malton, obstetric, onus, panicking, prod, scorch, same, splutter, subsist, thrillList of words: thrill, splutter, panicking, scorch, same, dot, prod, obstetric, malton, onus, drumhead, delmarva, barn, embezzle, it&t, damp, guru, subsist, entirety, greene\n", + "\n", + "Alphabetical sorting:\n", + "1. barn\n", + "2. damp\n", + "3. delmarva\n", + "4. dot\n", + "5. drumhead\n", + "6. embezzle\n", + "7. entirety\n", + "8. greene\n", + "9. guru\n", + "10. it&t\n", + "11. malton\n", + "12. obstetric\n", + "13. onus\n", + "14. panicking\n", + "15. prod\n", + "16. scorch\n", + "17. same\n", + "18. splutter\n", + "19. subsist\n", + "20. thrill\n", + "\n", + "Answer: barn, damp, delmarva, dot, drumhead, embezzle, entirety, greene, guru, it&t, malton, obstetric, onus, panicking, prod, scorch, same, splutter, subsist, thrillFalseFalse
bbh_word_sorting-0091abutted agamemnon aquatic capacity casualty essex guinea hitachi hondo islamic loosen loquacious niece planet roadway solstice steed suspicion tibetHere are the words sorted alphabetically:\n", + "\n", + "1. abutted\n", + "2. agamemnon\n", + "3. aquatic\n", + "4. casualty\n", + "5. capacity\n", + "6. hondo\n", + "7. hitachi\n", + "8. islamic\n", + "9. loosen\n", + "10. loquacious\n", + "11. niece\n", + "12. planet\n", + "13. roadway\n", + "14. solstice\n", + "15. steed\n", + "16. suspicion\n", + "17. tibet\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* abutted\n", + "* agamemnon\n", + "* aquatic\n", + "* casualty\n", + "* capacity\n", + "* hondo\n", + "* hitachi\n", + "* islamic\n", + "* loosen\n", + "* loquacious\n", + "* niece\n", + "* planet\n", + "* roadway\n", + "* solstice\n", + "* steed\n", + "* suspicion\n", + "* tibet\n", + "* essex (Wait, I missed 'essex' in the initial list check. Let's re-examine the original list: solstice roadway loquacious niece planet steed abutted hondo loosen agamemnon islamic capacity tibet hitachi essex casualty guinea aquatic suspicion)\n", + "\n", + "Let's re-sort the full list:\n", + "solstice, roadway, loquacious, niece, planet, steed, abutted, hondo, loosen, agamemnon, islamic, capacity, tibet, hitachi, essex, casualty, guinea, aquatic, suspicion\n", + "\n", + "1. abutted\n", + "2. agamemnon\n", + "3. aquatic\n", + "4. casualty\n", + "5. capacity\n", + "6. essex\n", + "7. guinea\n", + "8. hondo\n", + "9. hitachi\n", + "10. islamic\n", + "11. loosen\n", + "12. loquacious\n", + "13. niece\n", + "14. planet\n", + "15. roadway\n", + "16. solstice\n", + "17. steed\n", + "18. suspicion\n", + "19. tibet\n", + "\n", + "(Checking 'guinea' placement: G comes after E, before H. Correct.)\n", + "\n", + "Final sorted list:\n", + "abutted, agamemnon, aquatic, casualty, capacity, essex, guinea, hondo, hitachi, islamic, loosen, loquacious, niece, planet, roadway, solstice, steed, suspicion, tibet\n", + "\n", + "Answer: abutted, agamemnon, aquatic, casualty, capacity, essex, guinea, hondo, hitachi, islamic, loosen, loquacious, niece, planet, roadway, solstice, steed, suspicion, tibetAnswer: abutted, agamemnon, aquatic, casualty, capacity, essex, guinea, hondo, hitachi, islamic, loosen, loquacious, niece, planet, roadway, solstice, steed, suspicion, tibetFalseFalse
bbh_word_sorting-0088army emancipate envious planetaria pooh scotia winkThe given list of words is: wink, envious, scotia, planetaria, pooh, emancipate, army.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter:\n", + "\n", + "1. **emancipate** (Starts with 'e')\n", + "2. **envious** (Starts with 'e', 'm' comes before 'n')\n", + "3. **army** (Starts with 'a')\n", + "4. **planetaria** (Starts with 'p')\n", + "5. **pooh** (Starts with 'p', 'o' comes before 'p' in planetaria if we consider the second letter, but 'p' vs 'p' is the same. 'o' vs 'l' in planetaria. Let's compare 'planetaria' and 'pooh'. 'p' is the same. 'l' in planetaria vs 'o' in pooh. 'l' comes before 'o'.)\n", + " * Re-evaluating 'planetaria' and 'pooh':\n", + " * p l a n e t a r i a\n", + " * p o o h\n", + " * 'l' comes before 'o', so 'planetaria' comes before 'pooh'.\n", + "6. **scotia** (Starts with 's')\n", + "7. **wink** (Starts with 'w')\n", + "\n", + "Let's re-sort systematically:\n", + "\n", + "Words starting with 'a':\n", + "* army\n", + "\n", + "Words starting with 'e':\n", + "* emancipate\n", + "* envious\n", + " * Comparing 'emancipate' and 'envious': e-m vs e-n. 'm' comes before 'n'.\n", + " * Order: emancipate, envious\n", + "\n", + "Words starting with 'p':\n", + "* planetaria\n", + "* pooh\n", + " * Comparing 'planetaria' and 'pooh': p-l vs p-o. 'l' comes before 'o'.\n", + " * Order: planetaria, pooh\n", + "\n", + "Words starting with 's':\n", + "* scotia\n", + "\n", + "Words starting with 'w':\n", + "* wink\n", + "\n", + "Combining the sorted groups:\n", + "1. army\n", + "2. emancipate\n", + "3. envious\n", + "4. planetaria\n", + "5. pooh\n", + "6. scotia\n", + "7. wink\n", + "\n", + "Final alphabetical order: army, emancipate, envious, planetaria, pooh, scotia, wink.\n", + "\n", + "Answer: army, emancipate, envious, planetaria, pooh, scotia, winkAnswer: army, emancipate, envious, planetaria, pooh, scotia, winkFalseFalse
bbh_word_sorting-0124arapaho bacteria bela bock burleyThe given list of words is: burley, bela, arapaho, bacteria, bock.\n", + "\n", + "We need to sort these words alphabetically. We compare them letter by letter:\n", + "\n", + "1. **bela** (Starts with 'b', second letter 'e')\n", + "2. **bacteria** (Starts with 'b', second letter 'a')\n", + "3. **bock** (Starts with 'b', second letter 'o')\n", + "4. **burley** (Starts with 'b', second letter 'u')\n", + "5. **arapaho** (Starts with 'a')\n", + "\n", + "Comparing the first letters: 'a' comes before 'b'.\n", + "So, **arapaho** is the first word.\n", + "\n", + "Now we compare the words starting with 'b': burley, bela, bacteria, bock.\n", + "\n", + "Comparing the second letters:\n", + "* bela: 'e'\n", + "* bacteria: 'a'\n", + "* bock: 'o'\n", + "* burley: 'u'\n", + "\n", + "'a' comes before 'e', 'o', and 'u'. So, **bacteria** comes next.\n", + "\n", + "Remaining words: burley, bela, bock.\n", + "\n", + "Comparing the second letters of the remaining words:\n", + "* bela: 'e'\n", + "* bock: 'o'\n", + "* burley: 'u'\n", + "\n", + "'e' comes before 'o' and 'u'. So, **bela** comes next.\n", + "\n", + "Remaining words: burley, bock.\n", + "\n", + "Comparing the second letters:\n", + "* bock: 'o'\n", + "* burley: 'u'\n", + "\n", + "'o' comes before 'u'. So, **bock** comes next.\n", + "\n", + "The last word is **burley**.\n", + "\n", + "The sorted order is: arapaho, bacteria, bela, bock, burley.\n", + "\n", + "Answer: arapaho, bacteria, bela, bock, burley\n", + "Answer: arapaho, bacteria, bela, bock, burleyAnswer: arapaho, bacteria, bela, bock, burleyFalseFalse
bbh_word_sorting-0022murray sweatbandThe given words are: sweatband, murray.\n", + "\n", + "To sort them alphabetically, we compare them letter by letter:\n", + "\n", + "1. **s**weatband\n", + "2. **m**urray\n", + "\n", + "Since 'm' comes before 's' in the alphabet, "murray" comes before "sweatband".\n", + "\n", + "The alphabetical order is: murray, sweatband.\n", + "\n", + "Answer: murray, sweatband\n", + "Answer: murray, sweatbandAnswer: murray, sweatbandFalseFalse
bbh_word_sorting-0029ami bituminous decadent exeter knickerbockerThe given list of words is: bituminous, ami, decadent, knickerbocker, exeter.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter:\n", + "\n", + "1. **ami**: Starts with 'a'\n", + "2. **bituminous**: Starts with 'b'\n", + "3. **decadent**: Starts with 'd'\n", + "4. **exeter**: Starts with 'e'\n", + "5. **knickerbocker**: Starts with 'k'\n", + "\n", + "Comparing the first letters: a, b, d, e, k.\n", + "In alphabetical order: a, b, d, e, k.\n", + "\n", + "1. **ami** (a)\n", + "2. **bituminous** (b)\n", + "3. **decadent** (d)\n", + "4. **exeter** (e)\n", + "5. **knickerbocker** (k)\n", + "\n", + "The sorted list is: ami, bituminous, decadent, exeter, knickerbocker.\n", + "\n", + "Answer: ami, bituminous, decadent, exeter, knickerbockerAnswer: ami, bituminous, decadent, exeter, knickerbockerFalseFalse
bbh_word_sorting-0109greasy lapidary markThe given list of words is: greasy, lapidary, mark.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter:\n", + "\n", + "1. **greasy** (starts with 'g')\n", + "2. **lapidary** (starts with 'l')\n", + "3. **mark** (starts with 'm')\n", + "\n", + "Comparing the first letters: 'g', 'l', 'm'.\n", + "In alphabetical order, 'g' comes before 'l', and 'l' comes before 'm'.\n", + "\n", + "Therefore, the alphabetical order is: greasy, lapidary, mark.\n", + "\n", + "Answer: greasy, lapidary, markAnswer: greasy, lapidary, markFalseFalse
bbh_word_sorting-0039abuilding burgher celebrity chigger ernie exorcism indiscoverable medlar newsboy pow snuff synchronous thimble thirteenth tinfoil yapHere are the words sorted alphabetically:\n", + "\n", + "1. abuilding\n", + "2. celebrity\n", + "3. burgher\n", + "4. chigger\n", + "5. indiscoverable\n", + "6. medlar\n", + "7. newsboy\n", + "8. exorcism\n", + "9. pow\n", + "10. snuff\n", + "11. synchronous\n", + "12. thirteenth\n", + "13. tinfoil\n", + "14. thimble\n", + "15. ernie\n", + "16. yap\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **a**building\n", + "* **c**elebrity\n", + "* **b**urgher\n", + "* **c**higger\n", + "* **i**ndiscoverable\n", + "* **m**edlar\n", + "* **n**ewsboy\n", + "* **e**xorcism (comes before pow)\n", + "* **p**ow\n", + "* **s**nuff\n", + "* **s**ynchronous\n", + "* **t**hirteenth\n", + "* **t**infoil\n", + "* **t**himble\n", + "* **e**rnie (comes before thimble)\n", + "* **y**ap\n", + "\n", + "Let's do a strict alphabetical sort:\n", + "\n", + "1. abuilding\n", + "2. celebrity\n", + "3. burgher\n", + "4. chigger\n", + "5. ernie\n", + "6. indiscoverable\n", + "7. medlar\n", + "8. newsboy\n", + "9. exorcism\n", + "10. pow\n", + "11. snuff\n", + "12. synchronous\n", + "13. thirteenth\n", + "14. tinfoil\n", + "15. thimble\n", + "16. yap\n", + "\n", + "Wait, let's check the 't' words: thirteenth, tinfoil, thimble.\n", + "thirteenth (thir...)\n", + "tinfoil (tinf...)\n", + "thimble (thim...)\n", + "thimble comes before thirteenth.\n", + "tinfoil comes before thimble.\n", + "thirteenth comes after tinfoil.\n", + "\n", + "Corrected 't' order: tinfoil, thimble, thirteenth.\n", + "\n", + "Let's re-sort the whole list:\n", + "\n", + "1. abuilding\n", + "2. celebrity\n", + "3. burgher\n", + "4. chigger\n", + "5. ernie\n", + "6. indiscoverable\n", + "7. medlar\n", + "8. newsboy\n", + "9. exorcism\n", + "10. pow\n", + "11. snuff\n", + "12. synchronous\n", + "13. tinfoil\n", + "14. thimble\n", + "15. thirteenth\n", + "16. yap\n", + "\n", + "Answer: abuilding, celebrity, burgher,Answer: abuilding, celebrity, burgher, chigger, ernie, indiscoverable, medlar, newsboy, exorcism, pow, snuff, synchronous, thirteenth, tinfoil, thimble, yapFalseFalse
bbh_word_sorting-0122bertrand careful eyelid feign heterostructure libra paste snip southeastern wherewithHere are the words to sort: heterostructure, bertrand, careful, wherewith, libra, eyelid, feign, southeastern, paste, snip.\n", + "\n", + "Let's sort them alphabetically:\n", + "\n", + "1. careful\n", + "2. bertrand\n", + "3. eyelid\n", + "4. feign\n", + "5. heterostructure\n", + "6. libra\n", + "7. paste\n", + "8. southeastern\n", + "9. snip\n", + "10. wherewith\n", + "\n", + "Detailed comparison:\n", + "* **c**areful\n", + "* **b**ertrand\n", + "* **e**yelid\n", + "* **f**eign\n", + "* **h**eterostructure\n", + "* **l**ibra\n", + "* **p**aste\n", + "* **s**outheastern\n", + "* **s**nip (southeastern comes before snip because 'o' comes before 'n') - Wait, let's recheck 's' words.\n", + " * southeastern\n", + " * snip\n", + " * southeastern vs snip: southeastern comes before snip.\n", + "* **w**herewith\n", + "\n", + "Let's re-sort the 's' words:\n", + "* southeastern\n", + "* snip\n", + "\n", + "The final sorted list is:\n", + "1. careful\n", + "2. bertrand\n", + "3. eyelid\n", + "4. feign\n", + "5. heterostructure\n", + "6. libra\n", + "7. paste\n", + "8. southeastern\n", + "9. snip\n", + "10. wherewith\n", + "\n", + "Answer: careful, bertrand, eyelid, feign, heterostructure, libra, paste, southeastern, snip, wherewithAnswer: careful, bertrand, eyelid, feign, heterostructure, libra, paste, southeastern, snip, wherewithFalseFalse
bbh_word_sorting-0098allot chauncey clergymen coachmen coddington companion embark fatten gazpacho granular hobble murk muslim niggle pristine pvc singlet threefold too yeatsHere are the words sorted alphabetically:\n", + "\n", + "1. chauncey\n", + "2. coddington\n", + "3. companion\n", + "4. coachmen\n", + "5. clergymen\n", + "6. embark\n", + "7. fatten\n", + "8. threefold\n", + "9. granular\n", + "10. gazpacho\n", + "11. hobble\n", + "12. murk\n", + "13. muslim\n", + "14. niggle\n", + "15. pristine\n", + "16. pvc\n", + "17. singlet\n", + "18. too\n", + "19. yeats\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **c**hauncey\n", + "* **c**oddington\n", + "* **c**ompanion\n", + "* **c**oachmen\n", + "* **c**lergymen\n", + "* **e**mbark\n", + "* **f**atten\n", + "* **f**reedom (Wait, 'threefold' comes before 'fatten' if we look at the 't' sound, but alphabetically 'fatten' comes before 'threefold'.)\n", + "* **g**azpacho\n", + "* **g**ranular\n", + "* **h**obble\n", + "* **m**urk\n", + "* **m**uslim\n", + "* **n**iggle\n", + "* **p**ristine\n", + "* **p**vc\n", + "* **s**inglet\n", + "* **t**oo\n", + "* **y**eats\n", + "\n", + "Let's do a strict alphabetical sort:\n", + "\n", + "1. chauncey\n", + "2. coddington\n", + "3. companion\n", + "4. coachmen\n", + "5. clergymen\n", + "6. embark\n", + "7. fatten\n", + "8. threefold\n", + "9. granular\n", + "10. gazpacho\n", + "11. hobble\n", + "12. murk\n", + "13. muslim\n", + "14. niggle\n", + "15. pristine\n", + "16. pvc\n", + "17. singlet\n", + "18. too\n", + "19. yeats\n", + "\n", + "(Checking 'fatten' vs 'threefold': F-A-T-T-E-N vs T-H-R-E-E-F-O-L-D. 'fatten' comes first.)\n", + "(Checking 'granular' vs 'gazpacho': G-R vs G-A. 'gazpacho' comes before 'granular'.)\n", + "\n", + "Corrected final sort:\n", + "\n", + "1. chauncey\n", + "2. coddington\n", + "3. companion\n", + "4. coachmen\n", + "5. clergymen\n", + "6. embark\n", + "7. fatten\n", + "8. threefoldAnswer: chauncey, coddington, companion, coachmen, clergymen, embark, fatten, threefold, gazpacho, granular, hobble, murk, muslim, niggle, pristine, pvc, singlet, too, yeatsFalseFalse
bbh_word_sorting-0113alternate boone chalcedony charity genteel million olden satin sinaiHere are the words to sort: million, boone, chalcedony, sinai, satin, genteel, alternate, charity, olden\n", + "\n", + "1. **alternate**\n", + "2. **boone**\n", + "3. **chalcedony**\n", + "4. **charity**\n", + "5. **genteel**\n", + "6. **million**\n", + "7. **olden**\n", + "8. **satin**\n", + "9. **sinai**\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **a**lternate\n", + "* **b**oone\n", + "* **c**halcedony\n", + "* **c**harity (comes after chalcedony because 'h' comes before 'r')\n", + "* **g**enteel\n", + "* **m**illion\n", + "* **o**lden\n", + "* **s**atin\n", + "* **s**inai (satin comes before sinai because 'a' comes before 'i')\n", + "\n", + "Let's re-examine the 'c' words: chalcedony, charity.\n", + "chalcedony (ch-a-l)\n", + "charity (ch-a-r)\n", + "'l' comes before 'r', so chalcedony comes before charity.\n", + "\n", + "Let's re-examine the 's' words: satin, sinai.\n", + "satin (s-a-t)\n", + "sinai (s-i-n)\n", + "'a' comes before 'i', so satin comes before sinai.\n", + "\n", + "Final alphabetical order:\n", + "1. alternate\n", + "2. boone\n", + "3. chalcedony\n", + "4. charity\n", + "5. genteel\n", + "6. million\n", + "7. olden\n", + "8. satin\n", + "9. sinai\n", + "\n", + "Answer: alternate, boone, chalcedony, charity, genteel, million, olden, satin, sinaiAnswer: alternate, boone, chalcedony, charity, genteel, million, olden, satin, sinaiFalseFalse
bbh_word_sorting-0072appliance impede pulitzer superiorThe given list of words is: impede, appliance, pulitzer, superior.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter:\n", + "\n", + "1. **impede** (starts with 'i')\n", + "2. **appliance** (starts with 'a')\n", + "3. **pulitzer** (starts with 'p')\n", + "4. **superior** (starts with 's')\n", + "\n", + "Comparing the first letters: 'a', 'i', 'p', 's'.\n", + "Alphabetical order of the first letters: 'a', 'i', 'p', 's'.\n", + "\n", + "1. **appliance** (starts with 'a')\n", + "2. **impede** (starts with 'i')\n", + "3. **pulitzer** (starts with 'p')\n", + "4. **superior** (starts with 's')\n", + "\n", + "The sorted list is: appliance, impede, pulitzer, superior.\n", + "\n", + "Answer: appliance, impede, pulitzer, superior\n", + "Answer: appliance, impede, pulitzer, superiorThe given list of words is: impede, appliance, pulitzer, superior.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter based on the first letter of each word:\n", + "- impede starts with 'i'\n", + "- appliance starts with 'a'\n", + "- pulitzer starts with 'p'\n", + "- superior starts with 's'\n", + "\n", + "The starting letters are: 'i', 'a', 'p', 's'.\n", + "\n", + "Sorting these letters alphabetically: 'a', 'i', 'p', 's'.\n", + "\n", + "1. The word starting with 'a' is **appliance**.\n", + "2. The word starting with 'i' is **impede**.\n", + "3. The word starting with 'p' is **pulitzer**.\n", + "4. The word starting with 's' is **superior**.\n", + "\n", + "The correctly sorted list is: appliance, impede, pulitzer, superior.\n", + "\n", + "Answer: appliance, impede, pulitzer, superiorFalseFalse
bbh_word_sorting-0127affluent cheshire covalent diagnostician divisive epsilon folklore gideon gothic grover horowitz julio peanut quadrature salient spiderwort spiritualHere are the words sorted alphabetically:\n", + "\n", + "1. affluent\n", + "2. cheshire\n", + "3. covalent\n", + "4. diagnostician\n", + "5. divisive\n", + "6. epsilon\n", + "7. folklore\n", + "8. gothic\n", + "9. gideon\n", + "10. grover\n", + "11. horowitz\n", + "12. julio\n", + "13. peanut\n", + "14. quadrature\n", + "15. salient\n", + "16. spiderwort\n", + "17. spiritual\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **a**ffluent\n", + "* **c**ovalent, **c**heshire\n", + "* **d**iagnostician, **d**ivisive\n", + "* **e**psilon\n", + "* **f**olklore, **g**othic, **g**ideon, **g**rover\n", + "* **h**orowitz\n", + "* **j**ulio\n", + "* **p**eanut\n", + "* **q**uadrature\n", + "* **s**alient, **s**piderwort, **s**piritual\n", + "\n", + "**Detailed Alphabetical Sort:**\n", + "\n", + "1. affluent\n", + "2. cheshire\n", + "3. covalent\n", + "4. diagnostician\n", + "5. divisive\n", + "6. epsilon\n", + "7. folklore\n", + "8. gothic\n", + "9. gideon\n", + "10. grover\n", + "11. horowitz\n", + "12. julio\n", + "13. peanut\n", + "14. quadrature\n", + "15. salient\n", + "16. spiderwort\n", + "17. spiritual\n", + "\n", + "Wait, let's check the 'G' words: gothic, gideon, grover.\n", + "Gothic (go...)\n", + "Gideon (gi...)\n", + "Grover (gr...)\n", + "Order: gothic, gideon, grover.\n", + "\n", + "Let's check the 'S' words: salient, spiderwort, spiritual.\n", + "Salient (sal...)\n", + "Spiderwort (spi...)\n", + "Spiritual (spi...)\n", + "Salient comes before Spiderwort and Spiritual.\n", + "Spiderwort (spi**d**)\n", + "Spiritual (spi**r**)\n", + "Order: salient, spiderwort, spiritual.\n", + "\n", + "**Final Sorted List:**\n", + "\n", + "1. affluent\n", + "2. cheshire\n", + "3. covalent\n", + "4. diagnostician\n", + "5. divisive\n", + "6. epsilon\n", + "7. folklore\n", + "8. gothic\n", + "9. gideon\n", + "10. grover\n", + "11. horowitz\n", + "12. julio\n", + "13. peanut\n", + "14. quadrature\n", + "15. salient\n", + "16. spiderwort\n", + "17. spiritual\n", + "\n", + "(Re-checkingAnswer: affluent, cheshire, covalent, diagnostician, divisive, epsilon, folklore, gothic, gideon, grover, horowitz, julio, peanut, quadrature, salient, spiderwort, spiritualFalseFalse
bbh_word_sorting-0028academia amos beautiful butterscotch circuitous diatom europium extoller farrell fiducial ford glance kochab metzler molybdate monomer predatory veterinarianHere are the words sorted alphabetically:\n", + "\n", + "1. academia\n", + "2. amos\n", + "3. beautiful\n", + "4. butterscotch\n", + "5. circuitous\n", + "6. diatom\n", + "7. extoller\n", + "8. farrell\n", + "9. fiducial\n", + "10. glance\n", + "11. kochab\n", + "12. ladmoner (Wait, the list has 'monomer'. Let's re-check the list.)\n", + "\n", + "Original List: butterscotch veterinarian kochab molybdate extoller amos farrell academia glance predatory beautiful ford fiducial metzler europium circuitous diatom monomer\n", + "\n", + "Let's sort them systematically:\n", + "\n", + "1. academia\n", + "2. amos\n", + "3. beautiful\n", + "4. butterscotch\n", + "5. circuitous\n", + "6. diatom\n", + "7. extoller\n", + "8. farrell\n", + "9. fiducial\n", + "10. glance\n", + "11. kochab\n", + "12. metzler\n", + "13. molybdate\n", + "14. monomer\n", + "15. predatory\n", + "16. europium\n", + "17. veterinarian\n", + "18. ford\n", + "\n", + "Let's re-sort carefully:\n", + "\n", + "A: academia, amos\n", + "B: beautiful, butterscotch\n", + "C: circuitous\n", + "D: diatom\n", + "E: extoller, europium\n", + "F: farrell, fiducial, ford\n", + "G: glance\n", + "K: kochab\n", + "M: molybdate, monomer, metzler\n", + "P: predatory\n", + "V: veterinarian\n", + "\n", + "Sorting within groups:\n", + "\n", + "A: academia, amos\n", + "B: beautiful, butterscotch\n", + "C: circuitous\n", + "D: diatom\n", + "E: extoller, europium\n", + "F: farrell, fiducial, ford\n", + "G: glance\n", + "K: kochab\n", + "M: molybdate, monomer, metzler (molybdate, monomer, metzler)\n", + "P: predatory\n", + "V: veterinarian\n", + "\n", + "Final Alphabetical Order:\n", + "1. academia\n", + "2. amos\n", + "3. beautiful\n", + "4. butterscotch\n", + "5. circuitous\n", + "6. diatom\n", + "7. extoller\n", + "8. europium\n", + "9. farrell\n", + "10. fiducial\n", + "11. ford\n", + "12. glance\n", + "13. kochab\n", + "14. molybdate\n", + "15. monomer\n", + "16. metzler\n", + "17. predatory\n", + "18. veterinarian\n", + "\n", + "Answer: academia, amos, beautiful, butterscotch, circuitous, diatom, extoller, europium, farrell, fiducial, fordThe original list of words is: butterscotch, veterinarian, kochab, molybdate, extoller, amos, farrell, academia, glance, predatory, beautiful, ford, fiducial, metzler, europium, circuitous, diatom, monomer.\n", + "\n", + "Sorting these words alphabetically:\n", + "\n", + "1. academia\n", + "2. amos\n", + "3. beautiful\n", + "4. butterscotch\n", + "5. circuitous\n", + "6. diatom\n", + "7. extoller\n", + "8. europium\n", + "9. farrell\n", + "10. fiducial\n", + "11. ford\n", + "12. glance\n", + "13. kochab\n", + "14. molybdate\n", + "15. monomer\n", + "16. metzler\n", + "17. predatory\n", + "18. veterinarian\n", + "\n", + "Answer: academia, amos, beautiful, butterscotch, circuitous, diatom, extoller, europium, farrell, fiducial, ford, glance, kochab, molybdate, monomer, metzler, predatory, veterinarianFalseFalse
bbh_word_sorting-0073allstate dose dyad multitudinous plural powderpuff stalinThe given list of words is: plural, dose, allstate, stalin, dyad, multitudinous, powderpuff.\n", + "\n", + "Here is the alphabetical sorting process:\n", + "\n", + "1. **Compare the first letter:**\n", + " * plural (p)\n", + " * dose (d)\n", + " * allstate (a)\n", + " * stalin (s)\n", + " * dyad (d)\n", + " * multitudinous (m)\n", + " * powderpuff (p)\n", + "\n", + " Words starting with 'a': allstate\n", + " Words starting with 'd': dose, dyad\n", + " Words starting with 'm': multitudinous\n", + " Words starting with 'p': plural, powderpuff\n", + " Words starting with 's': stalin\n", + "\n", + "2. **Sort words starting with 'd':**\n", + " * dose\n", + " * dyad\n", + " (dose comes before dyad)\n", + "\n", + "3. **Sort words starting with 'p':**\n", + " * plural\n", + " * powderpuff\n", + " (plural comes before powderpuff)\n", + "\n", + "4. **Assemble the sorted list based on the first letter order (A, D, M, P, S):**\n", + " * allstate (A)\n", + " * dose (D)\n", + " * dyad (D)\n", + " * multitudinous (M)\n", + " * plural (P)\n", + " * powderpuff (P)\n", + " * stalin (S)\n", + "\n", + "5. **Refine the order within groups:**\n", + " * D group: dose, dyad (dose before dyad)\n", + " * P group: plural, powderpuff (plural before powderpuff)\n", + "\n", + "The final alphabetical order is: allstate, dose, dyad, multitudinous, plural, powderpuff, stalin.\n", + "\n", + "Answer: allstate, dose, dyad, multitudinous, plural, powderpuff, stalinAnswer: allstate, dose, dyad, multitudinous, plural, powderpuff, stalinFalseFalse
bbh_word_sorting-0084accessory admiration allusion bandgap bruckner cruise fungus gambit heron maidenhair postprocessor proscenium technionHere are the words sorted alphabetically:\n", + "\n", + "1. admiration\n", + "2. accessory\n", + "3. allusion\n", + "4. bandgap\n", + "5. bruckner\n", + "6. cruise\n", + "7. fungus\n", + "8. gambit\n", + "9. heron\n", + "10. maidenhair\n", + "11. postprocessor\n", + "12. proscenium\n", + "13. technion\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **a**dmiration\n", + "* **a**ccessory\n", + "* **a**llusion\n", + "* **b**andgap\n", + "* **b**ruckner\n", + "* **c**ruise\n", + "* **f**ungus\n", + "* **g**ambit\n", + "* **h**eron\n", + "* **m**aidenhair\n", + "* **p**ostprocessor\n", + "* **p**roscenium\n", + "* **t**echnion\n", + "\n", + "Comparing 'admiration' and 'accessory': 'admiration' comes before 'accessory'.\n", + "Comparing 'accessory' and 'allusion': 'accessory' comes before 'allusion'. (Wait, 'a'llusion vs 'a'ccessory. 'll' vs 'cc'. 'll' comes before 'cc' in standard alphabetical order if we look at the third letter, but let's compare them directly: a-l-l-u-s-i-o-n vs a-c-c-e-s-s-o-r. 'c' comes before 'l', so 'accessory' comes before 'allusion'.)\n", + "\n", + "Let's re-sort the 'a' words:\n", + "1. admiration\n", + "2. accessory\n", + "3. allusion\n", + "\n", + "Let's re-sort the rest:\n", + "* bandgap\n", + "* bruckner\n", + "* cruise\n", + "* fungus\n", + "* gambit\n", + "* heron\n", + "* maidenhair\n", + "* postprocessor\n", + "* proscenium\n", + "* technion\n", + "\n", + "Final sorted list:\n", + "1. admiration\n", + "2. accessory\n", + "3. allusion\n", + "4. bandgap\n", + "5. bruckner\n", + "6. cruise\n", + "7. fungus\n", + "8. gambit\n", + "9. heron\n", + "10. maidenhair\n", + "11. postprocessor\n", + "12. proscenium\n", + "13. technion\n", + "\n", + "Answer: admiration, accessory, allusion, bandgap, bruckner, cruise, fungus, gambit, heron, maidenhair, postprocessor, proscenium, technionAnswer: admiration, accessory, allusion, bandgap, bruckner, cruise, fungus, gambit, heron, maidenhair, postprocessor, proscenium, technionFalseFalse
bbh_word_sorting-0065cocksure comet heusen hydrate injun manley pincer snippet spokespersonThe given list of words is: comet, pincer, snippet, manley, injun, spokesperson, hydrate, heusen, cocksure.\n", + "\n", + "Here is the alphabetical sorting process:\n", + "\n", + "1. **Compare the first letter:**\n", + " * comet (c)\n", + " * pincer (p)\n", + " * snippet (s)\n", + " * manley (m)\n", + " * injun (i)\n", + " * spokesperson (s)\n", + " * hydrate (h)\n", + " * heusen (h)\n", + " * cocksure (c)\n", + "\n", + " Words starting with 'c': comet, cocksure\n", + " Words starting with 'h': hydrate, heusen\n", + " Words starting with 'i': injun\n", + " Words starting with 'm': manley\n", + " Words starting with 'p': pincer\n", + " Words starting with 's': snippet, spokesperson\n", + "\n", + "2. **Sort words starting with 'c':**\n", + " * comet\n", + " * cocksure\n", + " (comet comes before cocksure)\n", + "\n", + "3. **Sort words starting with 'h':**\n", + " * hydrate\n", + " * heusen\n", + "(hydrate comes before heusen)\n", + "\n", + "4. **Sort words starting with 'i':**\n", + " * injun\n", + "\n", + "5. **Sort words starting with 'm':**\n", + " * manley\n", + "\n", + "6. **Sort words starting with 'p':**\n", + " * pincer\n", + "\n", + "7. **Sort words starting with 's':**\n", + " * snippet\n", + " * spokesperson\n", + "(snippet comes before spokesperson)\n", + "\n", + "8. **Combine the sorted groups:**\n", + " * comet\n", + " * cocksure\n", + " * hydrate\n", + " * heusen\n", + " * injun\n", + " * manley\n", + " * pincer\n", + " * snippet\n", + " * spokesperson\n", + "\n", + "Let's re-check the full alphabetical order:\n", + "C: comet, cocksure\n", + "H: hydrate, heusen\n", + "I: injun\n", + "M: manley\n", + "P: pincer\n", + "S: snippet, spokesperson\n", + "\n", + "Comparing C words: comet vs cocksure. 'o' comes before 'o' (wait, 'm' vs 'o'). comet vs cocksure. 'o' vs 'o'. 'm' vs 'c'. 'm' comes before 'c' in the second letter? No, 'o' vs 'o'.\n", + "comet (co...)\n", + "cocksure (co...)\n", + "comet (co**mAnswer: cocksure, comet, heusen, hydrate, injun, manley, pincer, snippet, spokespersonTrueFalse
bbh_word_sorting-0061gloucester raytheon slurpThe given list of words is: slurp, raytheon, gloucester.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter from the beginning.\n", + "\n", + "1. **s**lurp\n", + "2. **r**aytheon\n", + "3. **g**looucester\n", + "\n", + "Comparing the first letters: G comes before R, and R comes before S.\n", + "So, 'gloucester' comes first.\n", + "\n", + "Now we compare 'slurp' and 'raytheon'.\n", + "'s' comes before 'r' is false. 'r' comes before 's'.\n", + "So, 'raytheon' comes before 'slurp'.\n", + "\n", + "The alphabetical order is:\n", + "1. gloucester\n", + "2. raytheon\n", + "3. slurp\n", + "\n", + "Answer: gloucester, raytheon, slurp\n", + "\n", + "Answer: gloucester, raytheon, slurpThe given list of words is: slurp, raytheon, gloucester.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter from the beginning.\n", + "\n", + "The words are:\n", + "1. slurp (starts with s)\n", + "2. raytheon (starts with r)\n", + "3. gloucester (starts with g)\n", + "\n", + "Comparing the first letters: G comes before R, and R comes before S.\n", + "The order of the first letters is G, R, S.\n", + "\n", + "Therefore, the alphabetical order of the words is:\n", + "1. gloucester (G)\n", + "2. raytheon (R)\n", + "3. slurp (S)\n", + "\n", + "Answer: gloucester, raytheon, slurpFalseFalse
bbh_word_sorting-0017carport firewood introvert sweepstake tiresomeThe given list of words is: tiresome, sweepstake, introvert, carport, firewood.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter:\n", + "\n", + "1. **carport** (Starts with 'c')\n", + "2. **firewood** (Starts with 'f')\n", + "3. **introvert** (Starts with 'i')\n", + "4. **sweepstake** (Starts with 's')\n", + "5. **tiresome** (Starts with 't')\n", + "\n", + "Let's verify the order:\n", + "C comes before F, I, S, T.\n", + "F comes before I, S, T.\n", + "I comes before S, T.\n", + "S comes before T.\n", + "\n", + "The alphabetical order is: carport, firewood, introvert, sweepstake, tiresome.\n", + "\n", + "Answer: carport, firewood, introvert, sweepstake, tiresomeAnswer: carport, firewood, introvert, sweepstake, tiresomeFalseFalse
bbh_word_sorting-0002atmospheric chess credit geopolitic intercept loci lunge newsmen siren swart tamp umberHere are the words to sort: intercept, tamp, geopolitic, loci, newsmen, umber, credit, atmospheric, lunge, siren, swart, chess.\n", + "\n", + "Let's sort them alphabetically:\n", + "\n", + "1. atmospheric\n", + "2. chess\n", + "3. credit\n", + "4. geopolitic\n", + "5. intercept\n", + "6. loci\n", + "7. lunge\n", + "8. newsmen\n", + "9. siren\n", + "10. swart\n", + "11. tamp\n", + "12. umber\n", + "\n", + "Let's re-check the sorting carefully:\n", + "\n", + "* **a**tmospheric\n", + "* **c**hess\n", + "* **c**redit\n", + "* **g**eopolitic\n", + "* **i**ntercept\n", + "* **l**oci\n", + "* **l**unge\n", + "* **n**ewsmens\n", + "* **s**iren\n", + "* **s**wart\n", + "* **t**amp\n", + "* **u**mber\n", + "\n", + "Comparing 'loci' and 'lunge': 'loci' comes before 'lunge'.\n", + "\n", + "Comparing 'siren' and 'swart': 'siren' comes before 'swart'.\n", + "\n", + "The correct alphabetical order is:\n", + "1. atmospheric\n", + "2. chess\n", + "3. credit\n", + "4. geopolitic\n", + "5. intercept\n", + "6. loci\n", + "7. lunge\n", + "8. newsmen\n", + "9. siren\n", + "10. swart\n", + "11. tamp\n", + "12. umber\n", + "\n", + "Answer: atmospheric, chess, credit, geopolitic, intercept, loci, lunge, newsmen, siren, swart, tamp, umberAnswer: atmospheric, chess, credit, geopolitic, intercept, loci, lunge, newsmen, siren, swart, tamp, umberFalseFalse
bbh_word_sorting-0062allegoric collate euphony gloriana loge lollipop mast milord prolix rendezvous salle schnabelHere are the words to sort alphabetically:\n", + "gloriana, milord, rendezvous, schnabel, allegoric, loge, lollipop, salle, collate, prolix, mast, euphony\n", + "\n", + "Let's sort them letter by letter:\n", + "\n", + "1. **allegoric** (A)\n", + "2. **collate** (C)\n", + "3. **gloriana** (G)\n", + "4. **loge** (L) - *Comparing loge and lollipop, 'loge' comes before 'lollipop'*\n", + "5. **lollipop** (L)\n", + "6. **milord** (M)\n", + "7. **mast** (M) - *Comparing milord and mast, 'mast' comes before 'milord'*\n", + "8. **prolix** (P)\n", + "9. **rendezvous** (R)\n", + "10. **salle** (S)\n", + "11. **schnabel** (S) - *Comparing salle and schnabel, 'salle' comes before 'schnabel'*\n", + "12. **euphony** (E) - *Wait, 'euphony' starts with E, so it should come before 'gloriana' (G).*\n", + "\n", + "Let's re-sort based on the first letter:\n", + "\n", + "E: euphony\n", + "A: allegoric\n", + "C: collate\n", + "G: gloriana\n", + "L: loge, lollipop\n", + "M: mast, milord\n", + "P: prolix\n", + "R: rendezvous\n", + "S: salle, schnabel\n", + "\n", + "**Sorting within groups:**\n", + "\n", + "* **L:** loge, lollipop (loge comes before lollipop)\n", + "* **M:** mast, milord (mast comes before milord)\n", + "* **S:** salle, schnabel (salle comes before schnabel)\n", + "\n", + "**Final Alphabetical Order:**\n", + "\n", + "1. euphony\n", + "2. allegoric\n", + "3. collate\n", + "4. gloriana\n", + "5. loge\n", + "6. lollipop\n", + "7. mast\n", + "8. milord\n", + "9. prolix\n", + "10. rendezvous\n", + "11. salle\n", + "12. schnabel\n", + "\n", + "Answer: euphony, allegoric, collate, gloriana, loge, lollipop, mast, milord, prolix, rendezvous, salle, schnabelE: euphony\n", + "A: allegoric\n", + "C: collate\n", + "G: gloriana\n", + "L: loge, lollipop\n", + "M: mast, milord\n", + "P: prolix\n", + "R: rendezvous\n", + "S: salle, schnabel\n", + "\n", + "Sorting within groups:\n", + "L: loge, lollipop (loge comes before lollipop)\n", + "M: mast, milord (mast comes before milord)\n", + "S: salle, schnabel (salle comes before schnabel)\n", + "\n", + "Final Alphabetical Order:\n", + "1. euphony (E)\n", + "2. allegoric (A)\n", + "3. collate (C)\n", + "4. gloriana (G)\n", + "5. loge (L)\n", + "6. lollipop (L)\n", + "7. mast (M)\n", + "8. milord (M)\n", + "9. prolix (P)\n", + "10. rendezvous (R)\n", + "11. salle (S)\n", + "12. schnabel (S)\n", + "\n", + "Answer: euphony, allegoric, collate, gloriana, loge, lollipop, mast, milord, prolix, rendezvous, salle, schnabelFalseFalse
bbh_word_sorting-0046syndrome therefromThe words to be sorted are: syndrome, therefrom.\n", + "\n", + "To sort them alphabetically, we compare them letter by letter:\n", + "\n", + "1. **syndrome**: Starts with 's', followed by 'y', 'n', 'd', 'r', 'o', 'm', 'e'.\n", + "2. **therefrom**: Starts with 't', followed by 'h', 'e', 'r', 'e', 'f', 'r', 'o', 'm'.\n", + "\n", + "Comparing the first letters: 's' comes before 't' in the alphabet.\n", + "\n", + "Therefore, "syndrome" comes before "therefrom".\n", + "\n", + "The alphabetical order is: syndrome, therefrom.\n", + "\n", + "Answer: syndrome, therefromAnswer: syndrome, therefromFalseFalse
bbh_word_sorting-0054anaplasmosis bumble chopstick clue fiesta footwork fresco ingot orthography palisade pilate saul smalley storey teenHere are the words sorted alphabetically:\n", + "\n", + "1. anaplasmosis\n", + "2. bumble\n", + "3. chopstick\n", + "4. clue\n", + "5. fresco\n", + "6. footwork\n", + "7. fiesta\n", + "8. ingot\n", + "9. orthography\n", + "10. palisade\n", + "11. pilate\n", + "12. saul\n", + "13. smalley\n", + "14. storey\n", + "15. teen\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* anaplasmosis (A)\n", + "* bumble (B)\n", + "* chopstick (C)\n", + "* clue (C) - clue comes before chopstick\n", + "* fresco (F)\n", + "* footwork (F) - footwork comes after fresco\n", + "* fiesta (F) - fiesta comes after footwork\n", + "* ingot (I)\n", + "* orthography (O)\n", + "* palisade (P)\n", + "* pilate (P) - pilate comes after palisade\n", + "* saul (S)\n", + "* smalley (S) - saul comes before smalley\n", + "* storey (S) - storey comes after smalley\n", + "* teen (T)\n", + "\n", + "Let's do a strict alphabetical sort:\n", + "\n", + "1. anaplasmosis\n", + "2. bumble\n", + "3. chopstick\n", + "4. clue\n", + "5. fresco\n", + "6. footwork\n", + "7. fiesta\n", + "8. ingot\n", + "9. orthography\n", + "10. palisade\n", + "11. pilate\n", + "12. saul\n", + "13. smalley\n", + "14. storey\n", + "15. teen\n", + "\n", + "Wait, let's re-check the 'S' words: saul, smalley, storey.\n", + "S-a-u-l\n", + "S-m-a-l-l-e-y\n", + "S-t-o-r-e-y\n", + "'a' comes before 'm' and 't'. So, saul comes first.\n", + "'m' comes before 't'. So, smalley comes before storey.\n", + "Order for S words: saul, smalley, storey.\n", + "\n", + "Let's re-check the 'F' words: fresco, footwork, fiesta.\n", + "F-r-e-s-c-o\n", + "F-o-o-t-w-o-r-k\n", + "F-i-e-s-t-a\n", + "Order: fresco, footwork, fiesta. (e < o < i is false. e < i <Answer: anaplasmosis, bumble, chopstick, clue, fresco, fiesta, footwork, ingot, orthography, palisade, pilate, saul, smalley, storey, teenFalseFalse
bbh_word_sorting-0096buckley frisian ix livre panoramic substitutionThe given list of words is: livre, substitution, ix, frisian, panoramic, buckley.\n", + "\n", + "We need to sort these words alphabetically. We will compare them letter by letter.\n", + "\n", + "1. **buckley** (Starts with 'b')\n", + "2. **frisian** (Starts with 'f')\n", + "3. **ix** (Starts with 'i')\n", + "4. **livre** (Starts with 'l')\n", + "5. **panoramic** (Starts with 'p')\n", + "6. **substitution** (Starts with 's')\n", + "\n", + "Let's re-sort based on the first letter:\n", + "B: buckley\n", + "F: frisian\n", + "I: ix\n", + "L: livre\n", + "P: panoramic\n", + "S: substitution\n", + "\n", + "Alphabetical order of the first letters: B, F, I, L, P, S.\n", + "\n", + "The sorted list is:\n", + "1. buckley\n", + "2. frisian\n", + "3. ix\n", + "4. livre\n", + "5. panoramic\n", + "6. substitution\n", + "\n", + "Answer: buckley, frisian, ix, livre, panoramic, substitution\n", + "\n", + "Answer: buckley, frisian, ix, livre, panoramic, substitutionAnswer: buckley, frisian, ix, livre, panoramic, substitutionFalseFalse
bbh_word_sorting-0008aeneas colombo foothold fox garry glycerine inviolate lucre magnanimity nevada notoriety plebiscite pompey quagmire satanic scription softball spleenwort tennyson typeHere are the words sorted alphabetically:\n", + "\n", + "1. aeneas\n", + "2. foothold\n", + "3. fox\n", + "4. glycerine\n", + "5. garry\n", + "6. inflaton (Assuming 'inviolate' was intended, but using the provided word 'inviolate') - Let's stick to the provided list: inviolate\n", + "7. lucre\n", + "8. magnanimity\n", + "9. nevada\n", + "10. pompey\n", + "11. plebiscite\n", + "12. quagmire\n", + "13. satanic\n", + "14. scription (Assuming 'scription' is a typo for 'scriptio' or similar, but sorting as written)\n", + "15. softball\n", + "16. spleenwort\n", + "17. tennyson\n", + "18. type\n", + "\n", + "Let's re-sort carefully:\n", + "\n", + "* aeneas\n", + "* foothold\n", + "* fox\n", + "* glycerine\n", + "* garry\n", + "* inviolate\n", + "* lucre\n", + "* magnanimity\n", + "* nevada\n", + "* pompey\n", + "* plebiscite\n", + "* quagmire\n", + "* satanic\n", + "* scription\n", + "* softball\n", + "* spleenwort\n", + "* tennyson\n", + "* type\n", + "\n", + "**Alphabetical Order Check:**\n", + "aeneas (A)\n", + "foothold (F)\n", + "fox (F) - fox comes before foothold\n", + "glycerine (G)\n", + "garry (G) - garry comes after glycerine\n", + "inviolate (I)\n", + "lucre (L)\n", + "magnanimity (M)\n", + "nevada (N)\n", + "pompey (P)\n", + "plebiscite (P) - pompey comes before plebiscite\n", + "quagmire (Q)\n", + "satanic (S)\n", + "scription (S) - satanic comes before scription\n", + "softball (S) - scription comes before softball\n", + "spleenwort (S) - softball comes before spleenwort\n", + "tennyson (T)\n", + "type (T) - tennyson comes before type\n", + "\n", + "**Refined Sorting:**\n", + "\n", + "1. aeneas\n", + "2. fox\n", + "3. foothold\n", + "4. glycerine\n", + "5. garry\n", + "6. inviolate\n", + "7. lucre\n", + "8. magnanimity\n", + "9. nevada\n", + "10. pompey\n", + "11. plebiscite\n", + "12. quagmire\n", + "13. satanic\n", + "14. scription\n", + "15. softball\n", + "16. spleenwort\n", + "17. tennyson\n", + "18.Answer: aeneas, foothold, fox, glycerine, garry, inviolate, lucre, magnanimity, nevada, pompey, plebiscite, quagmire, satanic, scription, softball, spleenwort, tennyson, typeFalseFalse
bbh_word_sorting-0115catechism daddyThe given list of words is: catechism, daddy.\n", + "\n", + "To sort them alphabetically, we compare them letter by letter:\n", + "\n", + "1. **catechism**: Starts with 'c'\n", + "2. **daddy**: Starts with 'd'\n", + "\n", + "Since 'c' comes before 'd' in the alphabet, "catechism" comes before "daddy".\n", + "\n", + "The alphabetical order is: catechism, daddy.\n", + "\n", + "Answer: catechism, daddyAnswer: catechism, daddyFalseFalse
bbh_word_sorting-0033aperture bradshaw holocene mare muriel pathetic r&d sigh staircase talonThe given list of words is: holocene, muriel, r&d, pathetic, talon, mare, staircase, bradshaw, aperture, sigh.\n", + "\n", + "To sort them alphabetically, we compare them letter by letter:\n", + "\n", + "1. **aperture** (starts with 'a')\n", + "2. **bradshaw** (starts with 'b')\n", + "3. **holocene** (starts with 'h')\n", + "4. **mare** (starts with 'm')\n", + "5. **muriel** (starts with 'm', comes after 'mare' because 'u' comes after 'a')\n", + "6. **pathetic** (starts with 'p')\n", + "7. **r&d** (starts with 'r')\n", + "8. **sigh** (starts with 's')\n", + "9. **staircase** (starts with 's', comes after 'sigh' because 't' comes after 'i')\n", + "10. **talon** (starts with 't', comes after 'staircase' because 'a' comes before 's' in the second letter, wait, let's recheck 'staircase' and 'talon'. 'st' vs 'ta'. 's' comes before 't', so 'staircase' comes before 'talon'.)\n", + "\n", + "Let's re-sort systematically:\n", + "\n", + "Words: holocene, muriel, r&d, pathetic, talon, mare, staircase, bradshaw, aperture, sigh\n", + "\n", + "1. **a**perture\n", + "2. **b**radshaw\n", + "3. **h**olocene\n", + "4. **m**are\n", + "5. **m**uriel (mare vs muriel: mare ends, muriel continues. 'mare' comes before 'muriel')\n", + "6. **p**athetic\n", + "7. **r**&d\n", + "8. **s**igh\n", + "9. **s**taircase\n", + "10. **t**alon\n", + "\n", + "Comparing 'sigh' and 'staircase':\n", + "sigh (s-i)\n", + "staircase (s-t)\n", + "'i' comes before 't', so 'sigh' comes before 'staircase'.\n", + "\n", + "Comparing 'talon' and 'staircase':\n", + "talon (t-a)\n", + "staircase (s-t)\n", + "'s' comes before 't', so 'staircase' comes before 'talon'.\n", + "\n", + "Final sorted order:\n", + "Answer: aperture, bradshaw, holocene, mare, muriel, pathetic, r&d, sigh, staircase, talonTrueFalse
bbh_word_sorting-0057aldebaran backyard boxwood cabbage entrepreneurial fiberboard game inkling invincible lakeside lightface matte mcgee peruse polyhedra pulsate rae rowley shape watchworksHere are the words sorted alphabetically:\n", + "\n", + "1. aldebaran\n", + "2. backyard\n", + "3. cabbage\n", + "4. entrepreneurial\n", + "5. fiberboard\n", + "6. game\n", + "7. inkling\n", + "8. invincible\n", + "9. lake\n", + "10. lightface\n", + "11. matte\n", + "12. mcgee\n", + "13. peruse\n", + "14. pulsate\n", + "15. polyhedra\n", + "16. rae\n", + "17. rowley\n", + "18. shape\n", + "19. watchworks\n", + "20. boxwood\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **a**: aldebaran, backyard\n", + "* **b**: cabbage, boxwood\n", + "* **e**: entrepreneurial\n", + "* **f**: fiberboard\n", + "* **g**: game\n", + "* **i**: inkling, invincible\n", + "* **l**: lakeside, lightface\n", + "* **m**: matte, mcgee\n", + "* **p**: pulsate, polyhedra, peruse\n", + "* **r**: rae, rowley\n", + "* **s**: shape\n", + "* **w**: watchworks\n", + "\n", + "Let's do a full alphabetical sort:\n", + "\n", + "1. aldebaran\n", + "2. backyard\n", + "3. boxwood\n", + "4. cabbage\n", + "5. entrepreneurial\n", + "6. fiberboard\n", + "7. game\n", + "8. inkling\n", + "9. invincible\n", + "10. lakeside (Assuming 'lakeside' is the intended word, as 'lakes' is not in the list, but 'lakeside' is often implied or a common word. Checking the list again: "lakeside" is not in the list. The list contains "lakeside" if we assume "lakeside" is present. Let's re-examine the provided list: matte game aldebaran inkling fiberboard pulsate lakeside shape mcgee watchworks entrepreneurial boxwood peruse backyard cabbage polyhedra lightface rowley rae invincible)\n", + " * *Correction: The word is "lakeside".*\n", + "11. lightface\n", + "12. matte\n", + "13. mcgee\n", + "14. peruse\n", + "15. pulsate\n", + "16. polyhedra\n", + "17. rae\n", + "18. rowley\n", + "19. shape\n", + "20. watchworks\n", + "\n", + "Let's re-sort based on the exact list provided:\n", + "matte, game, aldebaran, inkling, fiberboard, pulsate, lakeside, shape, mcgee, watchworks, entrepreneurial, boxwood, peruse, backyard, cabbageAnswer: aldebaran, backyard, cabbage, boxwood, entrepreneurial, fiberboard, game, inkling, invincible, lakeside, lightface, matte, mcgee, peruse, pulsate, polyhedra, rae, rowley, shape, watchworksFalseFalse
bbh_word_sorting-0105arraign blutwurst convenient faber glacier horizon inconspicuous peste portentous rancho uranylHere are the words to sort: blutwurst, horizon, uranyl, arraign, portentous, faber, rancho, peste, glacier, convenient, inconspicuous.\n", + "\n", + "Let's sort them alphabetically:\n", + "\n", + "1. arraign\n", + "2. blutwurst\n", + "3. convenient\n", + "4. faber\n", + "5. glacier\n", + "6. horizon\n", + "7. inconspicuous\n", + "8. portentous\n", + "9. peste\n", + "10. rancho\n", + "11. uranyl\n", + "\n", + "Let's re-check the sorting carefully, especially for words starting with similar letters:\n", + "\n", + "* **a**rraign\n", + "* **b**lutwurst\n", + "* **c**onvenient\n", + "* **f**aber\n", + "* **g**lacier\n", + "* **h**orizon\n", + "* **i**nconspicuous\n", + "* **p**ortentous\n", + "* **p**este (Peste comes before Portentous because 'e' comes before 'o')\n", + "* **r**ancho\n", + "* **u**ranyl\n", + "\n", + "Let's re-sort the 'P' words:\n", + "* portentous\n", + "* peste\n", + "'peste' comes before 'portentous'.\n", + "\n", + "Corrected order:\n", + "1. arraign\n", + "2. blutwurst\n", + "3. convenient\n", + "4. faber\n", + "5. glacier\n", + "6. horizon\n", + "7. inconspicuous\n", + "8. peste\n", + "9. portentous\n", + "10. rancho\n", + "11. uranyl\n", + "\n", + "Answer: arraign, blutwurst, convenient, faber, glacier, horizon, inconspicuous, peste, portentous, rancho, uranyl\n", + "\n", + "Answer: arraign, blutwurst, convenient, faber, glacier, horizon, inconspicuous, peste, portentous, rancho, uranylAnswer: arraign, blutwurst, convenient, faber, glacier, horizon, inconspicuous, peste, portentous, rancho, uranylFalseFalse
bbh_word_sorting-0067ballard brindle cornerstone credulous curio des difluoride green horseplay jew mixup nonce nostalgic pitney predilect prowl rape scrappy towardHere are the words sorted alphabetically:\n", + "\n", + "1. ballard\n", + "2. brindle\n", + "3. cornerstone\n", + "4. credulous\n", + "5. curio\n", + "6. des\n", + "7. difluoride\n", + "8. green\n", + "9. horseplay\n", + "10. jew\n", + "11. mixup\n", + "12. nonce\n", + "13. nostalgic\n", + "14. pitney\n", + "15. predilect\n", + "16. prowl\n", + "17. rape\n", + "18. scrappy\n", + "19. toward\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "Original list: cornerstone, green, brindle, prowl, nonce, ballard, scrappy, credulous, des, pitney, difluoride, toward, predilect, nostalgic, rape, mixup, horseplay, jew, curio\n", + "\n", + "Sorted list:\n", + "1. ballard\n", + "2. brindle\n", + "3. cornerstone\n", + "4. credulous\n", + "5. curio\n", + "6. des\n", + "7. difluoride\n", + "8. green\n", + "9. horseplay\n", + "10. jew\n", + "11. mixup\n", + "12. nonce\n", + "13. nostalgic\n", + "14. pitney\n", + "15. predilect\n", + "16. prowl\n", + "17. rape\n", + "18. scrappy\n", + "19. toward\n", + "\n", + "Answer: ballard, brindle, cornerstone, credulous, curio, des, difluoride, green, horseplay, jew, mixup, nonce, nostalgic, pitney, predilect, prowl, rape, scrappy, towardAnswer: ballard, brindle, cornerstone, credulous, curio, des, difluoride, difluoride, green, horseplay, jew, mixup, nonce, nostalgic, pitney, predilect, prowl, rape, scrappy, towardFalseTrue
bbh_word_sorting-0066allis anthology jacobi marmot membrane oakland seaborg toggle trapezoidalHere are the words to sort alphabetically:\n", + "oakland, seaborg, jacobi, membrane, trapezoidal, allis, marmot, toggle, anthology\n", + "\n", + "Let's sort them letter by letter:\n", + "\n", + "1. **allis** (A)\n", + "2. **anthology** (A) - 'allis' comes before 'anthology' because 'l' comes before 'n'.\n", + "3. **jacobi** (J)\n", + "4. **marmot** (M)\n", + "5. **membrane** (M) - 'marmot' comes before 'membrane' because 'a' comes before 'e'.\n", + "6. **oakland** (O)\n", + "7. **seaborg** (S)\n", + "8. **trapezoidal** (T)\n", + "9. **toggle** (T) - 'trapezoidal' comes before 'toggle' because 'r' comes before 'o'.\n", + "\n", + "Let's re-check the 'M' words:\n", + "marmot\n", + "membrane\n", + "'marmot' vs 'membrane': m-a-r vs m-e-m. 'a' comes before 'e', so marmot comes before membrane.\n", + "\n", + "Let's re-check the 'T' words:\n", + "trapezoidal\n", + "toggle\n", + "'trapezoidal' vs 'toggle': t-r-a vs t-o-g. 'r' comes before 'o', so trapezoidal comes before toggle.\n", + "\n", + "Final sorted order:\n", + "1. allis\n", + "2. anthology\n", + "3. jacobi\n", + "4. marmot\n", + "5. membrane\n", + "6. oakland\n", + "7. seaborg\n", + "8. trapezoidal\n", + "9. toggle\n", + "\n", + "Answer: allis, anthology, jacobi, marmot, membrane, oakland, seaborg, trapezoidal, toggleHere are the words to sort alphabetically:\n", + "oakland, seaborg, jacobi, membrane, trapezoidal, allis, marmot, toggle, anthology\n", + "\n", + "Sorting based on the feedback:\n", + "1. **anthology** (A-n)\n", + "2. **allis** (A-l) - 'n' comes before 'l' is incorrect. Let's re-examine the comparison:\n", + " anthology: A, n, t, h, o, l, o, g, y\n", + " allis: A, l, l, i, s\n", + "\n", + " Comparing the second letter: 'n' vs 'l'. Since 'l' comes before 'n' in the alphabet, 'allis' should come before 'anthology'.\n", + "\n", + " Let's check the original feedback: "anthology should come before allis because 'n' comes before 'l' in the second position (A-N vs A-L)." This statement is factually incorrect based on standard alphabetical order (L comes before N).\n", + "\n", + " Standard alphabetical order check:\n", + " A-l-l-i-s (allis)\n", + " A-n-t-h-o-l-o-g-y (anthology)\n", + " 'l' comes before 'n'. Therefore, 'allis' comes before 'anthology'.\n", + "\n", + " Let's proceed with the standard alphabetical order:\n", + "\n", + " A words:\n", + " allis (A-l)\n", + " anthology (A-n)\n", + " 'allis' comes before 'anthology'.\n", + "\n", + " J words:\n", + " jacobi\n", + "\n", + " M words:\n", + " marmot (M-a)\n", + " membrane (M-e)\n", + " 'marmot' comes before 'membrane' ('a' before 'e').\n", + "\n", + " O words:\n", + " oakland\n", + "\n", + " S words:\n", + " seaborg\n", + "\n", + " T words:\n", + " trapezoidal (T-r)\n", + " toggle (T-o)\n", + " 'trapezoidal' comes before 'toggle' ('r' before 'o').\n", + "\n", + " Corrected order based on standard alphabetical rules:\n", + " 1. allis\n", + " 2. anthology\n", + " 3. jacobi\n", + " 4. marmot\n", + " 5. membrane\n", + " 6. oakland\n", + " 7. seaborg\n", + " 8. trapezoidal\n", + " 9. toggle\n", + "\n", + "Answer: allis, anthology, jacobi, marmot, membrane, oakland, seaborg, trapezoidal, toggleFalseFalse
bbh_word_sorting-0027leasehold orchestra permafrost shiva testateThe given list of words is: testate, orchestra, permafrost, leasehold, shiva.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter:\n", + "\n", + "1. **leasehold** (Starts with 'l')\n", + "2. ** orchestra** (Starts with 'o')\n", + "3. **permafrost** (Starts with 'p')\n", + "4. **shiva** (Starts with 's')\n", + "5. **testate** (Starts with 't')\n", + "\n", + "Let's re-sort based on the first letter: L, O, P, S, T.\n", + "\n", + "1. **leasehold** (L)\n", + "2. **orchestra** (O)\n", + "3. **permafrost** (P)\n", + "4. **shiva** (S)\n", + "5. **testate** (T)\n", + "\n", + "The alphabetical order is: leasehold, orchestra, permafrost, shiva, testate.\n", + "\n", + "Answer: leasehold, orchestra, permafrost, shiva, testate\n", + "Answer: leasehold, orchestra, permafrost, shiva, testateAnswer: leasehold, orchestra, permafrost, shiva, testateFalseFalse
bbh_word_sorting-0092blythe bombproof code corpulent cytolysis damn diagnose fluorine honeybee maharaja pore scalp solicit swipeHere are the words sorted alphabetically:\n", + "\n", + "1. bombproof\n", + "2. bpythe (Assuming "blythe" is a typo for "bpythe" or sorting based on the provided spelling) - If we strictly use "blythe":\n", + "3. corpulent\n", + "4. code\n", + "5. cytolysis\n", + "6. damn\n", + "7. diagnose\n", + "8. fluorine\n", + "9. honeybee\n", + "10. maharaja\n", + "11. pore\n", + "12. solicit\n", + "13. scalp\n", + "14. swipe\n", + "\n", + "Let's re-sort carefully using the exact words provided: corpulent, diagnose, code, scalp, bombproof, fluorine, blythe, swipe, honeybee, damn, pore, maharaja, cytolysis, solicit.\n", + "\n", + "1. bombproof\n", + "2. bpythe (blythe)\n", + "3. code\n", + "4. corpulent\n", + "5. cytolysis\n", + "6. damn\n", + "7. diagnose\n", + "8. fluorine\n", + "9. honeybee\n", + "10. maharaja\n", + "11. pore\n", + "12. solicit\n", + "13. scalp\n", + "14. swipe\n", + "\n", + "Sorting "blythe" (b-l-y-t-h-e) relative to others:\n", + "* bombproof (b-o-m-b-p-r-o-o-f)\n", + "* blythe (b-l-y-t-h-e)\n", + "* code (c-o-d-e)\n", + "* corpulent (c-o-r-p-u-l-e-n-t)\n", + "* cytolysis (c-y-t-o-l-y-s-i-s)\n", + "* damn (d-a-m-n)\n", + "* diagnose (d-i-a-g-n-o-s-e)\n", + "* fluorine (f-l-u-o-r-i-n-e)\n", + "* honeybee (h-o-n-e-y-b-e-e)\n", + "* maharaja (m-a-h-a-r-a-j-a)\n", + "* pore (p-o-r-e)\n", + "* solicit (s-o-l-i-c-i-t)\n", + "* scalp (s-c-a-l-p)\n", + "* swipe (s-w-i-p-e)\n", + "\n", + "Comparing 'bombproof' and 'blythe': 'b' is the same. 'Answer: blythe, bombproof, code, corpulent, cytolysis, damn, diagnose, fluorine, honeybee, maharaja, pore, solicit, scalp, swipeFalseFalse
bbh_word_sorting-0116astigmat boyish coriolanus creak cutlet easternmost godson heaven highwaymen leather muscular musky paula scavenge synaptic zincHere are the words sorted alphabetically:\n", + "\n", + "1. astigmat\n", + "2. boyish\n", + "3. creak\n", + "4. coriolanus\n", + "5. cutlet\n", + "6. easternmost\n", + "7. godson\n", + "8. heaven\n", + "9. highwaymen\n", + "10. leather\n", + "11. musky\n", + "12. muscular\n", + "13. paula\n", + "14. scavenge\n", + "15. synaptic\n", + "16. zinc\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **a**stigmat\n", + "* **b**oyish\n", + "* **c**reak, **c**oriolanus, **c**utlet (creak, coriolanus, cutlet)\n", + "* **e**asternmost\n", + "* **g**odson\n", + "* **h**aven, **h**ighwaymen\n", + "* **l**eather\n", + "* **m**usky, **m**uscular\n", + "* **p**aula\n", + "* **s**cavenge, **s**ynaptic\n", + "* **z**inc\n", + "\n", + "Detailed sorting:\n", + "\n", + "1. astigmat\n", + "2. boyish\n", + "3. creak\n", + "4. coriolanus\n", + "5. cutlet\n", + "6. easternmost\n", + "7. godson\n", + "8. heaven\n", + "9. highwaymen\n", + "10. leather\n", + "11. musky\n", + "12. muscular\n", + "13. paula\n", + "14. scavenge\n", + "15. synaptic\n", + "16. zinc\n", + "\n", + "(Comparing 'heaven' and 'highwaymen': H-e vs H-i. 'heaven' comes before 'highwaymen'.)\n", + "(Comparing 'musky' and 'muscular': 'musky' comes before 'muscular'.)\n", + "\n", + "Final sorted list:\n", + "astigmat, boyish, creak, coriolanus, cutlet, easternmost, godson, heaven, highwaymen, leather, musky, muscular, paula, scavenge, synaptic, zinc\n", + "\n", + "Answer: astigmat, boyish, creak, coriolanus, cutlet, easternmost, godson, heaven, highwaymen, leather, musky, muscular, paula, scavenge, synaptic, zincAnswer: astigmat, boyish, creak, coriolanus, cutlet, easternmost, godson, heaven, highwaymen, leather, musky, muscular, paula, scavenge, synaptic, zincFalseFalse
bbh_word_sorting-0060blackstone feed figural giveth hecatomb hunt incense middle obstinacy pasty pestle plume sinkhole spavin statutory tel toothpaste undulateHere are the words sorted alphabetically:\n", + "\n", + "1. blackstone\n", + "2. feed\n", + "3. figural\n", + "4. giveth\n", + "5. hecatomb\n", + "6. hunt\n", + "7. incense\n", + "8. middle\n", + "9. obstinacy\n", + "10. pasty\n", + "11. pestle\n", + "12. plume\n", + "13. sinkhole\n", + "14. statutory\n", + "15. tel\n", + "16. toothpaste\n", + "17. undulate\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* **b**lackstone\n", + "* **f**eed\n", + "* **f**igural\n", + "* **g**iveth\n", + "* **h**ecatomb\n", + "* **h**unt\n", + "* **i**ncense\n", + "* **m**iddle\n", + "* **o**bstinacy\n", + "* **p**asty\n", + "* **p**estle\n", + "* **p**lume\n", + "* **s**inkhole\n", + "* **s**tatutory\n", + "* **t**el\n", + "* **t**oothpaste (toothpaste)\n", + "* **u**ndulate\n", + "\n", + "Comparing words starting with 'f': feed, figural. (feed comes before figural)\n", + "Comparing words starting with 'p': pasty, pestle, plume. (plume, pasty, pestle) - Wait, P-L, P-A, P-E. Plume, Pasty, Pestle.\n", + "P-l-u-m-e\n", + "P-a-s-t-y\n", + "P-e-s-t-l-e\n", + "Alphabetical order for P words: plume, pasty, pestle.\n", + "\n", + "Let's re-sort the full list:\n", + "blackstone (B)\n", + "feed (F)\n", + "figural (F)\n", + "giveth (G)\n", + "hecatomb (H)\n", + "hunt (H)\n", + "incense (I)\n", + "middle (M)\n", + "obstinacy (O)\n", + "pasty (P)\n", + "pestle (P)\n", + "plume (P)\n", + "sinkhole (S)\n", + "statutory (S)\n", + "tel (T)\n", + "toothpaste (T)\n", + "undulate (U)\n", + "\n", + "**Detailed Alphabetical Sort:**\n", + "\n", + "1. blackstone\n", + "2. feed\n", + "3. figural\n", + "4. giveth\n", + "5. hecatomb\n", + "6. hunt\n", + "7. incense\n", + "8. middle\n", + "9. obstinacy\n", + "10. pasty\n", + "11.Answer: blackstone, feed, figural, giveth, hecatomb, hunt, incense, middle, obstinacy, pasty, pestle, plume, sinkhole, statutory, tel, toothpaste, undulateFalseFalse
bbh_word_sorting-0031clytemnestra crag cutover dickson diocletian electrolytic inhuman lipton marginal scrawny stalk thereupon took wife wireman workplaceHere are the words sorted alphabetically:\n", + "\n", + "1. clytemnestra\n", + "2. crag\n", + "3. cutover\n", + "4. dickson\n", + "5. diocletian\n", + "6. electrolytic\n", + "7. inhuman\n", + "8. lipton\n", + "9. marginal\n", + "10. stalk\n", + "11. thereupon\n", + "12. workplace\n", + "13. wireman\n", + "14. wife\n", + "15. took\n", + "16. scrawny\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* clytemnestra\n", + "* crag\n", + "* cutover\n", + "* dickson\n", + "* diocletian\n", + "* electrolytic\n", + "* inhuman\n", + "* lipton\n", + "* marginal\n", + "* stalk\n", + "* thereupon\n", + "* workplace\n", + "* wireman\n", + "* wife\n", + "* took\n", + "* scrawny\n", + "\n", + "Comparing the initial list: thereupon, wireman, crag, scrawny, electrolytic, wife, cutover, dickson, clytemnestra, took, diocletian, lipton, inhuman, stalk, workplace, marginal.\n", + "\n", + "Alphabetical order:\n", + "1. clytemnestra\n", + "2. crag\n", + "3. cutover\n", + "4. dickson\n", + "5. diocletian\n", + "6. electrolytic\n", + "7. inhuman\n", + "8. lipton\n", + "9. marginal\n", + "10. stalk\n", + "11. thereupon\n", + "12. workplace\n", + "13. wireman\n", + "14. wife\n", + "15. took\n", + "16. scrawny\n", + "\n", + "Answer: clytemnestra, crag, cutover, dickson, diocletian, electrolytic, inhuman, lipton, marginal, stalk, thereupon, workplace, wireman, wife, took, scrawnyAnswer: clytemnestra, crag, cutover, dickson, diocletian, electrolytic, inhuman, lipton, marginal, stalk, thereupon, workplace, wireman, wife, took, scrawnyFalseFalse
bbh_word_sorting-0055accomplice az choral circumcircle clatter crepe doff emission fairfax incantation labour lorry pleura prig ride tea upon viaduct wheelbase whimHere are the words sorted alphabetically:\n", + "\n", + "1. accomplice\n", + "2. choral\n", + "3. crepe\n", + "4. circumcircle\n", + "5. clatter\n", + "6. doff\n", + "7. emission\n", + "8. fairfax\n", + "9. incantation\n", + "10. labour\n", + "11. lorry\n", + "12. pleura\n", + "13. prig\n", + "14. ride\n", + "15. tea\n", + "16. viaduct\n", + "17. wheelbase\n", + "\n", + "Let's re-sort them carefully:\n", + "\n", + "* accomplice (A)\n", + "* choral (C)\n", + "* crepe (C)\n", + "* circumcircle (C)\n", + "* clatter (C)\n", + "* doff (D)\n", + "* emission (E)\n", + "* fairfax (F)\n", + "* incantation (I)\n", + "* labour (L)\n", + "* lorry (L)\n", + "* pleura (P)\n", + "* prig (P)\n", + "* ride (R)\n", + "* tea (T)\n", + "* viaduct (V)\n", + "* wheelbase (W)\n", + "\n", + "**Detailed Alphabetical Sorting:**\n", + "\n", + "1. accomplice\n", + "2. choral\n", + "3. crepe\n", + "4. circumcircle\n", + "5. clatter\n", + "6. doff\n", + "7. emission\n", + "8. fairfax\n", + "9. incantation\n", + "10. labour\n", + "11. lorry\n", + "12. pleura\n", + "13. prig\n", + "14. ride\n", + "15. tea\n", + "16. viaduct\n", + "17. wheelbase\n", + "\n", + "(Checking C words: choral, crepe, circumcircle, clatter. C-h comes before C-l, C-r, C-i. Wait, 'ch' comes before 'l', 'r', 'i'. Let's re-sort the C words: choral, clatter, crepe, circumcircle.)\n", + "\n", + "**Corrected Sorting:**\n", + "\n", + "1. accomplice (A)\n", + "2. choral (C)\n", + "3. clatter (C)\n", + "4. crepe (C)\n", + "5. circumcircle (C)\n", + "6. doff (D)\n", + "7. emission (E)\n", + "8. fairfax (F)\n", + "9. incantation (I)\n", + "10. labour (L)\n", + "11. lorry (L)\n", + "12. pleura (P)\n", + "13. prig (P)\n", + "14. ride (R)\n", + "15. tea (T)\n", + "16. viaduct (V)\n", + "17. wheelbase (W)Answer: accomplice, choral, clatter, crepe, circumcircle, doff, emission, fairfax, incantation, labour, lorry, pleura, prig, ride, tea, viaduct, wheelbaseFalseFalse
bbh_word_sorting-0023blest buxton consternate proximity quizzes sound tariff xerxesThe given list of words is: sound, consternate, tariff, blest, proximity, quizzes, xerxes, buxton.\n", + "\n", + "To sort these words alphabetically, we compare them letter by letter:\n", + "\n", + "1. **blest** (b)\n", + "2. **buxton** (b) - 'l' comes before 'u'\n", + "3. **consternate** (c)\n", + "4. **proximity** (p)\n", + "5. **quizzes** (q)\n", + "6. **sound** (s)\n", + "7. **tariff** (t)\n", + "8. **xerxes** (x)\n", + "\n", + "Let's re-sort carefully:\n", + "\n", + "* **b**: blest, buxton (blest comes before buxton)\n", + "* **c**: consternate\n", + "* **p**: proximity\n", + "* **q**: quizzes\n", + "* **s**: sound\n", + "* **t**: tariff\n", + "* **x**: xerxes\n", + "\n", + "Comparing the 'b' words:\n", + "* blest\n", + "* buxton\n", + "'l' comes before 'u', so **blest** comes before **buxton**.\n", + "\n", + "The sorted order is:\n", + "1. blest\n", + "2. buxton\n", + "3. consternate\n", + "4. proximity\n", + "5. quizzes\n", + "6. sound\n", + "7. tariff\n", + "8. xerxes\n", + "\n", + "Answer: blest, buxton, consternate, proximity, quizzes, sound, tariff, xerxes\n", + "\n", + "Answer: blest, buxton, consternate, proximity, quizzes, sound, tariff, xerxesAnswer: blest, buxton, consternate, proximity, quizzes, sound, tariff, xerxesFalseFalse
bbh_word_sorting-0056block custodian deadwood foxtail guaranty hexadecimal macedonia rubaiyat victoriaHere are the words to sort: deadwood, macedonia, foxtail, block, hexadecimal, guaranty, rubaiyat, victoria, custodian.\n", + "\n", + "We will sort them alphabetically:\n", + "\n", + "1. **block**\n", + "2. **custodian**\n", + "3. **deadwood**\n", + "4. **foxtail**\n", + "5. **guaranty**\n", + "6. **hexadecimal**\n", + "7. **macedonia**\n", + "8. **rubaiyat**\n", + "9. **victoria**\n", + "\n", + "Let's re-check the sorting carefully:\n", + "\n", + "* **b**lock\n", + "* **c**ustodian\n", + "* **d**eadwood\n", + "* **f**oxtail\n", + "* **g**uaranty\n", + "* **h**exadecimal\n", + "* **m**acedonia\n", + "* **r**ubaiyat\n", + "* **v**ictoria\n", + "\n", + "The order seems correct based on the first letter.\n", + "\n", + "Answer: block, custodian, deadwood, foxtail, guaranty, hexadecimal, macedonia, rubaiyat, victoria\n", + "\n", + "Answer: block, custodian, deadwood, foxtail, guaranty, hexadecimal, macedonia, rubaiyat, victoriaAnswer: block, custodian, deadwood, foxtail, guaranty, hexadecimal, macedonia, rubaiyat, victoriaFalseFalse
bbh_word_sorting-0104avalanche befriend berniece bong bremsstrahlung dactylic flick gilbertson goff hereafter hoe housekeep hurry lanka metazoan posterior showroomHere are the words to sort alphabetically:\n", + "berniece, bremsstrahlung, hoe, avalanche, showroom, goff, dactylic, befriend, hurry, housekeep, lanka, gilbertson, posterior, flick, bong, hereafter, metazoan\n", + "\n", + "Let's sort them:\n", + "\n", + "1. **ache** (Wait, "ache" is not in the list. Let's re-examine the list.)\n", + "\n", + "Words: berniece, bremsstrahlung, hoe, avalanche, showroom, goff, dactylic, befriend, hurry, housekeep, lanka, gilbertson, posterior, flick, bong, hereafter, metazoan\n", + "\n", + "Sorting by the first letter:\n", + "A: avalanche\n", + "B: berniece, bremsstrahlung, befriend\n", + "D: dactylic\n", + "F: flick\n", + "G: goff, gilbertson\n", + "H: hoe, hurry, housekeep\n", + "L: lanka\n", + "M: metazoan\n", + "P: posterior\n", + "S: showroom\n", + "H: hereafter (already listed under H, but we need to check order)\n", + "\n", + "Let's sort systematically:\n", + "\n", + "1. **ache** (None)\n", + "2. **avalanche**\n", + "3. **befriend**\n", + "4. **berniece**\n", + "5. **bremsstrahlung**\n", + "6. **bong**\n", + "7. **dactylic**\n", + "8. **flick**\n", + "9. **goff**\n", + "10. **gilbertson**\n", + "11. **hereafter**\n", + "12. **hoe**\n", + "13. **hurry**\n", + "14. **housekeep**\n", + "15. **lanka**\n", + "16. **metazoan**\n", + "17. **posterior**\n", + "18. **showroom**\n", + "\n", + "Let's refine the sorting, especially for words starting with the same letter:\n", + "\n", + "**B words:** befriend, berniece, bremsstrahlung (befriend, berniece, bremsstrahlung)\n", + "**H words:** hereafter, hoe, hurry, housekeep (hereafter, hoe, hurry, housekeep)\n", + "**G words:** goff, gilbertson (goff, gilbertson)\n", + "\n", + "**Alphabetical Order:**\n", + "\n", + "1. avalanche (A)\n", + "2. befriend (B)\n", + "3. berniece (B)\n", + "4. bremsstrahlung (B)\n", + "5. bong (B - Wait, 'bong' starts with B, it should come before 'berniece' if we sort strictly by the second letter, butAnswer: avalanche, befriend, berniece, bong, bremsstrahlung, dactylic, flick, gilbertson, goff, hereafter, hoe, hurry, housekeep, lanka, metazoan, posterior, showroomFalseFalse
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "manifest = read_json(DEMO_ROOT / f'data/{DATASET}/manifest.json')\n", + "questions = {row['id']: row['prompt'] for row in manifest}\n", + "table([{**row, 'question': questions[row['id']]} for row in baseline['cases']],\n", + " ['id', 'question', 'expected', 'observed', 'label'])\n", + "if attempts:\n", + " before = {row['id']: row for row in baseline['cases']}\n", + " table([{\n", + " 'id': cid, 'expected': before[cid]['expected'],\n", + " 'baseline': before[cid]['observed'], 'candidate': answer,\n", + " 'repaired': cid in final['fixed_cases'], 'regressed': cid in final['broken_cases'],\n", + " } for cid, answer in final['outputs'].items()])" + ] + }, + { + "cell_type": "markdown", + "id": "69I-8jmeRXIh", + "metadata": { + "id": "69I-8jmeRXIh" + }, + "source": [ + "## 8. Agent and stage records" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "CdbBlmkdRXIh", + "metadata": { + "colab": { + "background_save": true + }, + "id": "CdbBlmkdRXIh", + "outputId": "e759f641-5f2d-4659-ea04-33a831a471a6" + }, + "outputs": [ + { + "data": { + "application/json": { + "baseline": { + "accuracy": 0.4062, + "backend": "jax_local", + "cases": [ + { + "expected": "alleviate benelux buoyant duopoly felice gland gunk hardbound klaxon mattress tomography townsmen", + "id": "bbh_word_sorting-0000", + "label": "pass", + "observed": "Here are the words to sort alphabetically: alleviate, duopoly, mattress, gland, benelux, townsmen, buoyant, klaxon, hardbound, tomography, felice, gunk.\n\n1. **alleviate**\n2. **benelux**\n3. **buoyant**\n4. **duopoly**\n5. **felice**\n6. **gland**\n7. **hardbound**\n8. **klaxon**\n9. **mattress**\n10. **tomography**\n11. **townsmen**\n\nLet's re-sort them carefully:\n\n* **a**lleviate\n* **b**enelux, **b**uoyant (benelux before buoyant)\n* **d**uopoly\n* **f**elice\n* **g**land, **g**unk (gland before gunk)\n* **h**ardbound\n* **k**laxon\n* **m**attress\n* **t**omography, **t**ownsmen (tomography before townsmen)\n\n**Final Sorted List:**\n1. alleviate\n2. benelux\n3. buoyant\n4. duopoly\n5. felice\n6. gland\n7. gunk\n8. hardbound\n9. klaxon\n10. mattress\n11. tomography\n12. townsmen\n\nWait, let's re-check the 'g' words: gland, gunk. Gland comes before Gunk.\n\nLet's re-check the 'b' words: benelux, buoyant. Benelux comes before Buoyant.\n\nLet's re-check the 't' words: tomography, townsmen. Tomography comes before Townsmen.\n\n**Final Alphabetical Order:**\n1. alleviate\n2. benelux\n3. buoyant\n4. duopoly\n5. felice\n6. gland\n7. gunk\n8. hardbound\n9. klaxon\n10. mattress\n11. tomography\n12. townsmen\n\nAnswer: alleviate, benelux, buoyant, duopoly, felice, gland, gunk, hardbound, klaxon, mattress, tomography, townsmen" + }, + { + "expected": "cheddar edt from oblivion pang poignant yuh", + "id": "bbh_word_sorting-0001", + "label": "pass", + "observed": "The given list of words is: yuh, edt, cheddar, oblivion, from, poignant, pang.\n\nWe need to sort these words alphabetically.\n\n1. **Compare the first letters:**\n - yuh (y)\n - edt (e)\n - cheddar (c)\n - oblivion (o)\n - from (f)\n - poignant (p)\n - pang (p)\n\n2. **Order based on the first letter:**\n - c (cheddar)\n - e (edt)\n - f (from)\n - o (oblivion)\n - p (poignant, pang)\n - y (yuh)\n\n3. **Sort the words starting with 'p':**\n - poignant\n - pang\n (Comparing the second letters: 'o' in poignant vs 'a' in pang. 'a' comes before 'o'.)\n So, the order is: pang, poignant.\n\n4. **Assemble the final sorted list:**\n - cheddar (c)\n - edt (e)\n - from (f)\n - oblivion (o)\n - pang (p)\n - poignant (p)\n - yuh (y)\n\nLet's re-check the 'p' words:\npang (p-a-n-g)\npoignant (p-o-i-g-n-a-n-t)\n'a' comes before 'o', so pang comes before poignant.\n\nFinal sorted order: cheddar, edt, from, oblivion, pang, poignant, yuh.\n\nAnswer: cheddar, edt, from, oblivion, pang, poignant, yuh" + }, + { + "expected": "atmospheric chess credit geopolitic intercept loci lunge newsmen siren swart tamp umber", + "id": "bbh_word_sorting-0002", + "label": "pass", + "observed": "Here are the words to sort: intercept, tamp, geopolitic, loci, newsmen, umber, credit, atmospheric, lunge, siren, swart, chess.\n\nLet's sort them alphabetically:\n\n1. atmospheric\n2. chess\n3. credit\n4. geopolitic\n5. intercept\n6. loci\n7. lunge\n8. newsmen\n9. siren\n10. swart\n11. tamp\n12. umber\n\nLet's re-check the sorting carefully:\n\n* **a**tmospheric\n* **c**hess\n* **c**redit\n* **g**eopolitic\n* **i**ntercept\n* **l**oci\n* **l**unge\n* **n**ewsmens\n* **s**iren\n* **s**wart\n* **t**amp\n* **u**mber\n\nComparing 'loci' and 'lunge': 'loci' comes before 'lunge'.\n\nComparing 'siren' and 'swart': 'siren' comes before 'swart'.\n\nThe correct alphabetical order is:\n1. atmospheric\n2. chess\n3. credit\n4. geopolitic\n5. intercept\n6. loci\n7. lunge\n8. newsmen\n9. siren\n10. swart\n11. tamp\n12. umber\n\nAnswer: atmospheric, chess, credit, geopolitic, intercept, loci, lunge, newsmen, siren, swart, tamp, umber" + }, + { + "expected": "above big broken coexist dominate irk olive prometheus screw thirdhand", + "id": "bbh_word_sorting-0003", + "label": "pass", + "observed": "Here are the words to sort: above, prometheus, broken, coexist, big, dominate, olive, screw, thirdhand, irk\n\nWe will sort them alphabetically:\n\n1. **above**\n2. **big**\n3. **broken**\n4. **coexist**\n5. **dominate**\n6. **irk**\n7. **olive**\n8. **prometheus**\n9. **screw**\n10. **thirdhand**\n\nLet's re-check the sorting carefully:\n\n* **a**bove\n* **b**ig\n* **b**roken (b-r comes after b-i)\n* **c**oexist\n* **d**ominate\n* **i**rk\n* **o**live\n* **p**rometheus\n* **s**crew\n* **t**hirdhand\n\nCorrect alphabetical order:\n1. above\n2. big\n3. broken\n4. coexist\n5. dominate\n6. irk\n7. olive\n8. prometheus\n9. screw\n10. thirdhand\n\nAnswer: above, big, broken, coexist, dominate, irk, olive, prometheus, screw, thirdhand\n\nAnswer: above, big, broken, coexist, dominate, irk, olive, prometheus, screw, thirdhand" + }, + { + "expected": "auerbach decor deoxyribose devisee dianne hodges incommensurable motorcade stratify troupe", + "id": "bbh_word_sorting-0004", + "label": "fail", + "observed": "Here are the words to sort alphabetically:\nhodges, deoxyribose, troupe, motorcade, devisee, stratify, auerbach, decor, incommensurable, dianne\n\nLet's sort them:\n\n1. **auerbach** (Starts with 'a')\n2. **decor** (Starts with 'd', comes before 'dianne')\n3. **devisee** (Starts with 'd', comes after 'decor')\n4. **dianne** (Starts with 'd', comes after 'devisee')\n5. **deoxyribose** (Starts with 'd', comes after 'dianne')\n6. **hodges** (Starts with 'h')\n7. **incommensurable** (Starts with 'i')\n8. **motorcade** (Starts with 'm')\n9. **stratify** (Starts with 's')\n10. **troupe** (Starts with 't')\n\nLet's re-check the 'd' words:\ndecor\ndevisee\ndianne\ndeoxyribose\n\nAlphabetical order for 'd' words:\ndecor\ndevisee (deci...)\ndianne (dia...)\ndeoxyribose (deox...)\nWait, comparing 'devisee' and 'dianne':\nd i a n n e\nd e v i s e e\n'i' comes before 'e' in the second position if we compare the full words:\nd i a n n e\nd e v i s e e\n'i' comes before 'e' is incorrect. Let's compare character by character:\nd i a n n e\nd e v i s e e\n'i' vs 'e'. 'e' comes before 'i'. So 'devisee' comes before 'dianne'.\n\nCorrected 'd' order:\n1. decor\n2. devisee\n3. dianne\n4. deoxyribose\n\nLet's re-sort the whole list:\n\n1. auerbach\n2. decor\n3. devisee\n4. dianne\n5. deoxyribose\n6. hodges\n7. incommensurable\n8. motorcade\n9. stratify\n10. troupe\n\nFinal check:\na - auerbach\nd - decor, devisee, dianne" + }, + { + "expected": "haughty seashore", + "id": "bbh_word_sorting-0005", + "label": "pass", + "observed": "The given words are: seashore, haughty.\n\nTo sort them alphabetically, we compare them letter by letter from the beginning.\n\n1. **s**eashore\n2. **h**aughty\n\nSince 'h' comes before 's' in the alphabet, \"haughty\" comes before \"seashore\".\n\nThe alphabetical order is: haughty, seashore.\n\nAnswer: haughty, seashore\nAnswer: haughty, seashore" + }, + { + "expected": "administer aeneid coachman decadent delhi dey gradate grim jacky littleneck phosphorescent pristine shrunk sinh systemwide tasting thrown torpedo verdict", + "id": "bbh_word_sorting-0006", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. gradate\n7. grim\n8. jacky\n9. littleneck\n10. phosphorescent\n11. pristine\n12. sinh\n13. systemwide\n14. tasting\n15. torpedo\n16. thrown\n17. verdict\n18. dey\n19. shrunk\n\nLet's re-sort them carefully:\n\n* administer\n* aeneid\n* decadent\n* coachman\n* delhi\n* grim\n* gradate\n* jacky\n* littleneck\n* phosphorescent\n* pristine\n* sinh\n* systemwide\n* tasting\n* torpedo\n* thrown\n* verdict\n* dey\n* shrunk\n\n**Alphabetical Sorting:**\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. dey\n7. grim\n8. gradate\n9. jacky\n10. littleneck\n11. phosphorescent\n12. pristine\n13. sinh\n14. shrunk\n15. systemwide\n16. tasting\n17. torpedo\n18. thrown\n19. verdict\n\nAnswer: administer, aeneid, decadent, coachman, delhi, dey, grim, gradate, jacky, littleneck, phosphorescent, pristine, sinh, shrunk, systemwide, tasting, torpedo, thrown, verdict" + }, + { + "expected": "boletus calypso conklin debugging deportee lucretia necktie omnipotent passband revving ulysses", + "id": "bbh_word_sorting-0007", + "label": "fail", + "observed": "Here are the words to sort alphabetically:\nboletus, ulysses, debugging, passband, lucretia, necktie, deportee, calypso, revving, conklin, omnipotent\n\nLet's sort them:\n\n1. **boletus**\n2. **calypso**\n3. **conklin**\n4. **deportee**\n5. **debugging**\n6. **lucretia**\n7. **necktie**\n8. **omnipotent**\n9. **passband**\n10. **revving**\n11. **ulysses**\n\nLet's re-check the sorting carefully:\n\n* b: boletus\n* c: calypso, conklin\n* d: debugging\n* l: lucretia\n* n: necktie\n* o: omnipotent\n* p: passband\n* r: revving\n* u: ulysses\n\nSorting the 'c' words: calypso comes before conklin.\n\nCorrect alphabetical order:\n1. boletus\n2. calypso\n3. conklin\n4. debugging\n5. lucretia\n6. necktie\n7. omnipotent\n8. passband\n9. revving\n10. ulysses\n11. deportee (Wait, 'deportee' starts with 'd'. Let's re-sort based on the first letter.)\n\nWords and first letters:\n* boletus (B)\n* ulysses (U)\n* debugging (D)\n* passband (P)\n* lucretia (L)\n* necktie (N)\n* deportee (D)\n* calypso (C)\n* revving (R)\n* conklin (C)\n* omnipotent (O)\n\nSorting by first letter: B, C, D, L, N, O, P, R, U\n\n**B:** boletus\n**C:** calypso, conklin (calypso before conklin)\n**D:** debugging, deportee (debugging before deportee)\n**L:** lucretia\n**N:** necktie\n**O:** omnipotent\n**P:** passband\n**R:** revving\n**U:** ulysses\n\nFinal sorted list:" + }, + { + "expected": "aeneas colombo foothold fox garry glycerine inviolate lucre magnanimity nevada notoriety plebiscite pompey quagmire satanic scription softball spleenwort tennyson type", + "id": "bbh_word_sorting-0008", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. aeneas\n2. foothold\n3. fox\n4. glycerine\n5. garry\n6. inflaton (Assuming 'inviolate' was intended, but using the provided word 'inviolate') - Let's stick to the provided list: inviolate\n7. lucre\n8. magnanimity\n9. nevada\n10. pompey\n11. plebiscite\n12. quagmire\n13. satanic\n14. scription (Assuming 'scription' is a typo for 'scriptio' or similar, but sorting as written)\n15. softball\n16. spleenwort\n17. tennyson\n18. type\n\nLet's re-sort carefully:\n\n* aeneas\n* foothold\n* fox\n* glycerine\n* garry\n* inviolate\n* lucre\n* magnanimity\n* nevada\n* pompey\n* plebiscite\n* quagmire\n* satanic\n* scription\n* softball\n* spleenwort\n* tennyson\n* type\n\n**Alphabetical Order Check:**\naeneas (A)\nfoothold (F)\nfox (F) - fox comes before foothold\nglycerine (G)\ngarry (G) - garry comes after glycerine\ninviolate (I)\nlucre (L)\nmagnanimity (M)\nnevada (N)\npompey (P)\nplebiscite (P) - pompey comes before plebiscite\nquagmire (Q)\nsatanic (S)\nscription (S) - satanic comes before scription\nsoftball (S) - scription comes before softball\nspleenwort (S) - softball comes before spleenwort\ntennyson (T)\ntype (T) - tennyson comes before type\n\n**Refined Sorting:**\n\n1. aeneas\n2. fox\n3. foothold\n4. glycerine\n5. garry\n6. inviolate\n7. lucre\n8. magnanimity\n9. nevada\n10. pompey\n11. plebiscite\n12. quagmire\n13. satanic\n14. scription\n15. softball\n16. spleenwort\n17. tennyson\n18." + }, + { + "expected": "allotted fate figural gorky grapple hydroxyl knives neapolitan nerve plainfield rampage saxon scottish scrumptious seventeen sidereal siena stooge thermal yakima", + "id": "bbh_word_sorting-0009", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. allotted\n2. fate\n3. figural\n4. gorky\n5. grapple\n6. hydroxyl\n7. knives\n8. neapolitan\n9. plainfield\n10. rampage\n11. scrumptious\n12. saxon\n13. siena\n14. scottish\n15. sidereal\n16. stooge\n17. seventeen\n18. thermal\n19. yakima\n20. nerve\n\nLet's re-sort them carefully:\n\n* allotted\n* fate\n* figural\n* gorky\n* grapple\n* hydroxyl\n* knives\n* neapolitan\n* plainfield\n* rampage\n* scrumptious\n* saxon\n* siena\n* scottish\n* sidereal\n* stooge\n* seventeen\n* thermal\n* yakima\n* nerve\n\nChecking the order again:\nA: allotted\nF: fate, figural\nG: gorky, grapple\nH: hydroxyl\nK: knives\nN: neapolitan, nerve\nP: plainfield\nR: rampage\nS: scrumptious, saxon, siena, scottish, sidereal\nS: stooge (S comes before T)\nS: seventeen\nT: thermal\nY: yakima\n\nCorrect alphabetical order:\n1. allotted\n2. fate\n3. figural\n4. gorky\n5. grapple\n6. hydroxyl\n7. knives\n8. neapolitan\n9. nerve\n10. plainfield\n11. rampage\n12. scrumptious\n13. saxon\n14. siena\n15. scottish\n16. sidereal\n17. stooge\n18. seventeen\n19. thermal\n20. yakima\n\nAnswer: allotted, fate, figural, gorky, grapple, hydroxyl, knives, neapolitan, nerve, plainfield, rampage, scrumptious, saxon, siena, scottish, sidereal, stooge, seventeen, thermal, yakima" + }, + { + "expected": "alterate aseptic cayenne chandigarh debauch declassify dingy equanimity excursion foamflower groupoid inclement kruger lawful october only scorch", + "id": "bbh_word_sorting-0010", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. alterate\n2. aseptic\n3. cayenne\n4. chandigarh\n5. declassify\n6. debauch\n7. dingy\n8. excursion\n9. equanimity\n10. foamflower\n11. lawful\n12. october\n13. only\n14. groupoid\n15. kruger\n16. scorch\n17. alterate (Wait, I listed alterate twice in my thought process, let's re-check the original list: foamflower lawful cayenne chandigarh only excursion declassify equanimity aseptic inclement october debauch kruger groupoid scorch dingy alterate)\n\nLet's re-sort carefully:\n\nOriginal List: foamflower, lawful, cayenne, chandigarh, only, excursion, declassify, equanimity, aseptic, inclement, october, debauch, kruger, groupoid, scorch, dingy, alterate\n\n1. alterate\n2. aseptic\n3. cayenne\n4. chandigarh\n5. declassify\n6. debauch\n7. dingy\n8. excursion\n9. equanimity\n10. foamflower\n11. inclement\n12. lawful\n13. october\n14. only\n15. groupoid\n16. kruger\n17. scorch\n\nLet's re-sort the remaining words based on the full list:\n\nA: alterate, aseptic\nC: cayenne, chandigarh\nD: declassify, debauch, dingy\nE: excursion, equanimity\nF: foamflower\nI: inclement\nL: lawful\nO: october, only\nG: groupoid\nK: kruger\nS: scorch\n\nCorrect Alphabetical Order:\n1. alterate\n2. aseptic\n3. cayenne\n4. chandigarh\n5. declassify\n6. debauch\n7. dingy\n8. excursion\n9. equanimity\n10. foamflower\n11. inclement\n12. lawful\n13. october\n14. only\n15. groupoid\n16. kruger\n17. scorch\n\nAnswer: alterate, aseptic, cayenne, chandigarh, declassify, debauch, dingy, excursion, equanimity, foamflower, inclement, lawful, october, only, groupoid, kruger, scorch" + }, + { + "expected": "bilk lethe perturb tactual", + "id": "bbh_word_sorting-0011", + "label": "pass", + "observed": "The given list of words is: lethe, bilk, perturb, tactual.\n\nTo sort these words alphabetically, we compare them letter by letter from the beginning.\n\n1. **lethe** (starts with 'l')\n2. **bilk** (starts with 'b')\n3. **perturb** (starts with 'p')\n4. **tactual** (starts with 't')\n\nComparing the first letters: 'b', 'l', 'p', 't'.\nIn alphabetical order: 'b', 'l', 'p', 't'.\n\n1. **bilk** (b)\n2. **lethe** (l)\n3. **perturb** (p)\n4. **tactual** (t)\n\nThe sorted list is: bilk, lethe, perturb, tactual.\n\nAnswer: bilk, lethe, perturb, tactual\n\nAnswer: bilk, lethe, perturb, tactual" + }, + { + "expected": "brainy cony enigma erudite fatuous gouda hoof impalpable isaacson lisbon malaria portrait portsmouth servomechanism stronghold succumb", + "id": "bbh_word_sorting-0012", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. brainy\n2. enigma\n3. erudite\n4. fatuous\n5. gouda\n6. impalpable\n7. isaacson\n8. lisbon\n9. malaria\n10. portsmouth\n11. portrait\n12. servomechanism\n13. stronghold\n14. succumb\n15. cony\n16. hoof\n\nLet's re-sort them carefully:\n\n* brainy\n* cony\n* erudite\n* enigma\n* fatuous\n* gouda\n* impalpable\n* isaacson\n* hoof\n* lisbon\n* malaria\n* portsmouth\n* portrait\n* servomechanism\n* stronghold\n* succumb\n\nLet's check the order again:\nB (brainy)\nC (cony)\nE (erudite, enigma) -> erudite before enigma\nF (fatuous)\nG (gouda)\nI (impalpable, isaacson) -> impalpable before isaacson\nH (hoof)\nL (lisbon)\nM (malaria)\nP (portsmouth, portrait) -> portsmouth before portrait\nS (servomechanism, succumb, stronghold) -> servomechanism, succumb, stronghold (S-e-r, S-u, S-t)\n\nCorrect alphabetical order:\n1. brainy\n2. cony\n3. erudite\n4. enigma\n5. fatuous\n6. gouda\n7. impalpable\n8. isaacson\n9. hoof\n10. lisbon\n11. malaria\n12. portsmouth\n13. portrait\n14. servomechanism\n15. stronghold\n16. succumb\n\nAnswer: brainy, cony, erudite, enigma, fatuous, gouda, impalpable, isaacson, hoof, lisbon, malaria, portsmouth, portrait, servomechanism, stronghold, succumb" + }, + { + "expected": "airlift butch cone homeowner inanimate incurring logarithm lumber maladapt micron newman profuse robertson sammy souvenir uganda wilcox", + "id": "bbh_word_sorting-0013", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. airlift\n2. maladapt\n3. cone\n4. butch\n5. homeowner\n6. incurring\n7. logarithm\n8. lumber\n9. micron\n10. newman\n11. inanimate\n12. profuse\n13. robertson\n14. sammy\n15. souvenir\n16. uganda\n17. wilcox\n\nLet's re-sort them carefully:\n\n* airlift\n* maladapt\n* cone\n* butch\n* homeowner\n* incurring\n* logarithm\n* lumber\n* micron\n* newman\n* inanimate\n* profuse\n* robertson\n* sammy\n* souvenir\n* uganda\n* wilcox\n\nComparing the initial list: logarithm, robertson, inanimate, uganda, micron, incurring, wilcox, lumber, sammy, souvenir, newman, profuse, maladapt, cone, butch, airlift, homeowner.\n\nAlphabetical order:\n1. airlift\n2. maladapt\n3. cone\n4. butch\n5. homeowner\n6. incurring\n7. logarithm\n8. lumber\n9. micron\n10. newman\n11. inanimate\n12. profuse\n13. robertson\n14. sammy\n15. souvenir\n16. uganda\n17. wilcox\n\nAnswer: airlift, maladapt, cone, butch, homeowner, incurring, logarithm, lumber, micron, newman, inanimate, profuse, robertson, sammy, souvenir, uganda, wilcox" + }, + { + "expected": "brownian coach eosine erudite flax inadvisable magnesium marriageable stahl vicksburg virgo", + "id": "bbh_word_sorting-0014", + "label": "fail", + "observed": "Here are the words to sort alphabetically:\nerudite, marriageable, stahl, coach, inadvisable, vicksburg, virgo, brownian, eosine, magnesium, flax\n\nLet's sort them:\n\n1. **brownian**\n2. **coach**\n3. **erudite**\n4. **eosine**\n5. **flax**\n6. **inadvisable**\n7. **magnesium**\n8. **marriageable**\n9. **stahl**\n10. **vicksburg**\n11. **virgo**\n\nLet's re-check the sorting carefully, especially for words starting with similar letters:\n\n* **b**rownian\n* **c**oach\n* **e**rudite, **e**osine (erudite comes before eosine)\n* **f**lax\n* **i**nadvisable\n* **m**agnesium, **m**arriageable (magnesium comes before marriageable)\n* **s**tahl\n* **v**icksburg, **v**irgo (vicksburg comes before virgo)\n\nCorrected alphabetical order:\n\n1. brownian\n2. coach\n3. erudite\n4. eosine\n5. flax\n6. inadvisable\n7. magnesium\n8. marriageable\n9. stahl\n10. vicksburg\n11. virgo\n\nAnswer: brownian, coach, erudite, eosine, flax, inadvisable, magnesium, marriageable, stahl, vicksburg, virgo" + }, + { + "expected": "consonant globule jacob musician sleight", + "id": "bbh_word_sorting-0015", + "label": "pass", + "observed": "The given list of words is: jacob, sleight, globule, consonant, musician.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **j**acob\n2. **s**leight\n3. **g**lobule\n4. **c**onsonant\n5. **m**usician\n\nComparing the first letters: J, S, G, C, M.\nIn alphabetical order: C, G, J, M, S.\n\n1. **consonant** (starts with C)\n2. **globule** (starts with G)\n3. **jacob** (starts with J)\n4. **musician** (starts with M)\n5. **sleight** (starts with S)\n\nThe sorted list is: consonant, globule, jacob, musician, sleight.\n\nAnswer: consonant, globule, jacob, musician, sleight\nAnswer: consonant, globule, jacob, musician, sleight" + }, + { + "expected": "crude cunard danubian inscribe peculate perceptive posterior tragedian upraise", + "id": "bbh_word_sorting-0016", + "label": "fail", + "observed": "The given list of words is: inscribe, upraise, perceptive, tragedian, peculate, cunard, posterior, crude, danubian.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter:**\n * inscribe (i)\n * upraise (u)\n * perceptive (p)\n * tragedian (t)\n * peculate (p)\n * cunard (c)\n * posterior (p)\n * crude (c)\n * danubian (d)\n\n Order based on the first letter: c, c, d, i, p, p, p, t, u.\n\n2. **Sort words starting with 'c':**\n * cunard\n * crude\n * Alphabetical order: crude, cunard (cr comes before cu)\n\n3. **Sort words starting with 'd':**\n * danubian\n\n4. **Sort words starting with 'i':**\n * inscribe\n\n5. **Sort words starting with 'p':**\n * perceptive\n * peculate\n * posterior\n * Alphabetical order: perceptive, peculate, posterior (per comes before pec, pec comes before pos)\n\n6. **Sort words starting with 't':**\n * tragedian\n\n7. **Sort words starting with 'u':**\n * upraise\n\n**Combining the sorted groups:**\n1. crude\n2. cunard\n3. danubian\n4. inscribe\n5. perceptive\n6. peculate\n7. posterior\n8. tragedian\n9. upraise\n\nLet's re-verify the 'c' words:\ncrude\ncunard (crude comes before cunard)\n\nLet's re-verify the 'p' words:\nperceptive (pe...)\npeculate (pe...)\nposterior (po...)\nperceptive vs peculate: 'r' vs 'c'. 'c' comes before 'r'.\nCorrect order for 'p' words: peculate, perceptive, posterior. (pec, per, pos)\n\nLet's re-verify the full list based on standard alphabetical order:\n\n* crude (cr)\n* cunard (cu)\n* danubian (d)\n* inscribe (i)\n* peculate (pe-c)\n* perceptive (pe" + }, + { + "expected": "carport firewood introvert sweepstake tiresome", + "id": "bbh_word_sorting-0017", + "label": "pass", + "observed": "The given list of words is: tiresome, sweepstake, introvert, carport, firewood.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **carport** (Starts with 'c')\n2. **firewood** (Starts with 'f')\n3. **introvert** (Starts with 'i')\n4. **sweepstake** (Starts with 's')\n5. **tiresome** (Starts with 't')\n\nLet's verify the order:\nC comes before F, I, S, T.\nF comes before I, S, T.\nI comes before S, T.\nS comes before T.\n\nThe alphabetical order is: carport, firewood, introvert, sweepstake, tiresome.\n\nAnswer: carport, firewood, introvert, sweepstake, tiresome" + }, + { + "expected": "barn damp delmarva dot drumhead embezzle entirety greene guru it&t malton obstetric onus panicking prod same scorch splutter subsist thrill", + "id": "bbh_word_sorting-0018", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. barn\n2. embezzle\n3. delmarva\n4. dot\n5. drumhead\n6. entirety\n7. guru\n8. it&t\n9. malton\n10. panicking\n11. prod\n12. scorch\n13. same\n14. splutter\n15. subsist\n16. thrill\n\n(Note: \"onus\" is missing from the provided list, but if it were included, it would come before \"obstetric\" and after \"onus\" would be before \"prod\" if we assume standard alphabetical order for the provided list. Let's re-check the provided list: thrill, splutter, panicking, scorch, same, dot, prod, obstetric, malton, onus, drumhead, delmarva, barn, embezzle, it&t, damp, guru, subsist, entirety, greene.)\n\nLet's re-sort the complete list:\nthrill, splutter, panicking, scorch, same, dot, prod, obstetric, malton, onus, drumhead, delmarva, barn, embezzle, it&t, damp, guru, subsist, entirety, greene\n\n1. barn\n2. damp\n3. delmarva\n4. dot\n5. drumhead\n6. embezzle\n7. entirety\n8. greene\n9. guru\n10. it&t\n11. malton\n12. obstetric\n13. onus\n14. panicking\n15. prod\n16. scorch\n17. same\n18. splutter\n19. subsist\n20. thrill\n\nAnswer: barn, damp, delmarva, dot, drumhead, embezzle, entirety, greene, guru, it&t, malton, obstetric, onus, panicking, prod, scorch, same, splutter, subsist, thrill" + }, + { + "expected": "broadcast cortland diffusible galvanometer gross gujarati incestuous larynx nomograph pewter scout sketchbook stag transition", + "id": "bbh_word_sorting-0019", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. broadcast\n2. cortland\n3. diffusible\n4. galvanometer\n5. gross\n6. gujarati\n7. incestuous\n8. larynx\n9. nomograph\n10. pewter\n11. scout\n12. sketchbook\n13. stag\n14. transition\n\nLet's re-sort them carefully:\n\n* **b**roadcast\n* **c**ortland\n* **d**iffusible\n* **g**alvometer\n* **g**ross\n* **g**ujarati\n* **i**ncestuous\n* **l**arynx\n* **n**omograph\n* **p**ewter\n* **s**cout\n* **s**teag (Wait, 'stag' comes before 'sketchbook' if we look at the second letter, but alphabetically 'stag' comes before 'sketchbook' if we compare 't' vs 'k'. Let's re-examine the list.)\n\nOriginal List: pewter gross gujarati cortland diffusible nomograph stag broadcast galvanometer transition incestuous sketchbook scout larynx\n\n1. broadcast\n2. cortland\n3. diffusible\n4. galvanometer\n5. gross\n6. gujarati\n7. incestuous\n8. larynx\n9. nomograph\n10. pewter\n11. scout\n12. sketchbook\n13. stag\n14. transition\n\nLet's perform a strict alphabetical sort:\n\n1. broadcast (b)\n2. cortland (c)\n3. diffusible (d)\n4. galvanometer (g)\n5. gross (g)\n6. gujarati (g)\n7. incestuous (i)\n8. larynx (l)\n9. nomograph (n)\n10. pewter (p)\n11. scout (s)\n12. sketchbook (s)\n13. stag (s)\n14. transition (t)\n\nSorting the 'g' words: galvanometer, gross, gujarati.\n* galvanometer\n* gross\n* gujarati\n\nSorting the 's' words: scout, sketchbook, stag.\n* sketchbook (sk)\n* scout (sc)\n* stag (st)\n* Therefore: sketchbook, scout, stag\n\nFinal sorted list:\n1. broadcast\n2. cortland\n" + }, + { + "expected": "bust chalk cowboy dentistry dumb fatty goucher horror masonry midshipmen musicale pathway resiny roadrunner rocket sapient serf tangential urea urinary", + "id": "bbh_word_sorting-0020", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. bust\n2. chalk\n3. cowboy\n4. dentistry\n5. dumb\n6. fatty\n7. goucher\n8. horror\n9. masonry\n10. midshipmen\n11. musical\n12. pathway\n13. resiny\n14. roadrunner\n15. rocket\n16. serf\n17. sapient\n18. tangential\n19. urinary\n20. urea\n\nLet's re-sort them carefully:\n\n* bust\n* chalk\n* cowboy\n* dentistry\n* dumb\n* fatty\n* goucher\n* horror\n* masonry\n* midshipmen\n* musical\n* pathway\n* resiny\n* roadrunner\n* rocket\n* serf\n* sapient\n* tangential\n* urinary\n* urea\n\nComparing the words:\nB (bust)\nC (chalk)\nC (cowboy) -> chalk before cowboy\nD (dentistry, dumb) -> dentistry before dumb\nF (fatty)\nG (goucher)\nH (horror)\nM (masonry, midshipmen) -> masonry before midshipmen\nM (musical)\nP (pathway)\nR (resiny, roadrunner, rocket) -> resiny, roadrunner, rocket\nS (serf, sapient) -> serf before sapient\nT (tangential)\nU (urinary, urea) -> urinary before urea\n\nLet's do a full alphabetical sort:\n\n1. bust\n2. chalk\n3. cowboy\n4. dentistry\n5. dumb\n6. fatty\n7. goucher\n8. horror\n9. masonry\n10. midshipmen\n11. musical\n12. pathway\n13. resiny\n14. roadrunner\n15. rocket\n16. serf\n17. sapient\n18. tangential\n19. urinary\n20. urea\n\nWait, let's re-check the 'M' words and 'R' words.\n\nM: masonry, midshipmen, musical\nM: masonry, midshipmen, musical (Correct order: masonry, midshipmen, musical)\n\nR: resiny, roadrunner, rocket\nR: resiny, roadrunner, rocket (Correct order: resiny, roadrunner, rocket)\n\nS: serf" + }, + { + "expected": "confidential faery fiction heterozygous horehound overture ursa", + "id": "bbh_word_sorting-0021", + "label": "pass", + "observed": "The given list of words is: confidential, ursa, fiction, heterozygous, faery, overture, horehound.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **confidential** (Starts with 'c')\n2. **faery** (Starts with 'f')\n3. **heterozygous** (Starts with 'h')\n4. **fiction** (Starts with 'f')\n5. **horehound** (Starts with 'h')\n6. **overture** (Starts with 'o')\n7. **ursa** (Starts with 'u')\n\nLet's sort the words starting with 'f':\n* faery\n* fiction\nComparing 'faery' and 'fiction': 'fai' vs 'fic'. 'a' comes before 'i', so 'faery' comes before 'fiction'.\n\nLet's sort the words starting with 'h':\n* heterozygous\n* horehound\nComparing 'heterozygous' and 'horehound': 'he' vs 'ho'. 'e' comes before 'o', so 'heterozygous' comes before 'horehound'.\n\nThe full alphabetical order is:\n1. confidential (c)\n2. faery (f)\n3. fiction (f)\n4. heterozygous (h)\n5. horehound (h)\n6. overture (o)\n7. ursa (u)\n\nLet's re-check the 'f' words:\nfaery\nfiction\nf-a-e-r-y\nf-i-c-t-i-o-n\n'a' comes before 'i', so faery comes before fiction.\n\nLet's re-check the 'h' words:\nheterozygous\nhorehound\nh-e-t-e-r-o-z-o-u-s\nh-o-r-e-h-o-u-n-d\n'e' comes before 'o', so heterozygous comes before horehound.\n\nFinal sorted list:\n1. confidential\n2. faery\n3. fiction\n4. heterozygous\n5. horehound\n6. overture\n7. ursa\n\nAnswer: confidential, faery, fiction, heterozygous, horehound, overture, ursa" + }, + { + "expected": "murray sweatband", + "id": "bbh_word_sorting-0022", + "label": "pass", + "observed": "The given words are: sweatband, murray.\n\nTo sort them alphabetically, we compare them letter by letter:\n\n1. **s**weatband\n2. **m**urray\n\nSince 'm' comes before 's' in the alphabet, \"murray\" comes before \"sweatband\".\n\nThe alphabetical order is: murray, sweatband.\n\nAnswer: murray, sweatband\nAnswer: murray, sweatband" + }, + { + "expected": "blest buxton consternate proximity quizzes sound tariff xerxes", + "id": "bbh_word_sorting-0023", + "label": "pass", + "observed": "The given list of words is: sound, consternate, tariff, blest, proximity, quizzes, xerxes, buxton.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **blest** (b)\n2. **buxton** (b) - 'l' comes before 'u'\n3. **consternate** (c)\n4. **proximity** (p)\n5. **quizzes** (q)\n6. **sound** (s)\n7. **tariff** (t)\n8. **xerxes** (x)\n\nLet's re-sort carefully:\n\n* **b**: blest, buxton (blest comes before buxton)\n* **c**: consternate\n* **p**: proximity\n* **q**: quizzes\n* **s**: sound\n* **t**: tariff\n* **x**: xerxes\n\nComparing the 'b' words:\n* blest\n* buxton\n'l' comes before 'u', so **blest** comes before **buxton**.\n\nThe sorted order is:\n1. blest\n2. buxton\n3. consternate\n4. proximity\n5. quizzes\n6. sound\n7. tariff\n8. xerxes\n\nAnswer: blest, buxton, consternate, proximity, quizzes, sound, tariff, xerxes\n\nAnswer: blest, buxton, consternate, proximity, quizzes, sound, tariff, xerxes" + }, + { + "expected": "aerodynamic botanist giacomo habitation jimmy nebulous offset padre panicking roosevelt schoolmate suburbia vector wv", + "id": "bbh_word_sorting-0024", + "label": "fail", + "observed": "Here are the words to sort alphabetically:\nwv, habitation, panicking, schoolmate, aerodynamic, giacomo, nebulous, offset, vector, roosevelt, jimmy, botanist, suburbia, padre\n\nLet's sort them:\n\n1. **aerodynamic**\n2. **botanist**\n3. **habitation**\n4. **giacomo**\n5. **jimmy**\n6. **nebulous**\n7. **offset**\n8. **panicking**\n9. **padre**\n10. **roosevelt**\n11. **schoolmate**\n12. **suburbia**\n13. **vector**\n14. **wv** (Assuming 'wv' is treated as a word, or placed based on its initial letters if it's an abbreviation. In standard alphabetical sorting, it comes before 'habitation' if we consider 'w' before 'h', but usually, if it's an initialism, it's treated as is. If we treat it strictly alphabetically based on the first letter 'w', it would be near the end.)\n\nLet's re-evaluate based on standard alphabetical order (case-insensitive, treating 'wv' as starting with 'w'):\n\n* aerodynamic (a)\n* botanist (b)\n* habitation (h)\n* giacomo (g)\n* jimmy (j)\n* nebulous (n)\n* offset (o)\n* panicking (p)\n* padre (p)\n* roosevelt (r)\n* schoolmate (s)\n* suburbia (s)\n* vector (v)\n* wv (w)\n\nSorting the 'p' words: panicking, padre. (panicking comes before padre)\nSorting the 's' words: schoolmate, suburbia. (schoolmate comes before suburbia)\n\nFinal sorted list:\n1. aerodynamic\n2. botanist\n3. habitation\n4. giacomo\n5. jimmy\n6. nebulous\n7. offset\n8. panicking\n9. padre\n10. roosevelt\n11. schoolmate\n12. suburbia\n13. vector\n14. wv\n\nAnswer: aerodynamic, botanist, habitation, giacomo, jimmy, nebulous, offset, panicking, padre, roosevelt, schoolmate, suburbia" + }, + { + "expected": "arrear brookside eavesdropping fasciculate henry hermaphrodite herodotus ibn incorrigible jane linchpin maritime postdoctoral shin sticky vehicular", + "id": "bbh_word_sorting-0025", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. arrear\n2. eavesdropping\n3. fasciculate\n4. herodotus\n5. hermaphrodite\n6. ibn henry\n7. incorrigible\n8. jane\n9. linchpin\n10. maritime\n11. postdoctoral\n12. shin\n13. sticky\n14. brookside\n15. vehicular\n\nLet's re-sort them carefully:\n\n* **a**rrear\n* **e**avesdropping\n* **f**asciculate\n* **h**erodotus\n* **h**ermaphrodite (H before I)\n* **i**bn henry\n* **i**ncorrigible (I before L)\n* **j**ane\n* **l**inchpin\n* **m**aritime\n* **p**ostdoctoral\n* **s**hin\n* **s**ticky\n* **b**rookside (B comes before S)\n* **v**ehicular\n\nLet's do a full alphabetical sort:\n\n1. arrear\n2. brookside\n3. eavesdropping\n4. fasciculate\n5. herodotus\n6. hermaphrodite\n7. ibn henry\n8. incorrigible\n9. jane\n10. linchpin\n11. maritime\n12. postdoctoral\n13. shin\n14. sticky\n15. vehicular\n\nWait, let's check the 'H' words: hermaphrodite and herodotus.\nH-e-r-m-a-p-h-r-o-d-i-t-e\nH-e-r-o-d-t-u-s\n'herm' comes before 'hero'. So, hermaphrodite comes before herodotus.\n\nLet's re-verify the full list:\narrear\nbrookside\neavesdropping\nfasciculate\nhermaphrodite\nherodotus\nibn henry\nincorrigible\njane\nlinchpin\nmaritime\npostdoctoral\nshin\nsticky\nvehicular\n\nFinal check on the order:\nA: arrear\nB: brookside\nE: eavesdropping\nF: fasciculate\nH: hermaphrodite, herodotus (herm before hero)\nI: ibn henry, incorrigible (ibn before incorrigible)\nJ:" + }, + { + "expected": "cite coleus fructose hurricane improbable irreducible tipoff tularemia vesper whereabout whitetail wier", + "id": "bbh_word_sorting-0026", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. cite\n2. coleus\n3. fructose\n4. hurricane\n5. irreducible\n6. improbable\n7. tipoff\n8. tularemia\n9. vesper\n10. whereabout\n11. whitetail\n\nLet's re-sort them carefully:\n\n* **c**ite\n* **c**oleus\n* **f**ructose\n* **h**urricane\n* **i**rreducible\n* **i**mprobable\n* **t**ipoff\n* **t**ularemia\n* **v**esper\n* **w**hereabout\n* **w**hitetail\n\nComparing 'cite' and 'coleus': 'cite' comes before 'coleus'.\nComparing 'irreducible' and 'improbable': 'irreducible' comes before 'improbable'.\nComparing 'tipoff' and 'tularemia': 'tipoff' comes before 'tularemia'.\nComparing 'whereabout' and 'whitetail': 'whereabout' comes before 'whitetail'.\n\nCorrect alphabetical order:\n1. cite\n2. coleus\n3. fructose\n4. hurricane\n5. irreducible\n6. improbable\n7. tipoff\n8. tularemia\n9. vesper\n10. whereabout\n11. whitetail\n\nWait, let's re-check the 'i' words: irreducible, improbable.\ni-r-r... vs i-m-p... $\\rightarrow$ irreducible comes before improbable. (Correct)\n\nLet's re-check the 't' words: tipoff, tularemia.\nt-i-p... vs t-u-l... $\\rightarrow$ tipoff comes before tularemia. (Correct)\n\nFinal sorted list:\n1. cite\n2. coleus\n3. fructose\n4. hurricane\n5. irreducible\n6. improbable\n7. tipoff\n8. tularemia\n9. vesper\n10. whereabout\n11. whitetail\n\nAnswer: cite, coleus, fructose, hurricane, irreducible, improbable, tipoff, tularemia, vesper, whereabout, whitetail" + }, + { + "expected": "leasehold orchestra permafrost shiva testate", + "id": "bbh_word_sorting-0027", + "label": "pass", + "observed": "The given list of words is: testate, orchestra, permafrost, leasehold, shiva.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **leasehold** (Starts with 'l')\n2. ** orchestra** (Starts with 'o')\n3. **permafrost** (Starts with 'p')\n4. **shiva** (Starts with 's')\n5. **testate** (Starts with 't')\n\nLet's re-sort based on the first letter: L, O, P, S, T.\n\n1. **leasehold** (L)\n2. **orchestra** (O)\n3. **permafrost** (P)\n4. **shiva** (S)\n5. **testate** (T)\n\nThe alphabetical order is: leasehold, orchestra, permafrost, shiva, testate.\n\nAnswer: leasehold, orchestra, permafrost, shiva, testate\nAnswer: leasehold, orchestra, permafrost, shiva, testate" + }, + { + "expected": "academia amos beautiful butterscotch circuitous diatom europium extoller farrell fiducial ford glance kochab metzler molybdate monomer predatory veterinarian", + "id": "bbh_word_sorting-0028", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. academia\n2. amos\n3. beautiful\n4. butterscotch\n5. circuitous\n6. diatom\n7. extoller\n8. farrell\n9. fiducial\n10. glance\n11. kochab\n12. ladmoner (Wait, the list has 'monomer'. Let's re-check the list.)\n\nOriginal List: butterscotch veterinarian kochab molybdate extoller amos farrell academia glance predatory beautiful ford fiducial metzler europium circuitous diatom monomer\n\nLet's sort them systematically:\n\n1. academia\n2. amos\n3. beautiful\n4. butterscotch\n5. circuitous\n6. diatom\n7. extoller\n8. farrell\n9. fiducial\n10. glance\n11. kochab\n12. metzler\n13. molybdate\n14. monomer\n15. predatory\n16. europium\n17. veterinarian\n18. ford\n\nLet's re-sort carefully:\n\nA: academia, amos\nB: beautiful, butterscotch\nC: circuitous\nD: diatom\nE: extoller, europium\nF: farrell, fiducial, ford\nG: glance\nK: kochab\nM: molybdate, monomer, metzler\nP: predatory\nV: veterinarian\n\nSorting within groups:\n\nA: academia, amos\nB: beautiful, butterscotch\nC: circuitous\nD: diatom\nE: extoller, europium\nF: farrell, fiducial, ford\nG: glance\nK: kochab\nM: molybdate, monomer, metzler (molybdate, monomer, metzler)\nP: predatory\nV: veterinarian\n\nFinal Alphabetical Order:\n1. academia\n2. amos\n3. beautiful\n4. butterscotch\n5. circuitous\n6. diatom\n7. extoller\n8. europium\n9. farrell\n10. fiducial\n11. ford\n12. glance\n13. kochab\n14. molybdate\n15. monomer\n16. metzler\n17. predatory\n18. veterinarian\n\nAnswer: academia, amos, beautiful, butterscotch, circuitous, diatom, extoller, europium, farrell, fiducial, ford" + }, + { + "expected": "ami bituminous decadent exeter knickerbocker", + "id": "bbh_word_sorting-0029", + "label": "pass", + "observed": "The given list of words is: bituminous, ami, decadent, knickerbocker, exeter.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **ami**: Starts with 'a'\n2. **bituminous**: Starts with 'b'\n3. **decadent**: Starts with 'd'\n4. **exeter**: Starts with 'e'\n5. **knickerbocker**: Starts with 'k'\n\nComparing the first letters: a, b, d, e, k.\nIn alphabetical order: a, b, d, e, k.\n\n1. **ami** (a)\n2. **bituminous** (b)\n3. **decadent** (d)\n4. **exeter** (e)\n5. **knickerbocker** (k)\n\nThe sorted list is: ami, bituminous, decadent, exeter, knickerbocker.\n\nAnswer: ami, bituminous, decadent, exeter, knickerbocker" + }, + { + "expected": "afternoon complementary dixie hesitate horsepower immaculate kind laughlin loire mechanism nimble sandia septuagenarian shuffleboard sierra toggle woebegone", + "id": "bbh_word_sorting-0030", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. afternoon\n2. complementary\n3. dixie\n4. horsepower\n5. immaculate\n6. kind\n7. laughlin\n8. loire\n9. mechanism\n10. nimble\n11. sandia\n12. septuagenarian\n13. shuffleboard\n14. sierra\n15. toggle\n16. woebegone\n\nLet's re-sort them carefully:\n\n* **a**fternoon\n* **c**omplementary\n* **d**ixie\n* **h**orsepower\n* **i**mmaculate\n* **k**ind\n* **l**aughlin\n* **l**oire (L-A vs L-O: afternoon comes before loire)\n* **m**echanism\n* **n**imble\n* **s**andia\n* **s**eptuagenarian\n* **s**huffleboard\n* **s**ierra\n* **t**oggle\n* **w**oebegone\n\nLet's do a strict alphabetical sort:\n\n1. afternoon\n2. complementary\n3. dixie\n4. horsepower\n5. immaculate\n6. kind\n7. laughlin\n8. loire\n9. mechanism\n10. nimble\n11. sandia\n12. septuagenarian\n13. shuffleboard\n14. sierra\n15. toggle\n16. woebegone\n\nWait, let's check 'laughlin' vs 'loire' and 'mechanism' vs 'nimble'.\n\nL words: laughlin, loire\n* laughlin\n* loire\n\nM words: mechanism\nN words: nimble\n\nS words: sandia, septuagenarian, shuffleboard, sierra\n* sandia\n* septuagenarian\n* shuffleboard\n* sierra\n\nFinal check of the full list:\nafternoon\ncomplementary\ndixie\nhorsepower\nimmaculate\nkind\nlaughlin\nloire\nmechanism\nnimble\nsandia\nseptuagenarian\nshuffleboard\nsierra\ntoggle\nwoebegone\n\nAnswer: afternoon, complementary, dixie, horsepower, immaculate, kind, laughlin, loire, mechanism, nimble, sandia, septuagenarian, shuffleboard, sierra, toggle, woebegone" + }, + { + "expected": "clytemnestra crag cutover dickson diocletian electrolytic inhuman lipton marginal scrawny stalk thereupon took wife wireman workplace", + "id": "bbh_word_sorting-0031", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. clytemnestra\n2. crag\n3. cutover\n4. dickson\n5. diocletian\n6. electrolytic\n7. inhuman\n8. lipton\n9. marginal\n10. stalk\n11. thereupon\n12. workplace\n13. wireman\n14. wife\n15. took\n16. scrawny\n\nLet's re-sort them carefully:\n\n* clytemnestra\n* crag\n* cutover\n* dickson\n* diocletian\n* electrolytic\n* inhuman\n* lipton\n* marginal\n* stalk\n* thereupon\n* workplace\n* wireman\n* wife\n* took\n* scrawny\n\nComparing the initial list: thereupon, wireman, crag, scrawny, electrolytic, wife, cutover, dickson, clytemnestra, took, diocletian, lipton, inhuman, stalk, workplace, marginal.\n\nAlphabetical order:\n1. clytemnestra\n2. crag\n3. cutover\n4. dickson\n5. diocletian\n6. electrolytic\n7. inhuman\n8. lipton\n9. marginal\n10. stalk\n11. thereupon\n12. workplace\n13. wireman\n14. wife\n15. took\n16. scrawny\n\nAnswer: clytemnestra, crag, cutover, dickson, diocletian, electrolytic, inhuman, lipton, marginal, stalk, thereupon, workplace, wireman, wife, took, scrawny" + }, + { + "expected": "agile blackguard butt clapeyron cognoscenti flamboyant geophysical lift lightfooted manumitted mathieu meager purposive reconnaissance sawbelly scribe seaworthy wiseacre woodcut yves", + "id": "bbh_word_sorting-0032", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. agile\n2. blackguard\n3. clapeyron\n4. cognoscenti\n5. geophysical\n6. flamboyant\n7. lift\n8. lightfooted\n9. manumitted\n10. meager\n11. mathieu\n12. purposive\n13. reconnaissance\n14. seaworthy\n15. scribe\n16. sawbelly\n17. wiseacre\n18. woodcut\n19. yves\n20. butt\n\nLet's re-sort them carefully:\n\n* **a**gile\n* **b**lackguard, butt\n* **c**lapeyron, cognoscenti\n* **g**eophysical\n* **l**ift, lightfooted\n* **m**anumitted, meager, mathieu\n* **p**urposeive\n* **r**econnaissance\n* **s**eaworthy, scribe, sawbelly\n* **w**iseacre, woodcut, yves\n\nDetailed sorting:\n\n1. agile\n2. blackguard\n3. butt\n4. clapeyron\n5. cognoscenti\n6. geophysical\n7. flamboyant\n8. lift\n9. lightfooted\n10. manumitted\n11. meager\n12. mathieu\n13. purposive\n14. reconnaissance\n15. seaworthy\n16. scribe\n17. sawbelly\n18. wiseacre\n19. woodcut\n20. yves\n\nAnswer: agile, blackguard, butt, clapeyron, cognoscenti, geophysical, flamboyant, lift, lightfooted, manumitted, meager, mathieu, purposive, reconnaissance, seaworthy, scribe, sawbelly, wiseacre, woodcut, yves" + }, + { + "expected": "aperture bradshaw holocene mare muriel pathetic r&d sigh staircase talon", + "id": "bbh_word_sorting-0033", + "label": "fail", + "observed": "The given list of words is: holocene, muriel, r&d, pathetic, talon, mare, staircase, bradshaw, aperture, sigh.\n\nTo sort them alphabetically, we compare them letter by letter:\n\n1. **aperture** (starts with 'a')\n2. **bradshaw** (starts with 'b')\n3. **holocene** (starts with 'h')\n4. **mare** (starts with 'm')\n5. **muriel** (starts with 'm', comes after 'mare' because 'u' comes after 'a')\n6. **pathetic** (starts with 'p')\n7. **r&d** (starts with 'r')\n8. **sigh** (starts with 's')\n9. **staircase** (starts with 's', comes after 'sigh' because 't' comes after 'i')\n10. **talon** (starts with 't', comes after 'staircase' because 'a' comes before 's' in the second letter, wait, let's recheck 'staircase' and 'talon'. 'st' vs 'ta'. 's' comes before 't', so 'staircase' comes before 'talon'.)\n\nLet's re-sort systematically:\n\nWords: holocene, muriel, r&d, pathetic, talon, mare, staircase, bradshaw, aperture, sigh\n\n1. **a**perture\n2. **b**radshaw\n3. **h**olocene\n4. **m**are\n5. **m**uriel (mare vs muriel: mare ends, muriel continues. 'mare' comes before 'muriel')\n6. **p**athetic\n7. **r**&d\n8. **s**igh\n9. **s**taircase\n10. **t**alon\n\nComparing 'sigh' and 'staircase':\nsigh (s-i)\nstaircase (s-t)\n'i' comes before 't', so 'sigh' comes before 'staircase'.\n\nComparing 'talon' and 'staircase':\ntalon (t-a)\nstaircase (s-t)\n's' comes before 't', so 'staircase' comes before 'talon'.\n\nFinal sorted order:\n" + }, + { + "expected": "adsorption align anastasia anastomotic apache award bobbin burrow calumny epaulet execrable hostelry hun macedon omnipotent putty roughshod smooth spontaneity", + "id": "bbh_word_sorting-0034", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. apache\n2. adsorption\n3. align\n4. anastomotic\n5. anastasia\n6. award\n7. bobbin\n8. burrow\n9. calumny\n10. hun\n11. hostelry\n12. macedon\n13. omnipotent\n14. epaulet\n15. putty\n16. roughshod\n17. spontaneity\n18. smooth\n\nLet's re-sort them carefully:\n\n* **a**pache\n* **a**dsorption\n* **a**lign\n* **a**nastomotic\n* **a**nastasia\n* **a**ward\n* **b**obbin\n* **b**urrow\n* **c**alumny\n* **h**on (assuming 'hun' is a typo or should be sorted as 'hun')\n* **h**ostelry\n* **m**acedon\n* **o**mnipotent\n* **e**paulet\n* **p**utty\n* **r**oughshod\n* **s**pontaneity\n* **s**mooth\n\nLet's re-examine the list provided: spontaneity, smooth, execrable, epaulet, bobbin, calumny, hun, anastasia, omnipotent, award, hostelry, macedon, roughshod, burrow, align, apache, putty, adsorption, anastomotic.\n\n**Alphabetical Sorting:**\n\n1. apache\n2. adsorption\n3. align\n4. anastomotic\n5. anastasia\n6. award\n7. bobbin\n8. burrow\n9. calumny\n10. execrable\n11. epaulet\n12. hun\n13. hostelry\n14. macedon\n15. omnipotent\n16. putty\n17. roughshod\n18. spontaneity\n19. smooth\n\n(Note: 'execrable' comes before 'epaulet' if we strictly follow the standard alphabet, but 'epaulet' starts with 'e' and 'execrable' starts with 'e'. E-P comes before E-X.)\n\nLet's re-check the 'E' words: execrable, epaulet.\nE-P-A-U-L-E-" + }, + { + "expected": "covenant davenport densitometer noisy scoreboard sonorant thence", + "id": "bbh_word_sorting-0035", + "label": "pass", + "observed": "The given list of words is: covenant, thence, davenport, sonorant, scoreboard, densitometer, noisy.\n\nWe need to sort these words alphabetically.\n\n1. **covenant**\n2. **davenport**\n3. **densitometer**\n4. **noisy**\n5. **scoreboard**\n6. **sonorant**\n7. **thence**\n\nLet's compare them letter by letter:\n\n* **c**ovenant\n* **d**avenport\n* **d**ensitometer (davenport comes before densitometer because 'a' comes before 'e')\n* **n**oisy\n* **s**coreboard\n* **s**onorant (scoreboard comes before sonorant because 'c' comes before 'o')\n* **t**hence\n\nLet's re-sort carefully:\n\n1. **covenant** (Starts with C)\n2. **davenport** (Starts with D, 'a')\n3. **densitometer** (Starts with D, 'e') $\\rightarrow$ davenport comes before densitometer.\n4. **noisy** (Starts with N)\n5. **scoreboard** (Starts with S, 'c')\n6. **sonorant** (Starts with S, 'o') $\\rightarrow$ scoreboard comes before sonorant.\n7. **thence** (Starts with T)\n\nThe correct alphabetical order is:\n1. covenant\n2. davenport\n3. densitometer\n4. noisy\n5. scoreboard\n6. sonorant\n7. thence\n\nLet's verify the D words:\ndavenport\ndensitometer\n'a' comes before 'e', so davenport comes before densitometer.\n\nLet's verify the S words:\nscoreboard\nsonorant\n'c' comes before 'o', so scoreboard comes before sonorant.\n\nFinal sorted list: covenant, davenport, densitometer, noisy, scoreboard, sonorant, thence.\n\nAnswer: covenant, davenport, densitometer, noisy, scoreboard, sonorant, thence" + }, + { + "expected": "cortex incident insane kangaroo marionette mcleod pillage roundabout sinter stipulate threshold trammel", + "id": "bbh_word_sorting-0036", + "label": "fail", + "observed": "Here are the words to sort alphabetically:\nsinter, cortex, kangaroo, pillage, threshold, insane, mcleod, roundabout, stipulate, marionette, trammel, incident\n\nLet's sort them:\n\n1. **cortex**\n2. **incident**\n3. **insane**\n4. **kangaroo**\n5. **mcleod**\n6. **marionette**\n7. **pillage**\n8. **roundabout**\n9. **sinter**\n10. **stipulate**\n11. **threshold**\n12. **trammel**\n\nLet's re-check the sorting carefully, especially for words starting with similar letters:\n\n* **c**ortex\n* **i**ncident, **i**nsane (incident before insane)\n* **k**angaroo\n* **m**cleod, **m**arionette (mcleod before marionette)\n* **p**illage\n* **r**oundabout\n* **s**inter, **s**tipulate (sinter before stipulate)\n* **t**hreshold, **t**rammel\n\nCorrected alphabetical order:\n\n1. cortex\n2. incident\n3. insane\n4. kangaroo\n5. mcleod\n6. marionette\n7. pillage\n8. roundabout\n9. sinter\n10. stipulate\n11. threshold\n12. trammel\n\nAnswer: cortex, incident, insane, kangaroo, mcleod, marionette, pillage, roundabout, sinter, stipulate, threshold, trammel\n\nAnswer: cortex, incident, insane, kangaroo, mcleod, marionette, pillage, roundabout, sinter, stipulate, threshold, trammel" + }, + { + "expected": "abdominal address berry bounty effusive fomalhaut hanoverian involve islamabad jordan optimal pay stearic stigmata swathe tattoo them tornado yang", + "id": "bbh_word_sorting-0037", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. abdominal\n2. address\n3. bounty\n4. berry\n5. effusive\n6. fomalhaut\n7. hanoverian\n8. islamabad\n9. involve\n10. pay\n11. stigmata\n12. stearic\n13. swathe\n14. tattoo\n15. them\n16. tornado\n17. optimal\n18. yang\n19. jordan\n\nLet's re-sort them carefully:\n\n* **a**bdominal\n* **a**ddress\n* **b**ounty\n* **b**erry\n* **e**ffusive\n* **f**omalhaut\n* **h**anoverian\n* **i**slamabad\n* **i**nvolve\n* **p**ay\n* **s**tigmata\n* **s**tearic\n* **s**wathe\n* **t**attoo\n* **t**hem\n* **t**ornado\n* **o**ptimal\n* **y**ang\n* **j**ordan (Wait, J comes before O and Y)\n\nLet's re-sort based on the first letter:\n\nA: abdominal, address\nB: bounty, berry\nE: effusive\nF: fomalhaut\nH: hanoverian\nI: islamabad, involve\nJ: jordan\nO: optimal\nS: stigmata, stearic, swathe\nT: tattoo, them, tornado\nY: yang\n\n**Detailed Sorting:**\n\n1. abdominal\n2. address\n3. bounty\n4. berry\n5. effusive\n6. fomalhaut\n7. hanoverian\n8. islamabad\n9. involve\n10. jordan\n11. optimal\n12. stigmata\n13. stearic\n14. swathe\n15. tattoo\n16. them\n17. tornado\n18. yang\n\nAnswer: abdominal, address, bounty, berry, effusive, fomalhaut, hanoverian, islamabad, involve, jordan, optimal, stigmata, stearic, swathe, tattoo, them, tornado, yang" + }, + { + "expected": "anharmonic beauteous coypu inflammation", + "id": "bbh_word_sorting-0038", + "label": "pass", + "observed": "The given list of words is: coypu, anharmonic, inflammation, beauteous.\n\nWe need to sort these words alphabetically.\n\n1. **Compare the first letters:**\n - coypu starts with 'c'\n - anharmonic starts with 'a'\n - inflammation starts with 'i'\n - beauteous starts with 'b'\n\n The alphabetical order of the starting letters is: a, b, c, i.\n\n2. **Order the words based on the starting letters:**\n - 'a': anharmonic\n - 'b': beauteous\n - 'c': coypu\n - 'i': inflammation\n\n3. **The sorted list is:** anharmonic, beauteous, coypu, inflammation.\n\nAnswer: anharmonic, beauteous, coypu, inflammation" + }, + { + "expected": "abuilding burgher celebrity chigger ernie exorcism indiscoverable medlar newsboy pow snuff synchronous thimble thirteenth tinfoil yap", + "id": "bbh_word_sorting-0039", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. abuilding\n2. celebrity\n3. burgher\n4. chigger\n5. indiscoverable\n6. medlar\n7. newsboy\n8. exorcism\n9. pow\n10. snuff\n11. synchronous\n12. thirteenth\n13. tinfoil\n14. thimble\n15. ernie\n16. yap\n\nLet's re-sort them carefully:\n\n* **a**building\n* **c**elebrity\n* **b**urgher\n* **c**higger\n* **i**ndiscoverable\n* **m**edlar\n* **n**ewsboy\n* **e**xorcism (comes before pow)\n* **p**ow\n* **s**nuff\n* **s**ynchronous\n* **t**hirteenth\n* **t**infoil\n* **t**himble\n* **e**rnie (comes before thimble)\n* **y**ap\n\nLet's do a strict alphabetical sort:\n\n1. abuilding\n2. celebrity\n3. burgher\n4. chigger\n5. ernie\n6. indiscoverable\n7. medlar\n8. newsboy\n9. exorcism\n10. pow\n11. snuff\n12. synchronous\n13. thirteenth\n14. tinfoil\n15. thimble\n16. yap\n\nWait, let's check the 't' words: thirteenth, tinfoil, thimble.\nthirteenth (thir...)\ntinfoil (tinf...)\nthimble (thim...)\nthimble comes before thirteenth.\ntinfoil comes before thimble.\nthirteenth comes after tinfoil.\n\nCorrected 't' order: tinfoil, thimble, thirteenth.\n\nLet's re-sort the whole list:\n\n1. abuilding\n2. celebrity\n3. burgher\n4. chigger\n5. ernie\n6. indiscoverable\n7. medlar\n8. newsboy\n9. exorcism\n10. pow\n11. snuff\n12. synchronous\n13. tinfoil\n14. thimble\n15. thirteenth\n16. yap\n\nAnswer: abuilding, celebrity, burgher," + }, + { + "expected": "accept avoid caramel carbuncle compressor conclave drib elegy embower error gaillardia grassland hostile pitfall rosa spectra stepchild utopia whimsey", + "id": "bbh_word_sorting-0040", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. accept\n2. avoid\n3. carbuncle\n4. caramel\n5. conclave\n6. compressor\n7. drib\n8. elegy\n9. embower\n10. error\n11. gaillardia\n12. hostile\n13. pitfall\n14. rosa\n15. spectra\n16. stepchild\n17. utopia\n18. whimsey\n19. grassland\n\nLet's re-sort them carefully:\n\n* **a**: accept, avoid\n* **c**: carbuncle, caramel, conclave, compressor\n* **d**: drib\n* **e**: elegy, error, embower\n* **g**: gaillardia, grassland\n* **h**: hostile\n* **p**: pitfall\n* **r**: rosa\n* **s**: spectra, stepchild\n* **u**: utopia\n* **w**: whimsey\n\n**Detailed Sorting:**\n\n1. accept\n2. avoid\n3. carbuncle\n4. caramel\n5. conclave\n6. compressor\n7. drib\n8. elegy\n9. embower\n10. error\n11. gaillardia\n12. grassland\n13. hostile\n14. pitfall\n15. rosa\n16. spectra\n17. stepchild\n18. utopia\n19. whimsey\n\n(Checking 'gaillardia' vs 'grassland': g-a-i vs g-r-a. 'gaillardia' comes before 'grassland'.)\n(Checking 'error' vs 'embower': e-r vs e-m. 'embower' comes before 'error'.)\n\n**Final Sorted List:**\naccept, avoid, carbuncle, caramel, conclave, compressor, drib, elegy, embower, error, gaillardia, grassland, hostile, pitfall, rosa, spectra, stepchild, utopia, whimsey\n\nAnswer: accept, avoid, carbuncle, caramel, conclave, compressor, drib, elegy, embower, error, gaillardia, grassland, hostile, pitfall, rosa, spectra, stepchild, utopia, whimsey" + }, + { + "expected": "bighorn contaminate demystify nigeria odysseus penny proton sociolinguistic stirrup voltaire", + "id": "bbh_word_sorting-0041", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. bighorn\n2. contaminate\n3. demystify\n4. odysseus\n5. penny\n6. proton\n7. sociolinguistic\n8. stirrup\n9. voltaire\n10. nigeria\n11. **voltaire** (Wait, I listed voltaire twice in my mental check, let's re-sort carefully)\n\nLet's list them and sort them:\nsociolinguistic, nigeria, odysseus, demystify, proton, voltaire, penny, contaminate, bighorn, stirrup\n\n1. bighorn\n2. contaminate\n3. demystify\n4. nigeria\n5. odysseus\n6. penny\n7. proton\n8. sociolinguistic\n9. stirrup\n10. voltaire\n\nLet's re-check the sorting based on the initial list: sociolinguistic, nigeria, odysseus, demystify, proton, voltaire, penny, contaminate, bighorn, stirrup\n\n* Bighorn (B)\n* Contaminate (C)\n* Demystify (D)\n* Nigeria (N)\n* Odysseus (O)\n* Penny (P)\n* Proton (P) - P-e comes before P-e-n, so Penny before Proton.\n* Sociolinguistic (S)\n* Stirrup (S) - Stirrup comes after Sociolinguistic (S-t vs S-o).\n* Voltaire (V)\n\nCorrect alphabetical order:\n1. bighorn\n2. contaminate\n3. demystify\n4. nigeria\n5. odysseus\n6. penny\n7. proton\n8. sociolinguistic\n9. stirrup\n10. voltaire\n\nWait, let's check S words: sociolinguistic, stirrup.\nS-o-c... vs S-t-i... $\\rightarrow$ sociolinguistic comes before stirrup.\n\nFinal check:\nBighorn\nContaminate\nDemystify\nNigeria\nOdysseus\nPenny\nProton\nSociolinguistic\nStirrup\nVoltaire\n\nAnswer: bighorn, contaminate, demystify, nigeria, odysseus, penny, proton, sociolinguistic, stirrup, voltaire\n\nAnswer: bighorn, contaminate, demystify, nigeria, odysseus, penny, proton, sociolinguistic, stir" + }, + { + "expected": "antler christiana falter invigorate jot kamikaze landlady libya ludlow mallow porridge residuary tuscarora wetland wrapup", + "id": "bbh_word_sorting-0042", + "label": "pass", + "observed": "Here are the words sorted alphabetically:\n\n1. antler\n2. christiana\n3. falter\n4. landlady\n5. libya\n6. ludlow\n7. mallow\n8. porridge\n9. residuary\n10. tuscarora\n11. wrapup\n12. invigorate\n13. jot\n14. kamikaze\n15. wetland\n\nLet's re-sort them carefully:\n\n* **a**ntler\n* **c**hristiana\n* **f**alter\n* **i**nvigorate\n* **j**ot\n* **k**amikaze\n* **l**andlady\n* **l**ibya\n* **l**udlow\n* **m**allow\n* **p**orridge\n* **r**esiduary\n* **t**uscarora\n* **w**etland\n* **w**rapup\n\nSorting the 'L' words: landlady, libya, ludlow.\n* landlady\n* libya\n* ludlow\n\nSorting the 'W' words: wetland, wrapup.\n* wetland\n* wrapup\n\nThe final sorted list:\n1. antler\n2. christiana\n3. falter\n4. invigorate\n5. jot\n6. kamikaze\n7. landlady\n8. libya\n9. ludlow\n10. mallow\n11. porridge\n12. residuary\n13. tuscarora\n14. wetland\n15. wrapup\n\nAnswer: antler, christiana, falter, invigorate, jot, kamikaze, landlady, libya, ludlow, mallow, porridge, residuary, tuscarora, wetland, wrapup" + }, + { + "expected": "coltish condescend date percolate placid rampant rochester significant", + "id": "bbh_word_sorting-0043", + "label": "fail", + "observed": "The given list of words is: condescend, rampant, percolate, coltish, date, rochester, placid, significant.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter:**\n * c: condescend\n * r: rampant, rochester\n * p: percolate, placid\n * c: coltish\n * d: date\n * r: (already listed)\n * p: (already listed)\n * s: significant\n\n2. **Sort words starting with 'c':**\n * condescend\n * coltish\n * (Order: condescend, coltish)\n\n3. **Sort words starting with 'd':**\n * date\n\n4. **Sort words starting with 'p':**\n * percolate\n * placid\n * (Comparing 'percolate' and 'placid': 'p' is the same. 'e' in percolate comes before 'l' in placid. Wait, 'l' comes before 'e' in the alphabet? No. 'e' comes before 'l'. Let's recheck: p-e-r-c-o-l-a-t-e vs p-l-a-c-i-d. 'e' comes before 'l'. So, percolate comes before placid.)\n * percolate\n * placid\n\n5. **Sort words starting with 'r':**\n * rampant\n * rochester\n * (Comparing 'rampant' and 'rochester': 'a' in rampant comes before 'o' in rochester.)\n * rampant\n * rochester\n\n6. **Sort words starting with 's':**\n * significant\n\n**Combining the sorted groups:**\n\n1. condescend\n2. coltish\n3. date\n4. percolate\n5. placid\n6. rampant\n7. rochester\n8. significant\n\nLet's re-verify the full alphabetical order:\n* condescend (c-o...)\n* coltish (c-o...) -> condescend vs coltish: 'n' vs 'l'. 'l' comes before 'n'. So, coltish comes before condescend.\n * Corrected 'c' order: coltish, condescend\n* date (d)\n* percolate (p-e" + }, + { + "expected": "affable almost antic apache astute dandelion deadlock delphic execution fortunate horntail leverage levitate libertarian sanction scathe semitic storehouse sweeney unbeknownst", + "id": "bbh_word_sorting-0044", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. almost\n2. apache\n3. affable\n4. antic\n5. astute\n6. delphic\n7. deadlock\n8. execution\n9. fortunate\n10. leverage\n11. horntail\n12. libertarian\n13. levitate\n14. scathe\n15. semitic\n16. sweeney\n17. sanction\n18. storehouse\n19. unbeknownst\n20. dandelion\n\nLet's re-sort them carefully:\n\n* **a**lmost\n* **a**pache\n* **a**ffable\n* **a**ntic\n* **a**stute\n* **d**elphic\n* **d**eadlock\n* **e**xecution\n* **f**ortunate\n* **l**everage\n* **l**ebitate\n* **l**ibertarian\n* **h**orntail\n* **s**cache\n* **s**emitic\n* **s**weeney\n* **s**anction\n* **s**torehouse\n* **u**nbeknownst\n* **d**andelion (Wait, 'd' comes before 'e' and 'f'. Let's re-check the full list.)\n\nOriginal List: almost, delphic, semitic, unbeknownst, astute, deadlock, libertarian, antic, levitate, execution, sanction, scathe, storehouse, dandelion, affable, sweeney, fortunate, leverage, horntail, apache\n\nAlphabetical Sorting:\n\n1. almost\n2. apache\n3. affable\n4. antic\n5. astute\n6. dandelion\n7. delphic\n8. deadlock\n9. execution\n10. fortunate\n11. leverage\n12. horntail\n13. levitate\n14. libertarian\n15. scathe\n16. sanction\n17. semitic\n18. sweeney\n19. storehouse\n20. unbeknownst\n\nAnswer: almost, apache, affable, antic, astute, dandelion, delphic, deadlock, execution, fortunate, leverage, horntail, levitate, libertarian, scathe, sanction, semitic, sweeney, storehouse, unbeknownst" + }, + { + "expected": "anastomosis backslide calvert commando gabriel hendrickson hollister jackson pizzicato quail separate shelter spongy sticktight syndicate variety washy", + "id": "bbh_word_sorting-0045", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. backslide\n2. calvert\n3. gabriel\n4. hendrickson\n5. hollister\n6. jackson\n7. pizzicato\n8. quail\n9. separate\n10. shelter\n11. sticktight\n12. spongy\n13. syndicate\n14. variety\n15. washy\n16. commando\n17. anastomosis\n\nLet's re-sort them carefully:\n\n* backslide\n* calvert\n* gabriel\n* hendrickson\n* hollister\n* jackson\n* pizzicato\n* quail\n* separate\n* shelter\n* sticktight\n* spongy\n* syndicate\n* variety\n* washy\n* commando\n* anastomosis\n\nComparing the words:\nB: backslide\nC: calvert, commando\nG: gabriel\nH: hendrickson, hollister\nJ: jackson\nP: pizzicato\nQ: quail\nS: separate, shelter, spongy, sticktight, syndicate\nV: variety\nW: washy\nA: anastomosis\n\nDetailed alphabetical sort:\n1. backslide\n2. calvert\n3. commando\n4. gabriel\n5. hendrickson\n6. hollister\n7. jackson\n8. anastomosis\n9. pizzicato\n10. quail\n11. separate\n12. shelter\n13. sticktight\n14. spongy\n15. syndicate\n16. variety\n17. washy\n\nAnswer: backslide, calvert, commando, gabriel, hendrickson, hollister, jackson, anastomosis, pizzicato, quail, separate, shelter, sticktight, spongy, syndicate, variety, washy" + }, + { + "expected": "syndrome therefrom", + "id": "bbh_word_sorting-0046", + "label": "pass", + "observed": "The words to be sorted are: syndrome, therefrom.\n\nTo sort them alphabetically, we compare them letter by letter:\n\n1. **syndrome**: Starts with 's', followed by 'y', 'n', 'd', 'r', 'o', 'm', 'e'.\n2. **therefrom**: Starts with 't', followed by 'h', 'e', 'r', 'e', 'f', 'r', 'o', 'm'.\n\nComparing the first letters: 's' comes before 't' in the alphabet.\n\nTherefore, \"syndrome\" comes before \"therefrom\".\n\nThe alphabetical order is: syndrome, therefrom.\n\nAnswer: syndrome, therefrom" + }, + { + "expected": "belize bolshevism cost dance deadline dietetic formulae foster hesitant huddle judson mantle odessa palace progeny proust rackety resplendent thirdhand warmth", + "id": "bbh_word_sorting-0047", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. bolshevism\n2. belize\n3. dance\n4. deadline\n5. dietetic\n6. foster\n7. formulae\n8. hesitant\n9. huddle\n10. judson\n11. mantle\n12. odessa\n13. palace\n14. proust\n15. rackety\n16. resplendent\n17. thirdhand\n18. warmth\n19. cost\n\nLet's re-sort them carefully:\n\n* **b**olshevism, **b**elize (bolshevism before belize)\n* **d**ance, **d**eadline, **d**ietetic\n* **f**oster, **f**ormulae\n* **h**esitant, **h**uddle\n* **j**udson\n* **m**antle\n* **o**dessa\n* **p**alace, **p**roust\n* **r**ackety, **r**esplendent\n* **t**hirdhand\n* **w**armth\n* **c**ost\n\n**Alphabetical Order:**\n\n1. bolshevism\n2. belize\n3. dance\n4. deadline\n5. dietetic\n6. foster\n7. formulae\n8. hesitant\n9. huddle\n10. judson\n11. mantle\n12. odessa\n13. palace\n14. proust\n15. rackety\n16. resplendent\n17. thirdhand\n18. warmth\n19. cost\n\nWait, let's check the 'b' words again: bolshevism, belize. 'b'e comes before 'b'o.\n\nLet's re-sort the full list:\nodessa, dance, formulae, dietetic, mantle, cost, huddle, resplendent, bolshevism, proust, warmth, deadline, progeny, hesitant, palace, rackety, judson, foster, belize, thirdhand\n\n(Note: I missed 'progeny' in my initial count, let's include it.)\n\nFull List: odessa, dance, formulae, dietetic, mantle, cost, huddle, resplendent, bolshevism, proust, warmth, deadline, progeny, hesitant, palace, rackety, jud" + }, + { + "expected": "abner abramson amity automate exquisite fruitful gurgle none shampoo shorten waterproof", + "id": "bbh_word_sorting-0048", + "label": "fail", + "observed": "Here are the words to sort: automate, abner, shampoo, gurgle, none, exquisite, abramson, amity, fruitful, shorten, waterproof.\n\nLet's sort them alphabetically:\n\n1. **abner**\n2. **abramson**\n3. **automate**\n4. **amity**\n5. **fruitful**\n6. **gurgle**\n7. **none**\n8. **shorten**\n9. **shampoo**\n10. **waterproof**\n11. **exquisite**\n\nLet's re-sort carefully, paying attention to the starting letters:\n\n* **a**: automate, abner, abramson, amity\n * abner\n * abramson\n * amity\n * automate\n* **f**: fruitful\n* **g**: gurgle\n* **n**: none\n* **s**: shampoo, shorten\n* **w**: waterproof\n* **e**: exquisite\n\nCorrect alphabetical order:\n\n1. abner\n2. abramson\n3. amity\n4. automate\n5. exquisite\n6. fruitful\n7. gurgle\n8. none\n9. shorten\n10. shampoo\n11. waterproof\n\nLet's check the 'a' words again:\nabner\nabramson\namity\nautomate\n(a, b, a, a) -> abner, abramson, amity, automate. This order is correct.\n\nLet's check the 'e' word:\nexquisite (comes after automate)\n\nLet's check 'f' and 'g':\nfruitful, gurgle (f comes before g)\n\nLet's check 'n' and 's':\nnone, shorten, shampoo (n comes before s)\n\nLet's check 'w':\nwaterproof (w is last among these)\n\nFinal sorted list:\n1. abner\n2. abramson\n3. amity\n4. automate\n5. exquisite\n6. fruitful\n7. gurgle\n8. none\n9. shorten\n10. shampoo\n11. waterproof\n\nAnswer: abner, abramson, amity, automate, exquisite, fruitful, gurgle, none, shorten, shampoo, waterproof" + }, + { + "expected": "bindle chiang crystallography dent mambo ram roadside rundown savannah shipshape spew strange survey won't", + "id": "bbh_word_sorting-0049", + "label": "fail", + "observed": "Here are the words to sort alphabetically:\ncrystallography, survey, bindle, rundown, shipshape, roadside, strange, chiang, dent, mambo, savannah, spew, won't, ram\n\nLet's sort them:\n\n1. bindle\n2. chiang\n3. crystallography\n4. dent\n5. mambo\n6. ram\n7. roadside\n8. rundown\n9. survey\n10. shipshape\n11. savannah\n12. strange\n13. spew\n14. won't\n\nLet's re-sort carefully, paying attention to the first letter:\n\n* **b**: bindle\n* **c**: crystallography, chiang\n* **d**: dent\n* **m**: mambo\n* **r**: ram, roadside, rundown\n* **s**: survey, shipshape, savannah, strange, spew\n* **w**: won't\n\n**Sorting within groups:**\n\n**C:** crystallography, chiang (cryst... comes before chiang)\n * crystallography\n * chiang\n**R:** ram, roadside, rundown (ram, roadside, rundown)\n * ram\n * roadside\n * rundown\n**S:** survey, shipshape, savannah, strange, spew (savannah, shipshape, spew, survey, strange)\n * savannah\n * shipshape\n * spew\n * survey\n * strange\n**W:** won't\n\n**Final Alphabetical Order:**\n\n1. bindle\n2. crystallography\n3. chiang\n4. dent\n5. mambo\n6. ram\n7. roadside\n8. rundown\n9. savannah\n10. shipshape\n11. spew\n12. survey\n13. strange\n14. won't\n\nLet's re-check the 'R' words: ram, roadside, rundown.\nram (ra...)\nroadside (ro...)\nrundown (ru...)\nOrder: ram, roadside, rundown. (Correct)\n\nLet's re-check the 'S' words: savannah, shipshape, spew, survey, strange.\nsavannah (sa...)\nshipshape (sh...)\nspew (sp...)\nsurvey (su...)\nstrange (st...)\nOrder: savannah, shipshape, spew, survey, strange. (Correct)\n\nThe" + }, + { + "expected": "aerospace allure common decoy denmark enviable exclusive frill griffith jibe loosestrife nanosecond saute screechy sow spermatozoa spitz swabby yates", + "id": "bbh_word_sorting-0050", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. aerospace\n2. allure\n3. decoy\n4. denmark\n5. enviable\n6. exclusive\n7. frill\n8. griffith\n9. jibe\n10. loosestrife\n11. nanosecond\n12. sow\n13. spitz\n14. screechy\n15. spermatozoa\n16. saute\n17. swabby\n18. yates\n19. common\n\nLet's re-sort them carefully:\n\n* **a**erospace\n* **a**llure\n* **c**ommon\n* **d**enmark\n* **d**ecoy\n* **e**nable\n* **e**xclusive\n* **f**rill\n* **g**riffith\n* **j**ibe\n* **l**oosestrife\n* **n**anosecond\n* **s**ow\n* **s**pitz\n* **s**creechy\n* **s** spermatozoa\n* **s**aute\n* **s**wabby\n* **y**ates\n\n**Detailed Alphabetical Sorting:**\n\n1. aerospace\n2. allure\n3. common\n4. denmark\n5. decoy\n6. enviable\n7. exclusive\n8. frill\n9. griffith\n10. jibe\n11. loosestrife\n12. nanosecond\n13. sow\n14. spitz\n15. screechy\n16. spermatozoa\n17. saute\n18. swabby\n19. yates\n\n(Checking 's' words: spermatozoa, saute, screechy, sow, spitz, swabby. Order: spermatozoa, saute, screechy, sow, spitz, swabby)\n\nLet's re-verify the 's' words:\n* spermatozoa\n* saute\n* screechy\n* sow\n* spitz\n* swabby\n\nCorrect order for 's' words:\n1. spermatozoa\n2. saute\n3. screechy\n4. sow\n5. spitz\n6. swabby\n\nFinal Sorted List:\n1. aerospace\n2. allure\n3. common\n4. denmark\n5. decoy\n6. enviable\n7. exclusive\n8. frill\n9. griffith\n10. jibe\n11." + }, + { + "expected": "conglomerate dynastic inflammable nebulae phosphide prick stagnate tackle tristan vitiate", + "id": "bbh_word_sorting-0051", + "label": "pass", + "observed": "Here are the words to sort: dynastic, inflammable, prick, tristan, vitiate, tackle, stagnate, conglomerate, nebulae, phosphide.\n\nWe will sort them alphabetically:\n\n1. **conglomerate**\n2. **dynastic**\n3. **inflammable**\n4. **nebulae**\n5. **phosphide**\n6. **prick**\n7. **stagnate**\n8. **tackle**\n9. **tristan**\n10. **vitiate**\n\nLet's re-check the sorting carefully:\n\n* **c**onglomerate\n* **d**ynastic\n* **i**nflammable\n* **n**ebulae\n* **p**hosphide\n* **p**rick (P-h comes before P-r)\n* **s**tagnate\n* **t**ackle\n* **t**ristan (T-a comes before T-r)\n* **v**itiate\n\nLet's re-sort the 'P' words: phosphide, prick.\nP-h-o...\nP-r-i...\n'h' comes before 'r', so phosphide comes before prick.\n\nCorrected order:\n1. conglomerate\n2. dynastic\n3. inflammable\n4. nebulae\n5. phosphide\n6. prick\n7. stagnate\n8. tackle\n9. tristan\n10. vitiate\n\nLet's check the 'T' words: tackle, tristan.\ntackle (ta...)\ntristan (tr...)\n'a' comes before 'r', so tackle comes before tristan.\n\nFinal sorted list:\n1. conglomerate\n2. dynastic\n3. inflammable\n4. nebulae\n5. phosphide\n6. prick\n7. stagnate\n8. tackle\n9. tristan\n10. vitiate\n\nAnswer: conglomerate, dynastic, inflammable, nebulae, phosphide, prick, stagnate, tackle, tristan, vitiate" + }, + { + "expected": "amerada craftsmen din eclipse gaillardia inroad jackboot jest jordan kill mirth nate pomade putt shortcoming spruce whelan", + "id": "bbh_word_sorting-0052", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. amerada\n2. craftsmen\n3. eclipse\n4. din\n5. gaillardia\n6. inroad\n7. jest\n8. jackboot\n9. jordan\n10. kill\n11. mirth\n12. nate\n13. pomade\n14. putt\n15. shortcoming\n16. spruce\n17. whelan\n\nLet's re-sort them carefully:\n\n* amerada\n* craftsmen\n* din\n* eclipse\n* gaillardia\n* inroad\n* jest\n* jackboot\n* jordan\n* kill\n* mirth\n* nate\n* pomade\n* putt\n* shortcoming\n* spruce\n* whelan\n\nComparing the initial list: din spruce nate pomade jordan gaillardia putt inroad jest kill jackboot whelan mirth amerada craftsmen shortcoming eclipse\n\n1. amerada\n2. craftsmen\n3. din\n4. eclipse\n5. gaillardia\n6. inroad\n7. jest\n8. jackboot\n9. jordan\n10. kill\n11. mirth\n12. nate\n13. pomade\n14. putt\n15. shortcoming\n16. spruce\n17. whelan\n\nAnswer: amerada, craftsmen, din, eclipse, gaillardia, inroad, jest, jackboot, jordan, kill, mirth, nate, pomade, putt, shortcoming, spruce, whelan" + }, + { + "expected": "absorption aristocratic bermuda cesium cheerful congo diagram eucre ezra fallen juvenile musty nigeria nod quartile screechy slack testicle", + "id": "bbh_word_sorting-0053", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. absorption\n2. aristocratic\n3. bermuda\n4. cheerful\n5. congo\n6. cesium\n7. diagram\n8. fallen\n9. eucre\n10. ezra\n11. junitile (Assuming \"juvenile\" was a typo or meant to be sorted as is, but standard sorting applies) - Let's stick to the provided list: juvenile\n12. kotlinx (Assuming \"screechy\" is a typo or meant to be sorted as is) - Let's stick to the provided list: screechy\n13. musty\n14. nod\n15. nigeria\n16. quartile\n17. slack\n18. testicle\n\nLet's re-sort carefully:\n\n* absorption\n* aristocratic\n* bermuda\n* cheerful\n* congo\n* cesium\n* diagram\n* fallen\n* eucre\n* ezra\n* juvenile\n* musty\n* nod\n* nigeria\n* quartile\n* screechy\n* slack\n* testicle\n\nComparing 'ezra' and 'eucre': 'ezra' comes before 'eucre'.\nComparing 'juvenile' and 'musty': 'juvenile' comes before 'musty'.\n\nFinal sorted list:\n1. absorption\n2. aristocratic\n3. bermuda\n4. cheerful\n5. congo\n6. cesium\n7. diagram\n8. fallen\n9. eucre\n10. ezra\n11. juvenile\n12. musty\n13. nod\n14. nigeria\n15. quartile\n16. screechy\n17. slack\n18. testicle\n\nAnswer: absorption, aristocratic, bermuda, cheerful, congo, cesium, diagram, fallen, eucre, ezra, juvenile, musty, nod, nigeria, quartile, screechy, slack, testicle" + }, + { + "expected": "anaplasmosis bumble chopstick clue fiesta footwork fresco ingot orthography palisade pilate saul smalley storey teen", + "id": "bbh_word_sorting-0054", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. anaplasmosis\n2. bumble\n3. chopstick\n4. clue\n5. fresco\n6. footwork\n7. fiesta\n8. ingot\n9. orthography\n10. palisade\n11. pilate\n12. saul\n13. smalley\n14. storey\n15. teen\n\nLet's re-sort them carefully:\n\n* anaplasmosis (A)\n* bumble (B)\n* chopstick (C)\n* clue (C) - clue comes before chopstick\n* fresco (F)\n* footwork (F) - footwork comes after fresco\n* fiesta (F) - fiesta comes after footwork\n* ingot (I)\n* orthography (O)\n* palisade (P)\n* pilate (P) - pilate comes after palisade\n* saul (S)\n* smalley (S) - saul comes before smalley\n* storey (S) - storey comes after smalley\n* teen (T)\n\nLet's do a strict alphabetical sort:\n\n1. anaplasmosis\n2. bumble\n3. chopstick\n4. clue\n5. fresco\n6. footwork\n7. fiesta\n8. ingot\n9. orthography\n10. palisade\n11. pilate\n12. saul\n13. smalley\n14. storey\n15. teen\n\nWait, let's re-check the 'S' words: saul, smalley, storey.\nS-a-u-l\nS-m-a-l-l-e-y\nS-t-o-r-e-y\n'a' comes before 'm' and 't'. So, saul comes first.\n'm' comes before 't'. So, smalley comes before storey.\nOrder for S words: saul, smalley, storey.\n\nLet's re-check the 'F' words: fresco, footwork, fiesta.\nF-r-e-s-c-o\nF-o-o-t-w-o-r-k\nF-i-e-s-t-a\nOrder: fresco, footwork, fiesta. (e < o < i is false. e < i <" + }, + { + "expected": "accomplice az choral circumcircle clatter crepe doff emission fairfax incantation labour lorry pleura prig ride tea upon viaduct wheelbase whim", + "id": "bbh_word_sorting-0055", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. accomplice\n2. choral\n3. crepe\n4. circumcircle\n5. clatter\n6. doff\n7. emission\n8. fairfax\n9. incantation\n10. labour\n11. lorry\n12. pleura\n13. prig\n14. ride\n15. tea\n16. viaduct\n17. wheelbase\n\nLet's re-sort them carefully:\n\n* accomplice (A)\n* choral (C)\n* crepe (C)\n* circumcircle (C)\n* clatter (C)\n* doff (D)\n* emission (E)\n* fairfax (F)\n* incantation (I)\n* labour (L)\n* lorry (L)\n* pleura (P)\n* prig (P)\n* ride (R)\n* tea (T)\n* viaduct (V)\n* wheelbase (W)\n\n**Detailed Alphabetical Sorting:**\n\n1. accomplice\n2. choral\n3. crepe\n4. circumcircle\n5. clatter\n6. doff\n7. emission\n8. fairfax\n9. incantation\n10. labour\n11. lorry\n12. pleura\n13. prig\n14. ride\n15. tea\n16. viaduct\n17. wheelbase\n\n(Checking C words: choral, crepe, circumcircle, clatter. C-h comes before C-l, C-r, C-i. Wait, 'ch' comes before 'l', 'r', 'i'. Let's re-sort the C words: choral, clatter, crepe, circumcircle.)\n\n**Corrected Sorting:**\n\n1. accomplice (A)\n2. choral (C)\n3. clatter (C)\n4. crepe (C)\n5. circumcircle (C)\n6. doff (D)\n7. emission (E)\n8. fairfax (F)\n9. incantation (I)\n10. labour (L)\n11. lorry (L)\n12. pleura (P)\n13. prig (P)\n14. ride (R)\n15. tea (T)\n16. viaduct (V)\n17. wheelbase (W)" + }, + { + "expected": "block custodian deadwood foxtail guaranty hexadecimal macedonia rubaiyat victoria", + "id": "bbh_word_sorting-0056", + "label": "pass", + "observed": "Here are the words to sort: deadwood, macedonia, foxtail, block, hexadecimal, guaranty, rubaiyat, victoria, custodian.\n\nWe will sort them alphabetically:\n\n1. **block**\n2. **custodian**\n3. **deadwood**\n4. **foxtail**\n5. **guaranty**\n6. **hexadecimal**\n7. **macedonia**\n8. **rubaiyat**\n9. **victoria**\n\nLet's re-check the sorting carefully:\n\n* **b**lock\n* **c**ustodian\n* **d**eadwood\n* **f**oxtail\n* **g**uaranty\n* **h**exadecimal\n* **m**acedonia\n* **r**ubaiyat\n* **v**ictoria\n\nThe order seems correct based on the first letter.\n\nAnswer: block, custodian, deadwood, foxtail, guaranty, hexadecimal, macedonia, rubaiyat, victoria\n\nAnswer: block, custodian, deadwood, foxtail, guaranty, hexadecimal, macedonia, rubaiyat, victoria" + }, + { + "expected": "aldebaran backyard boxwood cabbage entrepreneurial fiberboard game inkling invincible lakeside lightface matte mcgee peruse polyhedra pulsate rae rowley shape watchworks", + "id": "bbh_word_sorting-0057", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. aldebaran\n2. backyard\n3. cabbage\n4. entrepreneurial\n5. fiberboard\n6. game\n7. inkling\n8. invincible\n9. lake\n10. lightface\n11. matte\n12. mcgee\n13. peruse\n14. pulsate\n15. polyhedra\n16. rae\n17. rowley\n18. shape\n19. watchworks\n20. boxwood\n\nLet's re-sort them carefully:\n\n* **a**: aldebaran, backyard\n* **b**: cabbage, boxwood\n* **e**: entrepreneurial\n* **f**: fiberboard\n* **g**: game\n* **i**: inkling, invincible\n* **l**: lakeside, lightface\n* **m**: matte, mcgee\n* **p**: pulsate, polyhedra, peruse\n* **r**: rae, rowley\n* **s**: shape\n* **w**: watchworks\n\nLet's do a full alphabetical sort:\n\n1. aldebaran\n2. backyard\n3. boxwood\n4. cabbage\n5. entrepreneurial\n6. fiberboard\n7. game\n8. inkling\n9. invincible\n10. lakeside (Assuming 'lakeside' is the intended word, as 'lakes' is not in the list, but 'lakeside' is often implied or a common word. Checking the list again: \"lakeside\" is not in the list. The list contains \"lakeside\" if we assume \"lakeside\" is present. Let's re-examine the provided list: matte game aldebaran inkling fiberboard pulsate lakeside shape mcgee watchworks entrepreneurial boxwood peruse backyard cabbage polyhedra lightface rowley rae invincible)\n * *Correction: The word is \"lakeside\".*\n11. lightface\n12. matte\n13. mcgee\n14. peruse\n15. pulsate\n16. polyhedra\n17. rae\n18. rowley\n19. shape\n20. watchworks\n\nLet's re-sort based on the exact list provided:\nmatte, game, aldebaran, inkling, fiberboard, pulsate, lakeside, shape, mcgee, watchworks, entrepreneurial, boxwood, peruse, backyard, cabbage" + }, + { + "expected": "darkle erudite hookup instant lip moldboard olsen pea quadrant yonkers", + "id": "bbh_word_sorting-0058", + "label": "pass", + "observed": "The given list of words is: pea, yonkers, darkle, quadrant, moldboard, instant, olsen, hookup, erudite, lip.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter:**\n * darkle (d)\n * erudite (e)\n * hookup (h)\n * instant (i)\n * lip (l)\n * moldboard (m)\n * olsen (o)\n * pea (p)\n * quadrant (q)\n * yonkers (y)\n\n2. **Sort based on the first letter:**\n * darkle (d)\n * erudite (e)\n * hookup (h)\n * instant (i)\n * lip (l)\n * moldboard (m)\n * olsen (o)\n * pea (p)\n * quadrant (q)\n * yonkers (y)\n\n3. **Final sorted list:**\n * darkle\n * erudite\n * hookup\n * instant\n * lip\n * moldboard\n * olsen\n * pea\n * quadrant\n * yonkers\n\nAnswer: darkle, erudite, hookup, instant, lip, moldboard, olsen, pea, quadrant, yonkers" + }, + { + "expected": "amicable browne calumny coo deerstalker extreme henchman histology indoeuropean paginate pelvis sonority they've tramway turvy", + "id": "bbh_word_sorting-0059", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. amicable\n2. browne\n3. calumny\n4. coo\n5. deerstalker\n6. eminence (Wait, \"extreme\" is in the list, let's re-check)\n7. henchman\n8. histology\n9. indoeuropean\n10. paginate\n11. pelvis\n12. sonority\n13. tramway\n14. they've\n15. turvy\n\nLet's re-sort carefully:\n\n* amicable (A)\n* browne (B)\n* calumny (C)\n* coo (C) - coo comes before calumny if we look at the second letter, but alphabetically, 'cal' comes before 'coo'. Let's stick to standard alphabetical order.\n * amicable\n * browne\n * calumny\n * coo\n* deerstalker (D)\n* eminence (Not in list, using extreme)\n* extreme (E)\n* henchman (H)\n* histology (H) - henchman comes before histology.\n* indoeuropean (I)\n* paginate (P)\n* pelvis (P) - paginate comes before pelvis.\n* sonority (S)\n* tramway (T)\n* they've (T) - tramway comes before they've.\n* turvy (T) - turvy comes after tramway.\n\nLet's perform a strict alphabetical sort:\n\n1. amicable\n2. browne\n3. calumny\n4. coo\n5. deerstalker\n6. extreme\n7. henchman\n8. histology\n9. indoeuropean\n10. paginate\n11. pelvis\n12. sonority\n13. tramway\n14. they've\n15. turvy\n\nChecking the 'H' words: henchman, histology. H-e-n vs H-i-s. henchman comes first.\nChecking the 'P' words: paginate, pelvis. P-a vs P-e. paginate comes first.\nChecking the 'T' words: tramway, they've, turvy. T-r, T-h, T-u. tramway, they've, turvy.\n * tramway (tr" + }, + { + "expected": "blackstone feed figural giveth hecatomb hunt incense middle obstinacy pasty pestle plume sinkhole spavin statutory tel toothpaste undulate", + "id": "bbh_word_sorting-0060", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. blackstone\n2. feed\n3. figural\n4. giveth\n5. hecatomb\n6. hunt\n7. incense\n8. middle\n9. obstinacy\n10. pasty\n11. pestle\n12. plume\n13. sinkhole\n14. statutory\n15. tel\n16. toothpaste\n17. undulate\n\nLet's re-sort them carefully:\n\n* **b**lackstone\n* **f**eed\n* **f**igural\n* **g**iveth\n* **h**ecatomb\n* **h**unt\n* **i**ncense\n* **m**iddle\n* **o**bstinacy\n* **p**asty\n* **p**estle\n* **p**lume\n* **s**inkhole\n* **s**tatutory\n* **t**el\n* **t**oothpaste (toothpaste)\n* **u**ndulate\n\nComparing words starting with 'f': feed, figural. (feed comes before figural)\nComparing words starting with 'p': pasty, pestle, plume. (plume, pasty, pestle) - Wait, P-L, P-A, P-E. Plume, Pasty, Pestle.\nP-l-u-m-e\nP-a-s-t-y\nP-e-s-t-l-e\nAlphabetical order for P words: plume, pasty, pestle.\n\nLet's re-sort the full list:\nblackstone (B)\nfeed (F)\nfigural (F)\ngiveth (G)\nhecatomb (H)\nhunt (H)\nincense (I)\nmiddle (M)\nobstinacy (O)\npasty (P)\npestle (P)\nplume (P)\nsinkhole (S)\nstatutory (S)\ntel (T)\ntoothpaste (T)\nundulate (U)\n\n**Detailed Alphabetical Sort:**\n\n1. blackstone\n2. feed\n3. figural\n4. giveth\n5. hecatomb\n6. hunt\n7. incense\n8. middle\n9. obstinacy\n10. pasty\n11." + }, + { + "expected": "gloucester raytheon slurp", + "id": "bbh_word_sorting-0061", + "label": "pass", + "observed": "The given list of words is: slurp, raytheon, gloucester.\n\nTo sort these words alphabetically, we compare them letter by letter from the beginning.\n\n1. **s**lurp\n2. **r**aytheon\n3. **g**looucester\n\nComparing the first letters: G comes before R, and R comes before S.\nSo, 'gloucester' comes first.\n\nNow we compare 'slurp' and 'raytheon'.\n's' comes before 'r' is false. 'r' comes before 's'.\nSo, 'raytheon' comes before 'slurp'.\n\nThe alphabetical order is:\n1. gloucester\n2. raytheon\n3. slurp\n\nAnswer: gloucester, raytheon, slurp\n\nAnswer: gloucester, raytheon, slurp" + }, + { + "expected": "allegoric collate euphony gloriana loge lollipop mast milord prolix rendezvous salle schnabel", + "id": "bbh_word_sorting-0062", + "label": "fail", + "observed": "Here are the words to sort alphabetically:\ngloriana, milord, rendezvous, schnabel, allegoric, loge, lollipop, salle, collate, prolix, mast, euphony\n\nLet's sort them letter by letter:\n\n1. **allegoric** (A)\n2. **collate** (C)\n3. **gloriana** (G)\n4. **loge** (L) - *Comparing loge and lollipop, 'loge' comes before 'lollipop'*\n5. **lollipop** (L)\n6. **milord** (M)\n7. **mast** (M) - *Comparing milord and mast, 'mast' comes before 'milord'*\n8. **prolix** (P)\n9. **rendezvous** (R)\n10. **salle** (S)\n11. **schnabel** (S) - *Comparing salle and schnabel, 'salle' comes before 'schnabel'*\n12. **euphony** (E) - *Wait, 'euphony' starts with E, so it should come before 'gloriana' (G).*\n\nLet's re-sort based on the first letter:\n\nE: euphony\nA: allegoric\nC: collate\nG: gloriana\nL: loge, lollipop\nM: mast, milord\nP: prolix\nR: rendezvous\nS: salle, schnabel\n\n**Sorting within groups:**\n\n* **L:** loge, lollipop (loge comes before lollipop)\n* **M:** mast, milord (mast comes before milord)\n* **S:** salle, schnabel (salle comes before schnabel)\n\n**Final Alphabetical Order:**\n\n1. euphony\n2. allegoric\n3. collate\n4. gloriana\n5. loge\n6. lollipop\n7. mast\n8. milord\n9. prolix\n10. rendezvous\n11. salle\n12. schnabel\n\nAnswer: euphony, allegoric, collate, gloriana, loge, lollipop, mast, milord, prolix, rendezvous, salle, schnabel" + }, + { + "expected": "artillery bainite doris fda harm incongruous monkey prosody vegetate vivian", + "id": "bbh_word_sorting-0063", + "label": "pass", + "observed": "The given list of words is: vegetate, artillery, harm, fda, doris, prosody, bainite, incongruous, monkey, vivian.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter:**\n * bainite (b)\n * doris (d)\n * fda (f)\n * harm (h)\n * incongruous (i)\n * monkey (m)\n * prosody (p)\n * artillery (a)\n * vegetate (v)\n * vivian (v)\n\n2. **Sort based on the first letter:**\n * **a:** artillery\n * **b:** bainite\n * **d:** doris\n * **f:** fda\n * **h:** harm\n * **i:** incongruous\n * **m:** monkey\n * **p:** prosody\n * **v:** vegetate, vivian\n\n3. **Sort the 'v' words:**\n * vegetate\n * vivian\n * Comparing the second letter: 'e' in vegetate, 'i' in vivian. 'e' comes before 'i'.\n * So, vegetate comes before vivian.\n\n4. **Final sorted order:**\n 1. artillery\n 2. bainite\n 3. doris\n 4. fda\n 5. harm\n 6. incongruous\n 7. monkey\n 8. prosody\n 9. vegetate\n 10. vivian\n\nAnswer: artillery, bainite, doris, fda, harm, incongruous, monkey, prosody, vegetate, vivian" + }, + { + "expected": "alkali breach buckle falsetto hyperboloid liquidate mirth nagasaki parmesan", + "id": "bbh_word_sorting-0064", + "label": "pass", + "observed": "The given list of words is: hyperboloid, buckle, liquidate, alkali, nagasaki, breach, falsetto, parmesan, mirth.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter:**\n * b (buckle, breach)\n * f (falsetto)\n * h (hyperboloid)\n * l (liquidate)\n * m (mirth)\n * n (nagasaki)\n * p (parmesan)\n * a (alkali)\n\n Sorting by the first letter: a, b, f, h, l, m, n, p.\n\n2. **Sort words starting with 'a':**\n * alkali\n\n3. **Sort words starting with 'b':**\n * buckle\n * breach\n * (b**r**each comes before b**u**ckle)\n * breach, buckle\n\n4. **Sort words starting with 'f':**\n * falsetto\n\n5. **Sort words starting with 'h':**\n * hyperboloid\n\n6. **Sort words starting with 'l':**\n * liquidate\n\n7. **Sort words starting with 'm':**\n * mirth\n\n8. **Sort words starting with 'n':**\n * nagasaki\n\n9. **Sort words starting with 'p':**\n * parmesan\n\n**Combining the sorted lists:**\n\n1. alkali\n2. breach\n3. buckle\n4. falsetto\n5. hyperboloid\n6. liquidate\n7. mirth\n8. nagasaki\n9. parmesan\n\nLet's re-verify the order carefully:\n\n* **a**: alkali\n* **b**: breach, buckle (breach before buckle)\n* **f**: falsetto\n* **h**: hyperboloid\n* **l**: liquidate\n* **m**: mirth\n* **n**: nagasaki\n* **p**: parmesan\n\nFinal sorted list: alkali, breach, buckle, falsetto, hyperboloid, liquidate, mirth, nagasaki, parmesan.\n\nAnswer: alkali, breach, buckle, falsetto, hyperboloid, liquidate, mirth, nagasaki, parmesan" + }, + { + "expected": "cocksure comet heusen hydrate injun manley pincer snippet spokesperson", + "id": "bbh_word_sorting-0065", + "label": "fail", + "observed": "The given list of words is: comet, pincer, snippet, manley, injun, spokesperson, hydrate, heusen, cocksure.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter:**\n * comet (c)\n * pincer (p)\n * snippet (s)\n * manley (m)\n * injun (i)\n * spokesperson (s)\n * hydrate (h)\n * heusen (h)\n * cocksure (c)\n\n Words starting with 'c': comet, cocksure\n Words starting with 'h': hydrate, heusen\n Words starting with 'i': injun\n Words starting with 'm': manley\n Words starting with 'p': pincer\n Words starting with 's': snippet, spokesperson\n\n2. **Sort words starting with 'c':**\n * comet\n * cocksure\n (comet comes before cocksure)\n\n3. **Sort words starting with 'h':**\n * hydrate\n * heusen\n(hydrate comes before heusen)\n\n4. **Sort words starting with 'i':**\n * injun\n\n5. **Sort words starting with 'm':**\n * manley\n\n6. **Sort words starting with 'p':**\n * pincer\n\n7. **Sort words starting with 's':**\n * snippet\n * spokesperson\n(snippet comes before spokesperson)\n\n8. **Combine the sorted groups:**\n * comet\n * cocksure\n * hydrate\n * heusen\n * injun\n * manley\n * pincer\n * snippet\n * spokesperson\n\nLet's re-check the full alphabetical order:\nC: comet, cocksure\nH: hydrate, heusen\nI: injun\nM: manley\nP: pincer\nS: snippet, spokesperson\n\nComparing C words: comet vs cocksure. 'o' comes before 'o' (wait, 'm' vs 'o'). comet vs cocksure. 'o' vs 'o'. 'm' vs 'c'. 'm' comes before 'c' in the second letter? No, 'o' vs 'o'.\ncomet (co...)\ncocksure (co...)\ncomet (co**m" + }, + { + "expected": "allis anthology jacobi marmot membrane oakland seaborg toggle trapezoidal", + "id": "bbh_word_sorting-0066", + "label": "fail", + "observed": "Here are the words to sort alphabetically:\noakland, seaborg, jacobi, membrane, trapezoidal, allis, marmot, toggle, anthology\n\nLet's sort them letter by letter:\n\n1. **allis** (A)\n2. **anthology** (A) - 'allis' comes before 'anthology' because 'l' comes before 'n'.\n3. **jacobi** (J)\n4. **marmot** (M)\n5. **membrane** (M) - 'marmot' comes before 'membrane' because 'a' comes before 'e'.\n6. **oakland** (O)\n7. **seaborg** (S)\n8. **trapezoidal** (T)\n9. **toggle** (T) - 'trapezoidal' comes before 'toggle' because 'r' comes before 'o'.\n\nLet's re-check the 'M' words:\nmarmot\nmembrane\n'marmot' vs 'membrane': m-a-r vs m-e-m. 'a' comes before 'e', so marmot comes before membrane.\n\nLet's re-check the 'T' words:\ntrapezoidal\ntoggle\n'trapezoidal' vs 'toggle': t-r-a vs t-o-g. 'r' comes before 'o', so trapezoidal comes before toggle.\n\nFinal sorted order:\n1. allis\n2. anthology\n3. jacobi\n4. marmot\n5. membrane\n6. oakland\n7. seaborg\n8. trapezoidal\n9. toggle\n\nAnswer: allis, anthology, jacobi, marmot, membrane, oakland, seaborg, trapezoidal, toggle" + }, + { + "expected": "ballard brindle cornerstone credulous curio des difluoride green horseplay jew mixup nonce nostalgic pitney predilect prowl rape scrappy toward", + "id": "bbh_word_sorting-0067", + "label": "pass", + "observed": "Here are the words sorted alphabetically:\n\n1. ballard\n2. brindle\n3. cornerstone\n4. credulous\n5. curio\n6. des\n7. difluoride\n8. green\n9. horseplay\n10. jew\n11. mixup\n12. nonce\n13. nostalgic\n14. pitney\n15. predilect\n16. prowl\n17. rape\n18. scrappy\n19. toward\n\nLet's re-sort them carefully:\n\nOriginal list: cornerstone, green, brindle, prowl, nonce, ballard, scrappy, credulous, des, pitney, difluoride, toward, predilect, nostalgic, rape, mixup, horseplay, jew, curio\n\nSorted list:\n1. ballard\n2. brindle\n3. cornerstone\n4. credulous\n5. curio\n6. des\n7. difluoride\n8. green\n9. horseplay\n10. jew\n11. mixup\n12. nonce\n13. nostalgic\n14. pitney\n15. predilect\n16. prowl\n17. rape\n18. scrappy\n19. toward\n\nAnswer: ballard, brindle, cornerstone, credulous, curio, des, difluoride, green, horseplay, jew, mixup, nonce, nostalgic, pitney, predilect, prowl, rape, scrappy, toward" + }, + { + "expected": "afloat apostasy bechtel chattel conner ferment grosbeak hendrickson indonesia jacm lanthanide melancholy quark scavenge strove vibrate", + "id": "bbh_word_sorting-0068", + "label": "fail", + "observed": "Here are the words to sort alphabetically:\nstrove, hendrickson, apostasy, jacm, indonesia, vibrate, afloat, lanthanide, grosbeak, scavenge, chattel, conner, bechtel, melancholy, ferment\n\nLet's sort them:\n\n1. **afloat**\n2. **apostasy**\n3. **bechtel**\n4. **chattel**\n5. **conner**\n6. **ferment**\n7. **grosbeak**\n8. **hendrickson**\n9. **indonesia**\n10. **jacm**\n11. **lanthanide**\n12. **melancholy**\n13. **scavenge**\n14. **strove**\n15. **vibrate**\n\nLet's re-check the sorting, especially for words starting with similar letters:\n\n* **a**: afloat, apostasy\n* **b**: bechtel\n* **c**: chattel, conner\n* **f**: ferment\n* **g**: grosbeak\n* **h**: hendrickson\n* **i**: indonesia\n* **j**: jacm\n* **l**: lanthanide\n* **m**: melancholy\n* **s**: scavenge, strove\n* **v**: vibrate\n\nDetailed comparison:\n* afloat (a-f)\n* apostasy (a-p) $\\rightarrow$ afloat comes before apostasy.\n* bechtel (b)\n* chattel (c-h)\n* conner (c-o) $\\rightarrow$ chattel comes before conner.\n* ferment (f)\n* grosbeak (g)\n* hendrickson (h)\n* indonesia (i)\n* jacm (j)\n* lanthanide (l)\n* melancholy (m)\n* scavenge (s-c)\n* strove (s-t) $\\rightarrow$ scavenge comes before strove.\n* vibrate (v)\n\nCorrected order:\n1. afloat\n2. apostasy\n3. bechtel\n4. chattel\n5. conner\n6. ferment\n7. grosbeak\n8. hendrickson\n9. indonesia\n10. jacm\n11. lanthanide\n12. melancholy\n13. scavenge\n14. strove\n15. vibrate\n\nAnswer: afloat" + }, + { + "expected": "acquisitive annuity autocracy bruno custody dare exploitation lodge militant quench somatic thunderclap ventricle", + "id": "bbh_word_sorting-0069", + "label": "pass", + "observed": "Here are the words sorted alphabetically:\n\n1. acquisitive\n2. annuity\n3. autocracy\n4. bruno\n5. custody\n6. dare\n7. exploitation\n8. lodge\n9. militant\n10. quench\n11. somatic\n12. thunderclap\n13. ventricle\n\nLet's re-sort them carefully:\n\n* **a**cquisitive, **a**nnuity, **a**utocracy\n* **b**runo\n* **c**ustody\n* **d**are\n* **e**xploitation (Wait, 'e' comes before 'l', 'm', 'q', 's', 't', 'v')\n* **l**odge\n* **m**ilitant\n* **q**uench\n* **s**omatic\n* **t**hunderclap\n* **v**entricle\n\nLet's list them and compare:\n1. acquisitive\n2. annuity\n3. autocracy\n4. bruno\n5. custody\n6. dare\n7. exploitation\n8. lodge\n9. militant\n10. quench\n11. somatic\n12. thunderclap\n13. ventricle\n\nChecking the order:\nacquisitive (a-c)\nannuity (a-n)\nautocracy (a-u)\nbruno (b)\ncustody (c-u)\ndare (d)\nexploitation (e)\nlodge (l)\nmilitant (m)\nquench (q)\nsomatic (s)\nthunderclap (t)\nventricle (v)\n\nThe correct alphabetical order is:\n1. acquisitive\n2. annuity\n3. autocracy\n4. bruno\n5. custody\n6. dare\n7. exploitation\n8. lodge\n9. militant\n10. quench\n11. somatic\n12. thunderclap\n13. ventricle\n\nAnswer: acquisitive, annuity, autocracy, bruno, custody, dare, exploitation, lodge, militant, quench, somatic, thunderclap, ventricle" + }, + { + "expected": "bosporus bully cork edt flogging forfeit lexicographer minor multiple perceptive pizza pungent rancorous reedy referring sedition sell tit", + "id": "bbh_word_sorting-0070", + "label": "fail", + "observed": "Here are the words to sort alphabetically:\nreedy, bully, multiple, edt, sell, referring, lexicographer, tit, flogging, perceptive, minor, bosporus, rancorous, forfeit, pizza, pungent, sedition, cork\n\nLet's sort them:\n\n1. **bosporus**\n2. **bully**\n3. **cork**\n4. **edt**\n5. **forfeit**\n6. **flogging**\n7. **lexicographer**\n8. **multiple**\n9. **minor**\n10. **perceptive**\n11. **pizza**\n12. **pungent**\n13. **rancorous**\n14. **reedy**\n15. **referring**\n16. **sell**\n17. **sedition**\n18. **tit**\n\nLet's re-check the sorting, especially for words starting with similar letters:\n\n* **b**: bully, bosporus\n * bosporus (bo...)\n * bully (bu...) $\\rightarrow$ bosporus, bully\n* **c**: cork\n* **e**: edt\n* **f**: forfeit, flogging\n * forfeit (for...)\n * flogging (flo...) $\\rightarrow$ flogging, forfeit\n* **l**: lexicographer\n* **m**: multiple, minor\n * multiple (mul...)\n * minor (min...) $\\rightarrow$ minor, multiple\n* **p**: perceptive, pizza, pungent\n * perceptive (per...)\n * pizza (pi...)\n * pungent (pun...) $\\rightarrow$ perceptive, pizza, pungent\n* **r**: reedy, referring, rancorous\n * reedy (ree...)\n * referring (ref...)\n * rancorous (ran...) $\\rightarrow$ rancorous, reedy, referring\n* **s**: sell, sedition\n * sell (sel...)\n * sedition (sed...) $\\rightarrow$ sell, sedition\n* **t**: tit\n\nLet's perform a strict alphabetical sort:\n\n1. bosporus (B)\n2. bully (B)\n3. cork (C)\n4. edt (E)\n5. flogging (F)\n6. forfeit (F)\n7. lexicographer (L)\n8. minor (M)\n" + }, + { + "expected": "behold dew dissipate format hew maybe misogyny oxalic pray steel stiffen termcap", + "id": "bbh_word_sorting-0071", + "label": "pass", + "observed": "The given list of words is: behold, oxalic, maybe, hew, steel, termcap, pray, stiffen, dissipate, misogyny, format, dew.\n\nHere is the alphabetical sorting process:\n\n1. **behold**\n2. **dew**\n3. **dissipate**\n4. **format**\n5. **hew**\n6. **maybe**\n7. **misogyny**\n8. **oxalic**\n9. **pray**\n10. **steel**\n11. **stiffen**\n12. **termcap**\n\nLet's re-sort carefully:\n\n* **b**: behold\n* **d**: dew, dissipate\n * dew comes before dissipate\n* **f**: format\n* **h**: hew\n* **m**: maybe, misogyny\n * maybe comes before misogyny\n* **o**: oxalic\n* **p**: pray\n* **s**: steel, stiffen\n * steel comes before stiffen\n* **t**: termcap\n\nLet's re-evaluate the order based on the first letter:\nB: behold\nD: dew, dissipate\nF: format\nH: hew\nM: maybe, misogyny\nO: oxalic\nP: pray\nS: steel, stiffen\nT: termcap\n\nSorting within groups:\nD: dew, dissipate (dew before dissipate)\nM: maybe, misogyny (maybe before misogyny)\nS: steel, stiffen (steel before stiffen)\n\nFinal sorted list:\n1. behold\n2. dew\n3. dissipate\n4. format\n5. hew\n6. maybe\n7. misogyny\n8. oxalic\n9. pray\n10. steel\n11. stiffen\n12. termcap\n\nAnswer: behold, dew, dissipate, format, hew, maybe, misogyny, oxalic, pray, steel, stiffen, termcap" + }, + { + "expected": "appliance impede pulitzer superior", + "id": "bbh_word_sorting-0072", + "label": "pass", + "observed": "The given list of words is: impede, appliance, pulitzer, superior.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **impede** (starts with 'i')\n2. **appliance** (starts with 'a')\n3. **pulitzer** (starts with 'p')\n4. **superior** (starts with 's')\n\nComparing the first letters: 'a', 'i', 'p', 's'.\nAlphabetical order of the first letters: 'a', 'i', 'p', 's'.\n\n1. **appliance** (starts with 'a')\n2. **impede** (starts with 'i')\n3. **pulitzer** (starts with 'p')\n4. **superior** (starts with 's')\n\nThe sorted list is: appliance, impede, pulitzer, superior.\n\nAnswer: appliance, impede, pulitzer, superior\nAnswer: appliance, impede, pulitzer, superior" + }, + { + "expected": "allstate dose dyad multitudinous plural powderpuff stalin", + "id": "bbh_word_sorting-0073", + "label": "pass", + "observed": "The given list of words is: plural, dose, allstate, stalin, dyad, multitudinous, powderpuff.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter:**\n * plural (p)\n * dose (d)\n * allstate (a)\n * stalin (s)\n * dyad (d)\n * multitudinous (m)\n * powderpuff (p)\n\n Words starting with 'a': allstate\n Words starting with 'd': dose, dyad\n Words starting with 'm': multitudinous\n Words starting with 'p': plural, powderpuff\n Words starting with 's': stalin\n\n2. **Sort words starting with 'd':**\n * dose\n * dyad\n (dose comes before dyad)\n\n3. **Sort words starting with 'p':**\n * plural\n * powderpuff\n (plural comes before powderpuff)\n\n4. **Assemble the sorted list based on the first letter order (A, D, M, P, S):**\n * allstate (A)\n * dose (D)\n * dyad (D)\n * multitudinous (M)\n * plural (P)\n * powderpuff (P)\n * stalin (S)\n\n5. **Refine the order within groups:**\n * D group: dose, dyad (dose before dyad)\n * P group: plural, powderpuff (plural before powderpuff)\n\nThe final alphabetical order is: allstate, dose, dyad, multitudinous, plural, powderpuff, stalin.\n\nAnswer: allstate, dose, dyad, multitudinous, plural, powderpuff, stalin" + }, + { + "expected": "downtrodden gadgetry gamin hurst inertial maraud morphine parsonage propane", + "id": "bbh_word_sorting-0074", + "label": "pass", + "observed": "Here are the words to sort: maraud, downtrodden, hurst, propane, morphine, parsonage, gadgetry, inertial, gamin\n\nLet's sort them alphabetically:\n\n1. **gadgetry** (g)\n2. **gamin** (g)\n3. **hurst** (h)\n4. **inertial** (i)\n5. **maraud** (m)\n6. **morphine** (m)\n7. **parsonage** (p)\n8. **propane** (p)\n9. **downtrodden** (d)\n\nLet's re-sort carefully:\n\n* **d**owntrodden\n* **g**adgetry\n* **g**amin\n* **h**urst\n* **i**nertial\n* **m**araud\n* **m**orphine\n* **p**arsonage\n* **p**ropane\n\nComparing 'gadgetry' and 'gamin':\ngadgetry (g-a-d...)\ngamin (g-a-m...)\n'd' comes before 'm', so gadgetry comes before gamin.\n\nComparing 'maraud' and 'morphine':\nmaraud (m-a-r...)\nmorphine (m-o-r...)\n'a' comes before 'o', so maraud comes before morphine.\n\nComparing 'parsonage' and 'propane':\nparsonage (p-a-r-s...)\npropane (p-r-o-p...)\n'a' comes before 'r', so parsonage comes before propane.\n\nThe correct alphabetical order is:\n1. downtrodden\n2. gadgetry\n3. gamin\n4. hurst\n5. inertial\n6. maraud\n7. morphine\n8. parsonage\n9. propane\n\nAnswer: downtrodden, gadgetry, gamin, hurst, inertial, maraud, morphine, parsonage, propane" + }, + { + "expected": "confrontation daddy hirsute proofread proserpine quantitative", + "id": "bbh_word_sorting-0075", + "label": "fail", + "observed": "The given list of words is: confrontation, quantitative, proserpine, proofread, daddy, hirsute.\n\nTo sort them alphabetically, we compare them letter by letter:\n\n1. **confrontation** (C)\n2. **quantitative** (Q)\n3. **proserpine** (P)\n4. **proofread** (P)\n5. **daddy** (D)\n6. **hirsute** (H)\n\nLet's sort them based on the first letter:\nD: daddy\nC: confrontation\nH: hirsute\nP: proserpine, proofread\nQ: quantitative\n\nNow, let's sort the words starting with 'P':\nproserpine\nproofread\nComparing 'proserpine' and 'proofread':\nproserpine (p r o s e r p i n e)\nproofread (p r o o f r e a d)\nThe third letter: 'o' vs 'o'.\nThe fourth letter: 's' vs 'o'. Since 'o' comes before 's', 'proofread' comes before 'proserpine'.\nWait, let's recheck:\np r o **s** e r p i n e\np r o **o** f r e a d\n'o' comes before 's'. So, 'proofread' comes before 'proserpine'.\n\nCorrect order for P words: proofread, proserpine.\n\nThe complete alphabetical order is:\n1. daddy (D)\n2. confrontation (C) - Wait, C comes before D. Let's re-sort by the first letter:\n\nD: daddy\nC: confrontation\nH: hirsute\nP: proofread, proserpine\nQ: quantitative\n\nAlphabetical order of first letters: C, D, H, P, Q.\n\n1. **confrontation** (C)\n2. **daddy** (D)\n3. **hirsute** (H)\n4. **proofread** (P)\n5. **proserpine** (P)\n6. **quantitative** (Q)\n\nNow, sorting the P words:\nproofread vs proserpine\np r o **o** f r e a d\np r o **s** e r p i n e\n'o' comes before 's', so proofread comes" + }, + { + "expected": "biennial creedal cry eyesight fletch fraudulent j miltonic mirage titmice whisper", + "id": "bbh_word_sorting-0076", + "label": "fail", + "observed": "The given list of words is: whisper, eyesight, titmice, mirage, fletch, cry, biennial, creedal, miltonic, fraudulent, j.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter:**\n * fletch (f)\n * fraudulent (f)\n * j (j)\n * miltonic (m)\n * mirage (m)\n * creedal (c)\n * biennial (b)\n * cry (c)\n * whisper (w)\n * eyesight (e)\n * titmice (t)\n\n2. **Sort by the first letter:**\n * **b:** biennial\n * **c:** creedal, cry\n * **e:** eyesight\n * **f:** fletch, fraudulent\n * **j:** j\n * **m:** miltonic, mirage\n * **t:** titmice\n * **w:** whisper\n\n3. **Sort within groups:**\n\n * **c:** creedal, cry (creedal comes before cry)\n * **f:** fletch, fraudulent (fletch comes before fraudulent)\n * **m:** miltonic, mirage (miltonic comes before mirage)\n\n4. **Final ordered list:**\n * biennial (b)\n * creedal (c)\n * cry (c)\n * eyesight (e)\n * fletch (f)\n * fraudulent (f)\n * j (j)\n * miltonic (m)\n * mirage (m)\n * titmice (t)\n * whisper (w)\n\nLet's re-verify the order based on standard alphabetical rules:\n\n1. biennial (b)\n2. creedal (c)\n3. cry (c)\n4. eyesight (e)\n5. fletch (f)\n6. fraudulent (f)\n7. j (j)\n8. miltonic (m)\n9. mirage (m)\n10. titmice (t)\n11. whisper (w)\n\n(Note on 'j': In alphabetical sorting, 'j' comes after 'f' and before 'm'.)\n\nThe correct alphabetical order is: biennial, creedal, cry, eyesight, fletch," + }, + { + "expected": "dean eosine formula gibson inebriate mater mulligatawny rica sigmund vassar", + "id": "bbh_word_sorting-0077", + "label": "pass", + "observed": "Here are the words sorted alphabetically:\n\n1. dean\n2. eosine\n3. formula\n4. gibson\n5. inebriate\n6. mater\n7. mulligatawny\n8. rica\n9. sigmund\n10. vassar\n\nLet's re-sort them carefully:\n\n* **d**ean\n* **e**osine\n* **f**ormula\n* **g**ibson\n* **i**nebriate\n* **m**ater\n* **m**ulligatawny (mulligatawny comes after mater because 'u' comes after 'a')\n* **r**ica\n* **s**igmund\n* **v**assar\n\nLet's check the 'm' words again: mater, mulligatawny.\nmater\nmulligatawny (mulligatawny comes after mater)\n\nCorrect alphabetical order:\n1. dean\n2. eosine\n3. formula\n4. gibson\n5. inebriate\n6. mater\n7. mulligatawny\n8. rica\n9. sigmund\n10. vassar\n\nAnswer: dean, eosine, formula, gibson, inebriate, mater, mulligatawny, rica, sigmund, vassar" + }, + { + "expected": "abbe adposition arragon cast danbury emplace falsetto gavin income inhibit onerous palazzi tabletop", + "id": "bbh_word_sorting-0078", + "label": "fail", + "observed": "Here are the words to sort alphabetically:\ncast, palazzi, inhibit, arragon, tabletop, danbury, abbe, adposition, emplace, gavin, income, falsetto, onerous\n\nLet's sort them:\n\n1. **abbe**\n2. **adposition**\n3. **arragon**\n4. **cast**\n5. **danbury**\n6. **emplace**\n7. **falsetto**\n8. **gavin**\n9. **inhibit**\n10. **income**\n11. ** onerous**\n12. **palazzi**\n13. **tabletop**\n\nLet's re-sort carefully, focusing on the first letter and subsequent letters:\n\n* **a**: abbe, adposition, arragon\n * abbe\n * adposition (a-d)\n * arragon (a-r)\n* **c**: cast\n* **d**: danbury\n* **e**: emplace\n* **f**: falsetto\n* **g**: gavin\n* **i**: inhibit, income\n * inhibit (i-n-h)\n * income (i-n-c) $\\rightarrow$ inhibit comes before income\n* **o**: onerous\n* **p**: palazzi\n* **t**: tabletop\n\nLet's re-evaluate the 'a' words:\n1. abbe\n2. adposition\n3. arragon\n\nLet's check the order of the remaining words: cast, danbury, emplace, falsetto, gavin, inhibit, income, onerous, palazzi, tabletop.\n\n1. abbe\n2. adposition\n3. arragon\n4. cast\n5. danbury\n6. emplace\n7. falsetto\n8. gavin\n9. inhibit\n10. income\n11. onerous\n12. palazzi\n13. tabletop\n\nFinal check:\na-b-b-e\na-d-p-o-s-i-t-i-o-n\na-r-r-a-g-o-n\nc-a-s-t\nd-a-n-b-u-r-y\ne-m-p-l-a-c-e\nf-a-l-s-e" + }, + { + "expected": "bivalve mainstream malformed mortify o'connell paunchy sleuth twelvefold umbilical vinegar", + "id": "bbh_word_sorting-0079", + "label": "pass", + "observed": "Here are the words sorted alphabetically:\n\n1. bivalve\n2. formaldehyde (Wait, the list has \"malformed\", not \"formaldehyde\". Let's stick to the provided list.)\n3. mortify\n4. mainstream\n5. malformed\n6. o'connell\n7. paunchy\n8. sleuth\n9. twelvefold\n10. umbilical\n11. vinegar\n\nLet's re-sort the provided list: twelvefold, o'connell, mainstream, malformed, umbilical, bivalve, vinegar, mortify, sleuth, paunchy.\n\n1. bivalve\n2. mainstream\n3. malformed\n4. mortify\n5. o'connell\n6. paunchy\n7. sleuth\n8. twelvefold\n9. umbilical\n10. vinegar\n\nLet's check the sorting carefully:\nB: bivalve\nM: mainstream, malformed, mortify\nO: o'connell\nP: paunchy\nS: sleuth\nT: twelvefold\nU: umbilical\nV: vinegar\n\nComparing M words: mainstream, malformed, mortify\n* mainstream\n* malformed (m-a-l)\n* mortify (m-o-r)\nSo: mainstream, malformed, mortify\n\nFinal sorted list:\n1. bivalve\n2. mainstream\n3. malformed\n4. mortify\n5. o'connell\n6. paunchy\n7. sleuth\n8. twelvefold\n9. umbilical\n10. vinegar\n\nAnswer: bivalve, mainstream, malformed, mortify, o'connell, paunchy, sleuth, twelvefold, umbilical, vinegar" + }, + { + "expected": "anarchic bstj elution exhumation furl geld gradual j liniment locomote midshipman pantheist profess riddance rowley saline", + "id": "bbh_word_sorting-0080", + "label": "fail", + "observed": "Here are the words to sort alphabetically:\nrowley, midshipman, saline, furl, geld, locomote, exhumation, anarchic, bstj, gradual, riddance, j, liniment, pantheist, elution, profess\n\nLet's sort them:\n\n1. **anarchic**\n2. **bstj**\n3. **elution**\n4. **furl**\n5. **geld**\n6. **gradual**\n7. **j**\n8. **locomote**\n9. **liniment**\n10. **midshipman**\n11. **pantheist**\n12. **profess**\n13. **riddance**\n14. **rowley**\n15. **saline**\n16. **exhumation**\n\nLet's re-check the sorting, especially for words starting with similar letters:\n\n* **a**: anarchic\n* **b**: bstj\n* **e**: elution, exhumation\n * elution comes before exhumation (l before x)\n* **f**: furl\n* **g**: geld, gradual\n * geld comes before gradual (e before r)\n* **j**: j\n* **l**: locomote, liniment\n * locomote comes before liniment (o before i is false, l comes before o) - Wait, L comes before M.\n * locomote\n * liniment\n* **m**: midshipman\n* **p**: pantheist, profess\n * pantheist comes before profess (a before r)\n* **r**: riddance, rowley\n * riddance comes before rowley (i before o)\n* **s**: saline\n* **(remaining)**\n\nLet's perform a strict alphabetical sort:\n\n1. anarchic\n2. bstj\n3. elution\n4. exhumation\n5. furl\n6. geld\n7. gradual\n8. j\n9. liniment\n10. locomote\n11. midshipman\n12. pantheist\n13. profess\n14. riddance\n15. rowley\n16. saline\n\nLet's re-verify the L words: liniment, locomote. L-I vs L-O. I comes before O" + }, + { + "expected": "affirmative airframe arcing ballroom bassoon benefit buggy coupon decide dodge hypothermia intrepid junior ladle nineveh prorogue schmitt shagging sparse ulcerate", + "id": "bbh_word_sorting-0081", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. airframe\n2. arcing\n3. affirmative\n4. ballroom\n5. benefit\n6. buggy\n7. bassoon\n8. decide\n9. dodge\n10. hyphenmia (hypothermia)\n11. intrepid\n12. junior\n13. ladle\n14. nineveh\n15. prorogue\n16. schmitt\n17. shagging\n18. sparse\n19. ulcerate\n20. coupon\n\nLet's re-sort them carefully:\n\n* **a**: airframe, arcing, affirmative\n* **b**: ballroom, benefit, buggy, bassoon\n* **c**: coupon\n* **d**: decide, dodge\n* **h**: hyphenmia (hypothermia), shagging\n* **i**: intrepid\n* **j**: junior\n* **l**: ladle\n* **n**: nineveh\n* **p**: prorogue\n* **s**: schmitt, sparse\n* **u**: ulcerate\n\n**Detailed Alphabetical Sort:**\n\n1. airframe\n2. arcing\n3. affirmative\n4. ballroom\n5. benefit\n6. buggy\n7. bassoon\n8. coupon\n9. decide\n10. dodge\n11. hypothermia (or hyphenmia, assuming 'hypothermia' is the intended word)\n12. intrepid\n13. junior\n14. ladle\n15. nineveh\n16. prorogue\n17. schmitt\n18. shagging\n19. sparse\n20. ulcerate\n\n(Note: I treated \"hypothermia\" as the intended word from \"hypothermia\" and sorted it under H. If \"hyphenmia\" was intended, it would be placed before \"hypothermia\".)\n\nAnswer: airframe, arcing, affirmative, ballroom, benefit, buggy, bassoon, coupon, decide, dodge, hypothermia, intrepid, junior, ladle, nineveh, prorogue, schmitt, shagging, sparse, ulcerate" + }, + { + "expected": "chrysalis wallaby", + "id": "bbh_word_sorting-0082", + "label": "pass", + "observed": "The given words are: chrysalis, wallaby.\n\nTo sort them alphabetically, we compare them letter by letter from the beginning.\n\n1. **chrysalis** starts with 'c'.\n2. **wallaby** starts with 'w'.\n\nSince 'c' comes before 'w' in the alphabet, \"chrysalis\" comes before \"wallaby\".\n\nThe alphabetical order is: chrysalis, wallaby.\n\nAnswer: chrysalis, wallaby" + }, + { + "expected": "batavia canaan maladjust merry olefin ranch relinquish yang", + "id": "bbh_word_sorting-0083", + "label": "pass", + "observed": "The given list of words is: yang, relinquish, merry, maladjust, olefin, canaan, ranch, batavia.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **batavia** (Starts with 'b')\n2. **canaan** (Starts with 'c')\n3. **maladjust** (Starts with 'm')\n4. **merry** (Starts with 'm') - Compare 'maladjust' and 'merry'. 'a' comes before 'e', so 'maladjust' comes before 'merry'.\n5. **olefin** (Starts with 'o')\n6. **ranch** (Starts with 'r')\n7. **relinquish** (Starts with 'r') - Compare 'ranch' and 'relinquish'. 'a' comes before 'e', so 'ranch' comes before 'relinquish'.\n8. **yang** (Starts with 'y')\n\nLet's re-sort carefully:\n\nWords starting with 'b':\n* batavia\n\nWords starting with 'c':\n* canaan\n\nWords starting with 'm':\n* maladjust\n* merry\n * Comparing 'maladjust' and 'merry': m-a vs m-e. 'a' comes before 'e'. So, maladjust, merry.\n\nWords starting with 'o':\n* olefin\n\nWords starting with 'r':\n* ranch\n* relinquish\n * Comparing 'ranch' and 'relinquish': r-a vs r-e. 'a' comes before 'e'. So, ranch, relinquish.\n\nWords starting with 'y':\n* yang\n\nThe sorted order is:\n1. batavia\n2. canaan\n3. maladjust\n4. merry\n5. olefin\n6. ranch\n7. relinquish\n8. yang\n\nAnswer: batavia, canaan, maladjust, merry, olefin, ranch, relinquish, yang\n\nAnswer: batavia, canaan, maladjust, merry, olefin, ranch, relinquish, yang" + }, + { + "expected": "accessory admiration allusion bandgap bruckner cruise fungus gambit heron maidenhair postprocessor proscenium technion", + "id": "bbh_word_sorting-0084", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. admiration\n2. accessory\n3. allusion\n4. bandgap\n5. bruckner\n6. cruise\n7. fungus\n8. gambit\n9. heron\n10. maidenhair\n11. postprocessor\n12. proscenium\n13. technion\n\nLet's re-sort them carefully:\n\n* **a**dmiration\n* **a**ccessory\n* **a**llusion\n* **b**andgap\n* **b**ruckner\n* **c**ruise\n* **f**ungus\n* **g**ambit\n* **h**eron\n* **m**aidenhair\n* **p**ostprocessor\n* **p**roscenium\n* **t**echnion\n\nComparing 'admiration' and 'accessory': 'admiration' comes before 'accessory'.\nComparing 'accessory' and 'allusion': 'accessory' comes before 'allusion'. (Wait, 'a'llusion vs 'a'ccessory. 'll' vs 'cc'. 'll' comes before 'cc' in standard alphabetical order if we look at the third letter, but let's compare them directly: a-l-l-u-s-i-o-n vs a-c-c-e-s-s-o-r. 'c' comes before 'l', so 'accessory' comes before 'allusion'.)\n\nLet's re-sort the 'a' words:\n1. admiration\n2. accessory\n3. allusion\n\nLet's re-sort the rest:\n* bandgap\n* bruckner\n* cruise\n* fungus\n* gambit\n* heron\n* maidenhair\n* postprocessor\n* proscenium\n* technion\n\nFinal sorted list:\n1. admiration\n2. accessory\n3. allusion\n4. bandgap\n5. bruckner\n6. cruise\n7. fungus\n8. gambit\n9. heron\n10. maidenhair\n11. postprocessor\n12. proscenium\n13. technion\n\nAnswer: admiration, accessory, allusion, bandgap, bruckner, cruise, fungus, gambit, heron, maidenhair, postprocessor, proscenium, technion" + }, + { + "expected": "allele anthropocentric badinage banish bartok brunswick dale dar desolater dun fraternity goat martinson monomer morphemic pegging starkey underclassmen whoop yourselves", + "id": "bbh_word_sorting-0085", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. allele\n2. anthropocentric\n3. banish\n4. bartok\n5. badinage\n6. brunswick\n7. desolater\n8. dale\n9. dar\n10. fraternity\n11. goat\n12. martinson\n13. morphemic\n14. monomer\n15. pegging\n16. starkey\n17. yourselves\n18. underclassmen\n19. whoop\n\nLet's re-sort them carefully:\n\n* **a**llele\n* **a**nthropocentric\n* **b**adinage\n* **b**artok\n* **b**runswick\n* **d**ale\n* **d**ar\n* **f**raternity\n* **g**oat\n* **m**artinson\n* **m**orphemic\n* **m**onomer\n* **p**egging\n* **s**tarkey\n* **u**nderclassmen\n* **w**hoop\n\nWait, I missed some words in the initial list check. Let's list all the words provided:\nunderclassmen, pegging, dale, bartok, goat, martinson, whoop, badinage, brunswick, fraternity, allele, dar, yourselves, dun, anthropocentric, banish, starkey, morphemic, monomer, desolater\n\nLet's sort them systematically:\n\n1. allele\n2. anthropocentric\n3. banish\n4. badinage\n5. bartok\n6. brunswick\n7. desolater\n8. dale\n9. dar\n10. fraternity\n11. goat\n12. martinson\n13. morphemic\n14. monomer\n15. pegging\n16. starkey\n17. yourselves\n18. underclassmen\n19. whoop\n20. dun (This word was missed in the previous manual check, let's place it)\n\nSorting 'dun': 'd' comes before 'd'ale, 'd'ar, etc.\n\nCorrected alphabetical order:\n\n1. allele\n2. anthropocentric\n3. banish\n4. badinage\n5. bartok\n6. brunswick\n7. desolater\n8. dale" + }, + { + "expected": "alcohol behold escutcheon forth fumarole hackberry motif pease regret satisfy uptake walkie", + "id": "bbh_word_sorting-0086", + "label": "fail", + "observed": "Here are the words to sort alphabetically:\nregret, uptake, satisfy, behold, fumarole, motif, forth, alcohol, escutcheon, pease, hackberry, walkie\n\nLet's sort them:\n\n1. alcohol\n2. behold\n3. escutcheon\n4. fumarole\n5. forth\n6. hackberry\n7. motif\n8. pease\n9. regret\n10. satisfy\n11. uptake\n12. walkie\n\nDetailed comparison:\n* **a**lcohol\n* **b**ehold\n* **e**scutcheon\n* **f**umarole\n* **f**orth (fumarole comes before forth if we compare the third letter: u vs o. Wait, F-O-R-T-H vs F-U-M-A-R-O-L-E. O comes before U.)\n * Re-evaluating F words: fumarole (fu...), forth (fo...) -> forth comes before fumarole.\n * Let's re-sort F words: forth, fumarole.\n* **h**ackberry\n* **m**otif\n* **p**ease\n* **r**egret\n* **s**atisfy\n* **u**ptake\n* **w**alkie\n\nLet's perform a full alphabetical sort:\n\n1. alcohol (A)\n2. behold (B)\n3. escutcheon (E)\n4. forth (F)\n5. fumarole (F) (f-o vs f-u)\n6. hackberry (H)\n7. motif (M)\n8. pease (P)\n9. regret (R)\n10. satisfy (S)\n11. uptake (U)\n12. walkie (W)\n\nChecking F words again:\nforth: f-o-r-t-h\nfumarole: f-u-m-a-r-o-l-e\n'o' comes before 'u', so **forth** comes before **fumarole**.\n\nFinal sorted list:\n1. alcohol\n2. behold\n3. escutcheon\n4. forth\n5. fumarole\n6. hackberry\n7. motif\n8. pease\n9. regret\n10. satisfy\n11. uptake\n1" + }, + { + "expected": "almagest archenemy catawba councilwomen decrement gnome jungian limpid milt photolysis sagging transfusable", + "id": "bbh_word_sorting-0087", + "label": "fail", + "observed": "Here are the words to sort alphabetically:\ncatawba, almagest, transfusable, milt, photolysis, limpid, gnome, archenemy, sagging, councilwomen, decrement, jungian\n\nLet's sort them:\n\n1. **almagest** (Starts with 'a', followed by 'l')\n2. **achenemy** (Starts with 'a', followed by 'c')\n3. **catawba** (Starts with 'c', followed by 'a')\n4. **decrement** (Starts with 'd')\n5. **gnome** (Starts with 'g')\n6. **jungian** (Starts with 'j')\n7. **limpid** (Starts with 'l')\n8. **milt** (Starts with 'm')\n9. **photolysis** (Starts with 'p')\n10. **sagging** (Starts with 's')\n11. **councilwomen** (Starts with 'c', followed by 'o')\n12. **transfusable** (Starts with 't')\n\nLet's re-sort carefully, paying attention to the first letter and subsequent letters:\n\nWords starting with A:\n* almagest\n* archenemy\n\nComparing 'almagest' and 'achenemy':\nalmagest (a-l)\nachenemy (a-c)\n'c' comes before 'l'. So, archenemy comes before almagest.\n1. archenemy\n2. almagest\n\nWords starting with C:\n* catawba\n* councilwomen\n\nComparing 'catawba' and 'councilwomen':\ncatawba (c-a)\ncouncilwomen (c-o)\n'a' comes before 'o'. So, catawba comes before councilwomen.\n3. catawba\n4. councilwomen\n\nWords starting with D:\n5. decrement\n\nWords starting with G:\n6. gnome\n\nWords starting with J:\n7. jungian\n\nWords starting with L:\n8. limpid\n\nWords starting with M:\n9. milt\n\nWords starting with P:\n10. photolysis\n\nWords starting with S:\n11. sagging\n\nWords starting with T:\n12. transfusable\n\nFinal sorted list:\n1. archenemy\n2. almagest\n3" + }, + { + "expected": "army emancipate envious planetaria pooh scotia wink", + "id": "bbh_word_sorting-0088", + "label": "pass", + "observed": "The given list of words is: wink, envious, scotia, planetaria, pooh, emancipate, army.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **emancipate** (Starts with 'e')\n2. **envious** (Starts with 'e', 'm' comes before 'n')\n3. **army** (Starts with 'a')\n4. **planetaria** (Starts with 'p')\n5. **pooh** (Starts with 'p', 'o' comes before 'p' in planetaria if we consider the second letter, but 'p' vs 'p' is the same. 'o' vs 'l' in planetaria. Let's compare 'planetaria' and 'pooh'. 'p' is the same. 'l' in planetaria vs 'o' in pooh. 'l' comes before 'o'.)\n * Re-evaluating 'planetaria' and 'pooh':\n * p l a n e t a r i a\n * p o o h\n * 'l' comes before 'o', so 'planetaria' comes before 'pooh'.\n6. **scotia** (Starts with 's')\n7. **wink** (Starts with 'w')\n\nLet's re-sort systematically:\n\nWords starting with 'a':\n* army\n\nWords starting with 'e':\n* emancipate\n* envious\n * Comparing 'emancipate' and 'envious': e-m vs e-n. 'm' comes before 'n'.\n * Order: emancipate, envious\n\nWords starting with 'p':\n* planetaria\n* pooh\n * Comparing 'planetaria' and 'pooh': p-l vs p-o. 'l' comes before 'o'.\n * Order: planetaria, pooh\n\nWords starting with 's':\n* scotia\n\nWords starting with 'w':\n* wink\n\nCombining the sorted groups:\n1. army\n2. emancipate\n3. envious\n4. planetaria\n5. pooh\n6. scotia\n7. wink\n\nFinal alphabetical order: army, emancipate, envious, planetaria, pooh, scotia, wink.\n\nAnswer: army, emancipate, envious, planetaria, pooh, scotia, wink" + }, + { + "expected": "aroma carcinogen delmarva designate facetious nod parochial rally sawfly syllabus", + "id": "bbh_word_sorting-0089", + "label": "fail", + "observed": "The given list of words is: delmarva, sawfly, aroma, nod, carcinogen, parochial, facetious, designate, syllabus, rally.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter:**\n * a: aroma\n * d: delmarva, designate\n * f: facetious\n * n: nod\n * p: parochial\n * r: rally\n * s: sawfly, syllabus\n\n2. **Sort words starting with 'a':**\n * aroma\n\n3. **Sort words starting with 'd':**\n * delmarva\n * designate (d-e-l vs d-e-s) $\\rightarrow$ delmarva, designate\n\n4. **Sort words starting with 'f':**\n * facetious\n\n5. **Sort words starting with 'n':**\n * nod\n\n6. **Sort words starting with 'p':**\n * parochial\n\n7. **Sort words starting with 'r':**\n * rally\n\n8. **Sort words starting with 's':**\n * sawfly\n * syllabus (s-a-w vs s-y-l) $\\rightarrow$ syllabus, sawfly (Wait, 'a' comes before 'y') $\\rightarrow$ syllabus, sawfly\n\nLet's re-verify the 's' words:\n* syllabus (s-y-l-l-a-b-u-s)\n* sawfly (s-a-w-f-l-y)\n'a' comes before 'y', so 'sawfly' comes before 'syllabus'.\n* sawfly\n* syllabus\n\nLet's re-verify the 'd' words:\n* delmarva (d-e-l-m...)\n* designate (d-e-g-n...)\n'g' comes before 'l', so designate comes before delmarva.\n* designate\n* delmarva\n\n**Final Sorted Order:**\n1. aroma\n2. designate\n3. delmarva\n4. facetious\n5. nod\n6. parochial\n7. rally\n8. sawfly\n9. syllabus\n\nLet's check the full list again:\naroma\ndesignate\ndelmarva\nfacetious\nnod\nparochial\nrally\nsawfly\nsyllabus\n\nAnswer: aroma, designate, delmar" + }, + { + "expected": "afro blackbird blame calyx elgin emphases implacable jura mayapple perquisite vii whit", + "id": "bbh_word_sorting-0090", + "label": "fail", + "observed": "Here are the words to sort alphabetically:\ncalyx, emphases, vii, elgin, perquisite, implacable, blackbird, afro, jura, mayapple, whit, blame\n\nLet's sort them:\n\n1. **afro**\n2. **blame**\n3. **blackbird**\n4. **calyx**\n5. **emphases**\n6. **elgin**\n7. **implacable**\n8. **jura**\n9. **mayapple**\n10. **perquisite**\n11. **vii** (Numbers usually come before letters, or are sorted based on their character code)\n12. **whit**\n\nLet's re-evaluate the sorting, paying attention to capitalization (assuming standard alphabetical sorting, treating all as lowercase for comparison unless specified):\n\n* afro\n* blame\n* blackbird\n* calyx\n* emphases\n* elgin\n* implacable\n* jura\n* mayapple\n* perquisite\n* vii\n* whit\n\nComparing 'vii' with other words: 'v' comes after 's' in 'emphases', 'elgin', etc. If we treat 'vii' as the sequence of letters 'v', 'i', 'i', it would be sorted based on the first letter 'v'.\n\nStandard alphabetical order:\n1. afro\n2. blame\n3. blackbird\n4. calyx\n5. emphases\n6. elgin\n7. implacable\n8. jura\n9. mayapple\n10. perquisite\n11. vii (Starts with 'v')\n12. whit (Starts with 'w')\n\nLet's check the order of 'vii' and 'whit': 'v' comes before 'w'.\n\nFinal sorted list:\n1. afro\n2. blame\n3. blackbird\n4. calyx\n5. emphases\n6. elgin\n7. implacable\n8. jura\n9. mayapple\n10. perquisite\n11. vii\n12. whit\n\nAnswer: afro, blame, blackbird, calyx, emphases, elgin, implacable, jura, mayapple, perquisite, vii, whit" + }, + { + "expected": "abutted agamemnon aquatic capacity casualty essex guinea hitachi hondo islamic loosen loquacious niece planet roadway solstice steed suspicion tibet", + "id": "bbh_word_sorting-0091", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. abutted\n2. agamemnon\n3. aquatic\n4. casualty\n5. capacity\n6. hondo\n7. hitachi\n8. islamic\n9. loosen\n10. loquacious\n11. niece\n12. planet\n13. roadway\n14. solstice\n15. steed\n16. suspicion\n17. tibet\n\nLet's re-sort them carefully:\n\n* abutted\n* agamemnon\n* aquatic\n* casualty\n* capacity\n* hondo\n* hitachi\n* islamic\n* loosen\n* loquacious\n* niece\n* planet\n* roadway\n* solstice\n* steed\n* suspicion\n* tibet\n* essex (Wait, I missed 'essex' in the initial list check. Let's re-examine the original list: solstice roadway loquacious niece planet steed abutted hondo loosen agamemnon islamic capacity tibet hitachi essex casualty guinea aquatic suspicion)\n\nLet's re-sort the full list:\nsolstice, roadway, loquacious, niece, planet, steed, abutted, hondo, loosen, agamemnon, islamic, capacity, tibet, hitachi, essex, casualty, guinea, aquatic, suspicion\n\n1. abutted\n2. agamemnon\n3. aquatic\n4. casualty\n5. capacity\n6. essex\n7. guinea\n8. hondo\n9. hitachi\n10. islamic\n11. loosen\n12. loquacious\n13. niece\n14. planet\n15. roadway\n16. solstice\n17. steed\n18. suspicion\n19. tibet\n\n(Checking 'guinea' placement: G comes after E, before H. Correct.)\n\nFinal sorted list:\nabutted, agamemnon, aquatic, casualty, capacity, essex, guinea, hondo, hitachi, islamic, loosen, loquacious, niece, planet, roadway, solstice, steed, suspicion, tibet\n\nAnswer: abutted, agamemnon, aquatic, casualty, capacity, essex, guinea, hondo, hitachi, islamic, loosen, loquacious, niece, planet, roadway, solstice, steed, suspicion, tibet" + }, + { + "expected": "blythe bombproof code corpulent cytolysis damn diagnose fluorine honeybee maharaja pore scalp solicit swipe", + "id": "bbh_word_sorting-0092", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. bombproof\n2. bpythe (Assuming \"blythe\" is a typo for \"bpythe\" or sorting based on the provided spelling) - If we strictly use \"blythe\":\n3. corpulent\n4. code\n5. cytolysis\n6. damn\n7. diagnose\n8. fluorine\n9. honeybee\n10. maharaja\n11. pore\n12. solicit\n13. scalp\n14. swipe\n\nLet's re-sort carefully using the exact words provided: corpulent, diagnose, code, scalp, bombproof, fluorine, blythe, swipe, honeybee, damn, pore, maharaja, cytolysis, solicit.\n\n1. bombproof\n2. bpythe (blythe)\n3. code\n4. corpulent\n5. cytolysis\n6. damn\n7. diagnose\n8. fluorine\n9. honeybee\n10. maharaja\n11. pore\n12. solicit\n13. scalp\n14. swipe\n\nSorting \"blythe\" (b-l-y-t-h-e) relative to others:\n* bombproof (b-o-m-b-p-r-o-o-f)\n* blythe (b-l-y-t-h-e)\n* code (c-o-d-e)\n* corpulent (c-o-r-p-u-l-e-n-t)\n* cytolysis (c-y-t-o-l-y-s-i-s)\n* damn (d-a-m-n)\n* diagnose (d-i-a-g-n-o-s-e)\n* fluorine (f-l-u-o-r-i-n-e)\n* honeybee (h-o-n-e-y-b-e-e)\n* maharaja (m-a-h-a-r-a-j-a)\n* pore (p-o-r-e)\n* solicit (s-o-l-i-c-i-t)\n* scalp (s-c-a-l-p)\n* swipe (s-w-i-p-e)\n\nComparing 'bombproof' and 'blythe': 'b' is the same. '" + }, + { + "expected": "appoint baneberry biharmonic dyne moustache pirate windowsill wiry", + "id": "bbh_word_sorting-0093", + "label": "fail", + "observed": "The given list of words is: windowsill, appoint, biharmonic, moustache, baneberry, wiry, dyne, pirate.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter:**\n * windowsill (w)\n * appoint (a)\n * biharmonic (b)\n * moustache (m)\n * baneberry (b)\n * wiry (w)\n * dyne (d)\n * pirate (p)\n\n Order based on the first letter: a, b, b, d, m, p, w, w\n\n2. **Sort words starting with 'a':**\n * appoint\n\n3. **Sort words starting with 'b':**\n * biharmonic\n * baneberry\n * (Comparing 'biharmonic' and 'baneberry': 'bi' comes before 'ba' if we consider the second letter, but standard alphabetical order compares character by character. 'b' is the same. 'i' in biharmonic vs 'a' in baneberry. 'a' comes before 'i'.)\n * baneberry\n * biharmonic\n\n4. **Sort words starting with 'd':**\n * dyne\n\n5. **Sort words starting with 'm':**\n * moustache\n\n6. **Sort words starting with 'p':**\n * pirate\n\n7. **Sort words starting with 'w':**\n * windowsill\n * wiry\n * (Comparing 'windowsill' and 'wiry': 'in' comes before 'ir'.)\n * windowsill\n * wiry\n\n**Combining the sorted lists:**\n1. appoint\n2. baneberry\n3. biharmonic\n4. dyne\n5. moustache\n6. pirate\n7. windowsill\n8. wiry\n\nLet's re-verify the 'b' words:\nbaneberry (ba...)\nbiharmonic (bi...)\n'a' comes before 'i', so baneberry comes before biharmonic.\n\nLet's re-verify the 'w' words:\nwindowsill (win...)\nwiry (wir...)\n'in' comes before 'ir', so windowsill comes before wiry.\n\n**Final Sorted Order:**\n1. appoint\n2. baneberry\n3. biharmonic\n4. dyne\n5. moustache\n6. pirate\n7. windowsill\n8." + }, + { + "expected": "battery bushland capacitive contingent crossbill enigma jane lipton meager ricochet wacke wallet wysiwyg", + "id": "bbh_word_sorting-0094", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. battery\n2. bushland\n3. capacitive\n4. contingent\n5. crossbill\n6. enigma\n7. jane\n8. lipton\n9. meager\n10. ricochet\n11. wallet\n12. wacke\n13. wysiwyg\n\nLet's re-sort them carefully:\n\n* **b**attery\n* **b**ushland\n* **c**apacitive\n* **c**ontingent\n* **c**rossbill\n* **e**nigma\n* **j**ane\n* **l**ipton\n* **m**eager\n* **r**icochet\n* **w**allet\n* **w**acke\n* **w**ysiwyg\n\nComparing the 'c' words:\n* capacitive\n* contingent\n* crossbill\nAlphabetical order: capacitive, contingent, crossbill.\n\nComparing the 'w' words:\n* wallet\n* wacke\n* wysiwyg\nAlphabetical order: wallet, wacke, wysiwyg.\n\nThe final sorted list is:\n1. battery\n2. bushland\n3. capacitive\n4. contingent\n5. crossbill\n6. enigma\n7. jane\n8. lipton\n9. meager\n10. ricochet\n11. wallet\n12. wacke\n13. wysiwyg\n\nAnswer: battery, bushland, capacitive, contingent, crossbill, enigma, jane, lipton, meager, ricochet, wallet, wacke, wysiwyg" + }, + { + "expected": "assure bully butterball bye contend cornet deaf dinosaur frontage gunky indeterminable lustrous ostentatious paradigmatic rhyme sanderson sashimi smokestack taint", + "id": "bbh_word_sorting-0095", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. butterball\n2. bye\n3. bully\n4. cornet\n5. contend\n6. dinosaur\n7. deaf\n8. frontage\n9. gunky\n10. indeterminable\n11. lustrous\n12. rhyme\n13. sanderson\n14. sashimi\n15. smokestack\n16. taint\n17. assure\n18. ostentatious\n19. paradigmatic\n\nLet's re-sort them carefully:\n\n* **b**utterball\n* **b**ye\n* **b**ully\n* **c**ornet\n* **c**ontend\n* **d**inosaur\n* **d**eaf\n* **f**rontage\n* **g**unky\n* **i**ndeterminable\n* **l**ustrous\n* **r**hyme\n* **s**anderson\n* **s** sashimi\n* **s**mokestack\n* **t**aint\n* **a**ssure\n* **o**stentatious\n* **p**aradigmatic\n\nCorrect alphabetical order:\n\n1. assure\n2. butterball\n3. bye\n4. bully\n5. cornet\n6. contend\n7. dinosaur\n8. deaf\n9. frontage\n10. gunky\n11. indeterminable\n12. lustrous\n13. paradigmatic\n14. rhyme\n15. sanderson\n16. sashimi\n17. smokestack\n18. taint\n19. ostentatious\n\nLet's re-check the 'a' words and 's' words:\nAssure (A)\nButterball (B)\nBye (B)\nBully (B)\nCornet (C)\nContend (C)\nDinosaur (D)\nDeaf (D)\nFrontage (F)\nGunky (G)\nIndeterminable (I)\nLustrous (L)\nParadigmatic (P)\nRhyme (R)\nSanderson (S)\nSashimi (S)\nSmokestack (S)\nTaint (T)\nOstentatious (O)\n\nSorting the S words: Sanderson, Sashimi, Smokestack.\nSashimi (Sa)\nSanderson (" + }, + { + "expected": "buckley frisian ix livre panoramic substitution", + "id": "bbh_word_sorting-0096", + "label": "pass", + "observed": "The given list of words is: livre, substitution, ix, frisian, panoramic, buckley.\n\nWe need to sort these words alphabetically. We will compare them letter by letter.\n\n1. **buckley** (Starts with 'b')\n2. **frisian** (Starts with 'f')\n3. **ix** (Starts with 'i')\n4. **livre** (Starts with 'l')\n5. **panoramic** (Starts with 'p')\n6. **substitution** (Starts with 's')\n\nLet's re-sort based on the first letter:\nB: buckley\nF: frisian\nI: ix\nL: livre\nP: panoramic\nS: substitution\n\nAlphabetical order of the first letters: B, F, I, L, P, S.\n\nThe sorted list is:\n1. buckley\n2. frisian\n3. ix\n4. livre\n5. panoramic\n6. substitution\n\nAnswer: buckley, frisian, ix, livre, panoramic, substitution\n\nAnswer: buckley, frisian, ix, livre, panoramic, substitution" + }, + { + "expected": "chicanery fugue mountain", + "id": "bbh_word_sorting-0097", + "label": "pass", + "observed": "The given list of words is: fugue, chicanery, mountain.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **f**ugue\n2. **c**hicanery\n3. **m**ountain\n\nComparing the first letters: 'c' comes before 'f', and 'f' comes before 'm'.\n\nSo the order based on the first letter is:\n'c' (chicanery)\n'f' (fugue)\n'm' (mountain)\n\nThe alphabetical order is: chicanery, fugue, mountain.\n\nAnswer: chicanery, fugue, mountain\n\nAnswer: chicanery, fugue, mountain" + }, + { + "expected": "allot chauncey clergymen coachmen coddington companion embark fatten gazpacho granular hobble murk muslim niggle pristine pvc singlet threefold too yeats", + "id": "bbh_word_sorting-0098", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. chauncey\n2. coddington\n3. companion\n4. coachmen\n5. clergymen\n6. embark\n7. fatten\n8. threefold\n9. granular\n10. gazpacho\n11. hobble\n12. murk\n13. muslim\n14. niggle\n15. pristine\n16. pvc\n17. singlet\n18. too\n19. yeats\n\nLet's re-sort them carefully:\n\n* **c**hauncey\n* **c**oddington\n* **c**ompanion\n* **c**oachmen\n* **c**lergymen\n* **e**mbark\n* **f**atten\n* **f**reedom (Wait, 'threefold' comes before 'fatten' if we look at the 't' sound, but alphabetically 'fatten' comes before 'threefold'.)\n* **g**azpacho\n* **g**ranular\n* **h**obble\n* **m**urk\n* **m**uslim\n* **n**iggle\n* **p**ristine\n* **p**vc\n* **s**inglet\n* **t**oo\n* **y**eats\n\nLet's do a strict alphabetical sort:\n\n1. chauncey\n2. coddington\n3. companion\n4. coachmen\n5. clergymen\n6. embark\n7. fatten\n8. threefold\n9. granular\n10. gazpacho\n11. hobble\n12. murk\n13. muslim\n14. niggle\n15. pristine\n16. pvc\n17. singlet\n18. too\n19. yeats\n\n(Checking 'fatten' vs 'threefold': F-A-T-T-E-N vs T-H-R-E-E-F-O-L-D. 'fatten' comes first.)\n(Checking 'granular' vs 'gazpacho': G-R vs G-A. 'gazpacho' comes before 'granular'.)\n\nCorrected final sort:\n\n1. chauncey\n2. coddington\n3. companion\n4. coachmen\n5. clergymen\n6. embark\n7. fatten\n8. threefold" + }, + { + "expected": "amanita amatory annoy besiege boggle california canticle crocodilian dexter dissipate dizzy encephalitis hornblower notre pasture propylene psychiatric sepia snipe straight", + "id": "bbh_word_sorting-0099", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. amanita\n2. annoy\n3. besiege\n4. boggle\n5. california\n6. canticle\n7. encephalitis\n8. dissipate\n9. dizzy\n10. dexter\n11. hornblower\n12. notre\n13. pasture\n14. propylene\n15. psychiatric\n16. sepia\n17. snipe\n18. straight\n\nLet's re-sort them carefully:\n\n* amanita\n* annoy\n* besiege\n* boggle\n* california\n* canticle\n* encephalitis\n* dissipate\n* dizzy\n* dexter\n* hornblower\n* notre\n* pasture\n* propylene\n* psychiatric\n* sepia\n* snipe\n* straight\n\nComparing the initial list: hornblower, dissipate, amanita, canticle, annoy, besiege, straight, notre, propylene, sepia, california, pasture, encephalitis, boggle, crocodilian, dexter, snipe, amatory, dizzy, psychiatric.\n\nWait, I missed \"crocodilian\" and \"amatory\" from the initial list provided in the prompt. Let's use the exact list provided:\nList: hornblower dissipate amanita canticle annoy besiege straight notre propylene sepia california pasture encephalitis boggle crocodilian dexter snipe amatory dizzy psychiatric\n\nSorting the 20 words:\n\n1. amanita\n2. amatory\n3. annoy\n4. besiege\n5. boggle\n6. california\n7. canticle\n8. crocodilian\n9. dissipate\n10. dizzy\n11. dexter\n12. encephalitis\n13. hornblower\n14. notre\n15. pasture\n16. propylene\n17. psychiatric\n18. sepia\n19. snipe\n20. straight\n\nLet's re-verify the order, especially around 'a' and 'b':\nA: amanita, amatory\nB: besiege, boggle\nC: california, canticle, crocodilian\nD: dissipate, dizzy, dexter\nE: encephalitis\nH: hornblower\nN: notre\nP: pasture, propylene, psychiatric\nS: sepia, snipe, straight\n\nCorrect alphabetical order:\n1. amanita\n2. amatory\n3. annoy\n4. besiege\n5. boggle\n" + }, + { + "expected": "christen clearheaded despond driveway encapsulate fungi gob mendelevium midwinter purpose sisyphus stanhope strip studious symmetry trample vs wring", + "id": "bbh_word_sorting-0100", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. clearheaded\n2. christen\n3. despond\n4. driveway\n5. encapsulate\n6. fungi\n7. mendelevium\n8. midwinter\n9. purpose\n10. stanhope\n11. strip\n12. studious\n13. symmetry\n14. trample\n15. vs\n16. wring\n\nLet's re-sort them carefully:\n\n* clearheaded\n* christen\n* despond\n* driveway\n* encapsulate\n* fungi\n* mendelevium\n* midwinter\n* purpose\n* stanhope\n* strip\n* studious\n* symmetry\n* trample\n* vs\n* wring\n\n(Checking the initial list again: stanhope, sisyphus, fungi, purpose, midwinter, mendelevium, studious, driveway, symmetry, despond, trample, wring, strip, clearheaded, encapsulate, vs, gob, christen)\n\nWait, I missed \"sisyphus\" and \"gob\". Let's re-list and sort:\n\nOriginal List: stanhope, sisyphus, fungi, purpose, midwinter, mendelevium, studious, driveway, symmetry, despond, trample, wring, strip, clearheaded, encapsulate, vs, gob, christen\n\nSorted List:\n1. clearheaded\n2. christen\n3. despond\n4. driveway\n5. encapsulate\n6. fungi\n7. gob\n8. mendelevium\n9. midwinter\n10. purpose\n11. stanhope\n12. sisyphus\n13. strip\n14. studious\n15. symmetry\n16. trample\n17. vs\n18. wring\n\nAnswer: clearheaded, christen, despond, driveway, encapsulate, fungi, gob, mendelevium, midwinter, purpose, stanhope, sisyphus, strip, studious, symmetry, trample, vs, wring" + }, + { + "expected": "cotyledon more pepperoni regret starlight wallboard", + "id": "bbh_word_sorting-0101", + "label": "pass", + "observed": "The given list of words is: regret, starlight, wallboard, cotyledon, more, pepperoni.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **cotyledon** (Starts with 'c')\n2. **more** (Starts with 'm')\n3. **pepperoni** (Starts with 'p')\n4. **regret** (Starts with 'r')\n5. **starlight** (Starts with 's')\n6. **wallboard** (Starts with 'w')\n\nLet's verify the order:\n* c (cotyledon)\n* m (more)\n* p (pepperoni)\n* r (regret)\n* s (starlight)\n* w (wallboard)\n\nThe alphabetical order is: cotyledon, more, pepperoni, regret, starlight, wallboard.\n\nAnswer: cotyledon, more, pepperoni, regret, starlight, wallboard\n\nAnswer: cotyledon, more, pepperoni, regret, starlight, wallboard" + }, + { + "expected": "bedtime boon bottle chapati kenney okinawa", + "id": "bbh_word_sorting-0102", + "label": "fail", + "observed": "The given list of words is: bottle, kenney, chapati, okinawa, boon, bedtime.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **b**ottle\n2. **b**oon\n3. **b**edtime\n4. **c**hapati\n5. **k**enney\n6. **o**kinawa\n\nComparing the words starting with 'b':\n* bottle\n* boon\n* bedtime\n\nComparing the second letter:\n* bo**t**tle\n* bo**o**n\n* be**d**time\n\n'b' comes before 'd' and 't'.\n'boon' comes before 'bottle' because 'o' comes before 't'.\nSo the order for 'b' words is: boon, bottle, bedtime.\n\nNow we place the rest:\n* chapati (starts with 'c')\n* kenney (starts with 'k')\n* okinawa (starts with 'o')\n\nThe alphabetical order of the starting letters is: b, c, k, o.\n\nCombining the sorted groups:\n1. boon\n2. bottle\n3. bedtime\n4. chapati\n5. kenney\n6. okinawa\n\nLet's re-check the 'b' words:\nboon\nbottle\nbedtime\n'boon' vs 'bottle': 'o' vs 't'. 'o' comes before 't'. So, boon comes before bottle.\n'boon' vs 'bedtime': 'o' vs 'e'. 'e' comes before 'o'. So, bedtime comes before boon.\n\nLet's re-sort the 'b' words:\nbedtime (b-e)\nboon (b-o-o)\nbottle (b-o-t)\n'e' comes before 'o'. So, bedtime is first.\n'o' comes before 't'. So, boon is before bottle.\nOrder: bedtime, boon, bottle.\n\nThe full sorted list:\n1. bedtime\n2. boon\n3. bottle\n4. chapati\n5. kenney\n6. okinawa\n\nFinal check:\nbedtime\nboon\nbottle\nchapati\nkenney\nokinawa\n\nAnswer: bedtime, boon, bottle, chapati, kenney, okinawa\n\nAnswer: bedtime, boon, bottle, chapati, kenney" + }, + { + "expected": "acoustic anarchic bureaucracy diatom fabricate guelph immovable leftward liven neo phenomenology provide shortcut suggestive syndrome total trammel usage yarmulke", + "id": "bbh_word_sorting-0103", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. acoustic\n2. anarchic\n3. diatom\n4. fabricate\n5. phenomenology\n6. leftward\n7. liven\n8. neo\n9. immovable\n10. provide\n11. shortcut\n12. syndrome\n13. total\n14. trammel\n15. usage\n16. yarmulke\n17. bureaucracy\n18. guelph\n19. suggestive\n\nLet's re-sort them carefully:\n\n* **a**narchic, **a**coustic\n* **b**ureaucracy\n* **d**iatom\n* **f**abricate\n* **g**uelph\n* **i**mmovable\n* **l**eftward, **l**iven\n* **n**eo\n* **p**henomenology, **p**rovide\n* **s**hortcut, **s**yndrome, **s**uggestive\n* **t**otal, **t**rammel\n* **u**sage\n* **y**armulke\n\n**Detailed Alphabetical Sort:**\n\n1. acoustic\n2. anarchic\n3. bureaucracy\n4. diatom\n5. fabricate\n6. guelph\n7. immovable\n8. leftward\n9. liven\n10. neo\n11. phenomenology\n12. provide\n13. shortcut\n14. syndrome\n15. total\n16. trammel\n17. usage\n18. yarmulke\n19. suggestive\n\n(Checking L words: leftward, liven. L-e-f-t vs L-i-v-e. Leftward comes before liven.)\n(Checking P words: phenomenology, provide. Phenomenology comes before provide.)\n(Checking S words: shortcut, syndrome, suggestive. Shortcut, syndrome, suggestive.)\n\nFinal check of the full list:\nanarchic, acoustic, bureaucracy, diatom, fabricate, guelph, immovable, leftward, liven, neo, phenomenology, provide, shortcut, syndrome, total, trammel, usage, yarmulke, suggestive.\n\nAnswer: acoustic, anarchic, bureaucracy, diatom, fabricate, guelph, immovable, leftward, liven, neo, phenomenology, provide, shortcut, syndrome, total, trammel," + }, + { + "expected": "avalanche befriend berniece bong bremsstrahlung dactylic flick gilbertson goff hereafter hoe housekeep hurry lanka metazoan posterior showroom", + "id": "bbh_word_sorting-0104", + "label": "fail", + "observed": "Here are the words to sort alphabetically:\nberniece, bremsstrahlung, hoe, avalanche, showroom, goff, dactylic, befriend, hurry, housekeep, lanka, gilbertson, posterior, flick, bong, hereafter, metazoan\n\nLet's sort them:\n\n1. **ache** (Wait, \"ache\" is not in the list. Let's re-examine the list.)\n\nWords: berniece, bremsstrahlung, hoe, avalanche, showroom, goff, dactylic, befriend, hurry, housekeep, lanka, gilbertson, posterior, flick, bong, hereafter, metazoan\n\nSorting by the first letter:\nA: avalanche\nB: berniece, bremsstrahlung, befriend\nD: dactylic\nF: flick\nG: goff, gilbertson\nH: hoe, hurry, housekeep\nL: lanka\nM: metazoan\nP: posterior\nS: showroom\nH: hereafter (already listed under H, but we need to check order)\n\nLet's sort systematically:\n\n1. **ache** (None)\n2. **avalanche**\n3. **befriend**\n4. **berniece**\n5. **bremsstrahlung**\n6. **bong**\n7. **dactylic**\n8. **flick**\n9. **goff**\n10. **gilbertson**\n11. **hereafter**\n12. **hoe**\n13. **hurry**\n14. **housekeep**\n15. **lanka**\n16. **metazoan**\n17. **posterior**\n18. **showroom**\n\nLet's refine the sorting, especially for words starting with the same letter:\n\n**B words:** befriend, berniece, bremsstrahlung (befriend, berniece, bremsstrahlung)\n**H words:** hereafter, hoe, hurry, housekeep (hereafter, hoe, hurry, housekeep)\n**G words:** goff, gilbertson (goff, gilbertson)\n\n**Alphabetical Order:**\n\n1. avalanche (A)\n2. befriend (B)\n3. berniece (B)\n4. bremsstrahlung (B)\n5. bong (B - Wait, 'bong' starts with B, it should come before 'berniece' if we sort strictly by the second letter, but" + }, + { + "expected": "arraign blutwurst convenient faber glacier horizon inconspicuous peste portentous rancho uranyl", + "id": "bbh_word_sorting-0105", + "label": "pass", + "observed": "Here are the words to sort: blutwurst, horizon, uranyl, arraign, portentous, faber, rancho, peste, glacier, convenient, inconspicuous.\n\nLet's sort them alphabetically:\n\n1. arraign\n2. blutwurst\n3. convenient\n4. faber\n5. glacier\n6. horizon\n7. inconspicuous\n8. portentous\n9. peste\n10. rancho\n11. uranyl\n\nLet's re-check the sorting carefully, especially for words starting with similar letters:\n\n* **a**rraign\n* **b**lutwurst\n* **c**onvenient\n* **f**aber\n* **g**lacier\n* **h**orizon\n* **i**nconspicuous\n* **p**ortentous\n* **p**este (Peste comes before Portentous because 'e' comes before 'o')\n* **r**ancho\n* **u**ranyl\n\nLet's re-sort the 'P' words:\n* portentous\n* peste\n'peste' comes before 'portentous'.\n\nCorrected order:\n1. arraign\n2. blutwurst\n3. convenient\n4. faber\n5. glacier\n6. horizon\n7. inconspicuous\n8. peste\n9. portentous\n10. rancho\n11. uranyl\n\nAnswer: arraign, blutwurst, convenient, faber, glacier, horizon, inconspicuous, peste, portentous, rancho, uranyl\n\nAnswer: arraign, blutwurst, convenient, faber, glacier, horizon, inconspicuous, peste, portentous, rancho, uranyl" + }, + { + "expected": "aitken barycentric detest downey kajar nat solvate usable vision", + "id": "bbh_word_sorting-0106", + "label": "pass", + "observed": "The given list of words is: solvate, nat, usable, kajar, downey, vision, detest, aitken, barycentric.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter:**\n * a: aitken\n * b: barycentric\n * d: downey, detest\n * k: kajar\n * n: nat\n * s: solvate\n * u: usable\n * v: vision\n\n2. **Sort words starting with 'd':**\n * downey\n * detest (d-o comes before d-e, wait, 'd' is the same. 'o' in downey, 'e' in detest. 'e' comes before 'o'.)\n * Correct order: detest, downey\n\n3. **Combine the sorted list:**\n * aitken (a)\n * barycentric (b)\n * detest (d)\n * downey (d)\n * kajar (k)\n * nat (n)\n * solvate (s)\n * usable (u)\n * vision (v)\n\n4. **Final check of the order:**\n * aitken\n * barycentric\n * detest\n * downey\n * kajar\n * nat\n * solvate\n * usable\n * vision\n\nAnswer: aitken, barycentric, detest, downey, kajar, nat, solvate, usable, vision\nAnswer: aitken, barycentric, detest, downey, kajar, nat, solvate, usable, vision" + }, + { + "expected": "bandwidth hidebound wreak", + "id": "bbh_word_sorting-0107", + "label": "pass", + "observed": "The given words are: wreak, hidebound, bandwidth.\n\nTo sort them alphabetically, we compare them letter by letter:\n\n1. **wreak**: Starts with 'w'\n2. **hidebound**: Starts with 'h'\n3. **bandwidth**: Starts with 'b'\n\nComparing the first letters: 'b', 'h', 'w'.\nIn alphabetical order: 'b' comes before 'h', and 'h' comes before 'w'.\n\nSo, 'bandwidth' comes first.\n\nNow compare 'hidebound' and 'wreak'.\n'h' comes before 'w'.\nSo, 'hidebound' comes before 'wreak'.\n\nThe alphabetical order is:\n1. bandwidth\n2. hidebound\n3. wreak\n\nAnswer: bandwidth, hidebound, wreak\n\nAnswer: bandwidth, hidebound, wreak" + }, + { + "expected": "artful cancelled castrate citadel croon ear endpoint excite glaucous inspiration marque mckinley pesticide prig radiometer relish rothschild school tioga trianon", + "id": "bbh_word_sorting-0108", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. artful\n2. cancelled\n3. castrate\n4. citadel\n5. croon\n6. endpoint\n7. ear\n8. excite\n9. glaucous\n10. inspiration\n11. marque\n12. mckinley\n13. pesticide\n14. prig\n15. relish\n16. radiometer\n17. rothschild\n18. school\n19. tioga\n20. trianon\n\nLet's re-sort them carefully:\n\n* artful\n* cancelled\n* castrate\n* citadel\n* croon\n* endpoint\n* ear\n* excite\n* glaucous\n* inspiration\n* marque\n* mckinley\n* pesticide\n* prig\n* relish\n* radiometer\n* rothschild\n* school\n* tioga\n* trianon\n\nChecking the order again:\nA: artful\nC: cancelled, castrate, citadel, croon\nE: endpoint, ear, excite\nG: glaucous\nI: inspiration\nM: marque, mckinley\nP: pesticide, prig\nR: relish, radiometer, rothschild\nS: school\nT: tioga, trianon\n\nDetailed alphabetical sort:\n1. artful\n2. cancelled\n3. castrate\n4. citadel\n5. croon\n6. endpoint\n7. ear\n8. excite\n9. glaucous\n10. inspiration\n11. marque\n12. mckinley\n13. pesticide\n14. prig\n15. relish\n16. radiometer\n17. rothschild\n18. school\n19. tioga\n20. trianon\n\nAnswer: artful, cancelled, castrate, citadel, croon, endpoint, ear, excite, glaucous, inspiration, marque, mckinley, pesticide, prig, relish, radiometer, rothschild, school, tioga, trianon" + }, + { + "expected": "greasy lapidary mark", + "id": "bbh_word_sorting-0109", + "label": "pass", + "observed": "The given list of words is: greasy, lapidary, mark.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **greasy** (starts with 'g')\n2. **lapidary** (starts with 'l')\n3. **mark** (starts with 'm')\n\nComparing the first letters: 'g', 'l', 'm'.\nIn alphabetical order, 'g' comes before 'l', and 'l' comes before 'm'.\n\nTherefore, the alphabetical order is: greasy, lapidary, mark.\n\nAnswer: greasy, lapidary, mark" + }, + { + "expected": "assimilable bivariate bought calypso dogwood functor hideaway holeable lola monotonous nebuchadnezzar pacifism provocation slick", + "id": "bbh_word_sorting-0110", + "label": "pass", + "observed": "Here are the words sorted alphabetically:\n\n1. assimilable\n2. bivariate\n3. bought\n4. calypso\n5. dogwood\n6. functor\n7. hideaway\n8. holeable\n9. lola\n10. monotonous\n11. nebuchadnezzar\n12. pacifism\n13. provocation\n14. slick\n\nAnswer: assimilable, bivariate, bought, calypso, dogwood, functor, hideaway, holeable, lola, monotonous, nebuchadnezzar, pacifism, provocation, slick" + }, + { + "expected": "brewster inaudible synapse tithing tuba", + "id": "bbh_word_sorting-0111", + "label": "pass", + "observed": "The given list of words is: tuba, synapse, inaudible, brewster, tithing.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **brewster** (Starts with 'b')\n2. **inaudible** (Starts with 'i')\n3. **tithing** (Starts with 't')\n4. **tuba** (Starts with 't') - Compare 'tithing' and 'tuba'. 'i' comes before 'u', so 'tithing' comes before 'tuba'.\n5. **synapse** (Starts with 's')\n\nLet's re-sort based on the first letter:\nB: brewster\nI: inaudible\nS: synapse\nT: tithing, tuba\n\nNow compare the words starting with 'T':\ntithing\ntuba\n'i' comes before 'u', so 'tithing' comes before 'tuba'.\n\nThe alphabetical order is:\n1. brewster\n2. inaudible\n3. synapse\n4. tithing\n5. tuba\n\nAnswer: brewster, inaudible, synapse, tithing, tuba\n\nAnswer: brewster, inaudible, synapse, tithing, tuba" + }, + { + "expected": "beth kenya", + "id": "bbh_word_sorting-0112", + "label": "pass", + "observed": "The given list of words is: kenya, beth.\n\nTo sort these words alphabetically, we compare them letter by letter from the beginning.\n\n1. **Compare the first letter:**\n - 'k' in \"kenya\"\n - 'b' in \"beth\"\n Since 'b' comes before 'k' in the alphabet, \"beth\" comes before \"kenya\".\n\nTherefore, the alphabetical order is: beth, kenya.\n\nAnswer: beth, kenya" + }, + { + "expected": "alternate boone chalcedony charity genteel million olden satin sinai", + "id": "bbh_word_sorting-0113", + "label": "pass", + "observed": "Here are the words to sort: million, boone, chalcedony, sinai, satin, genteel, alternate, charity, olden\n\n1. **alternate**\n2. **boone**\n3. **chalcedony**\n4. **charity**\n5. **genteel**\n6. **million**\n7. **olden**\n8. **satin**\n9. **sinai**\n\nLet's re-sort them carefully:\n\n* **a**lternate\n* **b**oone\n* **c**halcedony\n* **c**harity (comes after chalcedony because 'h' comes before 'r')\n* **g**enteel\n* **m**illion\n* **o**lden\n* **s**atin\n* **s**inai (satin comes before sinai because 'a' comes before 'i')\n\nLet's re-examine the 'c' words: chalcedony, charity.\nchalcedony (ch-a-l)\ncharity (ch-a-r)\n'l' comes before 'r', so chalcedony comes before charity.\n\nLet's re-examine the 's' words: satin, sinai.\nsatin (s-a-t)\nsinai (s-i-n)\n'a' comes before 'i', so satin comes before sinai.\n\nFinal alphabetical order:\n1. alternate\n2. boone\n3. chalcedony\n4. charity\n5. genteel\n6. million\n7. olden\n8. satin\n9. sinai\n\nAnswer: alternate, boone, chalcedony, charity, genteel, million, olden, satin, sinai" + }, + { + "expected": "correspond herpes him seashore", + "id": "bbh_word_sorting-0114", + "label": "pass", + "observed": "The given list of words is: him, correspond, seashore, herpes.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **h**im\n2. **h**erpes\n3. **c**orrespond\n4. **s**eashore\n\nComparing the first letter: 'c' comes before 'h', and 'h' comes before 's'.\n\nSo, 'correspond' comes first among the words starting with 'c', 'h', or 's'. Wait, let's re-examine the starting letters:\n* him: h\n* correspond: c\n* seashore: s\n* herpes: h\n\nAlphabetical order of starting letters: c, h, h, s.\n\n1. **c**orrespond (starts with c)\n2. **h**im (starts with h)\n3. **h**erpes (starts with h)\n4. **s**eashore (starts with s)\n\nNow we compare the words starting with 'h': 'him' and 'herpes'.\n* hi**m**\n* he**r**pes\n'i' comes before 'e' is false. 'e' comes before 'i'.\nSo, 'herpes' comes before 'him'.\n\nThe sorted order is:\n1. correspond\n2. herpes\n3. him\n4. seashore\n\nLet's verify the comparison between 'herpes' and 'him':\nh e r p e s\nh i m\n'e' comes before 'i', so 'herpes' comes before 'him'.\n\nFinal sorted list: correspond, herpes, him, seashore.\n\nAnswer: correspond, herpes, him, seashore\n\nAnswer: correspond, herpes, him, seashore" + }, + { + "expected": "catechism daddy", + "id": "bbh_word_sorting-0115", + "label": "pass", + "observed": "The given list of words is: catechism, daddy.\n\nTo sort them alphabetically, we compare them letter by letter:\n\n1. **catechism**: Starts with 'c'\n2. **daddy**: Starts with 'd'\n\nSince 'c' comes before 'd' in the alphabet, \"catechism\" comes before \"daddy\".\n\nThe alphabetical order is: catechism, daddy.\n\nAnswer: catechism, daddy" + }, + { + "expected": "astigmat boyish coriolanus creak cutlet easternmost godson heaven highwaymen leather muscular musky paula scavenge synaptic zinc", + "id": "bbh_word_sorting-0116", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. astigmat\n2. boyish\n3. creak\n4. coriolanus\n5. cutlet\n6. easternmost\n7. godson\n8. heaven\n9. highwaymen\n10. leather\n11. musky\n12. muscular\n13. paula\n14. scavenge\n15. synaptic\n16. zinc\n\nLet's re-sort them carefully:\n\n* **a**stigmat\n* **b**oyish\n* **c**reak, **c**oriolanus, **c**utlet (creak, coriolanus, cutlet)\n* **e**asternmost\n* **g**odson\n* **h**aven, **h**ighwaymen\n* **l**eather\n* **m**usky, **m**uscular\n* **p**aula\n* **s**cavenge, **s**ynaptic\n* **z**inc\n\nDetailed sorting:\n\n1. astigmat\n2. boyish\n3. creak\n4. coriolanus\n5. cutlet\n6. easternmost\n7. godson\n8. heaven\n9. highwaymen\n10. leather\n11. musky\n12. muscular\n13. paula\n14. scavenge\n15. synaptic\n16. zinc\n\n(Comparing 'heaven' and 'highwaymen': H-e vs H-i. 'heaven' comes before 'highwaymen'.)\n(Comparing 'musky' and 'muscular': 'musky' comes before 'muscular'.)\n\nFinal sorted list:\nastigmat, boyish, creak, coriolanus, cutlet, easternmost, godson, heaven, highwaymen, leather, musky, muscular, paula, scavenge, synaptic, zinc\n\nAnswer: astigmat, boyish, creak, coriolanus, cutlet, easternmost, godson, heaven, highwaymen, leather, musky, muscular, paula, scavenge, synaptic, zinc" + }, + { + "expected": "astronomic cabdriver coherent loch pivot wagging", + "id": "bbh_word_sorting-0117", + "label": "pass", + "observed": "The given list of words is: wagging, cabdriver, astronomic, pivot, loch, coherent.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **astronomic** (Starts with 'a')\n2. **cabdriver** (Starts with 'c')\n3. **coherent** (Starts with 'c')\n4. **loch** (Starts with 'l')\n5. **pivot** (Starts with 'p')\n6. **wagging** (Starts with 'w')\n\nNow, let's compare the words starting with 'c':\n* cabdriver\n* coherent\n\nComparing 'cabdriver' and 'coherent':\n* c-a-b...\n* c-o-h...\n'a' comes before 'o', so 'cabdriver' comes before 'coherent'.\n\nThe sorted order for the 'c' words is: cabdriver, coherent.\n\nNow, let's put all the words in order:\n1. astronomic (a)\n2. cabdriver (c)\n3. coherent (c)\n4. loch (l)\n5. pivot (p)\n6. wagging (w)\n\nThe final alphabetical order is: astronomic, cabdriver, coherent, loch, pivot, wagging.\n\nAnswer: astronomic, cabdriver, coherent, loch, pivot, wagging" + }, + { + "expected": "cartilaginous no science spokane that'd", + "id": "bbh_word_sorting-0118", + "label": "pass", + "observed": "The given list of words is: no, spokane, cartilaginous, that'd, science.\n\nTo sort them alphabetically, we compare them letter by letter:\n\n1. **cartilaginous** (Starts with 'c')\n2. **no** (Starts with 'n')\n3. **science** (Starts with 's')\n4. **spokane** (Starts with 's', but 'sp' comes after 'sc')\n5. **that'd** (Starts with 't')\n\nLet's compare the words starting with 's': 'science' and 'spokane'.\n'science' comes before 'spokane' because 'ci' comes before 'po'.\n\nNow let's order the words starting with 'n' and 't':\n'no' comes before 'that'd'.\n\nThe complete alphabetical order is:\n1. cartilaginous\n2. no\n3. science\n4. spokane\n5. that'd\n\nAnswer: cartilaginous, no, science, spokane, that'd\n\nAnswer: cartilaginous, no, science, spokane, that'd" + }, + { + "expected": "aniline boletus eddy fontainebleau galveston gentle scandalous skat sportsmen wile", + "id": "bbh_word_sorting-0119", + "label": "fail", + "observed": "The given list of words is: gentle, boletus, galveston, aniline, eddy, fontainebleau, wile, scandalous, skat, sportsmen.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **aniline** (a)\n2. **boletus** (b)\n3. **eddy** (e)\n4. **fontainebleau** (f)\n5. **gentle** (g)\n6. **galveston** (g) - 'galveston' comes after 'gentle' because 'a' comes before 'e' in the second position of 'galveston' compared to 'gentle' (if we consider the first letter, 'g' is the same, then 'a' vs 'e'). Wait, let's re-evaluate 'gentle' and 'galveston'.\n - g e n t l e\n - g a l v e s t o n\n 'a' comes before 'e', so **galveston** comes before **gentle**.\n\nLet's re-sort based on the first letter:\nA: aniline\nB: boletus\nE: eddy\nF: fontainebleau\nG: gentle, galveston\nS: scandalous, skat, sportsmen, wile\n\nSorting the 'G' words:\ngentle (g e n t l e)\ngalveston (g a l v e s t o n)\n'a' comes before 'e', so **galveston** comes before **gentle**.\n\nSorting the 'S' words:\nscandalous (s c a n d a l o u s)\nskat (s k a t)\nsportsmen (s p o r t s m e n)\nwile (w i l e) - Wait, 'wile' starts with 'w', so it comes after 's' words.\n\nLet's list all words and sort them:\n1. aniline\n2. boletus\n3. eddy\n4. fontainebleau\n5. galveston (G, a)\n6. gentle (G, e)\n7. scandalous (S, c)\n8. skat (S, k)\n9. sportsmen (S, p)\n10. wile (W)\n\nComparing the 'S' words:\nscandalous (s c...)\nskat (" + }, + { + "expected": "allocable bertram boutique champlain crunchy dissipate facto highlight hydrology judaism labile necessity often phenol silage vale", + "id": "bbh_word_sorting-0120", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. allocable\n2. bertram\n3. boutique\n4. champlain\n5. crunchy\n6. dissipate\n7. facet (facto)\n8. highlight\n9. hydrology\n10. labile\n11. necessity\n12. often\n13. phenol\n14. silage\n15. vale\n16. judaism\n\nLet's re-sort carefully:\n\n* allocable\n* bertram\n* boutique\n* champlain\n* crunchy\n* dissipate\n* facet (facto) - Assuming 'facto' is a typo for 'fact' or sorting by the first letter 'f'. If we treat 'facto' as is:\n* highlight\n* hydrology\n* labile\n* necessity\n* often\n* phenol\n* silage\n* vale\n* judaism\n\nComparing 'facto' and 'highlight': 'f' comes before 'h'.\n\nLet's list them and sort:\nallocable (A)\nbertram (B)\nboutique (B) - bertram before boutique\nchamplain (C)\ncrunchy (C) - champlain before crunchy\ndissipate (D)\nfacto (F)\nhighlight (H)\nhydrology (H) - facto before hydrology\nlabile (L)\nnecessity (N)\noften (O)\nphenol (P)\nsilage (S)\nvale (V)\njudaism (J) - J comes before L\n\nCorrect alphabetical order:\n1. allocable\n2. bertram\n3. boutique\n4. champlain\n5. crunchy\n6. dissipate\n7. facto\n8. highlight\n9. hydrology\n10. labile\n11. judaism\n12. necessity\n13. often\n14. phenol\n15. silage\n16. vale\n\nWait, 'judaism' starts with J, which comes before L. Let's re-check the full list:\n\nlabile (L)\njudaism (J) -> J comes before L.\n\nCorrected order:\n1. allocable\n2. bertram\n3. boutique\n4. champlain\n5. crunchy\n6. dissipate\n7. facto\n8. highlight\n9. hydrology\n10. judaism\n11. labile\n12. necessity\n13" + }, + { + "expected": "borough hyperboloidal", + "id": "bbh_word_sorting-0121", + "label": "pass", + "observed": "The given words are: hyperboloidal, borough.\n\nTo sort them alphabetically, we compare them letter by letter:\n\n1. **h**yperboloidal\n2. **b**orough\n\nSince 'b' comes before 'h' in the alphabet, \"borough\" comes before \"hyperboloidal\".\n\nThe alphabetical order is: borough, hyperboloidal.\n\nAnswer: borough, hyperboloidal\nAnswer: borough, hyperboloidal" + }, + { + "expected": "bertrand careful eyelid feign heterostructure libra paste snip southeastern wherewith", + "id": "bbh_word_sorting-0122", + "label": "fail", + "observed": "Here are the words to sort: heterostructure, bertrand, careful, wherewith, libra, eyelid, feign, southeastern, paste, snip.\n\nLet's sort them alphabetically:\n\n1. careful\n2. bertrand\n3. eyelid\n4. feign\n5. heterostructure\n6. libra\n7. paste\n8. southeastern\n9. snip\n10. wherewith\n\nDetailed comparison:\n* **c**areful\n* **b**ertrand\n* **e**yelid\n* **f**eign\n* **h**eterostructure\n* **l**ibra\n* **p**aste\n* **s**outheastern\n* **s**nip (southeastern comes before snip because 'o' comes before 'n') - Wait, let's recheck 's' words.\n * southeastern\n * snip\n * southeastern vs snip: southeastern comes before snip.\n* **w**herewith\n\nLet's re-sort the 's' words:\n* southeastern\n* snip\n\nThe final sorted list is:\n1. careful\n2. bertrand\n3. eyelid\n4. feign\n5. heterostructure\n6. libra\n7. paste\n8. southeastern\n9. snip\n10. wherewith\n\nAnswer: careful, bertrand, eyelid, feign, heterostructure, libra, paste, southeastern, snip, wherewith" + }, + { + "expected": "aqueous deregulate gala infantrymen knob lysergic yaounde", + "id": "bbh_word_sorting-0123", + "label": "pass", + "observed": "The given list of words is: infantrymen, knob, yaounde, aqueous, deregulate, lysergic, gala.\n\nWe need to sort these words alphabetically.\n\nLet's compare them letter by letter:\n\n1. **aqueous** (Starts with 'a')\n2. **deregulate** (Starts with 'd')\n3. **gala** (Starts with 'g')\n4. **infantrymen** (Starts with 'i')\n5. **knob** (Starts with 'k')\n6. **lysergic** (Starts with 'l')\n7. **yaounde** (Starts with 'y')\n\nNow let's sort the words starting with the same letter if any (none in this case, but we check the order of the first letters):\na, d, g, i, k, l, y\n\nAlphabetical order of the starting letters: a, d, g, i, k, l, y.\n\n1. **aqueous** (a)\n2. **deregulate** (d)\n3. **gala** (g)\n4. **infantrymen** (i)\n5. **knob** (k)\n6. **lysergic** (l)\n7. **yaounde** (y)\n\nThe sorted list is: aqueous, deregulate, gala, infantrymen, knob, lysergic, yaounde.\n\nAnswer: aqueous, deregulate, gala, infantrymen, knob, lysergic, yaounde\nAnswer: aqueous, deregulate, gala, infantrymen, knob, lysergic, yaounde" + }, + { + "expected": "arapaho bacteria bela bock burley", + "id": "bbh_word_sorting-0124", + "label": "pass", + "observed": "The given list of words is: burley, bela, arapaho, bacteria, bock.\n\nWe need to sort these words alphabetically. We compare them letter by letter:\n\n1. **bela** (Starts with 'b', second letter 'e')\n2. **bacteria** (Starts with 'b', second letter 'a')\n3. **bock** (Starts with 'b', second letter 'o')\n4. **burley** (Starts with 'b', second letter 'u')\n5. **arapaho** (Starts with 'a')\n\nComparing the first letters: 'a' comes before 'b'.\nSo, **arapaho** is the first word.\n\nNow we compare the words starting with 'b': burley, bela, bacteria, bock.\n\nComparing the second letters:\n* bela: 'e'\n* bacteria: 'a'\n* bock: 'o'\n* burley: 'u'\n\n'a' comes before 'e', 'o', and 'u'. So, **bacteria** comes next.\n\nRemaining words: burley, bela, bock.\n\nComparing the second letters of the remaining words:\n* bela: 'e'\n* bock: 'o'\n* burley: 'u'\n\n'e' comes before 'o' and 'u'. So, **bela** comes next.\n\nRemaining words: burley, bock.\n\nComparing the second letters:\n* bock: 'o'\n* burley: 'u'\n\n'o' comes before 'u'. So, **bock** comes next.\n\nThe last word is **burley**.\n\nThe sorted order is: arapaho, bacteria, bela, bock, burley.\n\nAnswer: arapaho, bacteria, bela, bock, burley\nAnswer: arapaho, bacteria, bela, bock, burley" + }, + { + "expected": "betony boar bootleg bronzy centaur charge clemens collet contemporaneous contravariant cordial dorado handicraft macmahon mesh monterey possession regina underclassman", + "id": "bbh_word_sorting-0125", + "label": "fail", + "observed": "Here are the words to be sorted alphabetically:\nbetony, bronzy, charge, cordial, handicraft, boar, collet, clemens, bootleg, possession, mesh, centaur, contemporaneous, macmahon, monterey, dorado, contravariant, regina, underclassman\n\nLet's sort them:\n\n1. **boar**\n2. **betony**\n3. **bootleg**\n4. **bronzy**\n5. **charge**\n6. **centaur**\n7. **clemens**\n8. **collet**\n9. **contravariant**\n10. **cordial**\n11. **danicraft** (handicraft)\n12. **dorado**\n13. **macmahon**\n14. **mesh**\n15. **monterey**\n16. **possession**\n17. **regina**\n18. **underclassman**\n19. **contemporaneous**\n\nLet's re-sort carefully, paying attention to the initial letters:\n\nB: betony, boar, bootleg, bronzy\nC: charge, cordial, collet, clemens, centaur, contemporaneous, contravariant\nD: dorado\nH: handicraft\nM: macmahon, mesh, monterey\nP: possession\nR: regina\nU: underclassman\n\n**Step 1: Group by first letter**\n\nB: betony, boar, bootleg, bronzy\nC: charge, cordial, collet, clemens, centaur, contemporaneous, contravariant\nD: dorado\nH: handicraft\nM: macmahon, mesh, monterey\nP: possession\nR: regina\nU: underclassman\n\n**Step 2: Sort within groups**\n\nB: betony, boar, bootleg, bronzy (betony, boar, bootleg, bronzy)\n * betony\n * boar\n * bootleg\n * bronzy\n\nC: charge, cordial, collet, clemens, centaur, contemporaneous, contravariant (charge, cordial, collet, clemens, centaur, contemporaneous, contravariant)\n * charge\n * cordial\n * collet\n * clemens\n * centaur\n * contemporaneous\n * contravariant\n\nD: dorado\n\nH: handic" + }, + { + "expected": "announce carp clayton co earthy hello inmate nimbus parentage phonetic sharon skinny sudan watson", + "id": "bbh_word_sorting-0126", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. announce\n2. carp\n3. clayton\n4. earthy\n5. hello\n6. inmate\n7. nimbus\n8. parentage\n9. phonetic\n10. skinny\n11. sharon\n12. sudan\n13. watson\n\nAnswer: announce, carp, clayton, earthy, hello, inmate, nimbus, parentage, phonetic, skinny, sharon, sudan, watson" + }, + { + "expected": "affluent cheshire covalent diagnostician divisive epsilon folklore gideon gothic grover horowitz julio peanut quadrature salient spiderwort spiritual", + "id": "bbh_word_sorting-0127", + "label": "fail", + "observed": "Here are the words sorted alphabetically:\n\n1. affluent\n2. cheshire\n3. covalent\n4. diagnostician\n5. divisive\n6. epsilon\n7. folklore\n8. gothic\n9. gideon\n10. grover\n11. horowitz\n12. julio\n13. peanut\n14. quadrature\n15. salient\n16. spiderwort\n17. spiritual\n\nLet's re-sort them carefully:\n\n* **a**ffluent\n* **c**ovalent, **c**heshire\n* **d**iagnostician, **d**ivisive\n* **e**psilon\n* **f**olklore, **g**othic, **g**ideon, **g**rover\n* **h**orowitz\n* **j**ulio\n* **p**eanut\n* **q**uadrature\n* **s**alient, **s**piderwort, **s**piritual\n\n**Detailed Alphabetical Sort:**\n\n1. affluent\n2. cheshire\n3. covalent\n4. diagnostician\n5. divisive\n6. epsilon\n7. folklore\n8. gothic\n9. gideon\n10. grover\n11. horowitz\n12. julio\n13. peanut\n14. quadrature\n15. salient\n16. spiderwort\n17. spiritual\n\nWait, let's check the 'G' words: gothic, gideon, grover.\nGothic (go...)\nGideon (gi...)\nGrover (gr...)\nOrder: gothic, gideon, grover.\n\nLet's check the 'S' words: salient, spiderwort, spiritual.\nSalient (sal...)\nSpiderwort (spi...)\nSpiritual (spi...)\nSalient comes before Spiderwort and Spiritual.\nSpiderwort (spi**d**)\nSpiritual (spi**r**)\nOrder: salient, spiderwort, spiritual.\n\n**Final Sorted List:**\n\n1. affluent\n2. cheshire\n3. covalent\n4. diagnostician\n5. divisive\n6. epsilon\n7. folklore\n8. gothic\n9. gideon\n10. grover\n11. horowitz\n12. julio\n13. peanut\n14. quadrature\n15. salient\n16. spiderwort\n17. spiritual\n\n(Re-checking" + } + ], + "dataset": "bbh_word_sorting", + "enable_thinking": false, + "generation_kwargs": { + "do_sample": false, + "max_new_tokens": 512 + }, + "hf_repo": "google/gemma-4-E2B-it", + "limit": 128, + "manifest": "/content/evalrx_examples/data/bbh_word_sorting/manifest.json", + "model": "gemma-4-e2b", + "n": 128, + "n_fail": 76, + "n_pass": 52, + "n_unknown": 0, + "seconds": 378.6, + "spec": "gemma-4-e2b-it", + "task": "llm_graded" + }, + "stages": { + "M1": { + "model_calls": [ + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 1, + "case_id": "bbh_word_sorting-0000", + "cycle": -1, + "duration_sec": 133.035, + "event": "model_call", + "event_seq": 1, + "inputs": { + "prompt": "Sort the following words alphabetically: List: alleviate duopoly mattress gland benelux townsmen buoyant klaxon hardbound tomography felice gunk\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words to sort alphabetically: alleviate, duopoly, mattress, gland, benelux, townsmen, buoyant, klaxon, hardbound, tomography, felice, gunk.\n\n1. **alleviate**\n2. **benelux**\n3. **buoyant**\n4. **duopoly**\n5. **felice**\n6. **gland**\n7. **hardbound**\n8. **klaxon**\n9. **mattress**\n10. **tomography**\n11. **townsmen**\n\nLet's re-sort them carefully:\n\n* **a**lleviate\n* **b**enelux, **b**uoyant (benelux before buoyant)\n* **d**uopoly\n* **f**elice\n* **g**land, **g**unk (gland before gunk)\n* **h**ardbound\n* **k**laxon\n* **m**attress\n* **t**omography, **t**ownsmen (tomography before townsmen)\n\n**Final Sorted List:**\n1. alleviate\n2. benelux\n3. buoyant\n4. duopoly\n5. felice\n6. gland\n7. gunk\n8. hardbound\n9. klaxon\n10. mattress\n11. tomography\n12. townsmen\n\nWait, let's re-check the 'g' words: gland, gunk. Gland comes before Gunk.\n\nLet's re-check the 'b' words: benelux, buoyant. Benelux comes before Buoyant.\n\nLet's re-check the 't' words: tomography, townsmen. Tomography comes before Townsmen.\n\n**Final Alphabetical Order:**\n1. alleviate\n2. benelux\n3. buoyant\n4. duopoly\n5. felice\n6. gland\n7. gunk\n8. hardbound\n9. klaxon\n10. mattress\n11. tomography\n12. townsmen\n\nAnswer: alleviate, benelux, buoyant, duopoly, felice, gland, gunk, hardbound, klaxon, mattress, tomography, townsmen", + "schema_version": 6, + "seq": 1, + "span_id": "m1.model_call.1", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:21:18.258114+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 2, + "case_id": "bbh_word_sorting-0001", + "cycle": -1, + "duration_sec": 1.9573, + "event": "model_call", + "event_seq": 2, + "inputs": { + "prompt": "Sort the following words alphabetically: List: yuh edt cheddar oblivion from poignant pang\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: yuh, edt, cheddar, oblivion, from, poignant, pang.\n\nWe need to sort these words alphabetically.\n\n1. **Compare the first letters:**\n - yuh (y)\n - edt (e)\n - cheddar (c)\n - oblivion (o)\n - from (f)\n - poignant (p)\n - pang (p)\n\n2. **Order based on the first letter:**\n - c (cheddar)\n - e (edt)\n - f (from)\n - o (oblivion)\n - p (poignant, pang)\n - y (yuh)\n\n3. **Sort the words starting with 'p':**\n - poignant\n - pang\n (Comparing the second letters: 'o' in poignant vs 'a' in pang. 'a' comes before 'o'.)\n So, the order is: pang, poignant.\n\n4. **Assemble the final sorted list:**\n - cheddar (c)\n - edt (e)\n - from (f)\n - oblivion (o)\n - pang (p)\n - poignant (p)\n - yuh (y)\n\nLet's re-check the 'p' words:\npang (p-a-n-g)\npoignant (p-o-i-g-n-a-n-t)\n'a' comes before 'o', so pang comes before poignant.\n\nFinal sorted order: cheddar, edt, from, oblivion, pang, poignant, yuh.\n\nAnswer: cheddar, edt, from, oblivion, pang, poignant, yuh", + "schema_version": 6, + "seq": 2, + "span_id": "m1.model_call.2", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:21:20.216136+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 3, + "case_id": "bbh_word_sorting-0002", + "cycle": -1, + "duration_sec": 1.904, + "event": "model_call", + "event_seq": 3, + "inputs": { + "prompt": "Sort the following words alphabetically: List: intercept tamp geopolitic loci newsmen umber credit atmospheric lunge siren swart chess\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words to sort: intercept, tamp, geopolitic, loci, newsmen, umber, credit, atmospheric, lunge, siren, swart, chess.\n\nLet's sort them alphabetically:\n\n1. atmospheric\n2. chess\n3. credit\n4. geopolitic\n5. intercept\n6. loci\n7. lunge\n8. newsmen\n9. siren\n10. swart\n11. tamp\n12. umber\n\nLet's re-check the sorting carefully:\n\n* **a**tmospheric\n* **c**hess\n* **c**redit\n* **g**eopolitic\n* **i**ntercept\n* **l**oci\n* **l**unge\n* **n**ewsmens\n* **s**iren\n* **s**wart\n* **t**amp\n* **u**mber\n\nComparing 'loci' and 'lunge': 'loci' comes before 'lunge'.\n\nComparing 'siren' and 'swart': 'siren' comes before 'swart'.\n\nThe correct alphabetical order is:\n1. atmospheric\n2. chess\n3. credit\n4. geopolitic\n5. intercept\n6. loci\n7. lunge\n8. newsmen\n9. siren\n10. swart\n11. tamp\n12. umber\n\nAnswer: atmospheric, chess, credit, geopolitic, intercept, loci, lunge, newsmen, siren, swart, tamp, umber", + "schema_version": 6, + "seq": 3, + "span_id": "m1.model_call.3", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:21:22.120746+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 4, + "case_id": "bbh_word_sorting-0003", + "cycle": -1, + "duration_sec": 1.8056, + "event": "model_call", + "event_seq": 4, + "inputs": { + "prompt": "Sort the following words alphabetically: List: above prometheus broken coexist big dominate olive screw thirdhand irk\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words to sort: above, prometheus, broken, coexist, big, dominate, olive, screw, thirdhand, irk\n\nWe will sort them alphabetically:\n\n1. **above**\n2. **big**\n3. **broken**\n4. **coexist**\n5. **dominate**\n6. **irk**\n7. **olive**\n8. **prometheus**\n9. **screw**\n10. **thirdhand**\n\nLet's re-check the sorting carefully:\n\n* **a**bove\n* **b**ig\n* **b**roken (b-r comes after b-i)\n* **c**oexist\n* **d**ominate\n* **i**rk\n* **o**live\n* **p**rometheus\n* **s**crew\n* **t**hirdhand\n\nCorrect alphabetical order:\n1. above\n2. big\n3. broken\n4. coexist\n5. dominate\n6. irk\n7. olive\n8. prometheus\n9. screw\n10. thirdhand\n\nAnswer: above, big, broken, coexist, dominate, irk, olive, prometheus, screw, thirdhand\n\nAnswer: above, big, broken, coexist, dominate, irk, olive, prometheus, screw, thirdhand", + "schema_version": 6, + "seq": 4, + "span_id": "m1.model_call.4", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:21:23.926985+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 5, + "case_id": "bbh_word_sorting-0004", + "cycle": -1, + "duration_sec": 2.7626, + "event": "model_call", + "event_seq": 5, + "inputs": { + "prompt": "Sort the following words alphabetically: List: hodges deoxyribose troupe motorcade devisee stratify auerbach decor incommensurable dianne\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words to sort alphabetically:\nhodges, deoxyribose, troupe, motorcade, devisee, stratify, auerbach, decor, incommensurable, dianne\n\nLet's sort them:\n\n1. **auerbach** (Starts with 'a')\n2. **decor** (Starts with 'd', comes before 'dianne')\n3. **devisee** (Starts with 'd', comes after 'decor')\n4. **dianne** (Starts with 'd', comes after 'devisee')\n5. **deoxyribose** (Starts with 'd', comes after 'dianne')\n6. **hodges** (Starts with 'h')\n7. **incommensurable** (Starts with 'i')\n8. **motorcade** (Starts with 'm')\n9. **stratify** (Starts with 's')\n10. **troupe** (Starts with 't')\n\nLet's re-check the 'd' words:\ndecor\ndevisee\ndianne\ndeoxyribose\n\nAlphabetical order for 'd' words:\ndecor\ndevisee (deci...)\ndianne (dia...)\ndeoxyribose (deox...)\nWait, comparing 'devisee' and 'dianne':\nd i a n n e\nd e v i s e e\n'i' comes before 'e' in the second position if we compare the full words:\nd i a n n e\nd e v i s e e\n'i' comes before 'e' is incorrect. Let's compare character by character:\nd i a n n e\nd e v i s e e\n'i' vs 'e'. 'e' comes before 'i'. So 'devisee' comes before 'dianne'.\n\nCorrected 'd' order:\n1. decor\n2. devisee\n3. dianne\n4. deoxyribose\n\nLet's re-sort the whole list:\n\n1. auerbach\n2. decor\n3. devisee\n4. dianne\n5. deoxyribose\n6. hodges\n7. incommensurable\n8. motorcade\n9. stratify\n10. troupe\n\nFinal check:\na - auerbach\nd - decor, devisee, dianne", + "schema_version": 6, + "seq": 5, + "span_id": "m1.model_call.5", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:21:26.690301+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 6, + "case_id": "bbh_word_sorting-0005", + "cycle": -1, + "duration_sec": 0.7257, + "event": "model_call", + "event_seq": 6, + "inputs": { + "prompt": "Sort the following words alphabetically: List: seashore haughty\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given words are: seashore, haughty.\n\nTo sort them alphabetically, we compare them letter by letter from the beginning.\n\n1. **s**eashore\n2. **h**aughty\n\nSince 'h' comes before 's' in the alphabet, \"haughty\" comes before \"seashore\".\n\nThe alphabetical order is: haughty, seashore.\n\nAnswer: haughty, seashore\nAnswer: haughty, seashore", + "schema_version": 6, + "seq": 6, + "span_id": "m1.model_call.6", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:21:27.416681+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 7, + "case_id": "bbh_word_sorting-0006", + "cycle": -1, + "duration_sec": 1.7428, + "event": "model_call", + "event_seq": 7, + "inputs": { + "prompt": "Sort the following words alphabetically: List: torpedo phosphorescent pristine decadent shrunk dey administer gradate littleneck thrown jacky coachman aeneid verdict tasting sinh delhi systemwide grim\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. gradate\n7. grim\n8. jacky\n9. littleneck\n10. phosphorescent\n11. pristine\n12. sinh\n13. systemwide\n14. tasting\n15. torpedo\n16. thrown\n17. verdict\n18. dey\n19. shrunk\n\nLet's re-sort them carefully:\n\n* administer\n* aeneid\n* decadent\n* coachman\n* delhi\n* grim\n* gradate\n* jacky\n* littleneck\n* phosphorescent\n* pristine\n* sinh\n* systemwide\n* tasting\n* torpedo\n* thrown\n* verdict\n* dey\n* shrunk\n\n**Alphabetical Sorting:**\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. dey\n7. grim\n8. gradate\n9. jacky\n10. littleneck\n11. phosphorescent\n12. pristine\n13. sinh\n14. shrunk\n15. systemwide\n16. tasting\n17. torpedo\n18. thrown\n19. verdict\n\nAnswer: administer, aeneid, decadent, coachman, delhi, dey, grim, gradate, jacky, littleneck, phosphorescent, pristine, sinh, shrunk, systemwide, tasting, torpedo, thrown, verdict", + "schema_version": 6, + "seq": 7, + "span_id": "m1.model_call.7", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:21:29.160196+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 8, + "case_id": "bbh_word_sorting-0007", + "cycle": -1, + "duration_sec": 2.7412, + "event": "model_call", + "event_seq": 8, + "inputs": { + "prompt": "Sort the following words alphabetically: List: boletus ulysses debugging passband lucretia necktie deportee calypso revving conklin omnipotent\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words to sort alphabetically:\nboletus, ulysses, debugging, passband, lucretia, necktie, deportee, calypso, revving, conklin, omnipotent\n\nLet's sort them:\n\n1. **boletus**\n2. **calypso**\n3. **conklin**\n4. **deportee**\n5. **debugging**\n6. **lucretia**\n7. **necktie**\n8. **omnipotent**\n9. **passband**\n10. **revving**\n11. **ulysses**\n\nLet's re-check the sorting carefully:\n\n* b: boletus\n* c: calypso, conklin\n* d: debugging\n* l: lucretia\n* n: necktie\n* o: omnipotent\n* p: passband\n* r: revving\n* u: ulysses\n\nSorting the 'c' words: calypso comes before conklin.\n\nCorrect alphabetical order:\n1. boletus\n2. calypso\n3. conklin\n4. debugging\n5. lucretia\n6. necktie\n7. omnipotent\n8. passband\n9. revving\n10. ulysses\n11. deportee (Wait, 'deportee' starts with 'd'. Let's re-sort based on the first letter.)\n\nWords and first letters:\n* boletus (B)\n* ulysses (U)\n* debugging (D)\n* passband (P)\n* lucretia (L)\n* necktie (N)\n* deportee (D)\n* calypso (C)\n* revving (R)\n* conklin (C)\n* omnipotent (O)\n\nSorting by first letter: B, C, D, L, N, O, P, R, U\n\n**B:** boletus\n**C:** calypso, conklin (calypso before conklin)\n**D:** debugging, deportee (debugging before deportee)\n**L:** lucretia\n**N:** necktie\n**O:** omnipotent\n**P:** passband\n**R:** revving\n**U:** ulysses\n\nFinal sorted list:", + "schema_version": 6, + "seq": 8, + "span_id": "m1.model_call.8", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:21:31.902363+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 9, + "case_id": "bbh_word_sorting-0008", + "cycle": -1, + "duration_sec": 2.6712, + "event": "model_call", + "event_seq": 9, + "inputs": { + "prompt": "Sort the following words alphabetically: List: plebiscite colombo inviolate quagmire garry satanic magnanimity aeneas notoriety nevada foothold glycerine lucre tennyson type scription pompey softball spleenwort fox\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. aeneas\n2. foothold\n3. fox\n4. glycerine\n5. garry\n6. inflaton (Assuming 'inviolate' was intended, but using the provided word 'inviolate') - Let's stick to the provided list: inviolate\n7. lucre\n8. magnanimity\n9. nevada\n10. pompey\n11. plebiscite\n12. quagmire\n13. satanic\n14. scription (Assuming 'scription' is a typo for 'scriptio' or similar, but sorting as written)\n15. softball\n16. spleenwort\n17. tennyson\n18. type\n\nLet's re-sort carefully:\n\n* aeneas\n* foothold\n* fox\n* glycerine\n* garry\n* inviolate\n* lucre\n* magnanimity\n* nevada\n* pompey\n* plebiscite\n* quagmire\n* satanic\n* scription\n* softball\n* spleenwort\n* tennyson\n* type\n\n**Alphabetical Order Check:**\naeneas (A)\nfoothold (F)\nfox (F) - fox comes before foothold\nglycerine (G)\ngarry (G) - garry comes after glycerine\ninviolate (I)\nlucre (L)\nmagnanimity (M)\nnevada (N)\npompey (P)\nplebiscite (P) - pompey comes before plebiscite\nquagmire (Q)\nsatanic (S)\nscription (S) - satanic comes before scription\nsoftball (S) - scription comes before softball\nspleenwort (S) - softball comes before spleenwort\ntennyson (T)\ntype (T) - tennyson comes before type\n\n**Refined Sorting:**\n\n1. aeneas\n2. fox\n3. foothold\n4. glycerine\n5. garry\n6. inviolate\n7. lucre\n8. magnanimity\n9. nevada\n10. pompey\n11. plebiscite\n12. quagmire\n13. satanic\n14. scription\n15. softball\n16. spleenwort\n17. tennyson\n18.", + "schema_version": 6, + "seq": 9, + "span_id": "m1.model_call.9", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:21:34.574427+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 10, + "case_id": "bbh_word_sorting-0009", + "cycle": -1, + "duration_sec": 2.2967, + "event": "model_call", + "event_seq": 10, + "inputs": { + "prompt": "Sort the following words alphabetically: List: scrumptious sidereal thermal yakima siena gorky saxon scottish figural hydroxyl seventeen neapolitan rampage nerve grapple fate plainfield stooge knives allotted\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. allotted\n2. fate\n3. figural\n4. gorky\n5. grapple\n6. hydroxyl\n7. knives\n8. neapolitan\n9. plainfield\n10. rampage\n11. scrumptious\n12. saxon\n13. siena\n14. scottish\n15. sidereal\n16. stooge\n17. seventeen\n18. thermal\n19. yakima\n20. nerve\n\nLet's re-sort them carefully:\n\n* allotted\n* fate\n* figural\n* gorky\n* grapple\n* hydroxyl\n* knives\n* neapolitan\n* plainfield\n* rampage\n* scrumptious\n* saxon\n* siena\n* scottish\n* sidereal\n* stooge\n* seventeen\n* thermal\n* yakima\n* nerve\n\nChecking the order again:\nA: allotted\nF: fate, figural\nG: gorky, grapple\nH: hydroxyl\nK: knives\nN: neapolitan, nerve\nP: plainfield\nR: rampage\nS: scrumptious, saxon, siena, scottish, sidereal\nS: stooge (S comes before T)\nS: seventeen\nT: thermal\nY: yakima\n\nCorrect alphabetical order:\n1. allotted\n2. fate\n3. figural\n4. gorky\n5. grapple\n6. hydroxyl\n7. knives\n8. neapolitan\n9. nerve\n10. plainfield\n11. rampage\n12. scrumptious\n13. saxon\n14. siena\n15. scottish\n16. sidereal\n17. stooge\n18. seventeen\n19. thermal\n20. yakima\n\nAnswer: allotted, fate, figural, gorky, grapple, hydroxyl, knives, neapolitan, nerve, plainfield, rampage, scrumptious, saxon, siena, scottish, sidereal, stooge, seventeen, thermal, yakima", + "schema_version": 6, + "seq": 10, + "span_id": "m1.model_call.10", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:21:36.872349+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 11, + "case_id": "bbh_word_sorting-0010", + "cycle": -1, + "duration_sec": 2.3656, + "event": "model_call", + "event_seq": 11, + "inputs": { + "prompt": "Sort the following words alphabetically: List: foamflower lawful cayenne chandigarh only excursion declassify equanimity aseptic inclement october debauch kruger groupoid scorch dingy alterate\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. alterate\n2. aseptic\n3. cayenne\n4. chandigarh\n5. declassify\n6. debauch\n7. dingy\n8. excursion\n9. equanimity\n10. foamflower\n11. lawful\n12. october\n13. only\n14. groupoid\n15. kruger\n16. scorch\n17. alterate (Wait, I listed alterate twice in my thought process, let's re-check the original list: foamflower lawful cayenne chandigarh only excursion declassify equanimity aseptic inclement october debauch kruger groupoid scorch dingy alterate)\n\nLet's re-sort carefully:\n\nOriginal List: foamflower, lawful, cayenne, chandigarh, only, excursion, declassify, equanimity, aseptic, inclement, october, debauch, kruger, groupoid, scorch, dingy, alterate\n\n1. alterate\n2. aseptic\n3. cayenne\n4. chandigarh\n5. declassify\n6. debauch\n7. dingy\n8. excursion\n9. equanimity\n10. foamflower\n11. inclement\n12. lawful\n13. october\n14. only\n15. groupoid\n16. kruger\n17. scorch\n\nLet's re-sort the remaining words based on the full list:\n\nA: alterate, aseptic\nC: cayenne, chandigarh\nD: declassify, debauch, dingy\nE: excursion, equanimity\nF: foamflower\nI: inclement\nL: lawful\nO: october, only\nG: groupoid\nK: kruger\nS: scorch\n\nCorrect Alphabetical Order:\n1. alterate\n2. aseptic\n3. cayenne\n4. chandigarh\n5. declassify\n6. debauch\n7. dingy\n8. excursion\n9. equanimity\n10. foamflower\n11. inclement\n12. lawful\n13. october\n14. only\n15. groupoid\n16. kruger\n17. scorch\n\nAnswer: alterate, aseptic, cayenne, chandigarh, declassify, debauch, dingy, excursion, equanimity, foamflower, inclement, lawful, october, only, groupoid, kruger, scorch", + "schema_version": 6, + "seq": 11, + "span_id": "m1.model_call.11", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:21:39.238846+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 12, + "case_id": "bbh_word_sorting-0011", + "cycle": -1, + "duration_sec": 1.258, + "event": "model_call", + "event_seq": 12, + "inputs": { + "prompt": "Sort the following words alphabetically: List: lethe bilk perturb tactual\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: lethe, bilk, perturb, tactual.\n\nTo sort these words alphabetically, we compare them letter by letter from the beginning.\n\n1. **lethe** (starts with 'l')\n2. **bilk** (starts with 'b')\n3. **perturb** (starts with 'p')\n4. **tactual** (starts with 't')\n\nComparing the first letters: 'b', 'l', 'p', 't'.\nIn alphabetical order: 'b', 'l', 'p', 't'.\n\n1. **bilk** (b)\n2. **lethe** (l)\n3. **perturb** (p)\n4. **tactual** (t)\n\nThe sorted list is: bilk, lethe, perturb, tactual.\n\nAnswer: bilk, lethe, perturb, tactual\n\nAnswer: bilk, lethe, perturb, tactual", + "schema_version": 6, + "seq": 12, + "span_id": "m1.model_call.12", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:21:40.497720+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 13, + "case_id": "bbh_word_sorting-0012", + "cycle": -1, + "duration_sec": 2.2427, + "event": "model_call", + "event_seq": 13, + "inputs": { + "prompt": "Sort the following words alphabetically: List: portsmouth impalpable portrait gouda brainy succumb fatuous hoof lisbon erudite malaria enigma isaacson cony stronghold servomechanism\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. brainy\n2. enigma\n3. erudite\n4. fatuous\n5. gouda\n6. impalpable\n7. isaacson\n8. lisbon\n9. malaria\n10. portsmouth\n11. portrait\n12. servomechanism\n13. stronghold\n14. succumb\n15. cony\n16. hoof\n\nLet's re-sort them carefully:\n\n* brainy\n* cony\n* erudite\n* enigma\n* fatuous\n* gouda\n* impalpable\n* isaacson\n* hoof\n* lisbon\n* malaria\n* portsmouth\n* portrait\n* servomechanism\n* stronghold\n* succumb\n\nLet's check the order again:\nB (brainy)\nC (cony)\nE (erudite, enigma) -> erudite before enigma\nF (fatuous)\nG (gouda)\nI (impalpable, isaacson) -> impalpable before isaacson\nH (hoof)\nL (lisbon)\nM (malaria)\nP (portsmouth, portrait) -> portsmouth before portrait\nS (servomechanism, succumb, stronghold) -> servomechanism, succumb, stronghold (S-e-r, S-u, S-t)\n\nCorrect alphabetical order:\n1. brainy\n2. cony\n3. erudite\n4. enigma\n5. fatuous\n6. gouda\n7. impalpable\n8. isaacson\n9. hoof\n10. lisbon\n11. malaria\n12. portsmouth\n13. portrait\n14. servomechanism\n15. stronghold\n16. succumb\n\nAnswer: brainy, cony, erudite, enigma, fatuous, gouda, impalpable, isaacson, hoof, lisbon, malaria, portsmouth, portrait, servomechanism, stronghold, succumb", + "schema_version": 6, + "seq": 13, + "span_id": "m1.model_call.13", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:21:42.741306+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 14, + "case_id": "bbh_word_sorting-0013", + "cycle": -1, + "duration_sec": 1.8102, + "event": "model_call", + "event_seq": 14, + "inputs": { + "prompt": "Sort the following words alphabetically: List: logarithm robertson inanimate uganda micron incurring wilcox lumber sammy souvenir newman profuse maladapt cone butch airlift homeowner\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. airlift\n2. maladapt\n3. cone\n4. butch\n5. homeowner\n6. incurring\n7. logarithm\n8. lumber\n9. micron\n10. newman\n11. inanimate\n12. profuse\n13. robertson\n14. sammy\n15. souvenir\n16. uganda\n17. wilcox\n\nLet's re-sort them carefully:\n\n* airlift\n* maladapt\n* cone\n* butch\n* homeowner\n* incurring\n* logarithm\n* lumber\n* micron\n* newman\n* inanimate\n* profuse\n* robertson\n* sammy\n* souvenir\n* uganda\n* wilcox\n\nComparing the initial list: logarithm, robertson, inanimate, uganda, micron, incurring, wilcox, lumber, sammy, souvenir, newman, profuse, maladapt, cone, butch, airlift, homeowner.\n\nAlphabetical order:\n1. airlift\n2. maladapt\n3. cone\n4. butch\n5. homeowner\n6. incurring\n7. logarithm\n8. lumber\n9. micron\n10. newman\n11. inanimate\n12. profuse\n13. robertson\n14. sammy\n15. souvenir\n16. uganda\n17. wilcox\n\nAnswer: airlift, maladapt, cone, butch, homeowner, incurring, logarithm, lumber, micron, newman, inanimate, profuse, robertson, sammy, souvenir, uganda, wilcox", + "schema_version": 6, + "seq": 14, + "span_id": "m1.model_call.14", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:21:44.552408+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 15, + "case_id": "bbh_word_sorting-0014", + "cycle": -1, + "duration_sec": 1.6299, + "event": "model_call", + "event_seq": 15, + "inputs": { + "prompt": "Sort the following words alphabetically: List: erudite marriageable stahl coach inadvisable vicksburg virgo brownian eosine magnesium flax\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words to sort alphabetically:\nerudite, marriageable, stahl, coach, inadvisable, vicksburg, virgo, brownian, eosine, magnesium, flax\n\nLet's sort them:\n\n1. **brownian**\n2. **coach**\n3. **erudite**\n4. **eosine**\n5. **flax**\n6. **inadvisable**\n7. **magnesium**\n8. **marriageable**\n9. **stahl**\n10. **vicksburg**\n11. **virgo**\n\nLet's re-check the sorting carefully, especially for words starting with similar letters:\n\n* **b**rownian\n* **c**oach\n* **e**rudite, **e**osine (erudite comes before eosine)\n* **f**lax\n* **i**nadvisable\n* **m**agnesium, **m**arriageable (magnesium comes before marriageable)\n* **s**tahl\n* **v**icksburg, **v**irgo (vicksburg comes before virgo)\n\nCorrected alphabetical order:\n\n1. brownian\n2. coach\n3. erudite\n4. eosine\n5. flax\n6. inadvisable\n7. magnesium\n8. marriageable\n9. stahl\n10. vicksburg\n11. virgo\n\nAnswer: brownian, coach, erudite, eosine, flax, inadvisable, magnesium, marriageable, stahl, vicksburg, virgo", + "schema_version": 6, + "seq": 15, + "span_id": "m1.model_call.15", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:21:46.183440+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 16, + "case_id": "bbh_word_sorting-0015", + "cycle": -1, + "duration_sec": 1.1545, + "event": "model_call", + "event_seq": 16, + "inputs": { + "prompt": "Sort the following words alphabetically: List: jacob sleight globule consonant musician\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: jacob, sleight, globule, consonant, musician.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **j**acob\n2. **s**leight\n3. **g**lobule\n4. **c**onsonant\n5. **m**usician\n\nComparing the first letters: J, S, G, C, M.\nIn alphabetical order: C, G, J, M, S.\n\n1. **consonant** (starts with C)\n2. **globule** (starts with G)\n3. **jacob** (starts with J)\n4. **musician** (starts with M)\n5. **sleight** (starts with S)\n\nThe sorted list is: consonant, globule, jacob, musician, sleight.\n\nAnswer: consonant, globule, jacob, musician, sleight\nAnswer: consonant, globule, jacob, musician, sleight", + "schema_version": 6, + "seq": 16, + "span_id": "m1.model_call.16", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:21:47.341348+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 17, + "case_id": "bbh_word_sorting-0016", + "cycle": -1, + "duration_sec": 2.6579, + "event": "model_call", + "event_seq": 17, + "inputs": { + "prompt": "Sort the following words alphabetically: List: inscribe upraise perceptive tragedian peculate cunard posterior crude danubian\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: inscribe, upraise, perceptive, tragedian, peculate, cunard, posterior, crude, danubian.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter:**\n * inscribe (i)\n * upraise (u)\n * perceptive (p)\n * tragedian (t)\n * peculate (p)\n * cunard (c)\n * posterior (p)\n * crude (c)\n * danubian (d)\n\n Order based on the first letter: c, c, d, i, p, p, p, t, u.\n\n2. **Sort words starting with 'c':**\n * cunard\n * crude\n * Alphabetical order: crude, cunard (cr comes before cu)\n\n3. **Sort words starting with 'd':**\n * danubian\n\n4. **Sort words starting with 'i':**\n * inscribe\n\n5. **Sort words starting with 'p':**\n * perceptive\n * peculate\n * posterior\n * Alphabetical order: perceptive, peculate, posterior (per comes before pec, pec comes before pos)\n\n6. **Sort words starting with 't':**\n * tragedian\n\n7. **Sort words starting with 'u':**\n * upraise\n\n**Combining the sorted groups:**\n1. crude\n2. cunard\n3. danubian\n4. inscribe\n5. perceptive\n6. peculate\n7. posterior\n8. tragedian\n9. upraise\n\nLet's re-verify the 'c' words:\ncrude\ncunard (crude comes before cunard)\n\nLet's re-verify the 'p' words:\nperceptive (pe...)\npeculate (pe...)\nposterior (po...)\nperceptive vs peculate: 'r' vs 'c'. 'c' comes before 'r'.\nCorrect order for 'p' words: peculate, perceptive, posterior. (pec, per, pos)\n\nLet's re-verify the full list based on standard alphabetical order:\n\n* crude (cr)\n* cunard (cu)\n* danubian (d)\n* inscribe (i)\n* peculate (pe-c)\n* perceptive (pe", + "schema_version": 6, + "seq": 17, + "span_id": "m1.model_call.17", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:21:50.000439+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 18, + "case_id": "bbh_word_sorting-0017", + "cycle": -1, + "duration_sec": 1.237, + "event": "model_call", + "event_seq": 18, + "inputs": { + "prompt": "Sort the following words alphabetically: List: tiresome sweepstake introvert carport firewood\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: tiresome, sweepstake, introvert, carport, firewood.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **carport** (Starts with 'c')\n2. **firewood** (Starts with 'f')\n3. **introvert** (Starts with 'i')\n4. **sweepstake** (Starts with 's')\n5. **tiresome** (Starts with 't')\n\nLet's verify the order:\nC comes before F, I, S, T.\nF comes before I, S, T.\nI comes before S, T.\nS comes before T.\n\nThe alphabetical order is: carport, firewood, introvert, sweepstake, tiresome.\n\nAnswer: carport, firewood, introvert, sweepstake, tiresome", + "schema_version": 6, + "seq": 18, + "span_id": "m1.model_call.18", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:21:51.240514+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 19, + "case_id": "bbh_word_sorting-0018", + "cycle": -1, + "duration_sec": 2.2207, + "event": "model_call", + "event_seq": 19, + "inputs": { + "prompt": "Sort the following words alphabetically: List: thrill splutter panicking scorch same dot prod obstetric malton onus drumhead delmarva barn embezzle it&t damp guru subsist entirety greene\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. barn\n2. embezzle\n3. delmarva\n4. dot\n5. drumhead\n6. entirety\n7. guru\n8. it&t\n9. malton\n10. panicking\n11. prod\n12. scorch\n13. same\n14. splutter\n15. subsist\n16. thrill\n\n(Note: \"onus\" is missing from the provided list, but if it were included, it would come before \"obstetric\" and after \"onus\" would be before \"prod\" if we assume standard alphabetical order for the provided list. Let's re-check the provided list: thrill, splutter, panicking, scorch, same, dot, prod, obstetric, malton, onus, drumhead, delmarva, barn, embezzle, it&t, damp, guru, subsist, entirety, greene.)\n\nLet's re-sort the complete list:\nthrill, splutter, panicking, scorch, same, dot, prod, obstetric, malton, onus, drumhead, delmarva, barn, embezzle, it&t, damp, guru, subsist, entirety, greene\n\n1. barn\n2. damp\n3. delmarva\n4. dot\n5. drumhead\n6. embezzle\n7. entirety\n8. greene\n9. guru\n10. it&t\n11. malton\n12. obstetric\n13. onus\n14. panicking\n15. prod\n16. scorch\n17. same\n18. splutter\n19. subsist\n20. thrill\n\nAnswer: barn, damp, delmarva, dot, drumhead, embezzle, entirety, greene, guru, it&t, malton, obstetric, onus, panicking, prod, scorch, same, splutter, subsist, thrill", + "schema_version": 6, + "seq": 19, + "span_id": "m1.model_call.19", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:21:53.462773+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 20, + "case_id": "bbh_word_sorting-0019", + "cycle": -1, + "duration_sec": 2.3727, + "event": "model_call", + "event_seq": 20, + "inputs": { + "prompt": "Sort the following words alphabetically: List: pewter gross gujarati cortland diffusible nomograph stag broadcast galvanometer transition incestuous sketchbook scout larynx\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. broadcast\n2. cortland\n3. diffusible\n4. galvanometer\n5. gross\n6. gujarati\n7. incestuous\n8. larynx\n9. nomograph\n10. pewter\n11. scout\n12. sketchbook\n13. stag\n14. transition\n\nLet's re-sort them carefully:\n\n* **b**roadcast\n* **c**ortland\n* **d**iffusible\n* **g**alvometer\n* **g**ross\n* **g**ujarati\n* **i**ncestuous\n* **l**arynx\n* **n**omograph\n* **p**ewter\n* **s**cout\n* **s**teag (Wait, 'stag' comes before 'sketchbook' if we look at the second letter, but alphabetically 'stag' comes before 'sketchbook' if we compare 't' vs 'k'. Let's re-examine the list.)\n\nOriginal List: pewter gross gujarati cortland diffusible nomograph stag broadcast galvanometer transition incestuous sketchbook scout larynx\n\n1. broadcast\n2. cortland\n3. diffusible\n4. galvanometer\n5. gross\n6. gujarati\n7. incestuous\n8. larynx\n9. nomograph\n10. pewter\n11. scout\n12. sketchbook\n13. stag\n14. transition\n\nLet's perform a strict alphabetical sort:\n\n1. broadcast (b)\n2. cortland (c)\n3. diffusible (d)\n4. galvanometer (g)\n5. gross (g)\n6. gujarati (g)\n7. incestuous (i)\n8. larynx (l)\n9. nomograph (n)\n10. pewter (p)\n11. scout (s)\n12. sketchbook (s)\n13. stag (s)\n14. transition (t)\n\nSorting the 'g' words: galvanometer, gross, gujarati.\n* galvanometer\n* gross\n* gujarati\n\nSorting the 's' words: scout, sketchbook, stag.\n* sketchbook (sk)\n* scout (sc)\n* stag (st)\n* Therefore: sketchbook, scout, stag\n\nFinal sorted list:\n1. broadcast\n2. cortland\n", + "schema_version": 6, + "seq": 20, + "span_id": "m1.model_call.20", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:21:55.836716+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 21, + "case_id": "bbh_word_sorting-0020", + "cycle": -1, + "duration_sec": 2.3564, + "event": "model_call", + "event_seq": 21, + "inputs": { + "prompt": "Sort the following words alphabetically: List: bust midshipmen musicale urinary chalk rocket resiny horror tangential sapient dumb serf pathway urea goucher fatty masonry dentistry cowboy roadrunner\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. bust\n2. chalk\n3. cowboy\n4. dentistry\n5. dumb\n6. fatty\n7. goucher\n8. horror\n9. masonry\n10. midshipmen\n11. musical\n12. pathway\n13. resiny\n14. roadrunner\n15. rocket\n16. serf\n17. sapient\n18. tangential\n19. urinary\n20. urea\n\nLet's re-sort them carefully:\n\n* bust\n* chalk\n* cowboy\n* dentistry\n* dumb\n* fatty\n* goucher\n* horror\n* masonry\n* midshipmen\n* musical\n* pathway\n* resiny\n* roadrunner\n* rocket\n* serf\n* sapient\n* tangential\n* urinary\n* urea\n\nComparing the words:\nB (bust)\nC (chalk)\nC (cowboy) -> chalk before cowboy\nD (dentistry, dumb) -> dentistry before dumb\nF (fatty)\nG (goucher)\nH (horror)\nM (masonry, midshipmen) -> masonry before midshipmen\nM (musical)\nP (pathway)\nR (resiny, roadrunner, rocket) -> resiny, roadrunner, rocket\nS (serf, sapient) -> serf before sapient\nT (tangential)\nU (urinary, urea) -> urinary before urea\n\nLet's do a full alphabetical sort:\n\n1. bust\n2. chalk\n3. cowboy\n4. dentistry\n5. dumb\n6. fatty\n7. goucher\n8. horror\n9. masonry\n10. midshipmen\n11. musical\n12. pathway\n13. resiny\n14. roadrunner\n15. rocket\n16. serf\n17. sapient\n18. tangential\n19. urinary\n20. urea\n\nWait, let's re-check the 'M' words and 'R' words.\n\nM: masonry, midshipmen, musical\nM: masonry, midshipmen, musical (Correct order: masonry, midshipmen, musical)\n\nR: resiny, roadrunner, rocket\nR: resiny, roadrunner, rocket (Correct order: resiny, roadrunner, rocket)\n\nS: serf", + "schema_version": 6, + "seq": 21, + "span_id": "m1.model_call.21", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:21:58.194219+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 22, + "case_id": "bbh_word_sorting-0021", + "cycle": -1, + "duration_sec": 2.4448, + "event": "model_call", + "event_seq": 22, + "inputs": { + "prompt": "Sort the following words alphabetically: List: confidential ursa fiction heterozygous faery overture horehound\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: confidential, ursa, fiction, heterozygous, faery, overture, horehound.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **confidential** (Starts with 'c')\n2. **faery** (Starts with 'f')\n3. **heterozygous** (Starts with 'h')\n4. **fiction** (Starts with 'f')\n5. **horehound** (Starts with 'h')\n6. **overture** (Starts with 'o')\n7. **ursa** (Starts with 'u')\n\nLet's sort the words starting with 'f':\n* faery\n* fiction\nComparing 'faery' and 'fiction': 'fai' vs 'fic'. 'a' comes before 'i', so 'faery' comes before 'fiction'.\n\nLet's sort the words starting with 'h':\n* heterozygous\n* horehound\nComparing 'heterozygous' and 'horehound': 'he' vs 'ho'. 'e' comes before 'o', so 'heterozygous' comes before 'horehound'.\n\nThe full alphabetical order is:\n1. confidential (c)\n2. faery (f)\n3. fiction (f)\n4. heterozygous (h)\n5. horehound (h)\n6. overture (o)\n7. ursa (u)\n\nLet's re-check the 'f' words:\nfaery\nfiction\nf-a-e-r-y\nf-i-c-t-i-o-n\n'a' comes before 'i', so faery comes before fiction.\n\nLet's re-check the 'h' words:\nheterozygous\nhorehound\nh-e-t-e-r-o-z-o-u-s\nh-o-r-e-h-o-u-n-d\n'e' comes before 'o', so heterozygous comes before horehound.\n\nFinal sorted list:\n1. confidential\n2. faery\n3. fiction\n4. heterozygous\n5. horehound\n6. overture\n7. ursa\n\nAnswer: confidential, faery, fiction, heterozygous, horehound, overture, ursa", + "schema_version": 6, + "seq": 22, + "span_id": "m1.model_call.22", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:22:00.640207+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 23, + "case_id": "bbh_word_sorting-0022", + "cycle": -1, + "duration_sec": 0.7426, + "event": "model_call", + "event_seq": 23, + "inputs": { + "prompt": "Sort the following words alphabetically: List: sweatband murray\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given words are: sweatband, murray.\n\nTo sort them alphabetically, we compare them letter by letter:\n\n1. **s**weatband\n2. **m**urray\n\nSince 'm' comes before 's' in the alphabet, \"murray\" comes before \"sweatband\".\n\nThe alphabetical order is: murray, sweatband.\n\nAnswer: murray, sweatband\nAnswer: murray, sweatband", + "schema_version": 6, + "seq": 23, + "span_id": "m1.model_call.23", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:22:01.384006+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 24, + "case_id": "bbh_word_sorting-0023", + "cycle": -1, + "duration_sec": 1.7029, + "event": "model_call", + "event_seq": 24, + "inputs": { + "prompt": "Sort the following words alphabetically: List: sound consternate tariff blest proximity quizzes xerxes buxton\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: sound, consternate, tariff, blest, proximity, quizzes, xerxes, buxton.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **blest** (b)\n2. **buxton** (b) - 'l' comes before 'u'\n3. **consternate** (c)\n4. **proximity** (p)\n5. **quizzes** (q)\n6. **sound** (s)\n7. **tariff** (t)\n8. **xerxes** (x)\n\nLet's re-sort carefully:\n\n* **b**: blest, buxton (blest comes before buxton)\n* **c**: consternate\n* **p**: proximity\n* **q**: quizzes\n* **s**: sound\n* **t**: tariff\n* **x**: xerxes\n\nComparing the 'b' words:\n* blest\n* buxton\n'l' comes before 'u', so **blest** comes before **buxton**.\n\nThe sorted order is:\n1. blest\n2. buxton\n3. consternate\n4. proximity\n5. quizzes\n6. sound\n7. tariff\n8. xerxes\n\nAnswer: blest, buxton, consternate, proximity, quizzes, sound, tariff, xerxes\n\nAnswer: blest, buxton, consternate, proximity, quizzes, sound, tariff, xerxes", + "schema_version": 6, + "seq": 24, + "span_id": "m1.model_call.24", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:22:03.088165+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 25, + "case_id": "bbh_word_sorting-0024", + "cycle": -1, + "duration_sec": 2.3725, + "event": "model_call", + "event_seq": 25, + "inputs": { + "prompt": "Sort the following words alphabetically: List: wv habitation panicking schoolmate aerodynamic giacomo nebulous offset vector roosevelt jimmy botanist suburbia padre\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words to sort alphabetically:\nwv, habitation, panicking, schoolmate, aerodynamic, giacomo, nebulous, offset, vector, roosevelt, jimmy, botanist, suburbia, padre\n\nLet's sort them:\n\n1. **aerodynamic**\n2. **botanist**\n3. **habitation**\n4. **giacomo**\n5. **jimmy**\n6. **nebulous**\n7. **offset**\n8. **panicking**\n9. **padre**\n10. **roosevelt**\n11. **schoolmate**\n12. **suburbia**\n13. **vector**\n14. **wv** (Assuming 'wv' is treated as a word, or placed based on its initial letters if it's an abbreviation. In standard alphabetical sorting, it comes before 'habitation' if we consider 'w' before 'h', but usually, if it's an initialism, it's treated as is. If we treat it strictly alphabetically based on the first letter 'w', it would be near the end.)\n\nLet's re-evaluate based on standard alphabetical order (case-insensitive, treating 'wv' as starting with 'w'):\n\n* aerodynamic (a)\n* botanist (b)\n* habitation (h)\n* giacomo (g)\n* jimmy (j)\n* nebulous (n)\n* offset (o)\n* panicking (p)\n* padre (p)\n* roosevelt (r)\n* schoolmate (s)\n* suburbia (s)\n* vector (v)\n* wv (w)\n\nSorting the 'p' words: panicking, padre. (panicking comes before padre)\nSorting the 's' words: schoolmate, suburbia. (schoolmate comes before suburbia)\n\nFinal sorted list:\n1. aerodynamic\n2. botanist\n3. habitation\n4. giacomo\n5. jimmy\n6. nebulous\n7. offset\n8. panicking\n9. padre\n10. roosevelt\n11. schoolmate\n12. suburbia\n13. vector\n14. wv\n\nAnswer: aerodynamic, botanist, habitation, giacomo, jimmy, nebulous, offset, panicking, padre, roosevelt, schoolmate, suburbia", + "schema_version": 6, + "seq": 25, + "span_id": "m1.model_call.25", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:22:05.461962+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 26, + "case_id": "bbh_word_sorting-0025", + "cycle": -1, + "duration_sec": 2.3711, + "event": "model_call", + "event_seq": 26, + "inputs": { + "prompt": "Sort the following words alphabetically: List: linchpin arrear eavesdropping fasciculate shin postdoctoral maritime ibn henry hermaphrodite sticky jane brookside incorrigible vehicular herodotus\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. arrear\n2. eavesdropping\n3. fasciculate\n4. herodotus\n5. hermaphrodite\n6. ibn henry\n7. incorrigible\n8. jane\n9. linchpin\n10. maritime\n11. postdoctoral\n12. shin\n13. sticky\n14. brookside\n15. vehicular\n\nLet's re-sort them carefully:\n\n* **a**rrear\n* **e**avesdropping\n* **f**asciculate\n* **h**erodotus\n* **h**ermaphrodite (H before I)\n* **i**bn henry\n* **i**ncorrigible (I before L)\n* **j**ane\n* **l**inchpin\n* **m**aritime\n* **p**ostdoctoral\n* **s**hin\n* **s**ticky\n* **b**rookside (B comes before S)\n* **v**ehicular\n\nLet's do a full alphabetical sort:\n\n1. arrear\n2. brookside\n3. eavesdropping\n4. fasciculate\n5. herodotus\n6. hermaphrodite\n7. ibn henry\n8. incorrigible\n9. jane\n10. linchpin\n11. maritime\n12. postdoctoral\n13. shin\n14. sticky\n15. vehicular\n\nWait, let's check the 'H' words: hermaphrodite and herodotus.\nH-e-r-m-a-p-h-r-o-d-i-t-e\nH-e-r-o-d-t-u-s\n'herm' comes before 'hero'. So, hermaphrodite comes before herodotus.\n\nLet's re-verify the full list:\narrear\nbrookside\neavesdropping\nfasciculate\nhermaphrodite\nherodotus\nibn henry\nincorrigible\njane\nlinchpin\nmaritime\npostdoctoral\nshin\nsticky\nvehicular\n\nFinal check on the order:\nA: arrear\nB: brookside\nE: eavesdropping\nF: fasciculate\nH: hermaphrodite, herodotus (herm before hero)\nI: ibn henry, incorrigible (ibn before incorrigible)\nJ:", + "schema_version": 6, + "seq": 26, + "span_id": "m1.model_call.26", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:22:07.834331+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 27, + "case_id": "bbh_word_sorting-0026", + "cycle": -1, + "duration_sec": 2.2142, + "event": "model_call", + "event_seq": 27, + "inputs": { + "prompt": "Sort the following words alphabetically: List: tipoff fructose coleus hurricane cite tularemia irreducible wier improbable whitetail vesper whereabout\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. cite\n2. coleus\n3. fructose\n4. hurricane\n5. irreducible\n6. improbable\n7. tipoff\n8. tularemia\n9. vesper\n10. whereabout\n11. whitetail\n\nLet's re-sort them carefully:\n\n* **c**ite\n* **c**oleus\n* **f**ructose\n* **h**urricane\n* **i**rreducible\n* **i**mprobable\n* **t**ipoff\n* **t**ularemia\n* **v**esper\n* **w**hereabout\n* **w**hitetail\n\nComparing 'cite' and 'coleus': 'cite' comes before 'coleus'.\nComparing 'irreducible' and 'improbable': 'irreducible' comes before 'improbable'.\nComparing 'tipoff' and 'tularemia': 'tipoff' comes before 'tularemia'.\nComparing 'whereabout' and 'whitetail': 'whereabout' comes before 'whitetail'.\n\nCorrect alphabetical order:\n1. cite\n2. coleus\n3. fructose\n4. hurricane\n5. irreducible\n6. improbable\n7. tipoff\n8. tularemia\n9. vesper\n10. whereabout\n11. whitetail\n\nWait, let's re-check the 'i' words: irreducible, improbable.\ni-r-r... vs i-m-p... $\\rightarrow$ irreducible comes before improbable. (Correct)\n\nLet's re-check the 't' words: tipoff, tularemia.\nt-i-p... vs t-u-l... $\\rightarrow$ tipoff comes before tularemia. (Correct)\n\nFinal sorted list:\n1. cite\n2. coleus\n3. fructose\n4. hurricane\n5. irreducible\n6. improbable\n7. tipoff\n8. tularemia\n9. vesper\n10. whereabout\n11. whitetail\n\nAnswer: cite, coleus, fructose, hurricane, irreducible, improbable, tipoff, tularemia, vesper, whereabout, whitetail", + "schema_version": 6, + "seq": 27, + "span_id": "m1.model_call.27", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:22:10.049787+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 28, + "case_id": "bbh_word_sorting-0027", + "cycle": -1, + "duration_sec": 1.3343, + "event": "model_call", + "event_seq": 28, + "inputs": { + "prompt": "Sort the following words alphabetically: List: testate orchestra permafrost leasehold shiva\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: testate, orchestra, permafrost, leasehold, shiva.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **leasehold** (Starts with 'l')\n2. ** orchestra** (Starts with 'o')\n3. **permafrost** (Starts with 'p')\n4. **shiva** (Starts with 's')\n5. **testate** (Starts with 't')\n\nLet's re-sort based on the first letter: L, O, P, S, T.\n\n1. **leasehold** (L)\n2. **orchestra** (O)\n3. **permafrost** (P)\n4. **shiva** (S)\n5. **testate** (T)\n\nThe alphabetical order is: leasehold, orchestra, permafrost, shiva, testate.\n\nAnswer: leasehold, orchestra, permafrost, shiva, testate\nAnswer: leasehold, orchestra, permafrost, shiva, testate", + "schema_version": 6, + "seq": 28, + "span_id": "m1.model_call.28", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:22:11.385503+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 29, + "case_id": "bbh_word_sorting-0028", + "cycle": -1, + "duration_sec": 2.3699, + "event": "model_call", + "event_seq": 29, + "inputs": { + "prompt": "Sort the following words alphabetically: List: butterscotch veterinarian kochab molybdate extoller amos farrell academia glance predatory beautiful ford fiducial metzler europium circuitous diatom monomer\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. academia\n2. amos\n3. beautiful\n4. butterscotch\n5. circuitous\n6. diatom\n7. extoller\n8. farrell\n9. fiducial\n10. glance\n11. kochab\n12. ladmoner (Wait, the list has 'monomer'. Let's re-check the list.)\n\nOriginal List: butterscotch veterinarian kochab molybdate extoller amos farrell academia glance predatory beautiful ford fiducial metzler europium circuitous diatom monomer\n\nLet's sort them systematically:\n\n1. academia\n2. amos\n3. beautiful\n4. butterscotch\n5. circuitous\n6. diatom\n7. extoller\n8. farrell\n9. fiducial\n10. glance\n11. kochab\n12. metzler\n13. molybdate\n14. monomer\n15. predatory\n16. europium\n17. veterinarian\n18. ford\n\nLet's re-sort carefully:\n\nA: academia, amos\nB: beautiful, butterscotch\nC: circuitous\nD: diatom\nE: extoller, europium\nF: farrell, fiducial, ford\nG: glance\nK: kochab\nM: molybdate, monomer, metzler\nP: predatory\nV: veterinarian\n\nSorting within groups:\n\nA: academia, amos\nB: beautiful, butterscotch\nC: circuitous\nD: diatom\nE: extoller, europium\nF: farrell, fiducial, ford\nG: glance\nK: kochab\nM: molybdate, monomer, metzler (molybdate, monomer, metzler)\nP: predatory\nV: veterinarian\n\nFinal Alphabetical Order:\n1. academia\n2. amos\n3. beautiful\n4. butterscotch\n5. circuitous\n6. diatom\n7. extoller\n8. europium\n9. farrell\n10. fiducial\n11. ford\n12. glance\n13. kochab\n14. molybdate\n15. monomer\n16. metzler\n17. predatory\n18. veterinarian\n\nAnswer: academia, amos, beautiful, butterscotch, circuitous, diatom, extoller, europium, farrell, fiducial, ford", + "schema_version": 6, + "seq": 29, + "span_id": "m1.model_call.29", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:22:13.756933+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 30, + "case_id": "bbh_word_sorting-0029", + "cycle": -1, + "duration_sec": 1.2757, + "event": "model_call", + "event_seq": 30, + "inputs": { + "prompt": "Sort the following words alphabetically: List: bituminous ami decadent knickerbocker exeter\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: bituminous, ami, decadent, knickerbocker, exeter.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **ami**: Starts with 'a'\n2. **bituminous**: Starts with 'b'\n3. **decadent**: Starts with 'd'\n4. **exeter**: Starts with 'e'\n5. **knickerbocker**: Starts with 'k'\n\nComparing the first letters: a, b, d, e, k.\nIn alphabetical order: a, b, d, e, k.\n\n1. **ami** (a)\n2. **bituminous** (b)\n3. **decadent** (d)\n4. **exeter** (e)\n5. **knickerbocker** (k)\n\nThe sorted list is: ami, bituminous, decadent, exeter, knickerbocker.\n\nAnswer: ami, bituminous, decadent, exeter, knickerbocker", + "schema_version": 6, + "seq": 30, + "span_id": "m1.model_call.30", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:22:15.034054+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 31, + "case_id": "bbh_word_sorting-0030", + "cycle": -1, + "duration_sec": 2.5926, + "event": "model_call", + "event_seq": 31, + "inputs": { + "prompt": "Sort the following words alphabetically: List: woebegone nimble complementary afternoon immaculate laughlin kind hesitate loire septuagenarian sandia toggle dixie horsepower sierra shuffleboard mechanism\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. afternoon\n2. complementary\n3. dixie\n4. horsepower\n5. immaculate\n6. kind\n7. laughlin\n8. loire\n9. mechanism\n10. nimble\n11. sandia\n12. septuagenarian\n13. shuffleboard\n14. sierra\n15. toggle\n16. woebegone\n\nLet's re-sort them carefully:\n\n* **a**fternoon\n* **c**omplementary\n* **d**ixie\n* **h**orsepower\n* **i**mmaculate\n* **k**ind\n* **l**aughlin\n* **l**oire (L-A vs L-O: afternoon comes before loire)\n* **m**echanism\n* **n**imble\n* **s**andia\n* **s**eptuagenarian\n* **s**huffleboard\n* **s**ierra\n* **t**oggle\n* **w**oebegone\n\nLet's do a strict alphabetical sort:\n\n1. afternoon\n2. complementary\n3. dixie\n4. horsepower\n5. immaculate\n6. kind\n7. laughlin\n8. loire\n9. mechanism\n10. nimble\n11. sandia\n12. septuagenarian\n13. shuffleboard\n14. sierra\n15. toggle\n16. woebegone\n\nWait, let's check 'laughlin' vs 'loire' and 'mechanism' vs 'nimble'.\n\nL words: laughlin, loire\n* laughlin\n* loire\n\nM words: mechanism\nN words: nimble\n\nS words: sandia, septuagenarian, shuffleboard, sierra\n* sandia\n* septuagenarian\n* shuffleboard\n* sierra\n\nFinal check of the full list:\nafternoon\ncomplementary\ndixie\nhorsepower\nimmaculate\nkind\nlaughlin\nloire\nmechanism\nnimble\nsandia\nseptuagenarian\nshuffleboard\nsierra\ntoggle\nwoebegone\n\nAnswer: afternoon, complementary, dixie, horsepower, immaculate, kind, laughlin, loire, mechanism, nimble, sandia, septuagenarian, shuffleboard, sierra, toggle, woebegone", + "schema_version": 6, + "seq": 31, + "span_id": "m1.model_call.31", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:22:17.628073+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 32, + "case_id": "bbh_word_sorting-0031", + "cycle": -1, + "duration_sec": 1.7123, + "event": "model_call", + "event_seq": 32, + "inputs": { + "prompt": "Sort the following words alphabetically: List: thereupon wireman crag scrawny electrolytic wife cutover dickson clytemnestra took diocletian lipton inhuman stalk workplace marginal\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. clytemnestra\n2. crag\n3. cutover\n4. dickson\n5. diocletian\n6. electrolytic\n7. inhuman\n8. lipton\n9. marginal\n10. stalk\n11. thereupon\n12. workplace\n13. wireman\n14. wife\n15. took\n16. scrawny\n\nLet's re-sort them carefully:\n\n* clytemnestra\n* crag\n* cutover\n* dickson\n* diocletian\n* electrolytic\n* inhuman\n* lipton\n* marginal\n* stalk\n* thereupon\n* workplace\n* wireman\n* wife\n* took\n* scrawny\n\nComparing the initial list: thereupon, wireman, crag, scrawny, electrolytic, wife, cutover, dickson, clytemnestra, took, diocletian, lipton, inhuman, stalk, workplace, marginal.\n\nAlphabetical order:\n1. clytemnestra\n2. crag\n3. cutover\n4. dickson\n5. diocletian\n6. electrolytic\n7. inhuman\n8. lipton\n9. marginal\n10. stalk\n11. thereupon\n12. workplace\n13. wireman\n14. wife\n15. took\n16. scrawny\n\nAnswer: clytemnestra, crag, cutover, dickson, diocletian, electrolytic, inhuman, lipton, marginal, stalk, thereupon, workplace, wireman, wife, took, scrawny", + "schema_version": 6, + "seq": 32, + "span_id": "m1.model_call.32", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:22:19.342213+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 33, + "case_id": "bbh_word_sorting-0032", + "cycle": -1, + "duration_sec": 2.2014, + "event": "model_call", + "event_seq": 33, + "inputs": { + "prompt": "Sort the following words alphabetically: List: yves butt scribe seaworthy manumitted clapeyron lift blackguard reconnaissance agile woodcut lightfooted sawbelly meager geophysical wiseacre mathieu cognoscenti purposive flamboyant\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. agile\n2. blackguard\n3. clapeyron\n4. cognoscenti\n5. geophysical\n6. flamboyant\n7. lift\n8. lightfooted\n9. manumitted\n10. meager\n11. mathieu\n12. purposive\n13. reconnaissance\n14. seaworthy\n15. scribe\n16. sawbelly\n17. wiseacre\n18. woodcut\n19. yves\n20. butt\n\nLet's re-sort them carefully:\n\n* **a**gile\n* **b**lackguard, butt\n* **c**lapeyron, cognoscenti\n* **g**eophysical\n* **l**ift, lightfooted\n* **m**anumitted, meager, mathieu\n* **p**urposeive\n* **r**econnaissance\n* **s**eaworthy, scribe, sawbelly\n* **w**iseacre, woodcut, yves\n\nDetailed sorting:\n\n1. agile\n2. blackguard\n3. butt\n4. clapeyron\n5. cognoscenti\n6. geophysical\n7. flamboyant\n8. lift\n9. lightfooted\n10. manumitted\n11. meager\n12. mathieu\n13. purposive\n14. reconnaissance\n15. seaworthy\n16. scribe\n17. sawbelly\n18. wiseacre\n19. woodcut\n20. yves\n\nAnswer: agile, blackguard, butt, clapeyron, cognoscenti, geophysical, flamboyant, lift, lightfooted, manumitted, meager, mathieu, purposive, reconnaissance, seaworthy, scribe, sawbelly, wiseacre, woodcut, yves", + "schema_version": 6, + "seq": 33, + "span_id": "m1.model_call.33", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:22:21.546431+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 34, + "case_id": "bbh_word_sorting-0033", + "cycle": -1, + "duration_sec": 2.3829, + "event": "model_call", + "event_seq": 34, + "inputs": { + "prompt": "Sort the following words alphabetically: List: holocene muriel r&d pathetic talon mare staircase bradshaw aperture sigh\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: holocene, muriel, r&d, pathetic, talon, mare, staircase, bradshaw, aperture, sigh.\n\nTo sort them alphabetically, we compare them letter by letter:\n\n1. **aperture** (starts with 'a')\n2. **bradshaw** (starts with 'b')\n3. **holocene** (starts with 'h')\n4. **mare** (starts with 'm')\n5. **muriel** (starts with 'm', comes after 'mare' because 'u' comes after 'a')\n6. **pathetic** (starts with 'p')\n7. **r&d** (starts with 'r')\n8. **sigh** (starts with 's')\n9. **staircase** (starts with 's', comes after 'sigh' because 't' comes after 'i')\n10. **talon** (starts with 't', comes after 'staircase' because 'a' comes before 's' in the second letter, wait, let's recheck 'staircase' and 'talon'. 'st' vs 'ta'. 's' comes before 't', so 'staircase' comes before 'talon'.)\n\nLet's re-sort systematically:\n\nWords: holocene, muriel, r&d, pathetic, talon, mare, staircase, bradshaw, aperture, sigh\n\n1. **a**perture\n2. **b**radshaw\n3. **h**olocene\n4. **m**are\n5. **m**uriel (mare vs muriel: mare ends, muriel continues. 'mare' comes before 'muriel')\n6. **p**athetic\n7. **r**&d\n8. **s**igh\n9. **s**taircase\n10. **t**alon\n\nComparing 'sigh' and 'staircase':\nsigh (s-i)\nstaircase (s-t)\n'i' comes before 't', so 'sigh' comes before 'staircase'.\n\nComparing 'talon' and 'staircase':\ntalon (t-a)\nstaircase (s-t)\n's' comes before 't', so 'staircase' comes before 'talon'.\n\nFinal sorted order:\n", + "schema_version": 6, + "seq": 34, + "span_id": "m1.model_call.34", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:22:23.930962+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 35, + "case_id": "bbh_word_sorting-0034", + "cycle": -1, + "duration_sec": 2.5499, + "event": "model_call", + "event_seq": 35, + "inputs": { + "prompt": "Sort the following words alphabetically: List: spontaneity smooth execrable epaulet bobbin calumny hun anastasia omnipotent award hostelry macedon roughshod burrow align apache putty adsorption anastomotic\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. apache\n2. adsorption\n3. align\n4. anastomotic\n5. anastasia\n6. award\n7. bobbin\n8. burrow\n9. calumny\n10. hun\n11. hostelry\n12. macedon\n13. omnipotent\n14. epaulet\n15. putty\n16. roughshod\n17. spontaneity\n18. smooth\n\nLet's re-sort them carefully:\n\n* **a**pache\n* **a**dsorption\n* **a**lign\n* **a**nastomotic\n* **a**nastasia\n* **a**ward\n* **b**obbin\n* **b**urrow\n* **c**alumny\n* **h**on (assuming 'hun' is a typo or should be sorted as 'hun')\n* **h**ostelry\n* **m**acedon\n* **o**mnipotent\n* **e**paulet\n* **p**utty\n* **r**oughshod\n* **s**pontaneity\n* **s**mooth\n\nLet's re-examine the list provided: spontaneity, smooth, execrable, epaulet, bobbin, calumny, hun, anastasia, omnipotent, award, hostelry, macedon, roughshod, burrow, align, apache, putty, adsorption, anastomotic.\n\n**Alphabetical Sorting:**\n\n1. apache\n2. adsorption\n3. align\n4. anastomotic\n5. anastasia\n6. award\n7. bobbin\n8. burrow\n9. calumny\n10. execrable\n11. epaulet\n12. hun\n13. hostelry\n14. macedon\n15. omnipotent\n16. putty\n17. roughshod\n18. spontaneity\n19. smooth\n\n(Note: 'execrable' comes before 'epaulet' if we strictly follow the standard alphabet, but 'epaulet' starts with 'e' and 'execrable' starts with 'e'. E-P comes before E-X.)\n\nLet's re-check the 'E' words: execrable, epaulet.\nE-P-A-U-L-E-", + "schema_version": 6, + "seq": 35, + "span_id": "m1.model_call.35", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:22:26.482360+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 36, + "case_id": "bbh_word_sorting-0035", + "cycle": -1, + "duration_sec": 2.111, + "event": "model_call", + "event_seq": 36, + "inputs": { + "prompt": "Sort the following words alphabetically: List: covenant thence davenport sonorant scoreboard densitometer noisy\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: covenant, thence, davenport, sonorant, scoreboard, densitometer, noisy.\n\nWe need to sort these words alphabetically.\n\n1. **covenant**\n2. **davenport**\n3. **densitometer**\n4. **noisy**\n5. **scoreboard**\n6. **sonorant**\n7. **thence**\n\nLet's compare them letter by letter:\n\n* **c**ovenant\n* **d**avenport\n* **d**ensitometer (davenport comes before densitometer because 'a' comes before 'e')\n* **n**oisy\n* **s**coreboard\n* **s**onorant (scoreboard comes before sonorant because 'c' comes before 'o')\n* **t**hence\n\nLet's re-sort carefully:\n\n1. **covenant** (Starts with C)\n2. **davenport** (Starts with D, 'a')\n3. **densitometer** (Starts with D, 'e') $\\rightarrow$ davenport comes before densitometer.\n4. **noisy** (Starts with N)\n5. **scoreboard** (Starts with S, 'c')\n6. **sonorant** (Starts with S, 'o') $\\rightarrow$ scoreboard comes before sonorant.\n7. **thence** (Starts with T)\n\nThe correct alphabetical order is:\n1. covenant\n2. davenport\n3. densitometer\n4. noisy\n5. scoreboard\n6. sonorant\n7. thence\n\nLet's verify the D words:\ndavenport\ndensitometer\n'a' comes before 'e', so davenport comes before densitometer.\n\nLet's verify the S words:\nscoreboard\nsonorant\n'c' comes before 'o', so scoreboard comes before sonorant.\n\nFinal sorted list: covenant, davenport, densitometer, noisy, scoreboard, sonorant, thence.\n\nAnswer: covenant, davenport, densitometer, noisy, scoreboard, sonorant, thence", + "schema_version": 6, + "seq": 36, + "span_id": "m1.model_call.36", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:22:28.594917+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 37, + "case_id": "bbh_word_sorting-0036", + "cycle": -1, + "duration_sec": 2.0529, + "event": "model_call", + "event_seq": 37, + "inputs": { + "prompt": "Sort the following words alphabetically: List: sinter cortex kangaroo pillage threshold insane mcleod roundabout stipulate marionette trammel incident\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words to sort alphabetically:\nsinter, cortex, kangaroo, pillage, threshold, insane, mcleod, roundabout, stipulate, marionette, trammel, incident\n\nLet's sort them:\n\n1. **cortex**\n2. **incident**\n3. **insane**\n4. **kangaroo**\n5. **mcleod**\n6. **marionette**\n7. **pillage**\n8. **roundabout**\n9. **sinter**\n10. **stipulate**\n11. **threshold**\n12. **trammel**\n\nLet's re-check the sorting carefully, especially for words starting with similar letters:\n\n* **c**ortex\n* **i**ncident, **i**nsane (incident before insane)\n* **k**angaroo\n* **m**cleod, **m**arionette (mcleod before marionette)\n* **p**illage\n* **r**oundabout\n* **s**inter, **s**tipulate (sinter before stipulate)\n* **t**hreshold, **t**rammel\n\nCorrected alphabetical order:\n\n1. cortex\n2. incident\n3. insane\n4. kangaroo\n5. mcleod\n6. marionette\n7. pillage\n8. roundabout\n9. sinter\n10. stipulate\n11. threshold\n12. trammel\n\nAnswer: cortex, incident, insane, kangaroo, mcleod, marionette, pillage, roundabout, sinter, stipulate, threshold, trammel\n\nAnswer: cortex, incident, insane, kangaroo, mcleod, marionette, pillage, roundabout, sinter, stipulate, threshold, trammel", + "schema_version": 6, + "seq": 37, + "span_id": "m1.model_call.37", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:22:30.649663+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 38, + "case_id": "bbh_word_sorting-0037", + "cycle": -1, + "duration_sec": 2.2196, + "event": "model_call", + "event_seq": 38, + "inputs": { + "prompt": "Sort the following words alphabetically: List: them abdominal address fomalhaut pay optimal hanoverian yang berry tattoo islamabad swathe jordan bounty stearic stigmata involve effusive tornado\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. abdominal\n2. address\n3. bounty\n4. berry\n5. effusive\n6. fomalhaut\n7. hanoverian\n8. islamabad\n9. involve\n10. pay\n11. stigmata\n12. stearic\n13. swathe\n14. tattoo\n15. them\n16. tornado\n17. optimal\n18. yang\n19. jordan\n\nLet's re-sort them carefully:\n\n* **a**bdominal\n* **a**ddress\n* **b**ounty\n* **b**erry\n* **e**ffusive\n* **f**omalhaut\n* **h**anoverian\n* **i**slamabad\n* **i**nvolve\n* **p**ay\n* **s**tigmata\n* **s**tearic\n* **s**wathe\n* **t**attoo\n* **t**hem\n* **t**ornado\n* **o**ptimal\n* **y**ang\n* **j**ordan (Wait, J comes before O and Y)\n\nLet's re-sort based on the first letter:\n\nA: abdominal, address\nB: bounty, berry\nE: effusive\nF: fomalhaut\nH: hanoverian\nI: islamabad, involve\nJ: jordan\nO: optimal\nS: stigmata, stearic, swathe\nT: tattoo, them, tornado\nY: yang\n\n**Detailed Sorting:**\n\n1. abdominal\n2. address\n3. bounty\n4. berry\n5. effusive\n6. fomalhaut\n7. hanoverian\n8. islamabad\n9. involve\n10. jordan\n11. optimal\n12. stigmata\n13. stearic\n14. swathe\n15. tattoo\n16. them\n17. tornado\n18. yang\n\nAnswer: abdominal, address, bounty, berry, effusive, fomalhaut, hanoverian, islamabad, involve, jordan, optimal, stigmata, stearic, swathe, tattoo, them, tornado, yang", + "schema_version": 6, + "seq": 38, + "span_id": "m1.model_call.38", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:22:32.870968+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 39, + "case_id": "bbh_word_sorting-0038", + "cycle": -1, + "duration_sec": 0.8992, + "event": "model_call", + "event_seq": 39, + "inputs": { + "prompt": "Sort the following words alphabetically: List: coypu anharmonic inflammation beauteous\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: coypu, anharmonic, inflammation, beauteous.\n\nWe need to sort these words alphabetically.\n\n1. **Compare the first letters:**\n - coypu starts with 'c'\n - anharmonic starts with 'a'\n - inflammation starts with 'i'\n - beauteous starts with 'b'\n\n The alphabetical order of the starting letters is: a, b, c, i.\n\n2. **Order the words based on the starting letters:**\n - 'a': anharmonic\n - 'b': beauteous\n - 'c': coypu\n - 'i': inflammation\n\n3. **The sorted list is:** anharmonic, beauteous, coypu, inflammation.\n\nAnswer: anharmonic, beauteous, coypu, inflammation", + "schema_version": 6, + "seq": 39, + "span_id": "m1.model_call.39", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:22:33.771813+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 40, + "case_id": "bbh_word_sorting-0039", + "cycle": -1, + "duration_sec": 2.3725, + "event": "model_call", + "event_seq": 40, + "inputs": { + "prompt": "Sort the following words alphabetically: List: thirteenth tinfoil thimble snuff ernie pow celebrity abuilding indiscoverable chigger burgher synchronous yap medlar exorcism newsboy\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. abuilding\n2. celebrity\n3. burgher\n4. chigger\n5. indiscoverable\n6. medlar\n7. newsboy\n8. exorcism\n9. pow\n10. snuff\n11. synchronous\n12. thirteenth\n13. tinfoil\n14. thimble\n15. ernie\n16. yap\n\nLet's re-sort them carefully:\n\n* **a**building\n* **c**elebrity\n* **b**urgher\n* **c**higger\n* **i**ndiscoverable\n* **m**edlar\n* **n**ewsboy\n* **e**xorcism (comes before pow)\n* **p**ow\n* **s**nuff\n* **s**ynchronous\n* **t**hirteenth\n* **t**infoil\n* **t**himble\n* **e**rnie (comes before thimble)\n* **y**ap\n\nLet's do a strict alphabetical sort:\n\n1. abuilding\n2. celebrity\n3. burgher\n4. chigger\n5. ernie\n6. indiscoverable\n7. medlar\n8. newsboy\n9. exorcism\n10. pow\n11. snuff\n12. synchronous\n13. thirteenth\n14. tinfoil\n15. thimble\n16. yap\n\nWait, let's check the 't' words: thirteenth, tinfoil, thimble.\nthirteenth (thir...)\ntinfoil (tinf...)\nthimble (thim...)\nthimble comes before thirteenth.\ntinfoil comes before thimble.\nthirteenth comes after tinfoil.\n\nCorrected 't' order: tinfoil, thimble, thirteenth.\n\nLet's re-sort the whole list:\n\n1. abuilding\n2. celebrity\n3. burgher\n4. chigger\n5. ernie\n6. indiscoverable\n7. medlar\n8. newsboy\n9. exorcism\n10. pow\n11. snuff\n12. synchronous\n13. tinfoil\n14. thimble\n15. thirteenth\n16. yap\n\nAnswer: abuilding, celebrity, burgher,", + "schema_version": 6, + "seq": 40, + "span_id": "m1.model_call.40", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:22:36.146051+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 41, + "case_id": "bbh_word_sorting-0040", + "cycle": -1, + "duration_sec": 2.2221, + "event": "model_call", + "event_seq": 41, + "inputs": { + "prompt": "Sort the following words alphabetically: List: avoid whimsey grassland elegy pitfall error gaillardia hostile caramel conclave stepchild accept carbuncle compressor utopia drib spectra rosa embower\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. accept\n2. avoid\n3. carbuncle\n4. caramel\n5. conclave\n6. compressor\n7. drib\n8. elegy\n9. embower\n10. error\n11. gaillardia\n12. hostile\n13. pitfall\n14. rosa\n15. spectra\n16. stepchild\n17. utopia\n18. whimsey\n19. grassland\n\nLet's re-sort them carefully:\n\n* **a**: accept, avoid\n* **c**: carbuncle, caramel, conclave, compressor\n* **d**: drib\n* **e**: elegy, error, embower\n* **g**: gaillardia, grassland\n* **h**: hostile\n* **p**: pitfall\n* **r**: rosa\n* **s**: spectra, stepchild\n* **u**: utopia\n* **w**: whimsey\n\n**Detailed Sorting:**\n\n1. accept\n2. avoid\n3. carbuncle\n4. caramel\n5. conclave\n6. compressor\n7. drib\n8. elegy\n9. embower\n10. error\n11. gaillardia\n12. grassland\n13. hostile\n14. pitfall\n15. rosa\n16. spectra\n17. stepchild\n18. utopia\n19. whimsey\n\n(Checking 'gaillardia' vs 'grassland': g-a-i vs g-r-a. 'gaillardia' comes before 'grassland'.)\n(Checking 'error' vs 'embower': e-r vs e-m. 'embower' comes before 'error'.)\n\n**Final Sorted List:**\naccept, avoid, carbuncle, caramel, conclave, compressor, drib, elegy, embower, error, gaillardia, grassland, hostile, pitfall, rosa, spectra, stepchild, utopia, whimsey\n\nAnswer: accept, avoid, carbuncle, caramel, conclave, compressor, drib, elegy, embower, error, gaillardia, grassland, hostile, pitfall, rosa, spectra, stepchild, utopia, whimsey", + "schema_version": 6, + "seq": 41, + "span_id": "m1.model_call.41", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:22:38.370010+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 42, + "case_id": "bbh_word_sorting-0041", + "cycle": -1, + "duration_sec": 2.3724, + "event": "model_call", + "event_seq": 42, + "inputs": { + "prompt": "Sort the following words alphabetically: List: sociolinguistic nigeria odysseus demystify proton voltaire penny contaminate bighorn stirrup\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. bighorn\n2. contaminate\n3. demystify\n4. odysseus\n5. penny\n6. proton\n7. sociolinguistic\n8. stirrup\n9. voltaire\n10. nigeria\n11. **voltaire** (Wait, I listed voltaire twice in my mental check, let's re-sort carefully)\n\nLet's list them and sort them:\nsociolinguistic, nigeria, odysseus, demystify, proton, voltaire, penny, contaminate, bighorn, stirrup\n\n1. bighorn\n2. contaminate\n3. demystify\n4. nigeria\n5. odysseus\n6. penny\n7. proton\n8. sociolinguistic\n9. stirrup\n10. voltaire\n\nLet's re-check the sorting based on the initial list: sociolinguistic, nigeria, odysseus, demystify, proton, voltaire, penny, contaminate, bighorn, stirrup\n\n* Bighorn (B)\n* Contaminate (C)\n* Demystify (D)\n* Nigeria (N)\n* Odysseus (O)\n* Penny (P)\n* Proton (P) - P-e comes before P-e-n, so Penny before Proton.\n* Sociolinguistic (S)\n* Stirrup (S) - Stirrup comes after Sociolinguistic (S-t vs S-o).\n* Voltaire (V)\n\nCorrect alphabetical order:\n1. bighorn\n2. contaminate\n3. demystify\n4. nigeria\n5. odysseus\n6. penny\n7. proton\n8. sociolinguistic\n9. stirrup\n10. voltaire\n\nWait, let's check S words: sociolinguistic, stirrup.\nS-o-c... vs S-t-i... $\\rightarrow$ sociolinguistic comes before stirrup.\n\nFinal check:\nBighorn\nContaminate\nDemystify\nNigeria\nOdysseus\nPenny\nProton\nSociolinguistic\nStirrup\nVoltaire\n\nAnswer: bighorn, contaminate, demystify, nigeria, odysseus, penny, proton, sociolinguistic, stirrup, voltaire\n\nAnswer: bighorn, contaminate, demystify, nigeria, odysseus, penny, proton, sociolinguistic, stir", + "schema_version": 6, + "seq": 42, + "span_id": "m1.model_call.42", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:22:40.744246+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 43, + "case_id": "bbh_word_sorting-0042", + "cycle": -1, + "duration_sec": 1.7897, + "event": "model_call", + "event_seq": 43, + "inputs": { + "prompt": "Sort the following words alphabetically: List: falter tuscarora invigorate porridge wetland libya residuary wrapup ludlow landlady kamikaze christiana jot antler mallow\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. antler\n2. christiana\n3. falter\n4. landlady\n5. libya\n6. ludlow\n7. mallow\n8. porridge\n9. residuary\n10. tuscarora\n11. wrapup\n12. invigorate\n13. jot\n14. kamikaze\n15. wetland\n\nLet's re-sort them carefully:\n\n* **a**ntler\n* **c**hristiana\n* **f**alter\n* **i**nvigorate\n* **j**ot\n* **k**amikaze\n* **l**andlady\n* **l**ibya\n* **l**udlow\n* **m**allow\n* **p**orridge\n* **r**esiduary\n* **t**uscarora\n* **w**etland\n* **w**rapup\n\nSorting the 'L' words: landlady, libya, ludlow.\n* landlady\n* libya\n* ludlow\n\nSorting the 'W' words: wetland, wrapup.\n* wetland\n* wrapup\n\nThe final sorted list:\n1. antler\n2. christiana\n3. falter\n4. invigorate\n5. jot\n6. kamikaze\n7. landlady\n8. libya\n9. ludlow\n10. mallow\n11. porridge\n12. residuary\n13. tuscarora\n14. wetland\n15. wrapup\n\nAnswer: antler, christiana, falter, invigorate, jot, kamikaze, landlady, libya, ludlow, mallow, porridge, residuary, tuscarora, wetland, wrapup", + "schema_version": 6, + "seq": 43, + "span_id": "m1.model_call.43", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:22:42.535708+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 44, + "case_id": "bbh_word_sorting-0043", + "cycle": -1, + "duration_sec": 2.3719, + "event": "model_call", + "event_seq": 44, + "inputs": { + "prompt": "Sort the following words alphabetically: List: condescend rampant percolate coltish date rochester placid significant\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: condescend, rampant, percolate, coltish, date, rochester, placid, significant.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter:**\n * c: condescend\n * r: rampant, rochester\n * p: percolate, placid\n * c: coltish\n * d: date\n * r: (already listed)\n * p: (already listed)\n * s: significant\n\n2. **Sort words starting with 'c':**\n * condescend\n * coltish\n * (Order: condescend, coltish)\n\n3. **Sort words starting with 'd':**\n * date\n\n4. **Sort words starting with 'p':**\n * percolate\n * placid\n * (Comparing 'percolate' and 'placid': 'p' is the same. 'e' in percolate comes before 'l' in placid. Wait, 'l' comes before 'e' in the alphabet? No. 'e' comes before 'l'. Let's recheck: p-e-r-c-o-l-a-t-e vs p-l-a-c-i-d. 'e' comes before 'l'. So, percolate comes before placid.)\n * percolate\n * placid\n\n5. **Sort words starting with 'r':**\n * rampant\n * rochester\n * (Comparing 'rampant' and 'rochester': 'a' in rampant comes before 'o' in rochester.)\n * rampant\n * rochester\n\n6. **Sort words starting with 's':**\n * significant\n\n**Combining the sorted groups:**\n\n1. condescend\n2. coltish\n3. date\n4. percolate\n5. placid\n6. rampant\n7. rochester\n8. significant\n\nLet's re-verify the full alphabetical order:\n* condescend (c-o...)\n* coltish (c-o...) -> condescend vs coltish: 'n' vs 'l'. 'l' comes before 'n'. So, coltish comes before condescend.\n * Corrected 'c' order: coltish, condescend\n* date (d)\n* percolate (p-e", + "schema_version": 6, + "seq": 44, + "span_id": "m1.model_call.44", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:22:44.909637+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 45, + "case_id": "bbh_word_sorting-0044", + "cycle": -1, + "duration_sec": 2.5127, + "event": "model_call", + "event_seq": 45, + "inputs": { + "prompt": "Sort the following words alphabetically: List: almost delphic semitic unbeknownst astute deadlock libertarian antic levitate execution sanction scathe storehouse dandelion affable sweeney fortunate leverage horntail apache\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. almost\n2. apache\n3. affable\n4. antic\n5. astute\n6. delphic\n7. deadlock\n8. execution\n9. fortunate\n10. leverage\n11. horntail\n12. libertarian\n13. levitate\n14. scathe\n15. semitic\n16. sweeney\n17. sanction\n18. storehouse\n19. unbeknownst\n20. dandelion\n\nLet's re-sort them carefully:\n\n* **a**lmost\n* **a**pache\n* **a**ffable\n* **a**ntic\n* **a**stute\n* **d**elphic\n* **d**eadlock\n* **e**xecution\n* **f**ortunate\n* **l**everage\n* **l**ebitate\n* **l**ibertarian\n* **h**orntail\n* **s**cache\n* **s**emitic\n* **s**weeney\n* **s**anction\n* **s**torehouse\n* **u**nbeknownst\n* **d**andelion (Wait, 'd' comes before 'e' and 'f'. Let's re-check the full list.)\n\nOriginal List: almost, delphic, semitic, unbeknownst, astute, deadlock, libertarian, antic, levitate, execution, sanction, scathe, storehouse, dandelion, affable, sweeney, fortunate, leverage, horntail, apache\n\nAlphabetical Sorting:\n\n1. almost\n2. apache\n3. affable\n4. antic\n5. astute\n6. dandelion\n7. delphic\n8. deadlock\n9. execution\n10. fortunate\n11. leverage\n12. horntail\n13. levitate\n14. libertarian\n15. scathe\n16. sanction\n17. semitic\n18. sweeney\n19. storehouse\n20. unbeknownst\n\nAnswer: almost, apache, affable, antic, astute, dandelion, delphic, deadlock, execution, fortunate, leverage, horntail, levitate, libertarian, scathe, sanction, semitic, sweeney, storehouse, unbeknownst", + "schema_version": 6, + "seq": 45, + "span_id": "m1.model_call.45", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:22:47.424405+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 46, + "case_id": "bbh_word_sorting-0045", + "cycle": -1, + "duration_sec": 1.81, + "event": "model_call", + "event_seq": 46, + "inputs": { + "prompt": "Sort the following words alphabetically: List: spongy separate gabriel jackson quail hendrickson washy backslide shelter calvert anastomosis variety commando pizzicato sticktight syndicate hollister\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. backslide\n2. calvert\n3. gabriel\n4. hendrickson\n5. hollister\n6. jackson\n7. pizzicato\n8. quail\n9. separate\n10. shelter\n11. sticktight\n12. spongy\n13. syndicate\n14. variety\n15. washy\n16. commando\n17. anastomosis\n\nLet's re-sort them carefully:\n\n* backslide\n* calvert\n* gabriel\n* hendrickson\n* hollister\n* jackson\n* pizzicato\n* quail\n* separate\n* shelter\n* sticktight\n* spongy\n* syndicate\n* variety\n* washy\n* commando\n* anastomosis\n\nComparing the words:\nB: backslide\nC: calvert, commando\nG: gabriel\nH: hendrickson, hollister\nJ: jackson\nP: pizzicato\nQ: quail\nS: separate, shelter, spongy, sticktight, syndicate\nV: variety\nW: washy\nA: anastomosis\n\nDetailed alphabetical sort:\n1. backslide\n2. calvert\n3. commando\n4. gabriel\n5. hendrickson\n6. hollister\n7. jackson\n8. anastomosis\n9. pizzicato\n10. quail\n11. separate\n12. shelter\n13. sticktight\n14. spongy\n15. syndicate\n16. variety\n17. washy\n\nAnswer: backslide, calvert, commando, gabriel, hendrickson, hollister, jackson, anastomosis, pizzicato, quail, separate, shelter, sticktight, spongy, syndicate, variety, washy", + "schema_version": 6, + "seq": 46, + "span_id": "m1.model_call.46", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:22:49.236269+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 47, + "case_id": "bbh_word_sorting-0046", + "cycle": -1, + "duration_sec": 0.9465, + "event": "model_call", + "event_seq": 47, + "inputs": { + "prompt": "Sort the following words alphabetically: List: syndrome therefrom\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The words to be sorted are: syndrome, therefrom.\n\nTo sort them alphabetically, we compare them letter by letter:\n\n1. **syndrome**: Starts with 's', followed by 'y', 'n', 'd', 'r', 'o', 'm', 'e'.\n2. **therefrom**: Starts with 't', followed by 'h', 'e', 'r', 'e', 'f', 'r', 'o', 'm'.\n\nComparing the first letters: 's' comes before 't' in the alphabet.\n\nTherefore, \"syndrome\" comes before \"therefrom\".\n\nThe alphabetical order is: syndrome, therefrom.\n\nAnswer: syndrome, therefrom", + "schema_version": 6, + "seq": 47, + "span_id": "m1.model_call.47", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:22:50.184729+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 48, + "case_id": "bbh_word_sorting-0047", + "cycle": -1, + "duration_sec": 2.3676, + "event": "model_call", + "event_seq": 48, + "inputs": { + "prompt": "Sort the following words alphabetically: List: odessa dance formulae dietetic mantle cost huddle resplendent bolshevism proust warmth deadline progeny hesitant palace rackety judson foster belize thirdhand\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. bolshevism\n2. belize\n3. dance\n4. deadline\n5. dietetic\n6. foster\n7. formulae\n8. hesitant\n9. huddle\n10. judson\n11. mantle\n12. odessa\n13. palace\n14. proust\n15. rackety\n16. resplendent\n17. thirdhand\n18. warmth\n19. cost\n\nLet's re-sort them carefully:\n\n* **b**olshevism, **b**elize (bolshevism before belize)\n* **d**ance, **d**eadline, **d**ietetic\n* **f**oster, **f**ormulae\n* **h**esitant, **h**uddle\n* **j**udson\n* **m**antle\n* **o**dessa\n* **p**alace, **p**roust\n* **r**ackety, **r**esplendent\n* **t**hirdhand\n* **w**armth\n* **c**ost\n\n**Alphabetical Order:**\n\n1. bolshevism\n2. belize\n3. dance\n4. deadline\n5. dietetic\n6. foster\n7. formulae\n8. hesitant\n9. huddle\n10. judson\n11. mantle\n12. odessa\n13. palace\n14. proust\n15. rackety\n16. resplendent\n17. thirdhand\n18. warmth\n19. cost\n\nWait, let's check the 'b' words again: bolshevism, belize. 'b'e comes before 'b'o.\n\nLet's re-sort the full list:\nodessa, dance, formulae, dietetic, mantle, cost, huddle, resplendent, bolshevism, proust, warmth, deadline, progeny, hesitant, palace, rackety, judson, foster, belize, thirdhand\n\n(Note: I missed 'progeny' in my initial count, let's include it.)\n\nFull List: odessa, dance, formulae, dietetic, mantle, cost, huddle, resplendent, bolshevism, proust, warmth, deadline, progeny, hesitant, palace, rackety, jud", + "schema_version": 6, + "seq": 48, + "span_id": "m1.model_call.48", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:22:52.554388+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 49, + "case_id": "bbh_word_sorting-0048", + "cycle": -1, + "duration_sec": 2.3009, + "event": "model_call", + "event_seq": 49, + "inputs": { + "prompt": "Sort the following words alphabetically: List: automate abner shampoo gurgle none exquisite abramson amity fruitful shorten waterproof\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words to sort: automate, abner, shampoo, gurgle, none, exquisite, abramson, amity, fruitful, shorten, waterproof.\n\nLet's sort them alphabetically:\n\n1. **abner**\n2. **abramson**\n3. **automate**\n4. **amity**\n5. **fruitful**\n6. **gurgle**\n7. **none**\n8. **shorten**\n9. **shampoo**\n10. **waterproof**\n11. **exquisite**\n\nLet's re-sort carefully, paying attention to the starting letters:\n\n* **a**: automate, abner, abramson, amity\n * abner\n * abramson\n * amity\n * automate\n* **f**: fruitful\n* **g**: gurgle\n* **n**: none\n* **s**: shampoo, shorten\n* **w**: waterproof\n* **e**: exquisite\n\nCorrect alphabetical order:\n\n1. abner\n2. abramson\n3. amity\n4. automate\n5. exquisite\n6. fruitful\n7. gurgle\n8. none\n9. shorten\n10. shampoo\n11. waterproof\n\nLet's check the 'a' words again:\nabner\nabramson\namity\nautomate\n(a, b, a, a) -> abner, abramson, amity, automate. This order is correct.\n\nLet's check the 'e' word:\nexquisite (comes after automate)\n\nLet's check 'f' and 'g':\nfruitful, gurgle (f comes before g)\n\nLet's check 'n' and 's':\nnone, shorten, shampoo (n comes before s)\n\nLet's check 'w':\nwaterproof (w is last among these)\n\nFinal sorted list:\n1. abner\n2. abramson\n3. amity\n4. automate\n5. exquisite\n6. fruitful\n7. gurgle\n8. none\n9. shorten\n10. shampoo\n11. waterproof\n\nAnswer: abner, abramson, amity, automate, exquisite, fruitful, gurgle, none, shorten, shampoo, waterproof", + "schema_version": 6, + "seq": 49, + "span_id": "m1.model_call.49", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:22:54.857243+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 50, + "case_id": "bbh_word_sorting-0049", + "cycle": -1, + "duration_sec": 2.5593, + "event": "model_call", + "event_seq": 50, + "inputs": { + "prompt": "Sort the following words alphabetically: List: crystallography survey bindle rundown shipshape roadside strange chiang dent mambo savannah spew won't ram\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words to sort alphabetically:\ncrystallography, survey, bindle, rundown, shipshape, roadside, strange, chiang, dent, mambo, savannah, spew, won't, ram\n\nLet's sort them:\n\n1. bindle\n2. chiang\n3. crystallography\n4. dent\n5. mambo\n6. ram\n7. roadside\n8. rundown\n9. survey\n10. shipshape\n11. savannah\n12. strange\n13. spew\n14. won't\n\nLet's re-sort carefully, paying attention to the first letter:\n\n* **b**: bindle\n* **c**: crystallography, chiang\n* **d**: dent\n* **m**: mambo\n* **r**: ram, roadside, rundown\n* **s**: survey, shipshape, savannah, strange, spew\n* **w**: won't\n\n**Sorting within groups:**\n\n**C:** crystallography, chiang (cryst... comes before chiang)\n * crystallography\n * chiang\n**R:** ram, roadside, rundown (ram, roadside, rundown)\n * ram\n * roadside\n * rundown\n**S:** survey, shipshape, savannah, strange, spew (savannah, shipshape, spew, survey, strange)\n * savannah\n * shipshape\n * spew\n * survey\n * strange\n**W:** won't\n\n**Final Alphabetical Order:**\n\n1. bindle\n2. crystallography\n3. chiang\n4. dent\n5. mambo\n6. ram\n7. roadside\n8. rundown\n9. savannah\n10. shipshape\n11. spew\n12. survey\n13. strange\n14. won't\n\nLet's re-check the 'R' words: ram, roadside, rundown.\nram (ra...)\nroadside (ro...)\nrundown (ru...)\nOrder: ram, roadside, rundown. (Correct)\n\nLet's re-check the 'S' words: savannah, shipshape, spew, survey, strange.\nsavannah (sa...)\nshipshape (sh...)\nspew (sp...)\nsurvey (su...)\nstrange (st...)\nOrder: savannah, shipshape, spew, survey, strange. (Correct)\n\nThe", + "schema_version": 6, + "seq": 50, + "span_id": "m1.model_call.50", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:22:57.418646+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 51, + "case_id": "bbh_word_sorting-0050", + "cycle": -1, + "duration_sec": 2.3667, + "event": "model_call", + "event_seq": 51, + "inputs": { + "prompt": "Sort the following words alphabetically: List: griffith common nanosecond loosestrife decoy swabby aerospace denmark yates frill spitz enviable screechy allure sow spermatozoa saute jibe exclusive\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. aerospace\n2. allure\n3. decoy\n4. denmark\n5. enviable\n6. exclusive\n7. frill\n8. griffith\n9. jibe\n10. loosestrife\n11. nanosecond\n12. sow\n13. spitz\n14. screechy\n15. spermatozoa\n16. saute\n17. swabby\n18. yates\n19. common\n\nLet's re-sort them carefully:\n\n* **a**erospace\n* **a**llure\n* **c**ommon\n* **d**enmark\n* **d**ecoy\n* **e**nable\n* **e**xclusive\n* **f**rill\n* **g**riffith\n* **j**ibe\n* **l**oosestrife\n* **n**anosecond\n* **s**ow\n* **s**pitz\n* **s**creechy\n* **s** spermatozoa\n* **s**aute\n* **s**wabby\n* **y**ates\n\n**Detailed Alphabetical Sorting:**\n\n1. aerospace\n2. allure\n3. common\n4. denmark\n5. decoy\n6. enviable\n7. exclusive\n8. frill\n9. griffith\n10. jibe\n11. loosestrife\n12. nanosecond\n13. sow\n14. spitz\n15. screechy\n16. spermatozoa\n17. saute\n18. swabby\n19. yates\n\n(Checking 's' words: spermatozoa, saute, screechy, sow, spitz, swabby. Order: spermatozoa, saute, screechy, sow, spitz, swabby)\n\nLet's re-verify the 's' words:\n* spermatozoa\n* saute\n* screechy\n* sow\n* spitz\n* swabby\n\nCorrect order for 's' words:\n1. spermatozoa\n2. saute\n3. screechy\n4. sow\n5. spitz\n6. swabby\n\nFinal Sorted List:\n1. aerospace\n2. allure\n3. common\n4. denmark\n5. decoy\n6. enviable\n7. exclusive\n8. frill\n9. griffith\n10. jibe\n11.", + "schema_version": 6, + "seq": 51, + "span_id": "m1.model_call.51", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:22:59.787319+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 52, + "case_id": "bbh_word_sorting-0051", + "cycle": -1, + "duration_sec": 2.0881, + "event": "model_call", + "event_seq": 52, + "inputs": { + "prompt": "Sort the following words alphabetically: List: dynastic inflammable prick tristan vitiate tackle stagnate conglomerate nebulae phosphide\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words to sort: dynastic, inflammable, prick, tristan, vitiate, tackle, stagnate, conglomerate, nebulae, phosphide.\n\nWe will sort them alphabetically:\n\n1. **conglomerate**\n2. **dynastic**\n3. **inflammable**\n4. **nebulae**\n5. **phosphide**\n6. **prick**\n7. **stagnate**\n8. **tackle**\n9. **tristan**\n10. **vitiate**\n\nLet's re-check the sorting carefully:\n\n* **c**onglomerate\n* **d**ynastic\n* **i**nflammable\n* **n**ebulae\n* **p**hosphide\n* **p**rick (P-h comes before P-r)\n* **s**tagnate\n* **t**ackle\n* **t**ristan (T-a comes before T-r)\n* **v**itiate\n\nLet's re-sort the 'P' words: phosphide, prick.\nP-h-o...\nP-r-i...\n'h' comes before 'r', so phosphide comes before prick.\n\nCorrected order:\n1. conglomerate\n2. dynastic\n3. inflammable\n4. nebulae\n5. phosphide\n6. prick\n7. stagnate\n8. tackle\n9. tristan\n10. vitiate\n\nLet's check the 'T' words: tackle, tristan.\ntackle (ta...)\ntristan (tr...)\n'a' comes before 'r', so tackle comes before tristan.\n\nFinal sorted list:\n1. conglomerate\n2. dynastic\n3. inflammable\n4. nebulae\n5. phosphide\n6. prick\n7. stagnate\n8. tackle\n9. tristan\n10. vitiate\n\nAnswer: conglomerate, dynastic, inflammable, nebulae, phosphide, prick, stagnate, tackle, tristan, vitiate", + "schema_version": 6, + "seq": 52, + "span_id": "m1.model_call.52", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:23:01.877661+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 53, + "case_id": "bbh_word_sorting-0052", + "cycle": -1, + "duration_sec": 1.726, + "event": "model_call", + "event_seq": 53, + "inputs": { + "prompt": "Sort the following words alphabetically: List: din spruce nate pomade jordan gaillardia putt inroad jest kill jackboot whelan mirth amerada craftsmen shortcoming eclipse\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. amerada\n2. craftsmen\n3. eclipse\n4. din\n5. gaillardia\n6. inroad\n7. jest\n8. jackboot\n9. jordan\n10. kill\n11. mirth\n12. nate\n13. pomade\n14. putt\n15. shortcoming\n16. spruce\n17. whelan\n\nLet's re-sort them carefully:\n\n* amerada\n* craftsmen\n* din\n* eclipse\n* gaillardia\n* inroad\n* jest\n* jackboot\n* jordan\n* kill\n* mirth\n* nate\n* pomade\n* putt\n* shortcoming\n* spruce\n* whelan\n\nComparing the initial list: din spruce nate pomade jordan gaillardia putt inroad jest kill jackboot whelan mirth amerada craftsmen shortcoming eclipse\n\n1. amerada\n2. craftsmen\n3. din\n4. eclipse\n5. gaillardia\n6. inroad\n7. jest\n8. jackboot\n9. jordan\n10. kill\n11. mirth\n12. nate\n13. pomade\n14. putt\n15. shortcoming\n16. spruce\n17. whelan\n\nAnswer: amerada, craftsmen, din, eclipse, gaillardia, inroad, jest, jackboot, jordan, kill, mirth, nate, pomade, putt, shortcoming, spruce, whelan", + "schema_version": 6, + "seq": 53, + "span_id": "m1.model_call.53", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:23:03.605790+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 54, + "case_id": "bbh_word_sorting-0053", + "cycle": -1, + "duration_sec": 2.097, + "event": "model_call", + "event_seq": 54, + "inputs": { + "prompt": "Sort the following words alphabetically: List: fallen juvenile bermuda diagram ezra screechy aristocratic musty absorption slack quartile nod nigeria testicle congo cheerful eucre cesium\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. absorption\n2. aristocratic\n3. bermuda\n4. cheerful\n5. congo\n6. cesium\n7. diagram\n8. fallen\n9. eucre\n10. ezra\n11. junitile (Assuming \"juvenile\" was a typo or meant to be sorted as is, but standard sorting applies) - Let's stick to the provided list: juvenile\n12. kotlinx (Assuming \"screechy\" is a typo or meant to be sorted as is) - Let's stick to the provided list: screechy\n13. musty\n14. nod\n15. nigeria\n16. quartile\n17. slack\n18. testicle\n\nLet's re-sort carefully:\n\n* absorption\n* aristocratic\n* bermuda\n* cheerful\n* congo\n* cesium\n* diagram\n* fallen\n* eucre\n* ezra\n* juvenile\n* musty\n* nod\n* nigeria\n* quartile\n* screechy\n* slack\n* testicle\n\nComparing 'ezra' and 'eucre': 'ezra' comes before 'eucre'.\nComparing 'juvenile' and 'musty': 'juvenile' comes before 'musty'.\n\nFinal sorted list:\n1. absorption\n2. aristocratic\n3. bermuda\n4. cheerful\n5. congo\n6. cesium\n7. diagram\n8. fallen\n9. eucre\n10. ezra\n11. juvenile\n12. musty\n13. nod\n14. nigeria\n15. quartile\n16. screechy\n17. slack\n18. testicle\n\nAnswer: absorption, aristocratic, bermuda, cheerful, congo, cesium, diagram, fallen, eucre, ezra, juvenile, musty, nod, nigeria, quartile, screechy, slack, testicle", + "schema_version": 6, + "seq": 54, + "span_id": "m1.model_call.54", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:23:05.704806+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 55, + "case_id": "bbh_word_sorting-0054", + "cycle": -1, + "duration_sec": 2.383, + "event": "model_call", + "event_seq": 55, + "inputs": { + "prompt": "Sort the following words alphabetically: List: saul fresco teen chopstick footwork smalley anaplasmosis palisade orthography storey bumble pilate clue ingot fiesta\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. anaplasmosis\n2. bumble\n3. chopstick\n4. clue\n5. fresco\n6. footwork\n7. fiesta\n8. ingot\n9. orthography\n10. palisade\n11. pilate\n12. saul\n13. smalley\n14. storey\n15. teen\n\nLet's re-sort them carefully:\n\n* anaplasmosis (A)\n* bumble (B)\n* chopstick (C)\n* clue (C) - clue comes before chopstick\n* fresco (F)\n* footwork (F) - footwork comes after fresco\n* fiesta (F) - fiesta comes after footwork\n* ingot (I)\n* orthography (O)\n* palisade (P)\n* pilate (P) - pilate comes after palisade\n* saul (S)\n* smalley (S) - saul comes before smalley\n* storey (S) - storey comes after smalley\n* teen (T)\n\nLet's do a strict alphabetical sort:\n\n1. anaplasmosis\n2. bumble\n3. chopstick\n4. clue\n5. fresco\n6. footwork\n7. fiesta\n8. ingot\n9. orthography\n10. palisade\n11. pilate\n12. saul\n13. smalley\n14. storey\n15. teen\n\nWait, let's re-check the 'S' words: saul, smalley, storey.\nS-a-u-l\nS-m-a-l-l-e-y\nS-t-o-r-e-y\n'a' comes before 'm' and 't'. So, saul comes first.\n'm' comes before 't'. So, smalley comes before storey.\nOrder for S words: saul, smalley, storey.\n\nLet's re-check the 'F' words: fresco, footwork, fiesta.\nF-r-e-s-c-o\nF-o-o-t-w-o-r-k\nF-i-e-s-t-a\nOrder: fresco, footwork, fiesta. (e < o < i is false. e < i <", + "schema_version": 6, + "seq": 55, + "span_id": "m1.model_call.55", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:23:08.089975+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 56, + "case_id": "bbh_word_sorting-0055", + "cycle": -1, + "duration_sec": 2.388, + "event": "model_call", + "event_seq": 56, + "inputs": { + "prompt": "Sort the following words alphabetically: List: emission upon labour whim fairfax ride crepe prig accomplice az doff clatter circumcircle tea wheelbase pleura incantation choral viaduct lorry\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. accomplice\n2. choral\n3. crepe\n4. circumcircle\n5. clatter\n6. doff\n7. emission\n8. fairfax\n9. incantation\n10. labour\n11. lorry\n12. pleura\n13. prig\n14. ride\n15. tea\n16. viaduct\n17. wheelbase\n\nLet's re-sort them carefully:\n\n* accomplice (A)\n* choral (C)\n* crepe (C)\n* circumcircle (C)\n* clatter (C)\n* doff (D)\n* emission (E)\n* fairfax (F)\n* incantation (I)\n* labour (L)\n* lorry (L)\n* pleura (P)\n* prig (P)\n* ride (R)\n* tea (T)\n* viaduct (V)\n* wheelbase (W)\n\n**Detailed Alphabetical Sorting:**\n\n1. accomplice\n2. choral\n3. crepe\n4. circumcircle\n5. clatter\n6. doff\n7. emission\n8. fairfax\n9. incantation\n10. labour\n11. lorry\n12. pleura\n13. prig\n14. ride\n15. tea\n16. viaduct\n17. wheelbase\n\n(Checking C words: choral, crepe, circumcircle, clatter. C-h comes before C-l, C-r, C-i. Wait, 'ch' comes before 'l', 'r', 'i'. Let's re-sort the C words: choral, clatter, crepe, circumcircle.)\n\n**Corrected Sorting:**\n\n1. accomplice (A)\n2. choral (C)\n3. clatter (C)\n4. crepe (C)\n5. circumcircle (C)\n6. doff (D)\n7. emission (E)\n8. fairfax (F)\n9. incantation (I)\n10. labour (L)\n11. lorry (L)\n12. pleura (P)\n13. prig (P)\n14. ride (R)\n15. tea (T)\n16. viaduct (V)\n17. wheelbase (W)", + "schema_version": 6, + "seq": 56, + "span_id": "m1.model_call.56", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:23:10.480190+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 57, + "case_id": "bbh_word_sorting-0056", + "cycle": -1, + "duration_sec": 1.2064, + "event": "model_call", + "event_seq": 57, + "inputs": { + "prompt": "Sort the following words alphabetically: List: deadwood macedonia foxtail block hexadecimal guaranty rubaiyat victoria custodian\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words to sort: deadwood, macedonia, foxtail, block, hexadecimal, guaranty, rubaiyat, victoria, custodian.\n\nWe will sort them alphabetically:\n\n1. **block**\n2. **custodian**\n3. **deadwood**\n4. **foxtail**\n5. **guaranty**\n6. **hexadecimal**\n7. **macedonia**\n8. **rubaiyat**\n9. **victoria**\n\nLet's re-check the sorting carefully:\n\n* **b**lock\n* **c**ustodian\n* **d**eadwood\n* **f**oxtail\n* **g**uaranty\n* **h**exadecimal\n* **m**acedonia\n* **r**ubaiyat\n* **v**ictoria\n\nThe order seems correct based on the first letter.\n\nAnswer: block, custodian, deadwood, foxtail, guaranty, hexadecimal, macedonia, rubaiyat, victoria\n\nAnswer: block, custodian, deadwood, foxtail, guaranty, hexadecimal, macedonia, rubaiyat, victoria", + "schema_version": 6, + "seq": 57, + "span_id": "m1.model_call.57", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:23:11.688808+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 58, + "case_id": "bbh_word_sorting-0057", + "cycle": -1, + "duration_sec": 2.3763, + "event": "model_call", + "event_seq": 58, + "inputs": { + "prompt": "Sort the following words alphabetically: List: matte game aldebaran inkling fiberboard pulsate lakeside shape mcgee watchworks entrepreneurial boxwood peruse backyard cabbage polyhedra lightface rowley rae invincible\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. aldebaran\n2. backyard\n3. cabbage\n4. entrepreneurial\n5. fiberboard\n6. game\n7. inkling\n8. invincible\n9. lake\n10. lightface\n11. matte\n12. mcgee\n13. peruse\n14. pulsate\n15. polyhedra\n16. rae\n17. rowley\n18. shape\n19. watchworks\n20. boxwood\n\nLet's re-sort them carefully:\n\n* **a**: aldebaran, backyard\n* **b**: cabbage, boxwood\n* **e**: entrepreneurial\n* **f**: fiberboard\n* **g**: game\n* **i**: inkling, invincible\n* **l**: lakeside, lightface\n* **m**: matte, mcgee\n* **p**: pulsate, polyhedra, peruse\n* **r**: rae, rowley\n* **s**: shape\n* **w**: watchworks\n\nLet's do a full alphabetical sort:\n\n1. aldebaran\n2. backyard\n3. boxwood\n4. cabbage\n5. entrepreneurial\n6. fiberboard\n7. game\n8. inkling\n9. invincible\n10. lakeside (Assuming 'lakeside' is the intended word, as 'lakes' is not in the list, but 'lakeside' is often implied or a common word. Checking the list again: \"lakeside\" is not in the list. The list contains \"lakeside\" if we assume \"lakeside\" is present. Let's re-examine the provided list: matte game aldebaran inkling fiberboard pulsate lakeside shape mcgee watchworks entrepreneurial boxwood peruse backyard cabbage polyhedra lightface rowley rae invincible)\n * *Correction: The word is \"lakeside\".*\n11. lightface\n12. matte\n13. mcgee\n14. peruse\n15. pulsate\n16. polyhedra\n17. rae\n18. rowley\n19. shape\n20. watchworks\n\nLet's re-sort based on the exact list provided:\nmatte, game, aldebaran, inkling, fiberboard, pulsate, lakeside, shape, mcgee, watchworks, entrepreneurial, boxwood, peruse, backyard, cabbage", + "schema_version": 6, + "seq": 58, + "span_id": "m1.model_call.58", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:23:14.067542+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 59, + "case_id": "bbh_word_sorting-0058", + "cycle": -1, + "duration_sec": 1.4322, + "event": "model_call", + "event_seq": 59, + "inputs": { + "prompt": "Sort the following words alphabetically: List: pea yonkers darkle quadrant moldboard instant olsen hookup erudite lip\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: pea, yonkers, darkle, quadrant, moldboard, instant, olsen, hookup, erudite, lip.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter:**\n * darkle (d)\n * erudite (e)\n * hookup (h)\n * instant (i)\n * lip (l)\n * moldboard (m)\n * olsen (o)\n * pea (p)\n * quadrant (q)\n * yonkers (y)\n\n2. **Sort based on the first letter:**\n * darkle (d)\n * erudite (e)\n * hookup (h)\n * instant (i)\n * lip (l)\n * moldboard (m)\n * olsen (o)\n * pea (p)\n * quadrant (q)\n * yonkers (y)\n\n3. **Final sorted list:**\n * darkle\n * erudite\n * hookup\n * instant\n * lip\n * moldboard\n * olsen\n * pea\n * quadrant\n * yonkers\n\nAnswer: darkle, erudite, hookup, instant, lip, moldboard, olsen, pea, quadrant, yonkers", + "schema_version": 6, + "seq": 59, + "span_id": "m1.model_call.59", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:23:15.502000+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 60, + "case_id": "bbh_word_sorting-0059", + "cycle": -1, + "duration_sec": 2.3845, + "event": "model_call", + "event_seq": 60, + "inputs": { + "prompt": "Sort the following words alphabetically: List: paginate amicable they've histology extreme indoeuropean sonority tramway turvy coo deerstalker browne henchman pelvis calumny\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. amicable\n2. browne\n3. calumny\n4. coo\n5. deerstalker\n6. eminence (Wait, \"extreme\" is in the list, let's re-check)\n7. henchman\n8. histology\n9. indoeuropean\n10. paginate\n11. pelvis\n12. sonority\n13. tramway\n14. they've\n15. turvy\n\nLet's re-sort carefully:\n\n* amicable (A)\n* browne (B)\n* calumny (C)\n* coo (C) - coo comes before calumny if we look at the second letter, but alphabetically, 'cal' comes before 'coo'. Let's stick to standard alphabetical order.\n * amicable\n * browne\n * calumny\n * coo\n* deerstalker (D)\n* eminence (Not in list, using extreme)\n* extreme (E)\n* henchman (H)\n* histology (H) - henchman comes before histology.\n* indoeuropean (I)\n* paginate (P)\n* pelvis (P) - paginate comes before pelvis.\n* sonority (S)\n* tramway (T)\n* they've (T) - tramway comes before they've.\n* turvy (T) - turvy comes after tramway.\n\nLet's perform a strict alphabetical sort:\n\n1. amicable\n2. browne\n3. calumny\n4. coo\n5. deerstalker\n6. extreme\n7. henchman\n8. histology\n9. indoeuropean\n10. paginate\n11. pelvis\n12. sonority\n13. tramway\n14. they've\n15. turvy\n\nChecking the 'H' words: henchman, histology. H-e-n vs H-i-s. henchman comes first.\nChecking the 'P' words: paginate, pelvis. P-a vs P-e. paginate comes first.\nChecking the 'T' words: tramway, they've, turvy. T-r, T-h, T-u. tramway, they've, turvy.\n * tramway (tr", + "schema_version": 6, + "seq": 60, + "span_id": "m1.model_call.60", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:23:17.888817+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 61, + "case_id": "bbh_word_sorting-0060", + "cycle": -1, + "duration_sec": 2.374, + "event": "model_call", + "event_seq": 61, + "inputs": { + "prompt": "Sort the following words alphabetically: List: statutory feed spavin hecatomb pestle plume figural pasty giveth incense undulate middle blackstone tel obstinacy toothpaste hunt sinkhole\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. blackstone\n2. feed\n3. figural\n4. giveth\n5. hecatomb\n6. hunt\n7. incense\n8. middle\n9. obstinacy\n10. pasty\n11. pestle\n12. plume\n13. sinkhole\n14. statutory\n15. tel\n16. toothpaste\n17. undulate\n\nLet's re-sort them carefully:\n\n* **b**lackstone\n* **f**eed\n* **f**igural\n* **g**iveth\n* **h**ecatomb\n* **h**unt\n* **i**ncense\n* **m**iddle\n* **o**bstinacy\n* **p**asty\n* **p**estle\n* **p**lume\n* **s**inkhole\n* **s**tatutory\n* **t**el\n* **t**oothpaste (toothpaste)\n* **u**ndulate\n\nComparing words starting with 'f': feed, figural. (feed comes before figural)\nComparing words starting with 'p': pasty, pestle, plume. (plume, pasty, pestle) - Wait, P-L, P-A, P-E. Plume, Pasty, Pestle.\nP-l-u-m-e\nP-a-s-t-y\nP-e-s-t-l-e\nAlphabetical order for P words: plume, pasty, pestle.\n\nLet's re-sort the full list:\nblackstone (B)\nfeed (F)\nfigural (F)\ngiveth (G)\nhecatomb (H)\nhunt (H)\nincense (I)\nmiddle (M)\nobstinacy (O)\npasty (P)\npestle (P)\nplume (P)\nsinkhole (S)\nstatutory (S)\ntel (T)\ntoothpaste (T)\nundulate (U)\n\n**Detailed Alphabetical Sort:**\n\n1. blackstone\n2. feed\n3. figural\n4. giveth\n5. hecatomb\n6. hunt\n7. incense\n8. middle\n9. obstinacy\n10. pasty\n11.", + "schema_version": 6, + "seq": 61, + "span_id": "m1.model_call.61", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:23:20.265284+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 62, + "case_id": "bbh_word_sorting-0061", + "cycle": -1, + "duration_sec": 0.8917, + "event": "model_call", + "event_seq": 62, + "inputs": { + "prompt": "Sort the following words alphabetically: List: slurp raytheon gloucester\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: slurp, raytheon, gloucester.\n\nTo sort these words alphabetically, we compare them letter by letter from the beginning.\n\n1. **s**lurp\n2. **r**aytheon\n3. **g**looucester\n\nComparing the first letters: G comes before R, and R comes before S.\nSo, 'gloucester' comes first.\n\nNow we compare 'slurp' and 'raytheon'.\n's' comes before 'r' is false. 'r' comes before 's'.\nSo, 'raytheon' comes before 'slurp'.\n\nThe alphabetical order is:\n1. gloucester\n2. raytheon\n3. slurp\n\nAnswer: gloucester, raytheon, slurp\n\nAnswer: gloucester, raytheon, slurp", + "schema_version": 6, + "seq": 62, + "span_id": "m1.model_call.62", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:23:21.159383+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 63, + "case_id": "bbh_word_sorting-0062", + "cycle": -1, + "duration_sec": 2.202, + "event": "model_call", + "event_seq": 63, + "inputs": { + "prompt": "Sort the following words alphabetically: List: gloriana milord rendezvous schnabel allegoric loge lollipop salle collate prolix mast euphony\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words to sort alphabetically:\ngloriana, milord, rendezvous, schnabel, allegoric, loge, lollipop, salle, collate, prolix, mast, euphony\n\nLet's sort them letter by letter:\n\n1. **allegoric** (A)\n2. **collate** (C)\n3. **gloriana** (G)\n4. **loge** (L) - *Comparing loge and lollipop, 'loge' comes before 'lollipop'*\n5. **lollipop** (L)\n6. **milord** (M)\n7. **mast** (M) - *Comparing milord and mast, 'mast' comes before 'milord'*\n8. **prolix** (P)\n9. **rendezvous** (R)\n10. **salle** (S)\n11. **schnabel** (S) - *Comparing salle and schnabel, 'salle' comes before 'schnabel'*\n12. **euphony** (E) - *Wait, 'euphony' starts with E, so it should come before 'gloriana' (G).*\n\nLet's re-sort based on the first letter:\n\nE: euphony\nA: allegoric\nC: collate\nG: gloriana\nL: loge, lollipop\nM: mast, milord\nP: prolix\nR: rendezvous\nS: salle, schnabel\n\n**Sorting within groups:**\n\n* **L:** loge, lollipop (loge comes before lollipop)\n* **M:** mast, milord (mast comes before milord)\n* **S:** salle, schnabel (salle comes before schnabel)\n\n**Final Alphabetical Order:**\n\n1. euphony\n2. allegoric\n3. collate\n4. gloriana\n5. loge\n6. lollipop\n7. mast\n8. milord\n9. prolix\n10. rendezvous\n11. salle\n12. schnabel\n\nAnswer: euphony, allegoric, collate, gloriana, loge, lollipop, mast, milord, prolix, rendezvous, salle, schnabel", + "schema_version": 6, + "seq": 63, + "span_id": "m1.model_call.63", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:23:23.363786+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 64, + "case_id": "bbh_word_sorting-0063", + "cycle": -1, + "duration_sec": 1.7649, + "event": "model_call", + "event_seq": 64, + "inputs": { + "prompt": "Sort the following words alphabetically: List: vegetate artillery harm fda doris prosody bainite incongruous monkey vivian\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: vegetate, artillery, harm, fda, doris, prosody, bainite, incongruous, monkey, vivian.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter:**\n * bainite (b)\n * doris (d)\n * fda (f)\n * harm (h)\n * incongruous (i)\n * monkey (m)\n * prosody (p)\n * artillery (a)\n * vegetate (v)\n * vivian (v)\n\n2. **Sort based on the first letter:**\n * **a:** artillery\n * **b:** bainite\n * **d:** doris\n * **f:** fda\n * **h:** harm\n * **i:** incongruous\n * **m:** monkey\n * **p:** prosody\n * **v:** vegetate, vivian\n\n3. **Sort the 'v' words:**\n * vegetate\n * vivian\n * Comparing the second letter: 'e' in vegetate, 'i' in vivian. 'e' comes before 'i'.\n * So, vegetate comes before vivian.\n\n4. **Final sorted order:**\n 1. artillery\n 2. bainite\n 3. doris\n 4. fda\n 5. harm\n 6. incongruous\n 7. monkey\n 8. prosody\n 9. vegetate\n 10. vivian\n\nAnswer: artillery, bainite, doris, fda, harm, incongruous, monkey, prosody, vegetate, vivian", + "schema_version": 6, + "seq": 64, + "span_id": "m1.model_call.64", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:23:25.131114+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 65, + "case_id": "bbh_word_sorting-0064", + "cycle": -1, + "duration_sec": 2.418, + "event": "model_call", + "event_seq": 65, + "inputs": { + "prompt": "Sort the following words alphabetically: List: hyperboloid buckle liquidate alkali nagasaki breach falsetto parmesan mirth\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: hyperboloid, buckle, liquidate, alkali, nagasaki, breach, falsetto, parmesan, mirth.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter:**\n * b (buckle, breach)\n * f (falsetto)\n * h (hyperboloid)\n * l (liquidate)\n * m (mirth)\n * n (nagasaki)\n * p (parmesan)\n * a (alkali)\n\n Sorting by the first letter: a, b, f, h, l, m, n, p.\n\n2. **Sort words starting with 'a':**\n * alkali\n\n3. **Sort words starting with 'b':**\n * buckle\n * breach\n * (b**r**each comes before b**u**ckle)\n * breach, buckle\n\n4. **Sort words starting with 'f':**\n * falsetto\n\n5. **Sort words starting with 'h':**\n * hyperboloid\n\n6. **Sort words starting with 'l':**\n * liquidate\n\n7. **Sort words starting with 'm':**\n * mirth\n\n8. **Sort words starting with 'n':**\n * nagasaki\n\n9. **Sort words starting with 'p':**\n * parmesan\n\n**Combining the sorted lists:**\n\n1. alkali\n2. breach\n3. buckle\n4. falsetto\n5. hyperboloid\n6. liquidate\n7. mirth\n8. nagasaki\n9. parmesan\n\nLet's re-verify the order carefully:\n\n* **a**: alkali\n* **b**: breach, buckle (breach before buckle)\n* **f**: falsetto\n* **h**: hyperboloid\n* **l**: liquidate\n* **m**: mirth\n* **n**: nagasaki\n* **p**: parmesan\n\nFinal sorted list: alkali, breach, buckle, falsetto, hyperboloid, liquidate, mirth, nagasaki, parmesan.\n\nAnswer: alkali, breach, buckle, falsetto, hyperboloid, liquidate, mirth, nagasaki, parmesan", + "schema_version": 6, + "seq": 65, + "span_id": "m1.model_call.65", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:23:27.551725+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 66, + "case_id": "bbh_word_sorting-0065", + "cycle": -1, + "duration_sec": 2.5539, + "event": "model_call", + "event_seq": 66, + "inputs": { + "prompt": "Sort the following words alphabetically: List: comet pincer snippet manley injun spokesperson hydrate heusen cocksure\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: comet, pincer, snippet, manley, injun, spokesperson, hydrate, heusen, cocksure.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter:**\n * comet (c)\n * pincer (p)\n * snippet (s)\n * manley (m)\n * injun (i)\n * spokesperson (s)\n * hydrate (h)\n * heusen (h)\n * cocksure (c)\n\n Words starting with 'c': comet, cocksure\n Words starting with 'h': hydrate, heusen\n Words starting with 'i': injun\n Words starting with 'm': manley\n Words starting with 'p': pincer\n Words starting with 's': snippet, spokesperson\n\n2. **Sort words starting with 'c':**\n * comet\n * cocksure\n (comet comes before cocksure)\n\n3. **Sort words starting with 'h':**\n * hydrate\n * heusen\n(hydrate comes before heusen)\n\n4. **Sort words starting with 'i':**\n * injun\n\n5. **Sort words starting with 'm':**\n * manley\n\n6. **Sort words starting with 'p':**\n * pincer\n\n7. **Sort words starting with 's':**\n * snippet\n * spokesperson\n(snippet comes before spokesperson)\n\n8. **Combine the sorted groups:**\n * comet\n * cocksure\n * hydrate\n * heusen\n * injun\n * manley\n * pincer\n * snippet\n * spokesperson\n\nLet's re-check the full alphabetical order:\nC: comet, cocksure\nH: hydrate, heusen\nI: injun\nM: manley\nP: pincer\nS: snippet, spokesperson\n\nComparing C words: comet vs cocksure. 'o' comes before 'o' (wait, 'm' vs 'o'). comet vs cocksure. 'o' vs 'o'. 'm' vs 'c'. 'm' comes before 'c' in the second letter? No, 'o' vs 'o'.\ncomet (co...)\ncocksure (co...)\ncomet (co**m", + "schema_version": 6, + "seq": 66, + "span_id": "m1.model_call.66", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:23:30.108114+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 67, + "case_id": "bbh_word_sorting-0066", + "cycle": -1, + "duration_sec": 1.7872, + "event": "model_call", + "event_seq": 67, + "inputs": { + "prompt": "Sort the following words alphabetically: List: oakland seaborg jacobi membrane trapezoidal allis marmot toggle anthology\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words to sort alphabetically:\noakland, seaborg, jacobi, membrane, trapezoidal, allis, marmot, toggle, anthology\n\nLet's sort them letter by letter:\n\n1. **allis** (A)\n2. **anthology** (A) - 'allis' comes before 'anthology' because 'l' comes before 'n'.\n3. **jacobi** (J)\n4. **marmot** (M)\n5. **membrane** (M) - 'marmot' comes before 'membrane' because 'a' comes before 'e'.\n6. **oakland** (O)\n7. **seaborg** (S)\n8. **trapezoidal** (T)\n9. **toggle** (T) - 'trapezoidal' comes before 'toggle' because 'r' comes before 'o'.\n\nLet's re-check the 'M' words:\nmarmot\nmembrane\n'marmot' vs 'membrane': m-a-r vs m-e-m. 'a' comes before 'e', so marmot comes before membrane.\n\nLet's re-check the 'T' words:\ntrapezoidal\ntoggle\n'trapezoidal' vs 'toggle': t-r-a vs t-o-g. 'r' comes before 'o', so trapezoidal comes before toggle.\n\nFinal sorted order:\n1. allis\n2. anthology\n3. jacobi\n4. marmot\n5. membrane\n6. oakland\n7. seaborg\n8. trapezoidal\n9. toggle\n\nAnswer: allis, anthology, jacobi, marmot, membrane, oakland, seaborg, trapezoidal, toggle", + "schema_version": 6, + "seq": 67, + "span_id": "m1.model_call.67", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:23:31.897818+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 68, + "case_id": "bbh_word_sorting-0067", + "cycle": -1, + "duration_sec": 1.5675, + "event": "model_call", + "event_seq": 68, + "inputs": { + "prompt": "Sort the following words alphabetically: List: cornerstone green brindle prowl nonce ballard scrappy credulous des pitney difluoride toward predilect nostalgic rape mixup horseplay jew curio\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. ballard\n2. brindle\n3. cornerstone\n4. credulous\n5. curio\n6. des\n7. difluoride\n8. green\n9. horseplay\n10. jew\n11. mixup\n12. nonce\n13. nostalgic\n14. pitney\n15. predilect\n16. prowl\n17. rape\n18. scrappy\n19. toward\n\nLet's re-sort them carefully:\n\nOriginal list: cornerstone, green, brindle, prowl, nonce, ballard, scrappy, credulous, des, pitney, difluoride, toward, predilect, nostalgic, rape, mixup, horseplay, jew, curio\n\nSorted list:\n1. ballard\n2. brindle\n3. cornerstone\n4. credulous\n5. curio\n6. des\n7. difluoride\n8. green\n9. horseplay\n10. jew\n11. mixup\n12. nonce\n13. nostalgic\n14. pitney\n15. predilect\n16. prowl\n17. rape\n18. scrappy\n19. toward\n\nAnswer: ballard, brindle, cornerstone, credulous, curio, des, difluoride, green, horseplay, jew, mixup, nonce, nostalgic, pitney, predilect, prowl, rape, scrappy, toward", + "schema_version": 6, + "seq": 68, + "span_id": "m1.model_call.68", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:23:33.467874+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 69, + "case_id": "bbh_word_sorting-0068", + "cycle": -1, + "duration_sec": 2.3734, + "event": "model_call", + "event_seq": 69, + "inputs": { + "prompt": "Sort the following words alphabetically: List: strove hendrickson apostasy jacm indonesia vibrate afloat lanthanide grosbeak scavenge quark chattel conner bechtel melancholy ferment\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words to sort alphabetically:\nstrove, hendrickson, apostasy, jacm, indonesia, vibrate, afloat, lanthanide, grosbeak, scavenge, chattel, conner, bechtel, melancholy, ferment\n\nLet's sort them:\n\n1. **afloat**\n2. **apostasy**\n3. **bechtel**\n4. **chattel**\n5. **conner**\n6. **ferment**\n7. **grosbeak**\n8. **hendrickson**\n9. **indonesia**\n10. **jacm**\n11. **lanthanide**\n12. **melancholy**\n13. **scavenge**\n14. **strove**\n15. **vibrate**\n\nLet's re-check the sorting, especially for words starting with similar letters:\n\n* **a**: afloat, apostasy\n* **b**: bechtel\n* **c**: chattel, conner\n* **f**: ferment\n* **g**: grosbeak\n* **h**: hendrickson\n* **i**: indonesia\n* **j**: jacm\n* **l**: lanthanide\n* **m**: melancholy\n* **s**: scavenge, strove\n* **v**: vibrate\n\nDetailed comparison:\n* afloat (a-f)\n* apostasy (a-p) $\\rightarrow$ afloat comes before apostasy.\n* bechtel (b)\n* chattel (c-h)\n* conner (c-o) $\\rightarrow$ chattel comes before conner.\n* ferment (f)\n* grosbeak (g)\n* hendrickson (h)\n* indonesia (i)\n* jacm (j)\n* lanthanide (l)\n* melancholy (m)\n* scavenge (s-c)\n* strove (s-t) $\\rightarrow$ scavenge comes before strove.\n* vibrate (v)\n\nCorrected order:\n1. afloat\n2. apostasy\n3. bechtel\n4. chattel\n5. conner\n6. ferment\n7. grosbeak\n8. hendrickson\n9. indonesia\n10. jacm\n11. lanthanide\n12. melancholy\n13. scavenge\n14. strove\n15. vibrate\n\nAnswer: afloat", + "schema_version": 6, + "seq": 69, + "span_id": "m1.model_call.69", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:23:35.844137+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 70, + "case_id": "bbh_word_sorting-0069", + "cycle": -1, + "duration_sec": 2.1102, + "event": "model_call", + "event_seq": 70, + "inputs": { + "prompt": "Sort the following words alphabetically: List: quench autocracy lodge exploitation custody ventricle annuity bruno thunderclap militant somatic dare acquisitive\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. acquisitive\n2. annuity\n3. autocracy\n4. bruno\n5. custody\n6. dare\n7. exploitation\n8. lodge\n9. militant\n10. quench\n11. somatic\n12. thunderclap\n13. ventricle\n\nLet's re-sort them carefully:\n\n* **a**cquisitive, **a**nnuity, **a**utocracy\n* **b**runo\n* **c**ustody\n* **d**are\n* **e**xploitation (Wait, 'e' comes before 'l', 'm', 'q', 's', 't', 'v')\n* **l**odge\n* **m**ilitant\n* **q**uench\n* **s**omatic\n* **t**hunderclap\n* **v**entricle\n\nLet's list them and compare:\n1. acquisitive\n2. annuity\n3. autocracy\n4. bruno\n5. custody\n6. dare\n7. exploitation\n8. lodge\n9. militant\n10. quench\n11. somatic\n12. thunderclap\n13. ventricle\n\nChecking the order:\nacquisitive (a-c)\nannuity (a-n)\nautocracy (a-u)\nbruno (b)\ncustody (c-u)\ndare (d)\nexploitation (e)\nlodge (l)\nmilitant (m)\nquench (q)\nsomatic (s)\nthunderclap (t)\nventricle (v)\n\nThe correct alphabetical order is:\n1. acquisitive\n2. annuity\n3. autocracy\n4. bruno\n5. custody\n6. dare\n7. exploitation\n8. lodge\n9. militant\n10. quench\n11. somatic\n12. thunderclap\n13. ventricle\n\nAnswer: acquisitive, annuity, autocracy, bruno, custody, dare, exploitation, lodge, militant, quench, somatic, thunderclap, ventricle", + "schema_version": 6, + "seq": 70, + "span_id": "m1.model_call.70", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:23:37.956873+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 71, + "case_id": "bbh_word_sorting-0070", + "cycle": -1, + "duration_sec": 2.3825, + "event": "model_call", + "event_seq": 71, + "inputs": { + "prompt": "Sort the following words alphabetically: List: reedy bully multiple edt sell referring lexicographer tit flogging perceptive minor bosporus rancorous forfeit pizza pungent sedition cork\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words to sort alphabetically:\nreedy, bully, multiple, edt, sell, referring, lexicographer, tit, flogging, perceptive, minor, bosporus, rancorous, forfeit, pizza, pungent, sedition, cork\n\nLet's sort them:\n\n1. **bosporus**\n2. **bully**\n3. **cork**\n4. **edt**\n5. **forfeit**\n6. **flogging**\n7. **lexicographer**\n8. **multiple**\n9. **minor**\n10. **perceptive**\n11. **pizza**\n12. **pungent**\n13. **rancorous**\n14. **reedy**\n15. **referring**\n16. **sell**\n17. **sedition**\n18. **tit**\n\nLet's re-check the sorting, especially for words starting with similar letters:\n\n* **b**: bully, bosporus\n * bosporus (bo...)\n * bully (bu...) $\\rightarrow$ bosporus, bully\n* **c**: cork\n* **e**: edt\n* **f**: forfeit, flogging\n * forfeit (for...)\n * flogging (flo...) $\\rightarrow$ flogging, forfeit\n* **l**: lexicographer\n* **m**: multiple, minor\n * multiple (mul...)\n * minor (min...) $\\rightarrow$ minor, multiple\n* **p**: perceptive, pizza, pungent\n * perceptive (per...)\n * pizza (pi...)\n * pungent (pun...) $\\rightarrow$ perceptive, pizza, pungent\n* **r**: reedy, referring, rancorous\n * reedy (ree...)\n * referring (ref...)\n * rancorous (ran...) $\\rightarrow$ rancorous, reedy, referring\n* **s**: sell, sedition\n * sell (sel...)\n * sedition (sed...) $\\rightarrow$ sell, sedition\n* **t**: tit\n\nLet's perform a strict alphabetical sort:\n\n1. bosporus (B)\n2. bully (B)\n3. cork (C)\n4. edt (E)\n5. flogging (F)\n6. forfeit (F)\n7. lexicographer (L)\n8. minor (M)\n", + "schema_version": 6, + "seq": 71, + "span_id": "m1.model_call.71", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:23:40.342330+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 72, + "case_id": "bbh_word_sorting-0071", + "cycle": -1, + "duration_sec": 1.9583, + "event": "model_call", + "event_seq": 72, + "inputs": { + "prompt": "Sort the following words alphabetically: List: behold oxalic maybe hew steel termcap pray stiffen dissipate misogyny format dew\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: behold, oxalic, maybe, hew, steel, termcap, pray, stiffen, dissipate, misogyny, format, dew.\n\nHere is the alphabetical sorting process:\n\n1. **behold**\n2. **dew**\n3. **dissipate**\n4. **format**\n5. **hew**\n6. **maybe**\n7. **misogyny**\n8. **oxalic**\n9. **pray**\n10. **steel**\n11. **stiffen**\n12. **termcap**\n\nLet's re-sort carefully:\n\n* **b**: behold\n* **d**: dew, dissipate\n * dew comes before dissipate\n* **f**: format\n* **h**: hew\n* **m**: maybe, misogyny\n * maybe comes before misogyny\n* **o**: oxalic\n* **p**: pray\n* **s**: steel, stiffen\n * steel comes before stiffen\n* **t**: termcap\n\nLet's re-evaluate the order based on the first letter:\nB: behold\nD: dew, dissipate\nF: format\nH: hew\nM: maybe, misogyny\nO: oxalic\nP: pray\nS: steel, stiffen\nT: termcap\n\nSorting within groups:\nD: dew, dissipate (dew before dissipate)\nM: maybe, misogyny (maybe before misogyny)\nS: steel, stiffen (steel before stiffen)\n\nFinal sorted list:\n1. behold\n2. dew\n3. dissipate\n4. format\n5. hew\n6. maybe\n7. misogyny\n8. oxalic\n9. pray\n10. steel\n11. stiffen\n12. termcap\n\nAnswer: behold, dew, dissipate, format, hew, maybe, misogyny, oxalic, pray, steel, stiffen, termcap", + "schema_version": 6, + "seq": 72, + "span_id": "m1.model_call.72", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:23:42.303234+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 73, + "case_id": "bbh_word_sorting-0072", + "cycle": -1, + "duration_sec": 1.2486, + "event": "model_call", + "event_seq": 73, + "inputs": { + "prompt": "Sort the following words alphabetically: List: impede appliance pulitzer superior\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: impede, appliance, pulitzer, superior.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **impede** (starts with 'i')\n2. **appliance** (starts with 'a')\n3. **pulitzer** (starts with 'p')\n4. **superior** (starts with 's')\n\nComparing the first letters: 'a', 'i', 'p', 's'.\nAlphabetical order of the first letters: 'a', 'i', 'p', 's'.\n\n1. **appliance** (starts with 'a')\n2. **impede** (starts with 'i')\n3. **pulitzer** (starts with 'p')\n4. **superior** (starts with 's')\n\nThe sorted list is: appliance, impede, pulitzer, superior.\n\nAnswer: appliance, impede, pulitzer, superior\nAnswer: appliance, impede, pulitzer, superior", + "schema_version": 6, + "seq": 73, + "span_id": "m1.model_call.73", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:23:43.554507+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 74, + "case_id": "bbh_word_sorting-0073", + "cycle": -1, + "duration_sec": 1.8169, + "event": "model_call", + "event_seq": 74, + "inputs": { + "prompt": "Sort the following words alphabetically: List: plural dose allstate stalin dyad multitudinous powderpuff\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: plural, dose, allstate, stalin, dyad, multitudinous, powderpuff.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter:**\n * plural (p)\n * dose (d)\n * allstate (a)\n * stalin (s)\n * dyad (d)\n * multitudinous (m)\n * powderpuff (p)\n\n Words starting with 'a': allstate\n Words starting with 'd': dose, dyad\n Words starting with 'm': multitudinous\n Words starting with 'p': plural, powderpuff\n Words starting with 's': stalin\n\n2. **Sort words starting with 'd':**\n * dose\n * dyad\n (dose comes before dyad)\n\n3. **Sort words starting with 'p':**\n * plural\n * powderpuff\n (plural comes before powderpuff)\n\n4. **Assemble the sorted list based on the first letter order (A, D, M, P, S):**\n * allstate (A)\n * dose (D)\n * dyad (D)\n * multitudinous (M)\n * plural (P)\n * powderpuff (P)\n * stalin (S)\n\n5. **Refine the order within groups:**\n * D group: dose, dyad (dose before dyad)\n * P group: plural, powderpuff (plural before powderpuff)\n\nThe final alphabetical order is: allstate, dose, dyad, multitudinous, plural, powderpuff, stalin.\n\nAnswer: allstate, dose, dyad, multitudinous, plural, powderpuff, stalin", + "schema_version": 6, + "seq": 74, + "span_id": "m1.model_call.74", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:23:45.374148+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 75, + "case_id": "bbh_word_sorting-0074", + "cycle": -1, + "duration_sec": 2.0455, + "event": "model_call", + "event_seq": 75, + "inputs": { + "prompt": "Sort the following words alphabetically: List: maraud downtrodden hurst propane morphine parsonage gadgetry inertial gamin\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words to sort: maraud, downtrodden, hurst, propane, morphine, parsonage, gadgetry, inertial, gamin\n\nLet's sort them alphabetically:\n\n1. **gadgetry** (g)\n2. **gamin** (g)\n3. **hurst** (h)\n4. **inertial** (i)\n5. **maraud** (m)\n6. **morphine** (m)\n7. **parsonage** (p)\n8. **propane** (p)\n9. **downtrodden** (d)\n\nLet's re-sort carefully:\n\n* **d**owntrodden\n* **g**adgetry\n* **g**amin\n* **h**urst\n* **i**nertial\n* **m**araud\n* **m**orphine\n* **p**arsonage\n* **p**ropane\n\nComparing 'gadgetry' and 'gamin':\ngadgetry (g-a-d...)\ngamin (g-a-m...)\n'd' comes before 'm', so gadgetry comes before gamin.\n\nComparing 'maraud' and 'morphine':\nmaraud (m-a-r...)\nmorphine (m-o-r...)\n'a' comes before 'o', so maraud comes before morphine.\n\nComparing 'parsonage' and 'propane':\nparsonage (p-a-r-s...)\npropane (p-r-o-p...)\n'a' comes before 'r', so parsonage comes before propane.\n\nThe correct alphabetical order is:\n1. downtrodden\n2. gadgetry\n3. gamin\n4. hurst\n5. inertial\n6. maraud\n7. morphine\n8. parsonage\n9. propane\n\nAnswer: downtrodden, gadgetry, gamin, hurst, inertial, maraud, morphine, parsonage, propane", + "schema_version": 6, + "seq": 75, + "span_id": "m1.model_call.75", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:23:47.422269+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 76, + "case_id": "bbh_word_sorting-0075", + "cycle": -1, + "duration_sec": 2.3802, + "event": "model_call", + "event_seq": 76, + "inputs": { + "prompt": "Sort the following words alphabetically: List: confrontation quantitative proserpine proofread daddy hirsute\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: confrontation, quantitative, proserpine, proofread, daddy, hirsute.\n\nTo sort them alphabetically, we compare them letter by letter:\n\n1. **confrontation** (C)\n2. **quantitative** (Q)\n3. **proserpine** (P)\n4. **proofread** (P)\n5. **daddy** (D)\n6. **hirsute** (H)\n\nLet's sort them based on the first letter:\nD: daddy\nC: confrontation\nH: hirsute\nP: proserpine, proofread\nQ: quantitative\n\nNow, let's sort the words starting with 'P':\nproserpine\nproofread\nComparing 'proserpine' and 'proofread':\nproserpine (p r o s e r p i n e)\nproofread (p r o o f r e a d)\nThe third letter: 'o' vs 'o'.\nThe fourth letter: 's' vs 'o'. Since 'o' comes before 's', 'proofread' comes before 'proserpine'.\nWait, let's recheck:\np r o **s** e r p i n e\np r o **o** f r e a d\n'o' comes before 's'. So, 'proofread' comes before 'proserpine'.\n\nCorrect order for P words: proofread, proserpine.\n\nThe complete alphabetical order is:\n1. daddy (D)\n2. confrontation (C) - Wait, C comes before D. Let's re-sort by the first letter:\n\nD: daddy\nC: confrontation\nH: hirsute\nP: proofread, proserpine\nQ: quantitative\n\nAlphabetical order of first letters: C, D, H, P, Q.\n\n1. **confrontation** (C)\n2. **daddy** (D)\n3. **hirsute** (H)\n4. **proofread** (P)\n5. **proserpine** (P)\n6. **quantitative** (Q)\n\nNow, sorting the P words:\nproofread vs proserpine\np r o **o** f r e a d\np r o **s** e r p i n e\n'o' comes before 's', so proofread comes", + "schema_version": 6, + "seq": 76, + "span_id": "m1.model_call.76", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:23:49.805397+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 77, + "case_id": "bbh_word_sorting-0076", + "cycle": -1, + "duration_sec": 2.3757, + "event": "model_call", + "event_seq": 77, + "inputs": { + "prompt": "Sort the following words alphabetically: List: whisper eyesight titmice mirage fletch cry biennial creedal miltonic fraudulent j\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: whisper, eyesight, titmice, mirage, fletch, cry, biennial, creedal, miltonic, fraudulent, j.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter:**\n * fletch (f)\n * fraudulent (f)\n * j (j)\n * miltonic (m)\n * mirage (m)\n * creedal (c)\n * biennial (b)\n * cry (c)\n * whisper (w)\n * eyesight (e)\n * titmice (t)\n\n2. **Sort by the first letter:**\n * **b:** biennial\n * **c:** creedal, cry\n * **e:** eyesight\n * **f:** fletch, fraudulent\n * **j:** j\n * **m:** miltonic, mirage\n * **t:** titmice\n * **w:** whisper\n\n3. **Sort within groups:**\n\n * **c:** creedal, cry (creedal comes before cry)\n * **f:** fletch, fraudulent (fletch comes before fraudulent)\n * **m:** miltonic, mirage (miltonic comes before mirage)\n\n4. **Final ordered list:**\n * biennial (b)\n * creedal (c)\n * cry (c)\n * eyesight (e)\n * fletch (f)\n * fraudulent (f)\n * j (j)\n * miltonic (m)\n * mirage (m)\n * titmice (t)\n * whisper (w)\n\nLet's re-verify the order based on standard alphabetical rules:\n\n1. biennial (b)\n2. creedal (c)\n3. cry (c)\n4. eyesight (e)\n5. fletch (f)\n6. fraudulent (f)\n7. j (j)\n8. miltonic (m)\n9. mirage (m)\n10. titmice (t)\n11. whisper (w)\n\n(Note on 'j': In alphabetical sorting, 'j' comes after 'f' and before 'm'.)\n\nThe correct alphabetical order is: biennial, creedal, cry, eyesight, fletch,", + "schema_version": 6, + "seq": 77, + "span_id": "m1.model_call.77", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:23:52.184060+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 78, + "case_id": "bbh_word_sorting-0077", + "cycle": -1, + "duration_sec": 1.3253, + "event": "model_call", + "event_seq": 78, + "inputs": { + "prompt": "Sort the following words alphabetically: List: mater rica sigmund dean eosine inebriate gibson vassar mulligatawny formula\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. dean\n2. eosine\n3. formula\n4. gibson\n5. inebriate\n6. mater\n7. mulligatawny\n8. rica\n9. sigmund\n10. vassar\n\nLet's re-sort them carefully:\n\n* **d**ean\n* **e**osine\n* **f**ormula\n* **g**ibson\n* **i**nebriate\n* **m**ater\n* **m**ulligatawny (mulligatawny comes after mater because 'u' comes after 'a')\n* **r**ica\n* **s**igmund\n* **v**assar\n\nLet's check the 'm' words again: mater, mulligatawny.\nmater\nmulligatawny (mulligatawny comes after mater)\n\nCorrect alphabetical order:\n1. dean\n2. eosine\n3. formula\n4. gibson\n5. inebriate\n6. mater\n7. mulligatawny\n8. rica\n9. sigmund\n10. vassar\n\nAnswer: dean, eosine, formula, gibson, inebriate, mater, mulligatawny, rica, sigmund, vassar", + "schema_version": 6, + "seq": 78, + "span_id": "m1.model_call.78", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:23:53.512166+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 79, + "case_id": "bbh_word_sorting-0078", + "cycle": -1, + "duration_sec": 2.3725, + "event": "model_call", + "event_seq": 79, + "inputs": { + "prompt": "Sort the following words alphabetically: List: cast palazzi inhibit arragon tabletop danbury abbe adposition emplace gavin income falsetto onerous\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words to sort alphabetically:\ncast, palazzi, inhibit, arragon, tabletop, danbury, abbe, adposition, emplace, gavin, income, falsetto, onerous\n\nLet's sort them:\n\n1. **abbe**\n2. **adposition**\n3. **arragon**\n4. **cast**\n5. **danbury**\n6. **emplace**\n7. **falsetto**\n8. **gavin**\n9. **inhibit**\n10. **income**\n11. ** onerous**\n12. **palazzi**\n13. **tabletop**\n\nLet's re-sort carefully, focusing on the first letter and subsequent letters:\n\n* **a**: abbe, adposition, arragon\n * abbe\n * adposition (a-d)\n * arragon (a-r)\n* **c**: cast\n* **d**: danbury\n* **e**: emplace\n* **f**: falsetto\n* **g**: gavin\n* **i**: inhibit, income\n * inhibit (i-n-h)\n * income (i-n-c) $\\rightarrow$ inhibit comes before income\n* **o**: onerous\n* **p**: palazzi\n* **t**: tabletop\n\nLet's re-evaluate the 'a' words:\n1. abbe\n2. adposition\n3. arragon\n\nLet's check the order of the remaining words: cast, danbury, emplace, falsetto, gavin, inhibit, income, onerous, palazzi, tabletop.\n\n1. abbe\n2. adposition\n3. arragon\n4. cast\n5. danbury\n6. emplace\n7. falsetto\n8. gavin\n9. inhibit\n10. income\n11. onerous\n12. palazzi\n13. tabletop\n\nFinal check:\na-b-b-e\na-d-p-o-s-i-t-i-o-n\na-r-r-a-g-o-n\nc-a-s-t\nd-a-n-b-u-r-y\ne-m-p-l-a-c-e\nf-a-l-s-e", + "schema_version": 6, + "seq": 79, + "span_id": "m1.model_call.79", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:23:55.887577+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 80, + "case_id": "bbh_word_sorting-0079", + "cycle": -1, + "duration_sec": 1.7747, + "event": "model_call", + "event_seq": 80, + "inputs": { + "prompt": "Sort the following words alphabetically: List: twelvefold o'connell mainstream malformed umbilical bivalve vinegar mortify sleuth paunchy\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. bivalve\n2. formaldehyde (Wait, the list has \"malformed\", not \"formaldehyde\". Let's stick to the provided list.)\n3. mortify\n4. mainstream\n5. malformed\n6. o'connell\n7. paunchy\n8. sleuth\n9. twelvefold\n10. umbilical\n11. vinegar\n\nLet's re-sort the provided list: twelvefold, o'connell, mainstream, malformed, umbilical, bivalve, vinegar, mortify, sleuth, paunchy.\n\n1. bivalve\n2. mainstream\n3. malformed\n4. mortify\n5. o'connell\n6. paunchy\n7. sleuth\n8. twelvefold\n9. umbilical\n10. vinegar\n\nLet's check the sorting carefully:\nB: bivalve\nM: mainstream, malformed, mortify\nO: o'connell\nP: paunchy\nS: sleuth\nT: twelvefold\nU: umbilical\nV: vinegar\n\nComparing M words: mainstream, malformed, mortify\n* mainstream\n* malformed (m-a-l)\n* mortify (m-o-r)\nSo: mainstream, malformed, mortify\n\nFinal sorted list:\n1. bivalve\n2. mainstream\n3. malformed\n4. mortify\n5. o'connell\n6. paunchy\n7. sleuth\n8. twelvefold\n9. umbilical\n10. vinegar\n\nAnswer: bivalve, mainstream, malformed, mortify, o'connell, paunchy, sleuth, twelvefold, umbilical, vinegar", + "schema_version": 6, + "seq": 80, + "span_id": "m1.model_call.80", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:23:57.665342+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 81, + "case_id": "bbh_word_sorting-0080", + "cycle": -1, + "duration_sec": 2.3759, + "event": "model_call", + "event_seq": 81, + "inputs": { + "prompt": "Sort the following words alphabetically: List: rowley midshipman saline furl geld locomote exhumation anarchic bstj gradual riddance j liniment pantheist elution profess\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words to sort alphabetically:\nrowley, midshipman, saline, furl, geld, locomote, exhumation, anarchic, bstj, gradual, riddance, j, liniment, pantheist, elution, profess\n\nLet's sort them:\n\n1. **anarchic**\n2. **bstj**\n3. **elution**\n4. **furl**\n5. **geld**\n6. **gradual**\n7. **j**\n8. **locomote**\n9. **liniment**\n10. **midshipman**\n11. **pantheist**\n12. **profess**\n13. **riddance**\n14. **rowley**\n15. **saline**\n16. **exhumation**\n\nLet's re-check the sorting, especially for words starting with similar letters:\n\n* **a**: anarchic\n* **b**: bstj\n* **e**: elution, exhumation\n * elution comes before exhumation (l before x)\n* **f**: furl\n* **g**: geld, gradual\n * geld comes before gradual (e before r)\n* **j**: j\n* **l**: locomote, liniment\n * locomote comes before liniment (o before i is false, l comes before o) - Wait, L comes before M.\n * locomote\n * liniment\n* **m**: midshipman\n* **p**: pantheist, profess\n * pantheist comes before profess (a before r)\n* **r**: riddance, rowley\n * riddance comes before rowley (i before o)\n* **s**: saline\n* **(remaining)**\n\nLet's perform a strict alphabetical sort:\n\n1. anarchic\n2. bstj\n3. elution\n4. exhumation\n5. furl\n6. geld\n7. gradual\n8. j\n9. liniment\n10. locomote\n11. midshipman\n12. pantheist\n13. profess\n14. riddance\n15. rowley\n16. saline\n\nLet's re-verify the L words: liniment, locomote. L-I vs L-O. I comes before O", + "schema_version": 6, + "seq": 81, + "span_id": "m1.model_call.81", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:24:00.044160+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 82, + "case_id": "bbh_word_sorting-0081", + "cycle": -1, + "duration_sec": 2.0933, + "event": "model_call", + "event_seq": 82, + "inputs": { + "prompt": "Sort the following words alphabetically: List: ulcerate ballroom sparse intrepid arcing prorogue dodge benefit coupon ladle buggy shagging bassoon hypothermia decide affirmative airframe junior nineveh schmitt\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. airframe\n2. arcing\n3. affirmative\n4. ballroom\n5. benefit\n6. buggy\n7. bassoon\n8. decide\n9. dodge\n10. hyphenmia (hypothermia)\n11. intrepid\n12. junior\n13. ladle\n14. nineveh\n15. prorogue\n16. schmitt\n17. shagging\n18. sparse\n19. ulcerate\n20. coupon\n\nLet's re-sort them carefully:\n\n* **a**: airframe, arcing, affirmative\n* **b**: ballroom, benefit, buggy, bassoon\n* **c**: coupon\n* **d**: decide, dodge\n* **h**: hyphenmia (hypothermia), shagging\n* **i**: intrepid\n* **j**: junior\n* **l**: ladle\n* **n**: nineveh\n* **p**: prorogue\n* **s**: schmitt, sparse\n* **u**: ulcerate\n\n**Detailed Alphabetical Sort:**\n\n1. airframe\n2. arcing\n3. affirmative\n4. ballroom\n5. benefit\n6. buggy\n7. bassoon\n8. coupon\n9. decide\n10. dodge\n11. hypothermia (or hyphenmia, assuming 'hypothermia' is the intended word)\n12. intrepid\n13. junior\n14. ladle\n15. nineveh\n16. prorogue\n17. schmitt\n18. shagging\n19. sparse\n20. ulcerate\n\n(Note: I treated \"hypothermia\" as the intended word from \"hypothermia\" and sorted it under H. If \"hyphenmia\" was intended, it would be placed before \"hypothermia\".)\n\nAnswer: airframe, arcing, affirmative, ballroom, benefit, buggy, bassoon, coupon, decide, dodge, hypothermia, intrepid, junior, ladle, nineveh, prorogue, schmitt, shagging, sparse, ulcerate", + "schema_version": 6, + "seq": 82, + "span_id": "m1.model_call.82", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:24:02.140512+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 83, + "case_id": "bbh_word_sorting-0082", + "cycle": -1, + "duration_sec": 0.5983, + "event": "model_call", + "event_seq": 83, + "inputs": { + "prompt": "Sort the following words alphabetically: List: chrysalis wallaby\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given words are: chrysalis, wallaby.\n\nTo sort them alphabetically, we compare them letter by letter from the beginning.\n\n1. **chrysalis** starts with 'c'.\n2. **wallaby** starts with 'w'.\n\nSince 'c' comes before 'w' in the alphabet, \"chrysalis\" comes before \"wallaby\".\n\nThe alphabetical order is: chrysalis, wallaby.\n\nAnswer: chrysalis, wallaby", + "schema_version": 6, + "seq": 83, + "span_id": "m1.model_call.83", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:24:02.741997+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 84, + "case_id": "bbh_word_sorting-0083", + "cycle": -1, + "duration_sec": 2.0429, + "event": "model_call", + "event_seq": 84, + "inputs": { + "prompt": "Sort the following words alphabetically: List: yang relinquish merry maladjust olefin canaan ranch batavia\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: yang, relinquish, merry, maladjust, olefin, canaan, ranch, batavia.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **batavia** (Starts with 'b')\n2. **canaan** (Starts with 'c')\n3. **maladjust** (Starts with 'm')\n4. **merry** (Starts with 'm') - Compare 'maladjust' and 'merry'. 'a' comes before 'e', so 'maladjust' comes before 'merry'.\n5. **olefin** (Starts with 'o')\n6. **ranch** (Starts with 'r')\n7. **relinquish** (Starts with 'r') - Compare 'ranch' and 'relinquish'. 'a' comes before 'e', so 'ranch' comes before 'relinquish'.\n8. **yang** (Starts with 'y')\n\nLet's re-sort carefully:\n\nWords starting with 'b':\n* batavia\n\nWords starting with 'c':\n* canaan\n\nWords starting with 'm':\n* maladjust\n* merry\n * Comparing 'maladjust' and 'merry': m-a vs m-e. 'a' comes before 'e'. So, maladjust, merry.\n\nWords starting with 'o':\n* olefin\n\nWords starting with 'r':\n* ranch\n* relinquish\n * Comparing 'ranch' and 'relinquish': r-a vs r-e. 'a' comes before 'e'. So, ranch, relinquish.\n\nWords starting with 'y':\n* yang\n\nThe sorted order is:\n1. batavia\n2. canaan\n3. maladjust\n4. merry\n5. olefin\n6. ranch\n7. relinquish\n8. yang\n\nAnswer: batavia, canaan, maladjust, merry, olefin, ranch, relinquish, yang\n\nAnswer: batavia, canaan, maladjust, merry, olefin, ranch, relinquish, yang", + "schema_version": 6, + "seq": 84, + "span_id": "m1.model_call.84", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:24:04.787934+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 85, + "case_id": "bbh_word_sorting-0084", + "cycle": -1, + "duration_sec": 2.3261, + "event": "model_call", + "event_seq": 85, + "inputs": { + "prompt": "Sort the following words alphabetically: List: bruckner admiration technion bandgap heron postprocessor maidenhair fungus allusion gambit accessory proscenium cruise\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. admiration\n2. accessory\n3. allusion\n4. bandgap\n5. bruckner\n6. cruise\n7. fungus\n8. gambit\n9. heron\n10. maidenhair\n11. postprocessor\n12. proscenium\n13. technion\n\nLet's re-sort them carefully:\n\n* **a**dmiration\n* **a**ccessory\n* **a**llusion\n* **b**andgap\n* **b**ruckner\n* **c**ruise\n* **f**ungus\n* **g**ambit\n* **h**eron\n* **m**aidenhair\n* **p**ostprocessor\n* **p**roscenium\n* **t**echnion\n\nComparing 'admiration' and 'accessory': 'admiration' comes before 'accessory'.\nComparing 'accessory' and 'allusion': 'accessory' comes before 'allusion'. (Wait, 'a'llusion vs 'a'ccessory. 'll' vs 'cc'. 'll' comes before 'cc' in standard alphabetical order if we look at the third letter, but let's compare them directly: a-l-l-u-s-i-o-n vs a-c-c-e-s-s-o-r. 'c' comes before 'l', so 'accessory' comes before 'allusion'.)\n\nLet's re-sort the 'a' words:\n1. admiration\n2. accessory\n3. allusion\n\nLet's re-sort the rest:\n* bandgap\n* bruckner\n* cruise\n* fungus\n* gambit\n* heron\n* maidenhair\n* postprocessor\n* proscenium\n* technion\n\nFinal sorted list:\n1. admiration\n2. accessory\n3. allusion\n4. bandgap\n5. bruckner\n6. cruise\n7. fungus\n8. gambit\n9. heron\n10. maidenhair\n11. postprocessor\n12. proscenium\n13. technion\n\nAnswer: admiration, accessory, allusion, bandgap, bruckner, cruise, fungus, gambit, heron, maidenhair, postprocessor, proscenium, technion", + "schema_version": 6, + "seq": 85, + "span_id": "m1.model_call.85", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:24:07.117033+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 86, + "case_id": "bbh_word_sorting-0085", + "cycle": -1, + "duration_sec": 2.3653, + "event": "model_call", + "event_seq": 86, + "inputs": { + "prompt": "Sort the following words alphabetically: List: underclassmen pegging dale bartok goat martinson whoop badinage brunswick fraternity allele dar yourselves dun anthropocentric banish starkey morphemic monomer desolater\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. allele\n2. anthropocentric\n3. banish\n4. bartok\n5. badinage\n6. brunswick\n7. desolater\n8. dale\n9. dar\n10. fraternity\n11. goat\n12. martinson\n13. morphemic\n14. monomer\n15. pegging\n16. starkey\n17. yourselves\n18. underclassmen\n19. whoop\n\nLet's re-sort them carefully:\n\n* **a**llele\n* **a**nthropocentric\n* **b**adinage\n* **b**artok\n* **b**runswick\n* **d**ale\n* **d**ar\n* **f**raternity\n* **g**oat\n* **m**artinson\n* **m**orphemic\n* **m**onomer\n* **p**egging\n* **s**tarkey\n* **u**nderclassmen\n* **w**hoop\n\nWait, I missed some words in the initial list check. Let's list all the words provided:\nunderclassmen, pegging, dale, bartok, goat, martinson, whoop, badinage, brunswick, fraternity, allele, dar, yourselves, dun, anthropocentric, banish, starkey, morphemic, monomer, desolater\n\nLet's sort them systematically:\n\n1. allele\n2. anthropocentric\n3. banish\n4. badinage\n5. bartok\n6. brunswick\n7. desolater\n8. dale\n9. dar\n10. fraternity\n11. goat\n12. martinson\n13. morphemic\n14. monomer\n15. pegging\n16. starkey\n17. yourselves\n18. underclassmen\n19. whoop\n20. dun (This word was missed in the previous manual check, let's place it)\n\nSorting 'dun': 'd' comes before 'd'ale, 'd'ar, etc.\n\nCorrected alphabetical order:\n\n1. allele\n2. anthropocentric\n3. banish\n4. badinage\n5. bartok\n6. brunswick\n7. desolater\n8. dale", + "schema_version": 6, + "seq": 86, + "span_id": "m1.model_call.86", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:24:09.485583+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 87, + "case_id": "bbh_word_sorting-0086", + "cycle": -1, + "duration_sec": 2.5552, + "event": "model_call", + "event_seq": 87, + "inputs": { + "prompt": "Sort the following words alphabetically: List: regret uptake satisfy behold fumarole motif forth alcohol escutcheon pease hackberry walkie\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words to sort alphabetically:\nregret, uptake, satisfy, behold, fumarole, motif, forth, alcohol, escutcheon, pease, hackberry, walkie\n\nLet's sort them:\n\n1. alcohol\n2. behold\n3. escutcheon\n4. fumarole\n5. forth\n6. hackberry\n7. motif\n8. pease\n9. regret\n10. satisfy\n11. uptake\n12. walkie\n\nDetailed comparison:\n* **a**lcohol\n* **b**ehold\n* **e**scutcheon\n* **f**umarole\n* **f**orth (fumarole comes before forth if we compare the third letter: u vs o. Wait, F-O-R-T-H vs F-U-M-A-R-O-L-E. O comes before U.)\n * Re-evaluating F words: fumarole (fu...), forth (fo...) -> forth comes before fumarole.\n * Let's re-sort F words: forth, fumarole.\n* **h**ackberry\n* **m**otif\n* **p**ease\n* **r**egret\n* **s**atisfy\n* **u**ptake\n* **w**alkie\n\nLet's perform a full alphabetical sort:\n\n1. alcohol (A)\n2. behold (B)\n3. escutcheon (E)\n4. forth (F)\n5. fumarole (F) (f-o vs f-u)\n6. hackberry (H)\n7. motif (M)\n8. pease (P)\n9. regret (R)\n10. satisfy (S)\n11. uptake (U)\n12. walkie (W)\n\nChecking F words again:\nforth: f-o-r-t-h\nfumarole: f-u-m-a-r-o-l-e\n'o' comes before 'u', so **forth** comes before **fumarole**.\n\nFinal sorted list:\n1. alcohol\n2. behold\n3. escutcheon\n4. forth\n5. fumarole\n6. hackberry\n7. motif\n8. pease\n9. regret\n10. satisfy\n11. uptake\n1", + "schema_version": 6, + "seq": 87, + "span_id": "m1.model_call.87", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:24:12.043997+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 88, + "case_id": "bbh_word_sorting-0087", + "cycle": -1, + "duration_sec": 2.3655, + "event": "model_call", + "event_seq": 88, + "inputs": { + "prompt": "Sort the following words alphabetically: List: catawba almagest transfusable milt photolysis limpid gnome archenemy sagging councilwomen decrement jungian\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words to sort alphabetically:\ncatawba, almagest, transfusable, milt, photolysis, limpid, gnome, archenemy, sagging, councilwomen, decrement, jungian\n\nLet's sort them:\n\n1. **almagest** (Starts with 'a', followed by 'l')\n2. **achenemy** (Starts with 'a', followed by 'c')\n3. **catawba** (Starts with 'c', followed by 'a')\n4. **decrement** (Starts with 'd')\n5. **gnome** (Starts with 'g')\n6. **jungian** (Starts with 'j')\n7. **limpid** (Starts with 'l')\n8. **milt** (Starts with 'm')\n9. **photolysis** (Starts with 'p')\n10. **sagging** (Starts with 's')\n11. **councilwomen** (Starts with 'c', followed by 'o')\n12. **transfusable** (Starts with 't')\n\nLet's re-sort carefully, paying attention to the first letter and subsequent letters:\n\nWords starting with A:\n* almagest\n* archenemy\n\nComparing 'almagest' and 'achenemy':\nalmagest (a-l)\nachenemy (a-c)\n'c' comes before 'l'. So, archenemy comes before almagest.\n1. archenemy\n2. almagest\n\nWords starting with C:\n* catawba\n* councilwomen\n\nComparing 'catawba' and 'councilwomen':\ncatawba (c-a)\ncouncilwomen (c-o)\n'a' comes before 'o'. So, catawba comes before councilwomen.\n3. catawba\n4. councilwomen\n\nWords starting with D:\n5. decrement\n\nWords starting with G:\n6. gnome\n\nWords starting with J:\n7. jungian\n\nWords starting with L:\n8. limpid\n\nWords starting with M:\n9. milt\n\nWords starting with P:\n10. photolysis\n\nWords starting with S:\n11. sagging\n\nWords starting with T:\n12. transfusable\n\nFinal sorted list:\n1. archenemy\n2. almagest\n3", + "schema_version": 6, + "seq": 88, + "span_id": "m1.model_call.88", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:24:14.412595+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 89, + "case_id": "bbh_word_sorting-0088", + "cycle": -1, + "duration_sec": 2.3604, + "event": "model_call", + "event_seq": 89, + "inputs": { + "prompt": "Sort the following words alphabetically: List: wink envious scotia planetaria pooh emancipate army\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: wink, envious, scotia, planetaria, pooh, emancipate, army.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **emancipate** (Starts with 'e')\n2. **envious** (Starts with 'e', 'm' comes before 'n')\n3. **army** (Starts with 'a')\n4. **planetaria** (Starts with 'p')\n5. **pooh** (Starts with 'p', 'o' comes before 'p' in planetaria if we consider the second letter, but 'p' vs 'p' is the same. 'o' vs 'l' in planetaria. Let's compare 'planetaria' and 'pooh'. 'p' is the same. 'l' in planetaria vs 'o' in pooh. 'l' comes before 'o'.)\n * Re-evaluating 'planetaria' and 'pooh':\n * p l a n e t a r i a\n * p o o h\n * 'l' comes before 'o', so 'planetaria' comes before 'pooh'.\n6. **scotia** (Starts with 's')\n7. **wink** (Starts with 'w')\n\nLet's re-sort systematically:\n\nWords starting with 'a':\n* army\n\nWords starting with 'e':\n* emancipate\n* envious\n * Comparing 'emancipate' and 'envious': e-m vs e-n. 'm' comes before 'n'.\n * Order: emancipate, envious\n\nWords starting with 'p':\n* planetaria\n* pooh\n * Comparing 'planetaria' and 'pooh': p-l vs p-o. 'l' comes before 'o'.\n * Order: planetaria, pooh\n\nWords starting with 's':\n* scotia\n\nWords starting with 'w':\n* wink\n\nCombining the sorted groups:\n1. army\n2. emancipate\n3. envious\n4. planetaria\n5. pooh\n6. scotia\n7. wink\n\nFinal alphabetical order: army, emancipate, envious, planetaria, pooh, scotia, wink.\n\nAnswer: army, emancipate, envious, planetaria, pooh, scotia, wink", + "schema_version": 6, + "seq": 89, + "span_id": "m1.model_call.89", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:24:16.776261+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 90, + "case_id": "bbh_word_sorting-0089", + "cycle": -1, + "duration_sec": 2.4094, + "event": "model_call", + "event_seq": 90, + "inputs": { + "prompt": "Sort the following words alphabetically: List: delmarva sawfly aroma nod carcinogen parochial facetious designate syllabus rally\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: delmarva, sawfly, aroma, nod, carcinogen, parochial, facetious, designate, syllabus, rally.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter:**\n * a: aroma\n * d: delmarva, designate\n * f: facetious\n * n: nod\n * p: parochial\n * r: rally\n * s: sawfly, syllabus\n\n2. **Sort words starting with 'a':**\n * aroma\n\n3. **Sort words starting with 'd':**\n * delmarva\n * designate (d-e-l vs d-e-s) $\\rightarrow$ delmarva, designate\n\n4. **Sort words starting with 'f':**\n * facetious\n\n5. **Sort words starting with 'n':**\n * nod\n\n6. **Sort words starting with 'p':**\n * parochial\n\n7. **Sort words starting with 'r':**\n * rally\n\n8. **Sort words starting with 's':**\n * sawfly\n * syllabus (s-a-w vs s-y-l) $\\rightarrow$ syllabus, sawfly (Wait, 'a' comes before 'y') $\\rightarrow$ syllabus, sawfly\n\nLet's re-verify the 's' words:\n* syllabus (s-y-l-l-a-b-u-s)\n* sawfly (s-a-w-f-l-y)\n'a' comes before 'y', so 'sawfly' comes before 'syllabus'.\n* sawfly\n* syllabus\n\nLet's re-verify the 'd' words:\n* delmarva (d-e-l-m...)\n* designate (d-e-g-n...)\n'g' comes before 'l', so designate comes before delmarva.\n* designate\n* delmarva\n\n**Final Sorted Order:**\n1. aroma\n2. designate\n3. delmarva\n4. facetious\n5. nod\n6. parochial\n7. rally\n8. sawfly\n9. syllabus\n\nLet's check the full list again:\naroma\ndesignate\ndelmarva\nfacetious\nnod\nparochial\nrally\nsawfly\nsyllabus\n\nAnswer: aroma, designate, delmar", + "schema_version": 6, + "seq": 90, + "span_id": "m1.model_call.90", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:24:19.188948+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 91, + "case_id": "bbh_word_sorting-0090", + "cycle": -1, + "duration_sec": 2.2574, + "event": "model_call", + "event_seq": 91, + "inputs": { + "prompt": "Sort the following words alphabetically: List: calyx emphases vii elgin perquisite implacable blackbird afro jura mayapple whit blame\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words to sort alphabetically:\ncalyx, emphases, vii, elgin, perquisite, implacable, blackbird, afro, jura, mayapple, whit, blame\n\nLet's sort them:\n\n1. **afro**\n2. **blame**\n3. **blackbird**\n4. **calyx**\n5. **emphases**\n6. **elgin**\n7. **implacable**\n8. **jura**\n9. **mayapple**\n10. **perquisite**\n11. **vii** (Numbers usually come before letters, or are sorted based on their character code)\n12. **whit**\n\nLet's re-evaluate the sorting, paying attention to capitalization (assuming standard alphabetical sorting, treating all as lowercase for comparison unless specified):\n\n* afro\n* blame\n* blackbird\n* calyx\n* emphases\n* elgin\n* implacable\n* jura\n* mayapple\n* perquisite\n* vii\n* whit\n\nComparing 'vii' with other words: 'v' comes after 's' in 'emphases', 'elgin', etc. If we treat 'vii' as the sequence of letters 'v', 'i', 'i', it would be sorted based on the first letter 'v'.\n\nStandard alphabetical order:\n1. afro\n2. blame\n3. blackbird\n4. calyx\n5. emphases\n6. elgin\n7. implacable\n8. jura\n9. mayapple\n10. perquisite\n11. vii (Starts with 'v')\n12. whit (Starts with 'w')\n\nLet's check the order of 'vii' and 'whit': 'v' comes before 'w'.\n\nFinal sorted list:\n1. afro\n2. blame\n3. blackbird\n4. calyx\n5. emphases\n6. elgin\n7. implacable\n8. jura\n9. mayapple\n10. perquisite\n11. vii\n12. whit\n\nAnswer: afro, blame, blackbird, calyx, emphases, elgin, implacable, jura, mayapple, perquisite, vii, whit", + "schema_version": 6, + "seq": 91, + "span_id": "m1.model_call.91", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:24:21.449523+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 92, + "case_id": "bbh_word_sorting-0091", + "cycle": -1, + "duration_sec": 2.3187, + "event": "model_call", + "event_seq": 92, + "inputs": { + "prompt": "Sort the following words alphabetically: List: solstice roadway loquacious niece planet steed abutted hondo loosen agamemnon islamic capacity tibet hitachi essex casualty guinea aquatic suspicion\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. abutted\n2. agamemnon\n3. aquatic\n4. casualty\n5. capacity\n6. hondo\n7. hitachi\n8. islamic\n9. loosen\n10. loquacious\n11. niece\n12. planet\n13. roadway\n14. solstice\n15. steed\n16. suspicion\n17. tibet\n\nLet's re-sort them carefully:\n\n* abutted\n* agamemnon\n* aquatic\n* casualty\n* capacity\n* hondo\n* hitachi\n* islamic\n* loosen\n* loquacious\n* niece\n* planet\n* roadway\n* solstice\n* steed\n* suspicion\n* tibet\n* essex (Wait, I missed 'essex' in the initial list check. Let's re-examine the original list: solstice roadway loquacious niece planet steed abutted hondo loosen agamemnon islamic capacity tibet hitachi essex casualty guinea aquatic suspicion)\n\nLet's re-sort the full list:\nsolstice, roadway, loquacious, niece, planet, steed, abutted, hondo, loosen, agamemnon, islamic, capacity, tibet, hitachi, essex, casualty, guinea, aquatic, suspicion\n\n1. abutted\n2. agamemnon\n3. aquatic\n4. casualty\n5. capacity\n6. essex\n7. guinea\n8. hondo\n9. hitachi\n10. islamic\n11. loosen\n12. loquacious\n13. niece\n14. planet\n15. roadway\n16. solstice\n17. steed\n18. suspicion\n19. tibet\n\n(Checking 'guinea' placement: G comes after E, before H. Correct.)\n\nFinal sorted list:\nabutted, agamemnon, aquatic, casualty, capacity, essex, guinea, hondo, hitachi, islamic, loosen, loquacious, niece, planet, roadway, solstice, steed, suspicion, tibet\n\nAnswer: abutted, agamemnon, aquatic, casualty, capacity, essex, guinea, hondo, hitachi, islamic, loosen, loquacious, niece, planet, roadway, solstice, steed, suspicion, tibet", + "schema_version": 6, + "seq": 92, + "span_id": "m1.model_call.92", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:24:23.771459+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 93, + "case_id": "bbh_word_sorting-0092", + "cycle": -1, + "duration_sec": 2.3754, + "event": "model_call", + "event_seq": 93, + "inputs": { + "prompt": "Sort the following words alphabetically: List: corpulent diagnose code scalp bombproof fluorine blythe swipe honeybee damn pore maharaja cytolysis solicit\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. bombproof\n2. bpythe (Assuming \"blythe\" is a typo for \"bpythe\" or sorting based on the provided spelling) - If we strictly use \"blythe\":\n3. corpulent\n4. code\n5. cytolysis\n6. damn\n7. diagnose\n8. fluorine\n9. honeybee\n10. maharaja\n11. pore\n12. solicit\n13. scalp\n14. swipe\n\nLet's re-sort carefully using the exact words provided: corpulent, diagnose, code, scalp, bombproof, fluorine, blythe, swipe, honeybee, damn, pore, maharaja, cytolysis, solicit.\n\n1. bombproof\n2. bpythe (blythe)\n3. code\n4. corpulent\n5. cytolysis\n6. damn\n7. diagnose\n8. fluorine\n9. honeybee\n10. maharaja\n11. pore\n12. solicit\n13. scalp\n14. swipe\n\nSorting \"blythe\" (b-l-y-t-h-e) relative to others:\n* bombproof (b-o-m-b-p-r-o-o-f)\n* blythe (b-l-y-t-h-e)\n* code (c-o-d-e)\n* corpulent (c-o-r-p-u-l-e-n-t)\n* cytolysis (c-y-t-o-l-y-s-i-s)\n* damn (d-a-m-n)\n* diagnose (d-i-a-g-n-o-s-e)\n* fluorine (f-l-u-o-r-i-n-e)\n* honeybee (h-o-n-e-y-b-e-e)\n* maharaja (m-a-h-a-r-a-j-a)\n* pore (p-o-r-e)\n* solicit (s-o-l-i-c-i-t)\n* scalp (s-c-a-l-p)\n* swipe (s-w-i-p-e)\n\nComparing 'bombproof' and 'blythe': 'b' is the same. '", + "schema_version": 6, + "seq": 93, + "span_id": "m1.model_call.93", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:24:26.150169+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 94, + "case_id": "bbh_word_sorting-0093", + "cycle": -1, + "duration_sec": 2.3775, + "event": "model_call", + "event_seq": 94, + "inputs": { + "prompt": "Sort the following words alphabetically: List: windowsill appoint biharmonic moustache baneberry wiry dyne pirate\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: windowsill, appoint, biharmonic, moustache, baneberry, wiry, dyne, pirate.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter:**\n * windowsill (w)\n * appoint (a)\n * biharmonic (b)\n * moustache (m)\n * baneberry (b)\n * wiry (w)\n * dyne (d)\n * pirate (p)\n\n Order based on the first letter: a, b, b, d, m, p, w, w\n\n2. **Sort words starting with 'a':**\n * appoint\n\n3. **Sort words starting with 'b':**\n * biharmonic\n * baneberry\n * (Comparing 'biharmonic' and 'baneberry': 'bi' comes before 'ba' if we consider the second letter, but standard alphabetical order compares character by character. 'b' is the same. 'i' in biharmonic vs 'a' in baneberry. 'a' comes before 'i'.)\n * baneberry\n * biharmonic\n\n4. **Sort words starting with 'd':**\n * dyne\n\n5. **Sort words starting with 'm':**\n * moustache\n\n6. **Sort words starting with 'p':**\n * pirate\n\n7. **Sort words starting with 'w':**\n * windowsill\n * wiry\n * (Comparing 'windowsill' and 'wiry': 'in' comes before 'ir'.)\n * windowsill\n * wiry\n\n**Combining the sorted lists:**\n1. appoint\n2. baneberry\n3. biharmonic\n4. dyne\n5. moustache\n6. pirate\n7. windowsill\n8. wiry\n\nLet's re-verify the 'b' words:\nbaneberry (ba...)\nbiharmonic (bi...)\n'a' comes before 'i', so baneberry comes before biharmonic.\n\nLet's re-verify the 'w' words:\nwindowsill (win...)\nwiry (wir...)\n'in' comes before 'ir', so windowsill comes before wiry.\n\n**Final Sorted Order:**\n1. appoint\n2. baneberry\n3. biharmonic\n4. dyne\n5. moustache\n6. pirate\n7. windowsill\n8.", + "schema_version": 6, + "seq": 94, + "span_id": "m1.model_call.94", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:24:28.530907+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 95, + "case_id": "bbh_word_sorting-0094", + "cycle": -1, + "duration_sec": 1.6494, + "event": "model_call", + "event_seq": 95, + "inputs": { + "prompt": "Sort the following words alphabetically: List: crossbill bushland capacitive meager ricochet battery enigma wysiwyg wallet lipton jane wacke contingent\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. battery\n2. bushland\n3. capacitive\n4. contingent\n5. crossbill\n6. enigma\n7. jane\n8. lipton\n9. meager\n10. ricochet\n11. wallet\n12. wacke\n13. wysiwyg\n\nLet's re-sort them carefully:\n\n* **b**attery\n* **b**ushland\n* **c**apacitive\n* **c**ontingent\n* **c**rossbill\n* **e**nigma\n* **j**ane\n* **l**ipton\n* **m**eager\n* **r**icochet\n* **w**allet\n* **w**acke\n* **w**ysiwyg\n\nComparing the 'c' words:\n* capacitive\n* contingent\n* crossbill\nAlphabetical order: capacitive, contingent, crossbill.\n\nComparing the 'w' words:\n* wallet\n* wacke\n* wysiwyg\nAlphabetical order: wallet, wacke, wysiwyg.\n\nThe final sorted list is:\n1. battery\n2. bushland\n3. capacitive\n4. contingent\n5. crossbill\n6. enigma\n7. jane\n8. lipton\n9. meager\n10. ricochet\n11. wallet\n12. wacke\n13. wysiwyg\n\nAnswer: battery, bushland, capacitive, contingent, crossbill, enigma, jane, lipton, meager, ricochet, wallet, wacke, wysiwyg", + "schema_version": 6, + "seq": 95, + "span_id": "m1.model_call.95", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:24:30.183581+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 96, + "case_id": "bbh_word_sorting-0095", + "cycle": -1, + "duration_sec": 2.3731, + "event": "model_call", + "event_seq": 96, + "inputs": { + "prompt": "Sort the following words alphabetically: List: frontage gunky indeterminable smokestack taint assure ostentatious sanderson contend paradigmatic lustrous butterball deaf dinosaur bully rhyme sashimi bye cornet\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. butterball\n2. bye\n3. bully\n4. cornet\n5. contend\n6. dinosaur\n7. deaf\n8. frontage\n9. gunky\n10. indeterminable\n11. lustrous\n12. rhyme\n13. sanderson\n14. sashimi\n15. smokestack\n16. taint\n17. assure\n18. ostentatious\n19. paradigmatic\n\nLet's re-sort them carefully:\n\n* **b**utterball\n* **b**ye\n* **b**ully\n* **c**ornet\n* **c**ontend\n* **d**inosaur\n* **d**eaf\n* **f**rontage\n* **g**unky\n* **i**ndeterminable\n* **l**ustrous\n* **r**hyme\n* **s**anderson\n* **s** sashimi\n* **s**mokestack\n* **t**aint\n* **a**ssure\n* **o**stentatious\n* **p**aradigmatic\n\nCorrect alphabetical order:\n\n1. assure\n2. butterball\n3. bye\n4. bully\n5. cornet\n6. contend\n7. dinosaur\n8. deaf\n9. frontage\n10. gunky\n11. indeterminable\n12. lustrous\n13. paradigmatic\n14. rhyme\n15. sanderson\n16. sashimi\n17. smokestack\n18. taint\n19. ostentatious\n\nLet's re-check the 'a' words and 's' words:\nAssure (A)\nButterball (B)\nBye (B)\nBully (B)\nCornet (C)\nContend (C)\nDinosaur (D)\nDeaf (D)\nFrontage (F)\nGunky (G)\nIndeterminable (I)\nLustrous (L)\nParadigmatic (P)\nRhyme (R)\nSanderson (S)\nSashimi (S)\nSmokestack (S)\nTaint (T)\nOstentatious (O)\n\nSorting the S words: Sanderson, Sashimi, Smokestack.\nSashimi (Sa)\nSanderson (", + "schema_version": 6, + "seq": 96, + "span_id": "m1.model_call.96", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:24:32.560030+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 97, + "case_id": "bbh_word_sorting-0096", + "cycle": -1, + "duration_sec": 1.1757, + "event": "model_call", + "event_seq": 97, + "inputs": { + "prompt": "Sort the following words alphabetically: List: livre substitution ix frisian panoramic buckley\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: livre, substitution, ix, frisian, panoramic, buckley.\n\nWe need to sort these words alphabetically. We will compare them letter by letter.\n\n1. **buckley** (Starts with 'b')\n2. **frisian** (Starts with 'f')\n3. **ix** (Starts with 'i')\n4. **livre** (Starts with 'l')\n5. **panoramic** (Starts with 'p')\n6. **substitution** (Starts with 's')\n\nLet's re-sort based on the first letter:\nB: buckley\nF: frisian\nI: ix\nL: livre\nP: panoramic\nS: substitution\n\nAlphabetical order of the first letters: B, F, I, L, P, S.\n\nThe sorted list is:\n1. buckley\n2. frisian\n3. ix\n4. livre\n5. panoramic\n6. substitution\n\nAnswer: buckley, frisian, ix, livre, panoramic, substitution\n\nAnswer: buckley, frisian, ix, livre, panoramic, substitution", + "schema_version": 6, + "seq": 97, + "span_id": "m1.model_call.97", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:24:33.739212+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 98, + "case_id": "bbh_word_sorting-0097", + "cycle": -1, + "duration_sec": 0.7852, + "event": "model_call", + "event_seq": 98, + "inputs": { + "prompt": "Sort the following words alphabetically: List: fugue chicanery mountain\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: fugue, chicanery, mountain.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **f**ugue\n2. **c**hicanery\n3. **m**ountain\n\nComparing the first letters: 'c' comes before 'f', and 'f' comes before 'm'.\n\nSo the order based on the first letter is:\n'c' (chicanery)\n'f' (fugue)\n'm' (mountain)\n\nThe alphabetical order is: chicanery, fugue, mountain.\n\nAnswer: chicanery, fugue, mountain\n\nAnswer: chicanery, fugue, mountain", + "schema_version": 6, + "seq": 98, + "span_id": "m1.model_call.98", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:24:34.527760+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 99, + "case_id": "bbh_word_sorting-0098", + "cycle": -1, + "duration_sec": 2.3757, + "event": "model_call", + "event_seq": 99, + "inputs": { + "prompt": "Sort the following words alphabetically: List: murk pvc singlet yeats muslim coachmen clergymen companion gazpacho embark pristine granular hobble fatten allot too niggle chauncey threefold coddington\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. chauncey\n2. coddington\n3. companion\n4. coachmen\n5. clergymen\n6. embark\n7. fatten\n8. threefold\n9. granular\n10. gazpacho\n11. hobble\n12. murk\n13. muslim\n14. niggle\n15. pristine\n16. pvc\n17. singlet\n18. too\n19. yeats\n\nLet's re-sort them carefully:\n\n* **c**hauncey\n* **c**oddington\n* **c**ompanion\n* **c**oachmen\n* **c**lergymen\n* **e**mbark\n* **f**atten\n* **f**reedom (Wait, 'threefold' comes before 'fatten' if we look at the 't' sound, but alphabetically 'fatten' comes before 'threefold'.)\n* **g**azpacho\n* **g**ranular\n* **h**obble\n* **m**urk\n* **m**uslim\n* **n**iggle\n* **p**ristine\n* **p**vc\n* **s**inglet\n* **t**oo\n* **y**eats\n\nLet's do a strict alphabetical sort:\n\n1. chauncey\n2. coddington\n3. companion\n4. coachmen\n5. clergymen\n6. embark\n7. fatten\n8. threefold\n9. granular\n10. gazpacho\n11. hobble\n12. murk\n13. muslim\n14. niggle\n15. pristine\n16. pvc\n17. singlet\n18. too\n19. yeats\n\n(Checking 'fatten' vs 'threefold': F-A-T-T-E-N vs T-H-R-E-E-F-O-L-D. 'fatten' comes first.)\n(Checking 'granular' vs 'gazpacho': G-R vs G-A. 'gazpacho' comes before 'granular'.)\n\nCorrected final sort:\n\n1. chauncey\n2. coddington\n3. companion\n4. coachmen\n5. clergymen\n6. embark\n7. fatten\n8. threefold", + "schema_version": 6, + "seq": 99, + "span_id": "m1.model_call.99", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:24:36.906956+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 100, + "case_id": "bbh_word_sorting-0099", + "cycle": -1, + "duration_sec": 2.3764, + "event": "model_call", + "event_seq": 100, + "inputs": { + "prompt": "Sort the following words alphabetically: List: hornblower dissipate amanita canticle annoy besiege straight notre propylene sepia california pasture encephalitis boggle crocodilian dexter snipe amatory dizzy psychiatric\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. amanita\n2. annoy\n3. besiege\n4. boggle\n5. california\n6. canticle\n7. encephalitis\n8. dissipate\n9. dizzy\n10. dexter\n11. hornblower\n12. notre\n13. pasture\n14. propylene\n15. psychiatric\n16. sepia\n17. snipe\n18. straight\n\nLet's re-sort them carefully:\n\n* amanita\n* annoy\n* besiege\n* boggle\n* california\n* canticle\n* encephalitis\n* dissipate\n* dizzy\n* dexter\n* hornblower\n* notre\n* pasture\n* propylene\n* psychiatric\n* sepia\n* snipe\n* straight\n\nComparing the initial list: hornblower, dissipate, amanita, canticle, annoy, besiege, straight, notre, propylene, sepia, california, pasture, encephalitis, boggle, crocodilian, dexter, snipe, amatory, dizzy, psychiatric.\n\nWait, I missed \"crocodilian\" and \"amatory\" from the initial list provided in the prompt. Let's use the exact list provided:\nList: hornblower dissipate amanita canticle annoy besiege straight notre propylene sepia california pasture encephalitis boggle crocodilian dexter snipe amatory dizzy psychiatric\n\nSorting the 20 words:\n\n1. amanita\n2. amatory\n3. annoy\n4. besiege\n5. boggle\n6. california\n7. canticle\n8. crocodilian\n9. dissipate\n10. dizzy\n11. dexter\n12. encephalitis\n13. hornblower\n14. notre\n15. pasture\n16. propylene\n17. psychiatric\n18. sepia\n19. snipe\n20. straight\n\nLet's re-verify the order, especially around 'a' and 'b':\nA: amanita, amatory\nB: besiege, boggle\nC: california, canticle, crocodilian\nD: dissipate, dizzy, dexter\nE: encephalitis\nH: hornblower\nN: notre\nP: pasture, propylene, psychiatric\nS: sepia, snipe, straight\n\nCorrect alphabetical order:\n1. amanita\n2. amatory\n3. annoy\n4. besiege\n5. boggle\n", + "schema_version": 6, + "seq": 100, + "span_id": "m1.model_call.100", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:24:39.286934+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 101, + "case_id": "bbh_word_sorting-0100", + "cycle": -1, + "duration_sec": 2.0578, + "event": "model_call", + "event_seq": 101, + "inputs": { + "prompt": "Sort the following words alphabetically: List: stanhope sisyphus fungi purpose midwinter mendelevium studious driveway symmetry despond trample wring strip clearheaded encapsulate vs gob christen\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. clearheaded\n2. christen\n3. despond\n4. driveway\n5. encapsulate\n6. fungi\n7. mendelevium\n8. midwinter\n9. purpose\n10. stanhope\n11. strip\n12. studious\n13. symmetry\n14. trample\n15. vs\n16. wring\n\nLet's re-sort them carefully:\n\n* clearheaded\n* christen\n* despond\n* driveway\n* encapsulate\n* fungi\n* mendelevium\n* midwinter\n* purpose\n* stanhope\n* strip\n* studious\n* symmetry\n* trample\n* vs\n* wring\n\n(Checking the initial list again: stanhope, sisyphus, fungi, purpose, midwinter, mendelevium, studious, driveway, symmetry, despond, trample, wring, strip, clearheaded, encapsulate, vs, gob, christen)\n\nWait, I missed \"sisyphus\" and \"gob\". Let's re-list and sort:\n\nOriginal List: stanhope, sisyphus, fungi, purpose, midwinter, mendelevium, studious, driveway, symmetry, despond, trample, wring, strip, clearheaded, encapsulate, vs, gob, christen\n\nSorted List:\n1. clearheaded\n2. christen\n3. despond\n4. driveway\n5. encapsulate\n6. fungi\n7. gob\n8. mendelevium\n9. midwinter\n10. purpose\n11. stanhope\n12. sisyphus\n13. strip\n14. studious\n15. symmetry\n16. trample\n17. vs\n18. wring\n\nAnswer: clearheaded, christen, despond, driveway, encapsulate, fungi, gob, mendelevium, midwinter, purpose, stanhope, sisyphus, strip, studious, symmetry, trample, vs, wring", + "schema_version": 6, + "seq": 101, + "span_id": "m1.model_call.101", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:24:41.348094+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 102, + "case_id": "bbh_word_sorting-0101", + "cycle": -1, + "duration_sec": 1.1134, + "event": "model_call", + "event_seq": 102, + "inputs": { + "prompt": "Sort the following words alphabetically: List: regret starlight wallboard cotyledon more pepperoni\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: regret, starlight, wallboard, cotyledon, more, pepperoni.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **cotyledon** (Starts with 'c')\n2. **more** (Starts with 'm')\n3. **pepperoni** (Starts with 'p')\n4. **regret** (Starts with 'r')\n5. **starlight** (Starts with 's')\n6. **wallboard** (Starts with 'w')\n\nLet's verify the order:\n* c (cotyledon)\n* m (more)\n* p (pepperoni)\n* r (regret)\n* s (starlight)\n* w (wallboard)\n\nThe alphabetical order is: cotyledon, more, pepperoni, regret, starlight, wallboard.\n\nAnswer: cotyledon, more, pepperoni, regret, starlight, wallboard\n\nAnswer: cotyledon, more, pepperoni, regret, starlight, wallboard", + "schema_version": 6, + "seq": 102, + "span_id": "m1.model_call.102", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:24:42.465178+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 103, + "case_id": "bbh_word_sorting-0102", + "cycle": -1, + "duration_sec": 2.3708, + "event": "model_call", + "event_seq": 103, + "inputs": { + "prompt": "Sort the following words alphabetically: List: bottle kenney chapati okinawa boon bedtime\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: bottle, kenney, chapati, okinawa, boon, bedtime.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **b**ottle\n2. **b**oon\n3. **b**edtime\n4. **c**hapati\n5. **k**enney\n6. **o**kinawa\n\nComparing the words starting with 'b':\n* bottle\n* boon\n* bedtime\n\nComparing the second letter:\n* bo**t**tle\n* bo**o**n\n* be**d**time\n\n'b' comes before 'd' and 't'.\n'boon' comes before 'bottle' because 'o' comes before 't'.\nSo the order for 'b' words is: boon, bottle, bedtime.\n\nNow we place the rest:\n* chapati (starts with 'c')\n* kenney (starts with 'k')\n* okinawa (starts with 'o')\n\nThe alphabetical order of the starting letters is: b, c, k, o.\n\nCombining the sorted groups:\n1. boon\n2. bottle\n3. bedtime\n4. chapati\n5. kenney\n6. okinawa\n\nLet's re-check the 'b' words:\nboon\nbottle\nbedtime\n'boon' vs 'bottle': 'o' vs 't'. 'o' comes before 't'. So, boon comes before bottle.\n'boon' vs 'bedtime': 'o' vs 'e'. 'e' comes before 'o'. So, bedtime comes before boon.\n\nLet's re-sort the 'b' words:\nbedtime (b-e)\nboon (b-o-o)\nbottle (b-o-t)\n'e' comes before 'o'. So, bedtime is first.\n'o' comes before 't'. So, boon is before bottle.\nOrder: bedtime, boon, bottle.\n\nThe full sorted list:\n1. bedtime\n2. boon\n3. bottle\n4. chapati\n5. kenney\n6. okinawa\n\nFinal check:\nbedtime\nboon\nbottle\nchapati\nkenney\nokinawa\n\nAnswer: bedtime, boon, bottle, chapati, kenney, okinawa\n\nAnswer: bedtime, boon, bottle, chapati, kenney", + "schema_version": 6, + "seq": 103, + "span_id": "m1.model_call.103", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:24:44.839496+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 104, + "case_id": "bbh_word_sorting-0103", + "cycle": -1, + "duration_sec": 2.3702, + "event": "model_call", + "event_seq": 104, + "inputs": { + "prompt": "Sort the following words alphabetically: List: anarchic liven shortcut total diatom guelph usage acoustic yarmulke phenomenology neo bureaucracy trammel leftward immovable fabricate syndrome suggestive provide\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. acoustic\n2. anarchic\n3. diatom\n4. fabricate\n5. phenomenology\n6. leftward\n7. liven\n8. neo\n9. immovable\n10. provide\n11. shortcut\n12. syndrome\n13. total\n14. trammel\n15. usage\n16. yarmulke\n17. bureaucracy\n18. guelph\n19. suggestive\n\nLet's re-sort them carefully:\n\n* **a**narchic, **a**coustic\n* **b**ureaucracy\n* **d**iatom\n* **f**abricate\n* **g**uelph\n* **i**mmovable\n* **l**eftward, **l**iven\n* **n**eo\n* **p**henomenology, **p**rovide\n* **s**hortcut, **s**yndrome, **s**uggestive\n* **t**otal, **t**rammel\n* **u**sage\n* **y**armulke\n\n**Detailed Alphabetical Sort:**\n\n1. acoustic\n2. anarchic\n3. bureaucracy\n4. diatom\n5. fabricate\n6. guelph\n7. immovable\n8. leftward\n9. liven\n10. neo\n11. phenomenology\n12. provide\n13. shortcut\n14. syndrome\n15. total\n16. trammel\n17. usage\n18. yarmulke\n19. suggestive\n\n(Checking L words: leftward, liven. L-e-f-t vs L-i-v-e. Leftward comes before liven.)\n(Checking P words: phenomenology, provide. Phenomenology comes before provide.)\n(Checking S words: shortcut, syndrome, suggestive. Shortcut, syndrome, suggestive.)\n\nFinal check of the full list:\nanarchic, acoustic, bureaucracy, diatom, fabricate, guelph, immovable, leftward, liven, neo, phenomenology, provide, shortcut, syndrome, total, trammel, usage, yarmulke, suggestive.\n\nAnswer: acoustic, anarchic, bureaucracy, diatom, fabricate, guelph, immovable, leftward, liven, neo, phenomenology, provide, shortcut, syndrome, total, trammel,", + "schema_version": 6, + "seq": 104, + "span_id": "m1.model_call.104", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:24:47.213218+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 105, + "case_id": "bbh_word_sorting-0104", + "cycle": -1, + "duration_sec": 2.3753, + "event": "model_call", + "event_seq": 105, + "inputs": { + "prompt": "Sort the following words alphabetically: List: berniece bremsstrahlung hoe avalanche showroom goff dactylic befriend hurry housekeep lanka gilbertson posterior flick bong hereafter metazoan\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words to sort alphabetically:\nberniece, bremsstrahlung, hoe, avalanche, showroom, goff, dactylic, befriend, hurry, housekeep, lanka, gilbertson, posterior, flick, bong, hereafter, metazoan\n\nLet's sort them:\n\n1. **ache** (Wait, \"ache\" is not in the list. Let's re-examine the list.)\n\nWords: berniece, bremsstrahlung, hoe, avalanche, showroom, goff, dactylic, befriend, hurry, housekeep, lanka, gilbertson, posterior, flick, bong, hereafter, metazoan\n\nSorting by the first letter:\nA: avalanche\nB: berniece, bremsstrahlung, befriend\nD: dactylic\nF: flick\nG: goff, gilbertson\nH: hoe, hurry, housekeep\nL: lanka\nM: metazoan\nP: posterior\nS: showroom\nH: hereafter (already listed under H, but we need to check order)\n\nLet's sort systematically:\n\n1. **ache** (None)\n2. **avalanche**\n3. **befriend**\n4. **berniece**\n5. **bremsstrahlung**\n6. **bong**\n7. **dactylic**\n8. **flick**\n9. **goff**\n10. **gilbertson**\n11. **hereafter**\n12. **hoe**\n13. **hurry**\n14. **housekeep**\n15. **lanka**\n16. **metazoan**\n17. **posterior**\n18. **showroom**\n\nLet's refine the sorting, especially for words starting with the same letter:\n\n**B words:** befriend, berniece, bremsstrahlung (befriend, berniece, bremsstrahlung)\n**H words:** hereafter, hoe, hurry, housekeep (hereafter, hoe, hurry, housekeep)\n**G words:** goff, gilbertson (goff, gilbertson)\n\n**Alphabetical Order:**\n\n1. avalanche (A)\n2. befriend (B)\n3. berniece (B)\n4. bremsstrahlung (B)\n5. bong (B - Wait, 'bong' starts with B, it should come before 'berniece' if we sort strictly by the second letter, but", + "schema_version": 6, + "seq": 105, + "span_id": "m1.model_call.105", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:24:49.592023+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 106, + "case_id": "bbh_word_sorting-0105", + "cycle": -1, + "duration_sec": 1.7386, + "event": "model_call", + "event_seq": 106, + "inputs": { + "prompt": "Sort the following words alphabetically: List: blutwurst horizon uranyl arraign portentous faber rancho peste glacier convenient inconspicuous\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words to sort: blutwurst, horizon, uranyl, arraign, portentous, faber, rancho, peste, glacier, convenient, inconspicuous.\n\nLet's sort them alphabetically:\n\n1. arraign\n2. blutwurst\n3. convenient\n4. faber\n5. glacier\n6. horizon\n7. inconspicuous\n8. portentous\n9. peste\n10. rancho\n11. uranyl\n\nLet's re-check the sorting carefully, especially for words starting with similar letters:\n\n* **a**rraign\n* **b**lutwurst\n* **c**onvenient\n* **f**aber\n* **g**lacier\n* **h**orizon\n* **i**nconspicuous\n* **p**ortentous\n* **p**este (Peste comes before Portentous because 'e' comes before 'o')\n* **r**ancho\n* **u**ranyl\n\nLet's re-sort the 'P' words:\n* portentous\n* peste\n'peste' comes before 'portentous'.\n\nCorrected order:\n1. arraign\n2. blutwurst\n3. convenient\n4. faber\n5. glacier\n6. horizon\n7. inconspicuous\n8. peste\n9. portentous\n10. rancho\n11. uranyl\n\nAnswer: arraign, blutwurst, convenient, faber, glacier, horizon, inconspicuous, peste, portentous, rancho, uranyl\n\nAnswer: arraign, blutwurst, convenient, faber, glacier, horizon, inconspicuous, peste, portentous, rancho, uranyl", + "schema_version": 6, + "seq": 106, + "span_id": "m1.model_call.106", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:24:51.334229+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 107, + "case_id": "bbh_word_sorting-0106", + "cycle": -1, + "duration_sec": 1.7085, + "event": "model_call", + "event_seq": 107, + "inputs": { + "prompt": "Sort the following words alphabetically: List: solvate nat usable kajar downey vision detest aitken barycentric\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: solvate, nat, usable, kajar, downey, vision, detest, aitken, barycentric.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter:**\n * a: aitken\n * b: barycentric\n * d: downey, detest\n * k: kajar\n * n: nat\n * s: solvate\n * u: usable\n * v: vision\n\n2. **Sort words starting with 'd':**\n * downey\n * detest (d-o comes before d-e, wait, 'd' is the same. 'o' in downey, 'e' in detest. 'e' comes before 'o'.)\n * Correct order: detest, downey\n\n3. **Combine the sorted list:**\n * aitken (a)\n * barycentric (b)\n * detest (d)\n * downey (d)\n * kajar (k)\n * nat (n)\n * solvate (s)\n * usable (u)\n * vision (v)\n\n4. **Final check of the order:**\n * aitken\n * barycentric\n * detest\n * downey\n * kajar\n * nat\n * solvate\n * usable\n * vision\n\nAnswer: aitken, barycentric, detest, downey, kajar, nat, solvate, usable, vision\nAnswer: aitken, barycentric, detest, downey, kajar, nat, solvate, usable, vision", + "schema_version": 6, + "seq": 107, + "span_id": "m1.model_call.107", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:24:53.046440+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 108, + "case_id": "bbh_word_sorting-0107", + "cycle": -1, + "duration_sec": 0.9093, + "event": "model_call", + "event_seq": 108, + "inputs": { + "prompt": "Sort the following words alphabetically: List: wreak hidebound bandwidth\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given words are: wreak, hidebound, bandwidth.\n\nTo sort them alphabetically, we compare them letter by letter:\n\n1. **wreak**: Starts with 'w'\n2. **hidebound**: Starts with 'h'\n3. **bandwidth**: Starts with 'b'\n\nComparing the first letters: 'b', 'h', 'w'.\nIn alphabetical order: 'b' comes before 'h', and 'h' comes before 'w'.\n\nSo, 'bandwidth' comes first.\n\nNow compare 'hidebound' and 'wreak'.\n'h' comes before 'w'.\nSo, 'hidebound' comes before 'wreak'.\n\nThe alphabetical order is:\n1. bandwidth\n2. hidebound\n3. wreak\n\nAnswer: bandwidth, hidebound, wreak\n\nAnswer: bandwidth, hidebound, wreak", + "schema_version": 6, + "seq": 108, + "span_id": "m1.model_call.108", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:24:53.959503+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 109, + "case_id": "bbh_word_sorting-0108", + "cycle": -1, + "duration_sec": 2.0607, + "event": "model_call", + "event_seq": 109, + "inputs": { + "prompt": "Sort the following words alphabetically: List: mckinley pesticide school radiometer marque prig relish trianon inspiration artful cancelled castrate glaucous ear croon tioga rothschild excite endpoint citadel\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. artful\n2. cancelled\n3. castrate\n4. citadel\n5. croon\n6. endpoint\n7. ear\n8. excite\n9. glaucous\n10. inspiration\n11. marque\n12. mckinley\n13. pesticide\n14. prig\n15. relish\n16. radiometer\n17. rothschild\n18. school\n19. tioga\n20. trianon\n\nLet's re-sort them carefully:\n\n* artful\n* cancelled\n* castrate\n* citadel\n* croon\n* endpoint\n* ear\n* excite\n* glaucous\n* inspiration\n* marque\n* mckinley\n* pesticide\n* prig\n* relish\n* radiometer\n* rothschild\n* school\n* tioga\n* trianon\n\nChecking the order again:\nA: artful\nC: cancelled, castrate, citadel, croon\nE: endpoint, ear, excite\nG: glaucous\nI: inspiration\nM: marque, mckinley\nP: pesticide, prig\nR: relish, radiometer, rothschild\nS: school\nT: tioga, trianon\n\nDetailed alphabetical sort:\n1. artful\n2. cancelled\n3. castrate\n4. citadel\n5. croon\n6. endpoint\n7. ear\n8. excite\n9. glaucous\n10. inspiration\n11. marque\n12. mckinley\n13. pesticide\n14. prig\n15. relish\n16. radiometer\n17. rothschild\n18. school\n19. tioga\n20. trianon\n\nAnswer: artful, cancelled, castrate, citadel, croon, endpoint, ear, excite, glaucous, inspiration, marque, mckinley, pesticide, prig, relish, radiometer, rothschild, school, tioga, trianon", + "schema_version": 6, + "seq": 109, + "span_id": "m1.model_call.109", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:24:56.023914+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 110, + "case_id": "bbh_word_sorting-0109", + "cycle": -1, + "duration_sec": 0.6993, + "event": "model_call", + "event_seq": 110, + "inputs": { + "prompt": "Sort the following words alphabetically: List: greasy lapidary mark\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: greasy, lapidary, mark.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **greasy** (starts with 'g')\n2. **lapidary** (starts with 'l')\n3. **mark** (starts with 'm')\n\nComparing the first letters: 'g', 'l', 'm'.\nIn alphabetical order, 'g' comes before 'l', and 'l' comes before 'm'.\n\nTherefore, the alphabetical order is: greasy, lapidary, mark.\n\nAnswer: greasy, lapidary, mark", + "schema_version": 6, + "seq": 110, + "span_id": "m1.model_call.110", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:24:56.726947+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 111, + "case_id": "bbh_word_sorting-0110", + "cycle": -1, + "duration_sec": 0.6521, + "event": "model_call", + "event_seq": 111, + "inputs": { + "prompt": "Sort the following words alphabetically: List: monotonous slick provocation functor hideaway holeable assimilable lola calypso nebuchadnezzar dogwood pacifism bivariate bought\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. assimilable\n2. bivariate\n3. bought\n4. calypso\n5. dogwood\n6. functor\n7. hideaway\n8. holeable\n9. lola\n10. monotonous\n11. nebuchadnezzar\n12. pacifism\n13. provocation\n14. slick\n\nAnswer: assimilable, bivariate, bought, calypso, dogwood, functor, hideaway, holeable, lola, monotonous, nebuchadnezzar, pacifism, provocation, slick", + "schema_version": 6, + "seq": 111, + "span_id": "m1.model_call.111", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:24:57.382828+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 112, + "case_id": "bbh_word_sorting-0111", + "cycle": -1, + "duration_sec": 1.2813, + "event": "model_call", + "event_seq": 112, + "inputs": { + "prompt": "Sort the following words alphabetically: List: tuba synapse inaudible brewster tithing\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: tuba, synapse, inaudible, brewster, tithing.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **brewster** (Starts with 'b')\n2. **inaudible** (Starts with 'i')\n3. **tithing** (Starts with 't')\n4. **tuba** (Starts with 't') - Compare 'tithing' and 'tuba'. 'i' comes before 'u', so 'tithing' comes before 'tuba'.\n5. **synapse** (Starts with 's')\n\nLet's re-sort based on the first letter:\nB: brewster\nI: inaudible\nS: synapse\nT: tithing, tuba\n\nNow compare the words starting with 'T':\ntithing\ntuba\n'i' comes before 'u', so 'tithing' comes before 'tuba'.\n\nThe alphabetical order is:\n1. brewster\n2. inaudible\n3. synapse\n4. tithing\n5. tuba\n\nAnswer: brewster, inaudible, synapse, tithing, tuba\n\nAnswer: brewster, inaudible, synapse, tithing, tuba", + "schema_version": 6, + "seq": 112, + "span_id": "m1.model_call.112", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:24:58.668115+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 113, + "case_id": "bbh_word_sorting-0112", + "cycle": -1, + "duration_sec": 0.5752, + "event": "model_call", + "event_seq": 113, + "inputs": { + "prompt": "Sort the following words alphabetically: List: kenya beth\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: kenya, beth.\n\nTo sort these words alphabetically, we compare them letter by letter from the beginning.\n\n1. **Compare the first letter:**\n - 'k' in \"kenya\"\n - 'b' in \"beth\"\n Since 'b' comes before 'k' in the alphabet, \"beth\" comes before \"kenya\".\n\nTherefore, the alphabetical order is: beth, kenya.\n\nAnswer: beth, kenya", + "schema_version": 6, + "seq": 113, + "span_id": "m1.model_call.113", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:24:59.247123+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 114, + "case_id": "bbh_word_sorting-0113", + "cycle": -1, + "duration_sec": 1.7876, + "event": "model_call", + "event_seq": 114, + "inputs": { + "prompt": "Sort the following words alphabetically: List: million boone chalcedony sinai satin genteel alternate charity olden\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words to sort: million, boone, chalcedony, sinai, satin, genteel, alternate, charity, olden\n\n1. **alternate**\n2. **boone**\n3. **chalcedony**\n4. **charity**\n5. **genteel**\n6. **million**\n7. **olden**\n8. **satin**\n9. **sinai**\n\nLet's re-sort them carefully:\n\n* **a**lternate\n* **b**oone\n* **c**halcedony\n* **c**harity (comes after chalcedony because 'h' comes before 'r')\n* **g**enteel\n* **m**illion\n* **o**lden\n* **s**atin\n* **s**inai (satin comes before sinai because 'a' comes before 'i')\n\nLet's re-examine the 'c' words: chalcedony, charity.\nchalcedony (ch-a-l)\ncharity (ch-a-r)\n'l' comes before 'r', so chalcedony comes before charity.\n\nLet's re-examine the 's' words: satin, sinai.\nsatin (s-a-t)\nsinai (s-i-n)\n'a' comes before 'i', so satin comes before sinai.\n\nFinal alphabetical order:\n1. alternate\n2. boone\n3. chalcedony\n4. charity\n5. genteel\n6. million\n7. olden\n8. satin\n9. sinai\n\nAnswer: alternate, boone, chalcedony, charity, genteel, million, olden, satin, sinai", + "schema_version": 6, + "seq": 114, + "span_id": "m1.model_call.114", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:25:01.038938+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 115, + "case_id": "bbh_word_sorting-0114", + "cycle": -1, + "duration_sec": 1.7497, + "event": "model_call", + "event_seq": 115, + "inputs": { + "prompt": "Sort the following words alphabetically: List: him correspond seashore herpes\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: him, correspond, seashore, herpes.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **h**im\n2. **h**erpes\n3. **c**orrespond\n4. **s**eashore\n\nComparing the first letter: 'c' comes before 'h', and 'h' comes before 's'.\n\nSo, 'correspond' comes first among the words starting with 'c', 'h', or 's'. Wait, let's re-examine the starting letters:\n* him: h\n* correspond: c\n* seashore: s\n* herpes: h\n\nAlphabetical order of starting letters: c, h, h, s.\n\n1. **c**orrespond (starts with c)\n2. **h**im (starts with h)\n3. **h**erpes (starts with h)\n4. **s**eashore (starts with s)\n\nNow we compare the words starting with 'h': 'him' and 'herpes'.\n* hi**m**\n* he**r**pes\n'i' comes before 'e' is false. 'e' comes before 'i'.\nSo, 'herpes' comes before 'him'.\n\nThe sorted order is:\n1. correspond\n2. herpes\n3. him\n4. seashore\n\nLet's verify the comparison between 'herpes' and 'him':\nh e r p e s\nh i m\n'e' comes before 'i', so 'herpes' comes before 'him'.\n\nFinal sorted list: correspond, herpes, him, seashore.\n\nAnswer: correspond, herpes, him, seashore\n\nAnswer: correspond, herpes, him, seashore", + "schema_version": 6, + "seq": 115, + "span_id": "m1.model_call.115", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:25:02.792436+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 116, + "case_id": "bbh_word_sorting-0115", + "cycle": -1, + "duration_sec": 0.5337, + "event": "model_call", + "event_seq": 116, + "inputs": { + "prompt": "Sort the following words alphabetically: List: catechism daddy\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: catechism, daddy.\n\nTo sort them alphabetically, we compare them letter by letter:\n\n1. **catechism**: Starts with 'c'\n2. **daddy**: Starts with 'd'\n\nSince 'c' comes before 'd' in the alphabet, \"catechism\" comes before \"daddy\".\n\nThe alphabetical order is: catechism, daddy.\n\nAnswer: catechism, daddy", + "schema_version": 6, + "seq": 116, + "span_id": "m1.model_call.116", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:25:03.330307+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 117, + "case_id": "bbh_word_sorting-0116", + "cycle": -1, + "duration_sec": 2.1248, + "event": "model_call", + "event_seq": 117, + "inputs": { + "prompt": "Sort the following words alphabetically: List: zinc coriolanus leather easternmost highwaymen scavenge musky cutlet synaptic creak heaven paula astigmat muscular godson boyish\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. astigmat\n2. boyish\n3. creak\n4. coriolanus\n5. cutlet\n6. easternmost\n7. godson\n8. heaven\n9. highwaymen\n10. leather\n11. musky\n12. muscular\n13. paula\n14. scavenge\n15. synaptic\n16. zinc\n\nLet's re-sort them carefully:\n\n* **a**stigmat\n* **b**oyish\n* **c**reak, **c**oriolanus, **c**utlet (creak, coriolanus, cutlet)\n* **e**asternmost\n* **g**odson\n* **h**aven, **h**ighwaymen\n* **l**eather\n* **m**usky, **m**uscular\n* **p**aula\n* **s**cavenge, **s**ynaptic\n* **z**inc\n\nDetailed sorting:\n\n1. astigmat\n2. boyish\n3. creak\n4. coriolanus\n5. cutlet\n6. easternmost\n7. godson\n8. heaven\n9. highwaymen\n10. leather\n11. musky\n12. muscular\n13. paula\n14. scavenge\n15. synaptic\n16. zinc\n\n(Comparing 'heaven' and 'highwaymen': H-e vs H-i. 'heaven' comes before 'highwaymen'.)\n(Comparing 'musky' and 'muscular': 'musky' comes before 'muscular'.)\n\nFinal sorted list:\nastigmat, boyish, creak, coriolanus, cutlet, easternmost, godson, heaven, highwaymen, leather, musky, muscular, paula, scavenge, synaptic, zinc\n\nAnswer: astigmat, boyish, creak, coriolanus, cutlet, easternmost, godson, heaven, highwaymen, leather, musky, muscular, paula, scavenge, synaptic, zinc", + "schema_version": 6, + "seq": 117, + "span_id": "m1.model_call.117", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:25:05.459159+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 118, + "case_id": "bbh_word_sorting-0117", + "cycle": -1, + "duration_sec": 1.4722, + "event": "model_call", + "event_seq": 118, + "inputs": { + "prompt": "Sort the following words alphabetically: List: wagging cabdriver astronomic pivot loch coherent\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: wagging, cabdriver, astronomic, pivot, loch, coherent.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **astronomic** (Starts with 'a')\n2. **cabdriver** (Starts with 'c')\n3. **coherent** (Starts with 'c')\n4. **loch** (Starts with 'l')\n5. **pivot** (Starts with 'p')\n6. **wagging** (Starts with 'w')\n\nNow, let's compare the words starting with 'c':\n* cabdriver\n* coherent\n\nComparing 'cabdriver' and 'coherent':\n* c-a-b...\n* c-o-h...\n'a' comes before 'o', so 'cabdriver' comes before 'coherent'.\n\nThe sorted order for the 'c' words is: cabdriver, coherent.\n\nNow, let's put all the words in order:\n1. astronomic (a)\n2. cabdriver (c)\n3. coherent (c)\n4. loch (l)\n5. pivot (p)\n6. wagging (w)\n\nThe final alphabetical order is: astronomic, cabdriver, coherent, loch, pivot, wagging.\n\nAnswer: astronomic, cabdriver, coherent, loch, pivot, wagging", + "schema_version": 6, + "seq": 118, + "span_id": "m1.model_call.118", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:25:06.936344+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 119, + "case_id": "bbh_word_sorting-0118", + "cycle": -1, + "duration_sec": 1.1988, + "event": "model_call", + "event_seq": 119, + "inputs": { + "prompt": "Sort the following words alphabetically: List: no spokane cartilaginous that'd science\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: no, spokane, cartilaginous, that'd, science.\n\nTo sort them alphabetically, we compare them letter by letter:\n\n1. **cartilaginous** (Starts with 'c')\n2. **no** (Starts with 'n')\n3. **science** (Starts with 's')\n4. **spokane** (Starts with 's', but 'sp' comes after 'sc')\n5. **that'd** (Starts with 't')\n\nLet's compare the words starting with 's': 'science' and 'spokane'.\n'science' comes before 'spokane' because 'ci' comes before 'po'.\n\nNow let's order the words starting with 'n' and 't':\n'no' comes before 'that'd'.\n\nThe complete alphabetical order is:\n1. cartilaginous\n2. no\n3. science\n4. spokane\n5. that'd\n\nAnswer: cartilaginous, no, science, spokane, that'd\n\nAnswer: cartilaginous, no, science, spokane, that'd", + "schema_version": 6, + "seq": 119, + "span_id": "m1.model_call.119", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:25:08.139097+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 120, + "case_id": "bbh_word_sorting-0119", + "cycle": -1, + "duration_sec": 2.3747, + "event": "model_call", + "event_seq": 120, + "inputs": { + "prompt": "Sort the following words alphabetically: List: gentle boletus galveston aniline eddy fontainebleau wile scandalous skat sportsmen\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: gentle, boletus, galveston, aniline, eddy, fontainebleau, wile, scandalous, skat, sportsmen.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **aniline** (a)\n2. **boletus** (b)\n3. **eddy** (e)\n4. **fontainebleau** (f)\n5. **gentle** (g)\n6. **galveston** (g) - 'galveston' comes after 'gentle' because 'a' comes before 'e' in the second position of 'galveston' compared to 'gentle' (if we consider the first letter, 'g' is the same, then 'a' vs 'e'). Wait, let's re-evaluate 'gentle' and 'galveston'.\n - g e n t l e\n - g a l v e s t o n\n 'a' comes before 'e', so **galveston** comes before **gentle**.\n\nLet's re-sort based on the first letter:\nA: aniline\nB: boletus\nE: eddy\nF: fontainebleau\nG: gentle, galveston\nS: scandalous, skat, sportsmen, wile\n\nSorting the 'G' words:\ngentle (g e n t l e)\ngalveston (g a l v e s t o n)\n'a' comes before 'e', so **galveston** comes before **gentle**.\n\nSorting the 'S' words:\nscandalous (s c a n d a l o u s)\nskat (s k a t)\nsportsmen (s p o r t s m e n)\nwile (w i l e) - Wait, 'wile' starts with 'w', so it comes after 's' words.\n\nLet's list all words and sort them:\n1. aniline\n2. boletus\n3. eddy\n4. fontainebleau\n5. galveston (G, a)\n6. gentle (G, e)\n7. scandalous (S, c)\n8. skat (S, k)\n9. sportsmen (S, p)\n10. wile (W)\n\nComparing the 'S' words:\nscandalous (s c...)\nskat (", + "schema_version": 6, + "seq": 120, + "span_id": "m1.model_call.120", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:25:10.517806+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 121, + "case_id": "bbh_word_sorting-0120", + "cycle": -1, + "duration_sec": 2.3733, + "event": "model_call", + "event_seq": 121, + "inputs": { + "prompt": "Sort the following words alphabetically: List: labile crunchy highlight silage judaism allocable vale phenol dissipate bertram necessity champlain boutique hydrology facto often\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. allocable\n2. bertram\n3. boutique\n4. champlain\n5. crunchy\n6. dissipate\n7. facet (facto)\n8. highlight\n9. hydrology\n10. labile\n11. necessity\n12. often\n13. phenol\n14. silage\n15. vale\n16. judaism\n\nLet's re-sort carefully:\n\n* allocable\n* bertram\n* boutique\n* champlain\n* crunchy\n* dissipate\n* facet (facto) - Assuming 'facto' is a typo for 'fact' or sorting by the first letter 'f'. If we treat 'facto' as is:\n* highlight\n* hydrology\n* labile\n* necessity\n* often\n* phenol\n* silage\n* vale\n* judaism\n\nComparing 'facto' and 'highlight': 'f' comes before 'h'.\n\nLet's list them and sort:\nallocable (A)\nbertram (B)\nboutique (B) - bertram before boutique\nchamplain (C)\ncrunchy (C) - champlain before crunchy\ndissipate (D)\nfacto (F)\nhighlight (H)\nhydrology (H) - facto before hydrology\nlabile (L)\nnecessity (N)\noften (O)\nphenol (P)\nsilage (S)\nvale (V)\njudaism (J) - J comes before L\n\nCorrect alphabetical order:\n1. allocable\n2. bertram\n3. boutique\n4. champlain\n5. crunchy\n6. dissipate\n7. facto\n8. highlight\n9. hydrology\n10. labile\n11. judaism\n12. necessity\n13. often\n14. phenol\n15. silage\n16. vale\n\nWait, 'judaism' starts with J, which comes before L. Let's re-check the full list:\n\nlabile (L)\njudaism (J) -> J comes before L.\n\nCorrected order:\n1. allocable\n2. bertram\n3. boutique\n4. champlain\n5. crunchy\n6. dissipate\n7. facto\n8. highlight\n9. hydrology\n10. judaism\n11. labile\n12. necessity\n13", + "schema_version": 6, + "seq": 121, + "span_id": "m1.model_call.121", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:25:12.895134+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 122, + "case_id": "bbh_word_sorting-0121", + "cycle": -1, + "duration_sec": 0.5169, + "event": "model_call", + "event_seq": 122, + "inputs": { + "prompt": "Sort the following words alphabetically: List: hyperboloidal borough\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given words are: hyperboloidal, borough.\n\nTo sort them alphabetically, we compare them letter by letter:\n\n1. **h**yperboloidal\n2. **b**orough\n\nSince 'b' comes before 'h' in the alphabet, \"borough\" comes before \"hyperboloidal\".\n\nThe alphabetical order is: borough, hyperboloidal.\n\nAnswer: borough, hyperboloidal\nAnswer: borough, hyperboloidal", + "schema_version": 6, + "seq": 122, + "span_id": "m1.model_call.122", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:25:13.415972+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 123, + "case_id": "bbh_word_sorting-0122", + "cycle": -1, + "duration_sec": 1.515, + "event": "model_call", + "event_seq": 123, + "inputs": { + "prompt": "Sort the following words alphabetically: List: heterostructure bertrand careful wherewith libra eyelid feign southeastern paste snip\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words to sort: heterostructure, bertrand, careful, wherewith, libra, eyelid, feign, southeastern, paste, snip.\n\nLet's sort them alphabetically:\n\n1. careful\n2. bertrand\n3. eyelid\n4. feign\n5. heterostructure\n6. libra\n7. paste\n8. southeastern\n9. snip\n10. wherewith\n\nDetailed comparison:\n* **c**areful\n* **b**ertrand\n* **e**yelid\n* **f**eign\n* **h**eterostructure\n* **l**ibra\n* **p**aste\n* **s**outheastern\n* **s**nip (southeastern comes before snip because 'o' comes before 'n') - Wait, let's recheck 's' words.\n * southeastern\n * snip\n * southeastern vs snip: southeastern comes before snip.\n* **w**herewith\n\nLet's re-sort the 's' words:\n* southeastern\n* snip\n\nThe final sorted list is:\n1. careful\n2. bertrand\n3. eyelid\n4. feign\n5. heterostructure\n6. libra\n7. paste\n8. southeastern\n9. snip\n10. wherewith\n\nAnswer: careful, bertrand, eyelid, feign, heterostructure, libra, paste, southeastern, snip, wherewith", + "schema_version": 6, + "seq": 123, + "span_id": "m1.model_call.123", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:25:14.935113+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 124, + "case_id": "bbh_word_sorting-0123", + "cycle": -1, + "duration_sec": 1.6375, + "event": "model_call", + "event_seq": 124, + "inputs": { + "prompt": "Sort the following words alphabetically: List: infantrymen knob yaounde aqueous deregulate lysergic gala\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: infantrymen, knob, yaounde, aqueous, deregulate, lysergic, gala.\n\nWe need to sort these words alphabetically.\n\nLet's compare them letter by letter:\n\n1. **aqueous** (Starts with 'a')\n2. **deregulate** (Starts with 'd')\n3. **gala** (Starts with 'g')\n4. **infantrymen** (Starts with 'i')\n5. **knob** (Starts with 'k')\n6. **lysergic** (Starts with 'l')\n7. **yaounde** (Starts with 'y')\n\nNow let's sort the words starting with the same letter if any (none in this case, but we check the order of the first letters):\na, d, g, i, k, l, y\n\nAlphabetical order of the starting letters: a, d, g, i, k, l, y.\n\n1. **aqueous** (a)\n2. **deregulate** (d)\n3. **gala** (g)\n4. **infantrymen** (i)\n5. **knob** (k)\n6. **lysergic** (l)\n7. **yaounde** (y)\n\nThe sorted list is: aqueous, deregulate, gala, infantrymen, knob, lysergic, yaounde.\n\nAnswer: aqueous, deregulate, gala, infantrymen, knob, lysergic, yaounde\nAnswer: aqueous, deregulate, gala, infantrymen, knob, lysergic, yaounde", + "schema_version": 6, + "seq": 124, + "span_id": "m1.model_call.124", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:25:16.576671+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 125, + "case_id": "bbh_word_sorting-0124", + "cycle": -1, + "duration_sec": 1.8991, + "event": "model_call", + "event_seq": 125, + "inputs": { + "prompt": "Sort the following words alphabetically: List: burley bela arapaho bacteria bock\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "The given list of words is: burley, bela, arapaho, bacteria, bock.\n\nWe need to sort these words alphabetically. We compare them letter by letter:\n\n1. **bela** (Starts with 'b', second letter 'e')\n2. **bacteria** (Starts with 'b', second letter 'a')\n3. **bock** (Starts with 'b', second letter 'o')\n4. **burley** (Starts with 'b', second letter 'u')\n5. **arapaho** (Starts with 'a')\n\nComparing the first letters: 'a' comes before 'b'.\nSo, **arapaho** is the first word.\n\nNow we compare the words starting with 'b': burley, bela, bacteria, bock.\n\nComparing the second letters:\n* bela: 'e'\n* bacteria: 'a'\n* bock: 'o'\n* burley: 'u'\n\n'a' comes before 'e', 'o', and 'u'. So, **bacteria** comes next.\n\nRemaining words: burley, bela, bock.\n\nComparing the second letters of the remaining words:\n* bela: 'e'\n* bock: 'o'\n* burley: 'u'\n\n'e' comes before 'o' and 'u'. So, **bela** comes next.\n\nRemaining words: burley, bock.\n\nComparing the second letters:\n* bock: 'o'\n* burley: 'u'\n\n'o' comes before 'u'. So, **bock** comes next.\n\nThe last word is **burley**.\n\nThe sorted order is: arapaho, bacteria, bela, bock, burley.\n\nAnswer: arapaho, bacteria, bela, bock, burley\nAnswer: arapaho, bacteria, bela, bock, burley", + "schema_version": 6, + "seq": 125, + "span_id": "m1.model_call.125", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:25:18.479926+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 126, + "case_id": "bbh_word_sorting-0125", + "cycle": -1, + "duration_sec": 2.3634, + "event": "model_call", + "event_seq": 126, + "inputs": { + "prompt": "Sort the following words alphabetically: List: betony bronzy charge cordial handicraft boar collet clemens bootleg possession mesh centaur contemporaneous macmahon underclassman monterey dorado contravariant regina\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words to be sorted alphabetically:\nbetony, bronzy, charge, cordial, handicraft, boar, collet, clemens, bootleg, possession, mesh, centaur, contemporaneous, macmahon, monterey, dorado, contravariant, regina, underclassman\n\nLet's sort them:\n\n1. **boar**\n2. **betony**\n3. **bootleg**\n4. **bronzy**\n5. **charge**\n6. **centaur**\n7. **clemens**\n8. **collet**\n9. **contravariant**\n10. **cordial**\n11. **danicraft** (handicraft)\n12. **dorado**\n13. **macmahon**\n14. **mesh**\n15. **monterey**\n16. **possession**\n17. **regina**\n18. **underclassman**\n19. **contemporaneous**\n\nLet's re-sort carefully, paying attention to the initial letters:\n\nB: betony, boar, bootleg, bronzy\nC: charge, cordial, collet, clemens, centaur, contemporaneous, contravariant\nD: dorado\nH: handicraft\nM: macmahon, mesh, monterey\nP: possession\nR: regina\nU: underclassman\n\n**Step 1: Group by first letter**\n\nB: betony, boar, bootleg, bronzy\nC: charge, cordial, collet, clemens, centaur, contemporaneous, contravariant\nD: dorado\nH: handicraft\nM: macmahon, mesh, monterey\nP: possession\nR: regina\nU: underclassman\n\n**Step 2: Sort within groups**\n\nB: betony, boar, bootleg, bronzy (betony, boar, bootleg, bronzy)\n * betony\n * boar\n * bootleg\n * bronzy\n\nC: charge, cordial, collet, clemens, centaur, contemporaneous, contravariant (charge, cordial, collet, clemens, centaur, contemporaneous, contravariant)\n * charge\n * cordial\n * collet\n * clemens\n * centaur\n * contemporaneous\n * contravariant\n\nD: dorado\n\nH: handic", + "schema_version": 6, + "seq": 126, + "span_id": "m1.model_call.126", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:25:20.847386+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 127, + "case_id": "bbh_word_sorting-0126", + "cycle": -1, + "duration_sec": 0.5773, + "event": "model_call", + "event_seq": 127, + "inputs": { + "prompt": "Sort the following words alphabetically: List: skinny sharon sudan earthy parentage hello inmate announce co clayton carp phonetic watson nimbus\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. announce\n2. carp\n3. clayton\n4. earthy\n5. hello\n6. inmate\n7. nimbus\n8. parentage\n9. phonetic\n10. skinny\n11. sharon\n12. sudan\n13. watson\n\nAnswer: announce, carp, clayton, earthy, hello, inmate, nimbus, parentage, phonetic, skinny, sharon, sudan, watson", + "schema_version": 6, + "seq": 127, + "span_id": "m1.model_call.127", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:25:21.429000+00:00" + }, + { + "analyzer": "case_discovery", + "batch_case_ids": [ + "bbh_word_sorting-0000", + "bbh_word_sorting-0001", + "bbh_word_sorting-0002", + "bbh_word_sorting-0003", + "bbh_word_sorting-0004", + "bbh_word_sorting-0005", + "bbh_word_sorting-0006", + "bbh_word_sorting-0007", + "bbh_word_sorting-0008", + "bbh_word_sorting-0009", + "bbh_word_sorting-0010", + "bbh_word_sorting-0011", + "bbh_word_sorting-0012", + "bbh_word_sorting-0013", + "bbh_word_sorting-0014", + "bbh_word_sorting-0015", + "bbh_word_sorting-0016", + "bbh_word_sorting-0017", + "bbh_word_sorting-0018", + "bbh_word_sorting-0019" + ], + "call_index": 128, + "case_id": "bbh_word_sorting-0127", + "cycle": -1, + "duration_sec": 2.3643, + "event": "model_call", + "event_seq": 128, + "inputs": { + "prompt": "Sort the following words alphabetically: List: covalent spiderwort horowitz divisive spiritual cheshire affluent gideon quadrature julio peanut epsilon diagnostician grover folklore gothic salient\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "do_sample": "False", + "max_new_tokens": "512" + }, + "method": "generate", + "n_batch_cases": 128, + "output": "Here are the words sorted alphabetically:\n\n1. affluent\n2. cheshire\n3. covalent\n4. diagnostician\n5. divisive\n6. epsilon\n7. folklore\n8. gothic\n9. gideon\n10. grover\n11. horowitz\n12. julio\n13. peanut\n14. quadrature\n15. salient\n16. spiderwort\n17. spiritual\n\nLet's re-sort them carefully:\n\n* **a**ffluent\n* **c**ovalent, **c**heshire\n* **d**iagnostician, **d**ivisive\n* **e**psilon\n* **f**olklore, **g**othic, **g**ideon, **g**rover\n* **h**orowitz\n* **j**ulio\n* **p**eanut\n* **q**uadrature\n* **s**alient, **s**piderwort, **s**piritual\n\n**Detailed Alphabetical Sort:**\n\n1. affluent\n2. cheshire\n3. covalent\n4. diagnostician\n5. divisive\n6. epsilon\n7. folklore\n8. gothic\n9. gideon\n10. grover\n11. horowitz\n12. julio\n13. peanut\n14. quadrature\n15. salient\n16. spiderwort\n17. spiritual\n\nWait, let's check the 'G' words: gothic, gideon, grover.\nGothic (go...)\nGideon (gi...)\nGrover (gr...)\nOrder: gothic, gideon, grover.\n\nLet's check the 'S' words: salient, spiderwort, spiritual.\nSalient (sal...)\nSpiderwort (spi...)\nSpiritual (spi...)\nSalient comes before Spiderwort and Spiritual.\nSpiderwort (spi**d**)\nSpiritual (spi**r**)\nOrder: salient, spiderwort, spiritual.\n\n**Final Sorted List:**\n\n1. affluent\n2. cheshire\n3. covalent\n4. diagnostician\n5. divisive\n6. epsilon\n7. folklore\n8. gothic\n9. gideon\n10. grover\n11. horowitz\n12. julio\n13. peanut\n14. quadrature\n15. salient\n16. spiderwort\n17. spiritual\n\n(Re-checking", + "schema_version": 6, + "seq": 128, + "span_id": "m1.model_call.128", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:25:23.797471+00:00" + }, + { + "analyzer": "termination_audit", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 1, + "case_id": null, + "cycle": 0, + "duration_sec": 131.733, + "event": "model_call", + "event_seq": 258, + "inputs": { + "prompt": "Sort the following words alphabetically: List: linchpin arrear eavesdropping fasciculate shin postdoctoral maritime ibn henry hermaphrodite sticky jane brookside incorrigible vehicular herodotus\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nPartial response:\nHere are the words sorted alphabetically:\n\n1. arrear\n2. eavesdropping\n3. fasciculate\n4. herodotus\n5. hermaphrodite\n6. ibn henry\n7. incorrigible\n8. jane\n9. linchpin\n10. maritime\n11. postdoctoral\n12. shin\n13. sticky\n14. brookside\n15. vehicular\n\nLet's re-sort them carefully:\n\n* **a**rrear\n* **e**avesdropping\n* **f**asciculate\n* **h**erodotus\n* **h**ermaphrodite (H before I)\n* **i**bn henry\n* **i**ncorrigible (I before L)\n* **j**ane\n* **l**inchpin\n* **m**aritime\n* **p**ostdoctoral\n* **s**hin\n* **s**ticky\n* **b**rookside (B comes before S)\n* **v**ehicular\n\nLet's do a full alphabetical sort:\n\n1. arrear\n2. brookside\n3. eavesdropping\n4. fasciculate\n5. herodotus\n6. hermaphrodite\n7. ibn henry\n8. incorrigible\n9. jane\n10. linchpin\n11. maritime\n12. postdoctoral\n13. shin\n14. sticky\n15. vehicular\n\nWait, let's check the 'H' words: hermaphrodite and herodotus.\nH-e-r-m-a-p-h-r-o-d-i-t-e\nH-e-r-o-d-t-u-s\n'herm' comes before 'hero'. So, hermaphrodite comes before herodotus.\n\nLet's re-verify the full list:\narrear\nbrookside\neavesdropping\nfasciculate\nhermaphrodite\nherodotus\nibn henry\nincorrigible\njane\nlinchpin\nmaritime\npostdoctoral\nshin\nsticky\nvehicular\n\nFinal check on the order:\nA: arrear\nB: brookside\nE: eavesdropping\nF: fasciculate\nH: hermaphrodite, herodotus (herm before hero)\nI: ibn henry, incorrigible (ibn before incorrigible)\nJ:\n\nContinue the reasoning above from where it stops and finish it. End with the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: linchpin, arrear, eavesdropping, fasciculate, shin, postdoctoral, maritime, ibn henry, hermaphrodite, sticky, jane, brookside, incorrigible, vehicular, herodotus.\n\nLet's sort them alphabetically:\n\n1. **arrear** (A)\n2. **brookside** (B)\n3. **eavesdropping** (E)\n4. **fasciculate** (F)\n5. **hermaphrodite** (H - 'herm' comes before 'hero')\n6. **herodotus** (H)\n7. **ibn henry** (I - 'i' followed by 'b')\n8. **incorrigible** (I - 'i' followed by 'n')\n9. **jane** (J)\n10. **linchpin** (L)\n11. **maritime** (M)\n12. **postdoctoral** (P)\n13. **shin** (S - 'sh')\n14. **sticky** (S - 'st')\n15. **vehicular** (V)\n\nLet's confirm the order for the 'I' words:\nibn henry vs incorrigible. 'ibn' comes before 'incorrigible'. (Correct)\n\nLet's confirm the order for the 'H' words:\nhermaphrodite vs herodotus. 'herm' comes before 'hero'. (Correct)\n\nThe final sorted list is:\narrear, brookside, eavesdropping, fasciculate, hermaphrodite, herodotus, ibn henry, incorrigible, jane, linchpin, maritime, postdoctoral, shin, sticky, vehicular.\n\nAnswer: arrear, brookside, eavesdropping, fasciculate, hermaphrodite, herodotus, ibn henry, incorrigible, jane, linchpin, maritime, postdoctoral, shin, sticky, vehicular", + "schema_version": 6, + "seq": 129, + "span_id": "m1.model_call.258", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:27:35.933649+00:00" + }, + { + "analyzer": "termination_audit", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 2, + "case_id": null, + "cycle": 0, + "duration_sec": 2.6768, + "event": "model_call", + "event_seq": 259, + "inputs": { + "prompt": "Sort the following words alphabetically: List: whisper eyesight titmice mirage fletch cry biennial creedal miltonic fraudulent j\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nPartial response:\nThe given list of words is: whisper, eyesight, titmice, mirage, fletch, cry, biennial, creedal, miltonic, fraudulent, j.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter:**\n * fletch (f)\n * fraudulent (f)\n * j (j)\n * miltonic (m)\n * mirage (m)\n * creedal (c)\n * biennial (b)\n * cry (c)\n * whisper (w)\n * eyesight (e)\n * titmice (t)\n\n2. **Sort by the first letter:**\n * **b:** biennial\n * **c:** creedal, cry\n * **e:** eyesight\n * **f:** fletch, fraudulent\n * **j:** j\n * **m:** miltonic, mirage\n * **t:** titmice\n * **w:** whisper\n\n3. **Sort within groups:**\n\n * **c:** creedal, cry (creedal comes before cry)\n * **f:** fletch, fraudulent (fletch comes before fraudulent)\n * **m:** miltonic, mirage (miltonic comes before mirage)\n\n4. **Final ordered list:**\n * biennial (b)\n * creedal (c)\n * cry (c)\n * eyesight (e)\n * fletch (f)\n * fraudulent (f)\n * j (j)\n * miltonic (m)\n * mirage (m)\n * titmice (t)\n * whisper (w)\n\nLet's re-verify the order based on standard alphabetical rules:\n\n1. biennial (b)\n2. creedal (c)\n3. cry (c)\n4. eyesight (e)\n5. fletch (f)\n6. fraudulent (f)\n7. j (j)\n8. miltonic (m)\n9. mirage (m)\n10. titmice (t)\n11. whisper (w)\n\n(Note on 'j': In alphabetical sorting, 'j' comes after 'f' and before 'm'.)\n\nThe correct alphabetical order is: biennial, creedal, cry, eyesight, fletch,\n\nContinue the reasoning above from where it stops and finish it. End with the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: whisper, eyesight, titmice, mirage, fletch, cry, biennial, creedal, miltonic, fraudulent, j.\n\nWe will sort these words alphabetically:\n\n1. **Identify the first letters:**\n * w (whisper)\n * e (eyesight)\n * t (titmice)\n * m (mirage, miltonic)\n * f (fletch, fraudulent)\n * c (cry, creedal)\n * b (biennial)\n * c (cry, creedal)\n * m (mirage, miltonic)\n * f (fletch, fraudulent)\n * j (j)\n\n2. **Sort based on the first letter (A to Z):**\n * **b:** biennial\n * **c:** creedal, cry\n * **e:** eyesight\n * **f:** fletch, fraudulent\n * **j:** j\n * **m:** miltonic, mirage\n * **t:** titmice\n * **w:** whisper\n\n3. **Sort within groups (handling ties):**\n\n * **b:** biennial (1st)\n * **c:** creedal, cry. Comparing 'creedal' and 'cry': 'creedal' comes before 'cry'.\n * creedal, cry\n * **e:** eyesight (3rd)\n * **f:** fletch, fraudulent. Comparing 'fletch' and 'fraudulent': 'fletch' comes before 'fraudulent'.\n * fletch, fraudulent\n * **j:** j (6th)\n * **m:** miltonic, mirage. Comparing 'miltonic' and 'mirage': 'miltonic' comes before 'mirage'.\n * miltonic, mirage\n * **t:** titmice (9th)\n * **w:** whisper (10th)\n\n4. **Assemble the final sorted list:**\n biennial, creedal, cry, eyesight, fletch, fraudulent, j, miltonic, mirage, titmice, whisper.\n\nLet's re-check the placement of 'j'. 'j' comes after 'f' and before 'm'.\n\nCorrect sequence based on the initial letter order:\nB (biennial)\nC", + "schema_version": 6, + "seq": 130, + "span_id": "m1.model_call.259", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:27:38.616620+00:00" + }, + { + "analyzer": "selfcheck_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 1, + "case_id": "bbh_word_sorting-0006", + "cycle": 0, + "duration_sec": 77.5429, + "event": "model_call", + "event_seq": 260, + "inputs": { + "prompt": "Sort the following words alphabetically: List: torpedo phosphorescent pristine decadent shrunk dey administer gradate littleneck thrown jacky coachman aeneid verdict tasting sinh delhi systemwide grim\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. aeneid\n2. decadent\n3. administer\n4. delhi\n5. gradate\n6. grim\n7. jacky\n8. littleneck\n9. phosphorescent\n10. pristine\n11. sinh\n12. systemwide\n13. tasting\n14. torpedo\n15. thrown\n16. verdict\n\nLet's re-sort this list carefully:\n\n* aeneid\n* decadent\n* delhi\n* gradate\n* grim\n* jacky\n* littleneck\n* phosphorescent\n* pristine\n* sinh\n* systemwide\n* tasting\n* torpedo\n* thrown\n* verdict\n\nComparing the two \"t\" words: tasting comes before torpedo.\n\nLet's perform the full sort:\n\n1. aeneid\n2. decadent\n3. administer\n4. delhi\n5. gradate\n6. grim\n7. jacky\n8. littleneck\n9. phosphorescent\n10. pristine\n11. sinh\n12. systemwide\n13. tasting\n14. torpedo\n15. thrown\n16. verdict\n\nWait, I missed 'systemwide' placement relative to 'sinh'.\ns-i-n-g-h\ns-y-s-t-e-m-w-i-d-e\n\n'sinh' comes before 'systemwide'.\n\nLet's re-verify alphabetically:\n* aeneid\n* administer\n* decadent\n* delhi\n* gradate\n* grim\n* jacky\n* littleneck\n* phosphorescent\n* pristine\n* sinh\n* systemwide\n* tasting\n* torpedo\n* thrown\n* verdict\n\n(Checking 'administer' vs 'aeneid': a-d vs a-e. 'administer' comes before 'aeneid' if we strictly follow the second letter rules, but 'aeneid' is standard. Let's use standard dictionary order.)\n\naeneid\nadminister\ndecadent\ndelhi\ngradate\ngrim\njacky\nlittleneck\nphosphorescent\npristine\nsinh\nsystemwide\n", + "schema_version": 6, + "seq": 131, + "span_id": "m1.model_call.260", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:28:56.166540+00:00" + }, + { + "analyzer": "selfcheck_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 2, + "case_id": "bbh_word_sorting-0006", + "cycle": 0, + "duration_sec": 2.3733, + "event": "model_call", + "event_seq": 261, + "inputs": { + "prompt": "Sort the following words alphabetically: List: torpedo phosphorescent pristine decadent shrunk dey administer gradate littleneck thrown jacky coachman aeneid verdict tasting sinh delhi systemwide grim\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. gradate\n7. grim\n8. jacky\n9. littleneck\n10. phosphorescent\n11. pristine\n12. sinh\n13. systemwide\n14. tasting\n15. torpedo\n16. thrown\n17. verdict\n18. dey\n19. shrunk\n\nLet's re-sort them carefully:\n\n* **aeneid**\n* **administer**\n* **decadent**\n* **delhi**\n* **gradate**\n* **grim**\n* **jacky**\n* **littleneck**\n* **phosphorescent**\n* **pristine**\n* **sinh**\n* **systemwide**\n* **tasting**\n* **torpedo**\n* **thrown**\n* **verdict**\n* **dey**\n* **shrunk**\n\nComparing the initial list: torpedo, phosphorescent, pristine, decadent, shrunk, dey, administer, gradate, littleneck, thrown, jacky, coachman, aeneid, verdict, tasting, sinh, delhi, systemwide, grim.\n\nSorted list:\n1. administer\n2. aeneid\n3. decadent\n4. delhi\n5. grim\n6. gradate\n7. jacky\n8. littleneck\n9. phosphorescent\n10. pristine\n11. sinh\n12. systemwide\n13. tasting\n14. torpedo\n15. thrown\n16. verdict\n17. dey\n18. shrunk\n19. coachman (Wait, 'coachman' should come before 'decadent' if we follow standard alphabetical order, let's re-check)\n\nRe-sorting based strictly on alphabetical order:\n1. administer (a)\n2. aeneid (a)\n3. decadent (d)\n4. delhi (d)\n5. coachman (c) - C comes before D. Let's re-evaluate.\n\nList for easy sorting: torpedo, phosphorescent, pristine, decadent, shrunk, dey, administer, gradate, littleneck, thrown, jacky, coachman, aeneid, verdict, tasting, sinh, delhi, systemwide, grim.\n\nA", + "schema_version": 6, + "seq": 132, + "span_id": "m1.model_call.261", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:28:58.545056+00:00" + }, + { + "analyzer": "selfcheck_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 3, + "case_id": "bbh_word_sorting-0006", + "cycle": 0, + "duration_sec": 2.3764, + "event": "model_call", + "event_seq": 262, + "inputs": { + "prompt": "Sort the following words alphabetically: List: torpedo phosphorescent pristine decadent shrunk dey administer gradate littleneck thrown jacky coachman aeneid verdict tasting sinh delhi systemwide grim\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. gradate\n7. grim\n8. jacky\n9. littleneck\n10. phosphorescent\n11. pristine\n12. sinh\n13. systemwide\n14. tasting\n15. torpedo\n16. thrown\n17. verdict\n18. dey\n19. shrunk\n\nLet's re-sort them carefully:\n\n* administer (A)\n* aeneid (A) - 'aeneid' comes before 'administer' if we strictly follow standard alphabetical order (administer vs aeneid). A-d vs A-e. 'administer' comes first.\n* decadent (C)\n* coachman (C)\n* delhi (D)\n* gradate (G)\n* grim (G)\n* jacky (J)\n* littleneck (L)\n* phosphorescent (P)\n* pristine (P)\n* sinh (S)\n* systemwide (S)\n* tasting (T)\n* torpedo (T)\n* thrown (T)\n* verdict (V)\n* dey (D)\n* shrunk (S)\n\nLet's use a standard alphabetical sort:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. dey\n7. gradate\n8. grim\n9. jacky\n10. littleneck\n11. phosphorescent\n12. pristine\n13. sinh\n14. shrunk\n15. systemwide\n16. tasting\n17. torpedo\n18. thrown\n19. verdict\n\nLet's check the S words: sinh, shrunk, systemwide.\n* sinh\n* shrunk\n* systemwide\n\n'sinh' (S-i-n)\n'shrunk' (S-h-r)\n'systemwide' (S-y-s)\n\nCorrect order for S: sinh, shrunk, systemwide. (S-i comes before S-h, which comes before S-y) - Wait, 'shrunk' starts with 'sh'. 'sinh' starts with 'si'. 'systemwide' starts with 'sy'.\n\nLet's re-verify the full list:\nadmin", + "schema_version": 6, + "seq": 133, + "span_id": "m1.model_call.262", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:29:00.926891+00:00" + }, + { + "analyzer": "selfcheck_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 4, + "case_id": "bbh_word_sorting-0006", + "cycle": 0, + "duration_sec": 1.5398, + "event": "model_call", + "event_seq": 263, + "inputs": { + "prompt": "Sort the following words alphabetically: List: torpedo phosphorescent pristine decadent shrunk dey administer gradate littleneck thrown jacky coachman aeneid verdict tasting sinh delhi systemwide grim\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. gradate\n7. grim\n8. jacky\n9. littleneck\n10. phosphorescent\n11. pristine\n12. sinh\n13. systemwide\n14. tasting\n15. torpedo\n16. thrown\n17. verdict\n18. dey\n19. shrunk\n\nLet's re-sort them carefully:\n\n* administer\n* aeneid\n* decadent\n* coachman\n* delhi\n* grim\n* gradate\n* jacky\n* littleneck\n* phosphorescent\n* pristine\n* sinh\n* systemwide\n* tasting\n* torpedo\n* thrown\n* verdict\n* dey\n* shrunk\n\nCorrect alphabetical order:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. dey\n7. grim\n8. gradate\n9. jacky\n10. littleneck\n11. phosphorescent\n12. pristine\n13. sinh\n14. shrunk\n15. systemwide\n16. tasting\n17. torpedo\n18. thrown\n19. verdict\n\nAnswer: administer, aeneid, decadent, coachman, delhi, dey, grim, gradate, jacky, littleneck, phosphorescent, pristine, sinh, shrunk, systemwide, tasting, torpedo, thrown, verdict", + "schema_version": 6, + "seq": 134, + "span_id": "m1.model_call.263", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:29:02.471818+00:00" + }, + { + "analyzer": "selfcheck_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 5, + "case_id": "bbh_word_sorting-0025", + "cycle": 0, + "duration_sec": 2.3601, + "event": "model_call", + "event_seq": 264, + "inputs": { + "prompt": "Sort the following words alphabetically: List: linchpin arrear eavesdropping fasciculate shin postdoctoral maritime ibn henry hermaphrodite sticky jane brookside incorrigible vehicular herodotus\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. arrear\n2. eavesdropping\n3. fasciculate\n4. herodotus\n5. hermaphrodite\n6. ibn henry\n7. incorrigible\n8. jane\n9. linchpin\n10. maritime\n11. postdoctoral\n12. shin\n13. sticky\n14. vehicular\n15. brookside\n\nLet's re-sort them carefully:\n\n* **a**: arrear\n* **e**: eavesdropping\n* **f**: fasciculate\n* **h**: hermaphrodite, herodotus\n* **i**: ibn henry, incorrigible\n* **j**: jane\n* **l**: linchpin\n* **m**: maritime\n* **p**: postdoctoral\n* **s**: shin, sticky\n* **v**: vehicular\n* **b**: brookside\n\n**Detailed Sorting:**\n\n1. arrear\n2. eavesdropping\n3. fasciculate\n4. hermaphrodite (H, e comes before h)\n5. herodotus\n6. ibn henry (I, b comes before i/i) - Wait, 'i' comes before 'j' and 'l'. Let's re-check the structure.\n7. incorrigible\n8. jane\n9. linchpin\n10. maritime\n11. postdoctoral\n12. shin\n13. sticky\n14. vehicular\n15. brookside\n\n**Correct Alphabetical Order:**\n\n1. arrear\n2. eavesdropping\n3. fasciculate\n4. hermaphrodite\n5. herodotus\n6. ibn henry\n7. incorrigible\n8. jane\n9. linchpin\n10. maritime\n11. postdoctoral\n12. shin\n13. sticky\n14. vehicular\n15. brookside (B comes before others starting with B or A, E, F, H, I, J, L, M, P, S, V)\n\nLet's check the initial letters: A, E, F, H, I, I, J, L, M, P, S, S, V, B (Wait, I had two 'I' words: ibn henry and incorrigible. 'i' vs 'i'. 'n' vs 'n'. 'b' vs 'n'. 'b' comes before 'n').\n\nRevis", + "schema_version": 6, + "seq": 135, + "span_id": "m1.model_call.264", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:29:04.839571+00:00" + }, + { + "analyzer": "selfcheck_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 6, + "case_id": "bbh_word_sorting-0025", + "cycle": 0, + "duration_sec": 2.0041, + "event": "model_call", + "event_seq": 265, + "inputs": { + "prompt": "Sort the following words alphabetically: List: linchpin arrear eavesdropping fasciculate shin postdoctoral maritime ibn henry hermaphrodite sticky jane brookside incorrigible vehicular herodotus\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. arrear\n2. eavesdropping\n3. fasciculate\n4. herodotus\n5. hermaphrodite\n6. ibn henry\n7. incorrigible\n8. jane\n9. linchpin\n10. maritime\n11. postdoctoral\n12. shin\n13. sticky\n14. brookside\n15. vehicular\n\nLet's re-sort them systematically:\n\n* **A:** arrear\n* **B:** brookside\n* **E:** eavesdropping\n* **F:** fasciculate\n* **H:** hermaphrodite, herodotus\n* **I:** ibn henry, incorrigible\n* **J:** jane\n* **L:** linchpin\n* **M:** maritime\n* **P:** postdoctoral\n* **S:** shin, sticky\n* **V:** vehicular\n\nNow, sorting within the groups:\n\n* **H:** hermaphrodite, herodotus (herm comes before her) $\\rightarrow$ hermaphrodite, herodotus (Wait, 'herm' vs 'her'). 'herm' comes before 'her'.\n * hermaphrodite\n * herodotus\n* **I:** ibn henry, incorrigible (i.n. before i.n.c) $\\rightarrow$ ibn henry, incorrigible\n* **S:** shin, sticky (shin before sticky) $\\rightarrow$ shin, sticky\n\n**Final Sorted List:**\n\n1. arrear\n2. brookside\n3. eavesdropping\n4. fasciculate\n5. hermaphrodite\n6. herodotus\n7. ibn henry\n8. incorrigible\n9. jane\n10. linchpin\n11. maritime\n12. postdoctoral\n13. shin\n14. sticky\n15. vehicular\n\nAnswer: arrear, brookside, eavesdropping, fasciculate, hermaphrodite, herodotus, ibn henry, incorrigible, jane, linchpin, maritime, postdoctoral, shin, sticky, vehicular", + "schema_version": 6, + "seq": 136, + "span_id": "m1.model_call.265", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:29:06.849029+00:00" + }, + { + "analyzer": "selfcheck_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 7, + "case_id": "bbh_word_sorting-0025", + "cycle": 0, + "duration_sec": 2.3585, + "event": "model_call", + "event_seq": 266, + "inputs": { + "prompt": "Sort the following words alphabetically: List: linchpin arrear eavesdropping fasciculate shin postdoctoral maritime ibn henry hermaphrodite sticky jane brookside incorrigible vehicular herodotus\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. arrear\n2. eavesdropping\n3. fasciculate\n4. herodotus\n5. hermaphrodite\n6. ibn henry\n7. incorrigible\n8. jane\n9. linchpin\n10. maritime\n11. postdoctoral\n12. shin\n13. sticky\n14. brookside\n15. vehicular\n\nLet's re-sort them carefully:\n\n* arrear (A)\n* eavesdropping (E)\n* fasciculate (F)\n* herodotus (H)\n* hermaphrodite (H) - H-e comes before H-e, 'r' comes before 'm'? No, 'herm' vs 'herod'. 'hermaphrodite' comes after 'herodotus'.\n* ibn henry (I)\n* incorrigible (I) - 'ibn' vs 'incor'. 'i'n vs 'i'n. 'b' vs 'n'. 'ibn' comes before 'incor'.\n* jane (J)\n* linchpin (L)\n* maritime (M)\n* postdoctoral (P)\n* shin (S)\n* sticky (S) - 'shin' before 'sticky'.\n* brookside (B) - Mistake here, 'brookside' starts with B.\n\nLet's do a proper alphabetical sort:\n\n1. arrear\n2. brookside\n3. eavesdropping\n4. fasciculate\n5. herodotus\n6. hermaphrodite\n7. ibn henry\n8. incorrigible\n9. jane\n10. linchpin\n11. maritime\n12. postdoctoral\n13. shin\n14. sticky\n15. vehicular\n\nComparing 'herodotus' and 'hermaphrodite':\nher**d**otus vs her**m**aphrodite. 'd' comes before 'm'. So, herodotus comes before hermaphrodite. (Wait, checking standard dictionary order: d -> m)\nherodotus\nhermaphrodite\n\nComparing 'ibn henry' and 'incorrigible':\ni**b**n vs i**n**corrigible. 'b' comes before 'n'. So, ibn henry comes before incorrigible.\n\nComparing", + "schema_version": 6, + "seq": 137, + "span_id": "m1.model_call.266", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:29:09.213215+00:00" + }, + { + "analyzer": "selfcheck_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 8, + "case_id": "bbh_word_sorting-0025", + "cycle": 0, + "duration_sec": 2.3608, + "event": "model_call", + "event_seq": 267, + "inputs": { + "prompt": "Sort the following words alphabetically: List: linchpin arrear eavesdropping fasciculate shin postdoctoral maritime ibn henry hermaphrodite sticky jane brookside incorrigible vehicular herodotus\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words from the list sorted alphabetically:\n\n1. arrear\n2. eavesdropping\n3. fasciculate\n4. herodotus\n5. hermaphrodite\n6. ibn henry\n7. incorrigible\n8. jane\n9. linchpin\n10. maritime\n11. postdoctoral\n12. shin\n13. sticky\n14. brookside\n15. vehicular\n\nLet's re-sort them carefully:\n\n* **a**rrear\n* **e**avesdropping\n* **f**asciculate\n* **h**erodotus\n* **h**ermaphrodite (H-e-r-m...) comes before H-i-b...)\n* **i**bn henry\n* **i**ncorrigible (I-b... comes before I-n...)\n* **j**ane\n* **l**inchpin\n* **m**aritime\n* **p**ostdoctoral\n* **s**hin\n* **s**ticky\n* **b**rookside (B comes before S) - *Error in initial quick sort, let's do a full A-Z sort.*\n\n**Full Alphabetical Sort:**\n\n1. arrear (A)\n2. brookside (B)\n3. eavesdropping (E)\n4. fasciculate (F)\n5. herodotus (H)\n6. hermaphrodite (H) (herm comes before herodotus? H-e-r-m vs H-e-r-o. Yes, M before O.)\n7. ibn henry (I)\n8. incorrigible (I) (I-b before I-n)\n9. jane (J)\n10. linchpin (L)\n11. maritime (M)\n12. postdoctoral (P)\n13. shin (S)\n14. sticky (S) (shin before sticky)\n15. vehicular (V)\n\nLet's verify the H words:\n* hermaphrodite\n* herodotus\nH-e-r-m-a...\nH-e-r-o-d...\n'm' comes before 'o'. So, hermaphrodite comes before herodotus.\n\nLet's verify the I words:\n* ibn henry (I-b...)\n* incorrigible (I-", + "schema_version": 6, + "seq": 138, + "span_id": "m1.model_call.267", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:29:11.579779+00:00" + }, + { + "analyzer": "selfcheck_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 9, + "case_id": "bbh_word_sorting-0045", + "cycle": 0, + "duration_sec": 2.091, + "event": "model_call", + "event_seq": 268, + "inputs": { + "prompt": "Sort the following words alphabetically: List: spongy separate gabriel jackson quail hendrickson washy backslide shelter calvert anastomosis variety commando pizzicato sticktight syndicate hollister\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. backslide\n2. calvert\n3. gabriel\n4. hendrickson\n5. hollister\n6. jackson\n7. pizzicato\n8. quail\n9. separate\n10. shelter\n11. sticktight\n12. spongy\n13. syndicate\n14. variety\n15. washy\n\nLet's re-sort them carefully:\n\n* backslide\n* calvert\n* gabriel\n* hendrickson\n* hollister\n* jackson\n* pizzicato\n* quail\n* separate\n* shelter\n* sticktight\n* spongy\n* syndicate\n* variety\n* washy\n\nChecking the alphabetical order:\nb... (backslide)\nc... (calvert)\ng... (gabriel)\nh... (hendrickson, hollister) $\\rightarrow$ hendrickson, hollister\nj... (jackson)\np... (pizzicato)\nq... (quail)\ns... (separate, shelter, spongy, sticktight, syndicate) $\\rightarrow$ separate, shelter, sticktight, spongy, syndicate\nv... (variety)\nw... (washy)\n\nLet's refine the 'h' and 's' sections:\nH: hendrickson, hollister (h-e comes before h-o)\nS: separate, shelter, sticktight, spongy, syndicate (sep, shel, stick, spon, syn)\n\nFinal sorted list:\n1. backslide\n2. calvert\n3. gabriel\n4. hendrickson\n5. hollister\n6. jackson\n7. pizzicato\n8. quail\n9. separate\n10. shelter\n11. sticktight\n12. spongy\n13. syndicate\n14. variety\n15. washy\n\nAnswer: backslide, calvert, gabriel, hendrickson, hollister, jackson, pizzicato, quail, separate, shelter, sticktight, spongy, syndicate, variety, washy", + "schema_version": 6, + "seq": 139, + "span_id": "m1.model_call.268", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:29:13.682554+00:00" + }, + { + "analyzer": "selfcheck_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 10, + "case_id": "bbh_word_sorting-0045", + "cycle": 0, + "duration_sec": 2.354, + "event": "model_call", + "event_seq": 269, + "inputs": { + "prompt": "Sort the following words alphabetically: List: spongy separate gabriel jackson quail hendrickson washy backslide shelter calvert anastomosis variety commando pizzicato sticktight syndicate hollister\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. backslide\n2. calvert\n3. gabriel\n4. hendrickson\n5. hollister\n6. jackson\n7. pizzicato\n8. quail\n9. separate\n10. shelteer\n11. sticktight\n12. spongy\n13. syndicate\n14. variety\n15. washy\n\nLet's re-sort them carefully:\n\nOriginal List: spongy, separate, gabriel, jackson, quail, hendrickson, washy, backslide, shelter, calvert, anastomosis, variety, commando, pizzicato, sticktight, syndicate, hollister\n\nFor sorting, we treat words as single units.\n\n1. backslide\n2. calvert\n3. anastomosis\n4. commando\n5. gabriel\n6. hendrickson\n7. hollister\n8. jackson\n9. pizzicato\n10. quail\n11. separate\n12. shelteer (Assuming \"shelter\" was a typo for \"shelteer\" based on the list, or we use \"shelter\" - let's use \"shelter\")\n13. sticktight\n14. spongy\n15. syndicate\n16. variety\n17. washy\n\nLet's re-sort using the exact words provided: spongy, separate, gabriel, jackson, quail, hendrickson, washy, backslide, shelter, calvert, anastomosis, variety, commando, pizzicato, sticktight, syndicate, hollister\n\n1. backslide\n2. calvert\n3. anastomosis\n4. commando\n5. gabriel\n6. hendrickson\n7. hollister\n8. jackson\n9. pizzicato\n10. quail\n11. separate\n12. shelter\n13. sticktight\n14. spongy\n15. syndicate\n16. variety\n17. washy\n\n(Note: I assumed \"shelter\" was intended. If the list strictly contains \"shelter\" without an 'e' at the end, it fits here.)\n\nAnswer: backslide, calvert, anastomosis, commando, gabriel, hendrickson, hollister, jackson, pizzicato, quail, separate, shelter, sticktight, spongy, syndicate, variety, washy", + "schema_version": 6, + "seq": 140, + "span_id": "m1.model_call.269", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:29:16.042580+00:00" + }, + { + "analyzer": "selfcheck_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 11, + "case_id": "bbh_word_sorting-0045", + "cycle": 0, + "duration_sec": 1.8342, + "event": "model_call", + "event_seq": 270, + "inputs": { + "prompt": "Sort the following words alphabetically: List: spongy separate gabriel jackson quail hendrickson washy backslide shelter calvert anastomosis variety commando pizzicato sticktight syndicate hollister\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. backslide\n2. gabriel\n3. hendrickson\n4. hollister\n5. jackson\n6. pizzicato\n7. quail\n8. separate\n9. shelter\n10. sticktight\n11. spongy\n12. syndicate\n13. variety\n14. washy\n15. calvert\n16. anastomosis\n17. commando\n\nLet's re-sort them carefully:\n\n* backslide\n* calvert\n* commando\n* gabriel\n* hendrickson\n* hollister\n* jackson\n* pizzicato\n* quail\n* separate\n* shelter\n* sticktight\n* spongy\n* syndicate\n* variety\n* washy\n* anastomosis\n\nLet's check the sorting again (focusing on initial letters):\nB: backslide\nC: calvert, commando\nG: gabriel\nH: hendrickson, hollister\nJ: jackson\nP: pizzicato\nQ: quail\nS: separate, shelter, sticktight, spongy, syndicate\nV: variety\nW: washy\nA: anastomosis\n\nCorrect alphabetical order:\n1. backslide\n2. calvert\n3. commando\n4. gabriel\n5. hendrickson\n6. hollister\n7. jackson\n8. anastomosis\n9. pizzicato\n10. quail\n11. separate\n12. shelter\n13. sticktight\n14. spongy\n15. syndicate\n16. variety\n17. washy\n\nAnswer: backslide, calvert, commando, gabriel, hendrickson, hollister, jackson, anastomosis, pizzicato, quail, separate, shelter, sticktight, spongy, syndicate, variety, washy", + "schema_version": 6, + "seq": 141, + "span_id": "m1.model_call.270", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:29:17.882537+00:00" + }, + { + "analyzer": "selfcheck_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 12, + "case_id": "bbh_word_sorting-0045", + "cycle": 0, + "duration_sec": 2.3772, + "event": "model_call", + "event_seq": 271, + "inputs": { + "prompt": "Sort the following words alphabetically: List: spongy separate gabriel jackson quail hendrickson washy backslide shelter calvert anastomosis variety commando pizzicato sticktight syndicate hollister\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. backslide\n2. calvert\n3. gabriel\n4. hendrickson\n5. hollister\n6. jackson\n7. pizzicato\n8. quail\n9. separate\n10. shelter\n11. sticktight\n12. spongy\n13. syndicate\n14. variety\n15. washy\n16. commando\n17. anastomosis\n\nLet's re-sort carefully, especially handling names and words starting with similar letters:\n\nOriginal list: spongy, separate, gabriel, jackson, quail, hendrickson, washy, backslide, shelter, calvert, anastomosis, variety, commando, pizzicato, sticktight, syndicate, hollister\n\nSorting by the first letter:\nB: backslide\nC: calvert, commando\nG: gabriel\nH: hendrickson, hollister\nJ: jackson\nP: pizzicato\nQ: quail\nS: spongy, separate, shelter, sticktight, syndicate\nV: variety\nW: washy\n\nSorting within groups:\nC: calvert, commando (calvert comes before commando)\nH: hendrickson, hollister (hendrickson comes before hollister)\nS: backslide (Wait, 'backslide' starts with B)\nS words: spongy, separate, shelter, sticktight, syndicate (backslide was missed in the initial mental grouping)\n\nCorrect sorting based on standard alphabetical order:\n\n1. backslide (B)\n2. calvert (C)\n3. commando (C)\n4. gabriel (G)\n5. hendrickson (H)\n6. hollister (H)\n7. jackson (J)\n8. pizzicato (P)\n9. quail (Q)\n10. separate (S)\n11. shelter (S)\n12. sticktight (S)\n13. spongy (S)\n14. syndicate (S)\n15. variety (V)\n16. washy (W)\n17. anastomosis (A - moved to the beginning)\n\nLet's re-list exactly and sort properly:\n\nWords: spongy, separate, gabriel, jackson, quail, hendrickson, washy, backslide, shelter, calvert, anastomosis, variety, comm", + "schema_version": 6, + "seq": 142, + "span_id": "m1.model_call.271", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:29:20.265752+00:00" + }, + { + "analyzer": "selfcheck_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 13, + "case_id": "bbh_word_sorting-0076", + "cycle": 0, + "duration_sec": 2.4266, + "event": "model_call", + "event_seq": 272, + "inputs": { + "prompt": "Sort the following words alphabetically: List: whisper eyesight titmice mirage fletch cry biennial creedal miltonic fraudulent j\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words to sort alphabetically:\nwhisper, eyesight, titmice, mirage, fletch, cry, biennial, creedal, miltonic, fraudulent, j\n\nLet's sort them:\n\n1. **biennial**\n2. **creedal**\n3. **cry**\n4. **eyesight**\n5. **fletch**\n6. **fraudulent**\n7. **j** (Treating 'j' as a single item, usually sorted before letters)\n8. **miltonic**\n9. **mirage**\n10. **whisper**\n11. **titmice**\n\nLet's re-check the sorting, especially focusing on initial letters:\n\n* **b**iennial\n* **c**reedal, **c**ry\n* **e**yesight\n* **f**letch, **f**raudulent\n* **j**\n* **m**iltonic, **m**irage\n* **w**hisper\n* **t**itmice\n\nSorting 'creedal' and 'cry': creedal comes before cry.\nSorting 'fletch' and 'fraudulent': fraudulent comes before fletch.\nSorting 'miltonic' and 'mirage': miltonic comes before mirage.\n\nFinal alphabetical order:\n1. biennial\n2. creedal\n3. cry\n4. eyesight\n5. fraudulent\n6. fletch\n7. j\n8. miltonic\n9. mirage\n10. whisper\n11. titmice\n\nWait, let's re-examine 'eyesight' vs 'fraudulent' and 'fletch'.\nE-yesight\nF-fraudulent\nF-letch\nF-raudulent\nIf we compare F-words: fraudulent vs fletch. $f-r$ comes before $f-l$.\nSo: fraudulent, fletch.\n\nRevised check:\n1. biennial (B)\n2. creedal (C)\n3. cry (C)\n4. eyesight (E)\n5. fraudulent (F)\n6. fletch (F)\n7. j (J)\n8. miltonic (M)\n9. mirage (M)\n10. whisper (W)\n11. titmice (T)\n\nComparing F words: fraudulent vs f", + "schema_version": 6, + "seq": 143, + "span_id": "m1.model_call.272", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:29:22.702063+00:00" + }, + { + "analyzer": "selfcheck_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 14, + "case_id": "bbh_word_sorting-0076", + "cycle": 0, + "duration_sec": 2.3825, + "event": "model_call", + "event_seq": 273, + "inputs": { + "prompt": "Sort the following words alphabetically: List: whisper eyesight titmice mirage fletch cry biennial creedal miltonic fraudulent j\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: whisper, eyesight, titmice, mirage, fletch, cry, biennial, creedal, miltonic, fraudulent, j.\n\nTo sort these words alphabetically, we compare them letter by letter.\n\n1. **b**iennial\n2. **c**reedal\n3. **c**ry\n4. **f**letch\n5. **f**raudulent\n6. **i**sight\n7. **j**\n8. **m**ilage\n9. **m**iltonic\n10. **m**irage\n11. **t**itmice\n12. **w**hisper\n\nLet's perform a more systematic alphabetical sort:\n\n* **biennial** (starts with 'b')\n* **creedal** (starts with 'c', second letter 'r')\n* **cry** (starts with 'c', second letter 'r', third letter 'y') $\\rightarrow$ creedal comes before cry because 'e' comes before 'y'.\n* **fletch** (starts with 'f', second letter 'l')\n* **fraudulent** (starts with 'f', second letter 'r') $\\rightarrow$ fletch comes before fraudulent because 'l' comes before 'r'.\n* **eyesight** (starts with 'e') - Wait, I missed 'eyesight'. Let's re-sort by the first letter.\n\nWords: whisper, eyesight, titmice, mirage, fletch, cry, biennial, creedal, miltonic, fraudulent, j\n\n1. **biennial** (b)\n2. **creedal** (c)\n3. **cry** (c)\n4. **fletch** (f)\n5. **fraudulent** (f)\n6. **eyesight** (e) - Should come before 'f'. Let's re-examine.\n\nCorrect alphabetical order based on the first letter:\n\n* **b**: biennial\n* **c**: creedal, cry\n* **e**: eyesight\n* **f**: fletch, fraudulent\n* **j**: j\n* **m**: mirage, miltonic\n* **t**: titmice\n* **w**: whisper\n\nDetailed sorting for groups:\n\n**C group:**\n* creedal\n* cry\n (creedal comes before cry)\n\n**F group", + "schema_version": 6, + "seq": 144, + "span_id": "m1.model_call.273", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:29:25.090605+00:00" + }, + { + "analyzer": "selfcheck_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 15, + "case_id": "bbh_word_sorting-0076", + "cycle": 0, + "duration_sec": 2.4739, + "event": "model_call", + "event_seq": 274, + "inputs": { + "prompt": "Sort the following words alphabetically: List: whisper eyesight titmice mirage fletch cry biennial creedal miltonic fraudulent j\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: whisper, eyesight, titmice, mirage, fletch, cry, biennial, creedal, miltonic, fraudulent, j.\n\nTo sort these words alphabetically, we compare them letter by letter.\n\n1. **biennial** (b)\n2. **creedal** (c)\n3. **fletch** (f)\n4. **fraudulent** (f) - Comparing fletch and fraudulent: fletch vs fraudulent. 'l' vs 'r'. 'l' comes before 'r'.\n * fletch\n * fraudulent\n5. **eyesight** (e)\n6. **j** (j) (Usually treated as a single letter word, often placed early or based on the letter order)\n7. **miltonic** (m)\n8. **mirage** (m) - Comparing miltonic and mirage: miltonic vs mirage. 'i' vs 'i', 'l' vs 'r'. 'l' comes before 'r'.\n * miltonic\n * mirage\n9. **whisper** (w)\n10. **titmice** (t)\n11. **cry** (c)\n\nLet's re-sort systematically:\n\nWords: whisper, eyesight, titmice, mirage, fletch, cry, biennial, creedal, miltonic, fraudulent, j\n\nComparing the first letters: b, c, c, f, f, i, m, m, w, t, j\n\n1. **b**: biennial\n2. **c**: creedal, cry (creedal vs cry: c-r-e-e-d-a-l vs c-r-y. 'e' comes before 'y') -> creedal, cry\n3. **f**: fletch, fraudulent (fletch vs fraudulent: f-l-e-t-c-h vs f-r-a-u-d-e-n-t-.) -> fletch, fraudulent\n4. **i**: eyesight\n5. **j**: j\n6. **m**: miltonic, mirage (miltonic vs mirage: m-i-l-t-o-n-i-c vs m-i-r-a-g-e. 'l' comes before 'r') -> miltonic,", + "schema_version": 6, + "seq": 145, + "span_id": "m1.model_call.274", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:29:27.575547+00:00" + }, + { + "analyzer": "selfcheck_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 16, + "case_id": "bbh_word_sorting-0076", + "cycle": 0, + "duration_sec": 2.4579, + "event": "model_call", + "event_seq": 275, + "inputs": { + "prompt": "Sort the following words alphabetically: List: whisper eyesight titmice mirage fletch cry biennial creedal miltonic fraudulent j\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: whisper, eyesight, titmice, mirage, fletch, cry, biennial, creedal, miltonic, fraudulent, j\n\nWe need to sort these words alphabetically.\n\nLet's list the words and compare them letter by letter:\n1. whisper\n2. eyesight\n3. titmice\n4. mirage\n5. fletch\n6. cry\n7. biennial\n8. creedal\n9. miltonic\n10. fraudulent\n11. j\n\nComparing them (alphabetical order):\n\n- **b**iennial\n- **c**reedal\n- **c**ry\n- **f**letch\n- **f**raudulent\n- **i**sight\n- **j** (Since 'j' is a letter, it usually comes before letters with other starting letters, or we consider it based on the letter itself. In standard dictionary order, 'j' comes before 'm'.)\n- **m**ilage (mirage)\n- **m**iltonic\n- **t**itmice\n- **w**hisper\n\nLet's re-sort carefully:\n\nStart with 'b':\n* biennial\n\nStart with 'c':\n* creedal\n* cry (c-r comes before c-r-e, but we compare fully: creedal vs cry. 'e' comes before 'y', so creedal before cry.)\n * creedal\n * cry\n\nStart with 'f':\n* fletch\n* fraudulent (f-l comes before f-r, so fletch before fraudulent)\n * fletch\n * fraudulent\n\nStart with 'i':\n* eyesight\n\nStart with 'j':\n* j\n\nStart with 'm':\n* mirage\n* miltonic (m-i-l vs m-i-r. 'l' comes before 'r', so miltonic before mirage.)\n * miltonic\n * mirage\n\nStart with 't':\n* titmice\n\nStart with 'w':\n* whisper\n\nCombining the sorted lists:\n1. biennial\n2. creedal\n3. cry\n4. fletch\n5. fraudulent\n6. eyesight\n7. j\n8. miltonic\n9. mirage\n10. titmice\n11. whisper\n\nLet's double-check the comparison between '", + "schema_version": 6, + "seq": 146, + "span_id": "m1.model_call.275", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:29:30.045751+00:00" + }, + { + "analyzer": "selfcheck_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 17, + "case_id": "bbh_word_sorting-0094", + "cycle": 0, + "duration_sec": 2.3816, + "event": "model_call", + "event_seq": 276, + "inputs": { + "prompt": "Sort the following words alphabetically: List: crossbill bushland capacitive meager ricochet battery enigma wysiwyg wallet lipton jane wacke contingent\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: crossbill, bushland, capacitive, meager, ricochet, battery, enigma, wysiwyg, wallet, lipton, jane, wacke, contingent.\n\nHere is the alphabetical sorting process:\n\n1. **Battery** (B)\n2. **Bushland** (B) - 'u' comes before 'a' in the next position, but 'u' comes after 'u' in bushland vs battery. Let's compare carefully: Ba**t**t... vs Bu**s**h... 'a' comes before 'u'. So Battery before Bushland.)\n3. **Capacitive** (C)\n4. **Contingent** (C) - 'o' comes before 'a'. So Contingent before Capacitive.\n5. **Crossbill** (C) - 'r' comes after 'a'. Comparing Capacitive, Contingent, Crossbill:\n * Capacitive\n * Contingent\n * Crossbill\n (Capacitive vs Contingent: C-a-p vs C-o-n. 'a' comes before 'o'. So Capacitive before Contingent.)\n * Capacitive\n * Contingent\n * Crossbill\n6. **Enigma** (E)\n7. **Jane** (J)\n8. **Lipton** (L)\n9. **Meager** (M)\n10. **Ricochet** (R)\n11. **Wacke** (W)\n12. **Wallet** (W)\n13. **Wisywyg** (W)\n\nLet's re-sort by initial letter:\n\n**B:** battery, bushland\n * battery (ba...)\n * bushland (bu...) -> battery before bushland\n\n**C:** capacitive, contingent, crossbill\n * capacitive (cap...)\n * contingent (con...)\n * crossbill (cro...) -> capacitive before contingent before crossbill\n\n**E:** enigma\n\n**J:** jane\n\n**L:** lipton\n\n**M:** meager\n\n**R:** ricochet\n\n**W:** wacke, wallet, wysiwyg\n * wacke (wa...)\n * wallet (wa...)\n * wysiwyg (wy...)\n * Comparing wacke and wallet: wacke vs wallet. 'a' comes", + "schema_version": 6, + "seq": 147, + "span_id": "m1.model_call.276", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:29:32.439895+00:00" + }, + { + "analyzer": "selfcheck_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 18, + "case_id": "bbh_word_sorting-0094", + "cycle": 0, + "duration_sec": 2.4187, + "event": "model_call", + "event_seq": 277, + "inputs": { + "prompt": "Sort the following words alphabetically: List: crossbill bushland capacitive meager ricochet battery enigma wysiwyg wallet lipton jane wacke contingent\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. battery\n2. bushland\n3. capacitive\n4. contingent\n5. crossbill\n6. enigma\n7. jane\n8. lipton\n9. meager\n10. ricochet\n11. wallet\n12. wacke\n13. wysiwyg\n\nLet's double-check the sorting:\n\n* **b**attery\n* **b**ushland (b-u comes after b-a) - Wait, 'bushland' vs 'battery'. 'a' comes before 'u', so battery comes first.\n* **c**apacitive\n* **c**ontingent (c-o comes after c-a)\n* **c**rossbill (c-r comes after c-o)\n* **e**nigma\n* **j**ane\n* **l**ipton\n* **m**eager\n* **r**icochet\n* **w**allet\n* **w**acke\n* **w**ysiwyg\n\nLet's re-sort carefully:\n\n1. battery (b-a)\n2. bushland (b-u)\n3. capacitive (c-a-p)\n4. contingent (c-o-n)\n5. crossbill (c-r-o)\n6. enigma (e)\n7. jane (j)\n8. lipton (l)\n9. meager (m)\n10. ricochet (r-i)\n11. wallet (w-a)\n12. wacke (w-a-c) - 'wallet' vs 'wacke'. 'l' comes before 'c'.\n13. wysiwyg (w-y)\n\nComparing 'wallet' and 'wacke':\nW-A-L-L-E-T\nW-A-C-K-E\n'C' comes before 'L', so 'wacke' comes before 'wallet'.\n\nCorrected W sorting:\n* wallet (w-a-l)\n* wacke (w-a-c) - Wait, 'c' comes before 'l'. So wacke comes before wallet.\n* wysiwyg (w-y)\n\nLet's re-sort the W words:\n1. wacke (w-", + "schema_version": 6, + "seq": 148, + "span_id": "m1.model_call.277", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:29:34.865516+00:00" + }, + { + "analyzer": "selfcheck_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 19, + "case_id": "bbh_word_sorting-0094", + "cycle": 0, + "duration_sec": 2.3773, + "event": "model_call", + "event_seq": 278, + "inputs": { + "prompt": "Sort the following words alphabetically: List: crossbill bushland capacitive meager ricochet battery enigma wysiwyg wallet lipton jane wacke contingent\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: crossbill, bushland, capacitive, meager, ricochet, battery, enigma, wysiwyg, wallet, lipton, jane, wacke, contingent.\n\nHere is the alphabetical sorting process:\n\n1. **b**attery\n2. **b**ushland\n3. **c**apacity\n4. **c**rossbill\n5. **c**ontingent\n6. **e**nigma\n7. **j**ane\n8. **l**ipton\n9. **m**eager\n10. **r**icochet\n11. **w**acke\n12. **w**allet\n13. **w**ysiwyg\n\nLet's sort them systematically:\n\n* **b**: battery, bushland\n * battery comes before bushland.\n* **c**: capacitive, crossbill, contingent\n * capacitive\n * crossbill\n * contingent\n* **e**: enigma\n* **j**: jane\n* **l**: lipton\n* **m**: meager\n* **r**: ricochet\n* **w**: wacke, wallet, wysiwyg\n * wacke\n * wallet\n * wysiwyg\n\nLet's re-verify the order for 'w' words:\nwacke (wac...)\nwallet (wal...)\nwysiwyg (wys...)\nOrder: wacke, wallet, wysiwyg.\n\nCombining everything:\n\n1. battery\n2. bushland\n3. capacitive\n4. crossbill\n5. contingent\n6. enigma\n7. jane\n8. lipton\n9. meager\n10. ricochet\n11. wacke\n12. wallet\n13. wysiwyg\n\nLet's re-check the 'b' words:\nbattery\nbushland\n(Correct: battery before bushland)\n\nLet's re-check the 'c' words:\ncapacitive (c a p...)\ncrossbill (c r o...)\ncontingent (c o n...)\nAlphabetical order: capacitive, crossbill, contingent. (Wait, 'o' comes before 'r'. Let's re-evaluate 'crossbill' and 'contingent'.)\n\nc a p a", + "schema_version": 6, + "seq": 149, + "span_id": "m1.model_call.278", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:29:37.248900+00:00" + }, + { + "analyzer": "selfcheck_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 20, + "case_id": "bbh_word_sorting-0094", + "cycle": 0, + "duration_sec": 2.3998, + "event": "model_call", + "event_seq": 279, + "inputs": { + "prompt": "Sort the following words alphabetically: List: crossbill bushland capacitive meager ricochet battery enigma wysiwyg wallet lipton jane wacke contingent\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words to sort alphabetically:\ncrossbill, bushland, capacitive, meager, ricochet, battery, enigma, wysiwyg, wallet, lipton, jane, wacke, contingent\n\nLet's sort them:\n\n1. **battery**\n2. **bushland**\n3. **capacitive**\n4. **contingent**\n5. **crossbill**\n6. **enigma**\n7. **jane**\n8. **lipton**\n9. **meager**\n10. **ricochet**\n11. **wacke**\n12. **wallet**\n13. **wysiwyg**\n\nLet's re-sort carefully:\n\n* **b**: battery, bushland\n* **c**: capacitive, contingent, crossbill\n* **e**: enigma\n* **j**: jane\n* **l**: lipton\n* **m**: meager\n* **r**: ricochet\n* **w**: wacke, wallet, wysiwyg\n\nSorting within groups:\n\n* **b**: battery, bushland (battery comes before bushland)\n* **c**: capacitive, contingent, crossbill (capacitive, contingent, crossbill)\n* **e**: enigma\n* **j**: jane\n* **l**: lipton\n* **m**: meager\n* **r**: ricochet\n* **w**: wacke, wallet, wysiwyg (wacke, wallet, wysiwyg)\n\nDetailed alphabetical check:\n1. battery (b-a)\n2. bushland (b-u)\n3. capacitive (c-a-p)\n4. contingent (c-o)\n5. crossbill (c-r)\n6. enigma (e)\n7. jane (j)\n8. lipton (l)\n9. meager (m)\n10. ricochet (r)\n11. wacke (w-a-c-k)\n12. wallet (w-a-l)\n13. wysiwyg (w-y)\n\nComparing 'w' words:\nwacke vs wallet vs wysiwyg\nwacke (w-a-c)\nwallet (w-a-l)\nwysiwyg (w-y)\nOrder: wacke, wallet, wysiwyg (since c < l", + "schema_version": 6, + "seq": 150, + "span_id": "m1.model_call.279", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:29:39.654964+00:00" + }, + { + "analyzer": "selfcheck_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 21, + "case_id": "bbh_word_sorting-0000", + "cycle": 0, + "duration_sec": 2.2121, + "event": "model_call", + "event_seq": 280, + "inputs": { + "prompt": "Sort the following words alphabetically: List: alleviate duopoly mattress gland benelux townsmen buoyant klaxon hardbound tomography felice gunk\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: alleviate, duopoly, mattress, gland, benelux, townsmen, buoyant, klaxon, hardbound, tomography, felice, gunk.\n\nHere is the alphabetical sorting process:\n\n1. **Alleviate** (A)\n2. **Benelux** (B)\n3. **Buoyant** (B) - *Buoyant comes after Benelux (Bu > Be)*\n4. **Duopoly** (D)\n5. **Felice** (F)\n6. **Gland** (G)\n7. **Hardbound** (H)\n8. **Klaxon** (K)\n9. **Mattress** (M)\n10. **Tomography** (T)\n11. **Townsmen** (T)\n12. **Gunk** (G) - *Correction: Gunk comes before Gland in alphabetical order if we compare 'gunk' vs 'gland'. L vs L, U vs A. Wait, 'g' vs 'g'. U vs L. U comes after L. So Gland comes before Gunk.*\n\nLet's re-sort systematically:\n\nWords starting with A:\n* alleviate\n\nWords starting with B:\n* benelux\n* buoyant\n\nWords starting with D:\n* duopoly\n\nWords starting with F:\n* felice\n\nWords starting with G:\n* gland\n* gunk (Gland comes before Gunk)\n\nWords starting with H:\n* hardbound\n\nWords starting with K:\n* klaxon\n\nWords starting with M:\n* mattress\n\nWords starting with T:\n* tomography\n* townsmen (Tomography comes before Townsmen)\n\nFinal Sorted List:\n1. alleviate\n2. benelux\n3. buoyant\n4. duopoly\n5. felice\n6. gland\n7. gunk\n8. hardbound\n9. klaxon\n10. mattress\n11. tomography\n12. townsmen\n\nAnswer: alleviate, benelux, buoyant, duopoly, felice, gland, gunk, hardbound, klaxon, mattress, tomography, townsmen", + "schema_version": 6, + "seq": 151, + "span_id": "m1.model_call.280", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:29:41.877031+00:00" + }, + { + "analyzer": "selfcheck_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 22, + "case_id": "bbh_word_sorting-0000", + "cycle": 0, + "duration_sec": 2.2486, + "event": "model_call", + "event_seq": 281, + "inputs": { + "prompt": "Sort the following words alphabetically: List: alleviate duopoly mattress gland benelux townsmen buoyant klaxon hardbound tomography felice gunk\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. alleviate\n2. benelux\n3. buoyant\n4. gunk\n5. felice\n6. gland\n7. hardbound\n8. klaxon\n9. mattress\n10. tomography\n11. townsmen\n12. duopoly\n\nLet's re-sort them carefully:\n\n* **a**lleviate\n* **b**enelux, **b**uoyant\n* **d**uopoly\n* **g**land, **g**unk\n* **h**ardbound\n* **k**laxon\n* **m**attress\n* **t**omography, **t**ownsmen\n* **f**elice (Wait, 'f' should come before 'g'. Let's re-check.)\n\nLet's list the initial set: alleviate, duopoly, mattress, gland, benelux, townsmen, buoyant, klaxon, hardbound, tomography, felice, gunk.\n\n1. **a**lleviate\n2. **b**enelux\n3. **b**uoyant\n4. **d**uopoly\n5. **f**elice\n6. **g**land\n7. **g**unk\n8. **h**ardbound\n9. **k**laxon\n10. **m**attress\n11. **t**omography\n12. **t**ownsmen\n\nChecking 'g' words: gland vs gunk. 'gland' comes before 'gunk'.\nChecking 't' words: tomography vs townsmen. 'tomography' comes before 'townsmen'.\n\nThe final alphabetical order is:\n1. alleviate\n2. benelux\n3. buoyant\n4. duopoly\n5. felice\n6. gland\n7. gunk\n8. hardbound\n9. klaxon\n10. mattress\n11. tomography\n12. townsmen\n\nAnswer: alleviate, benelux, buoyant, duopoly, felice, gland, gunk, hardbound, klaxon, mattress, tomography, townsmen", + "schema_version": 6, + "seq": 152, + "span_id": "m1.model_call.281", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:29:44.139645+00:00" + }, + { + "analyzer": "selfcheck_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 23, + "case_id": "bbh_word_sorting-0000", + "cycle": 0, + "duration_sec": 2.3864, + "event": "model_call", + "event_seq": 282, + "inputs": { + "prompt": "Sort the following words alphabetically: List: alleviate duopoly mattress gland benelux townsmen buoyant klaxon hardbound tomography felice gunk\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: alleviate, duopoly, mattress, gland, benelux, townsmen, buoyant, klaxon, hardbound, tomography, felice, gunk.\n\nWe need to sort these words alphabetically.\n\n1. **alleviate**\n2. **benelux**\n3. **buoyant**\n4. **duopoly**\n5. **felice**\n6. **gland**\n7. **hardbound**\n8. **klaxon**\n9. **mattress**\n10. **tomography**\n11. **townsmen**\n\nLet's re-sort them systematically:\n\n* **a**: alleviate\n* **b**: benelux, buoyant\n * benelux comes before buoyant (b-e vs b-u)\n* **d**: duopoly\n* **f**: felice\n* **g**: gland, gunk\n * gland comes before gunk (g-l vs g-u)\n* **h**: hardbound\n* **k**: klaxon\n* **m**: mattress\n* **t**: tomography, townsmen\n\nLet's do the full alphabetical sort:\n\n1. alleviate (a)\n2. benelux (b, e)\n3. buoyant (b, u)\n4. duopoly (d)\n5. felice (f)\n6. gland (g, l)\n7. gunk (g, u)\n8. hardbound (h)\n9. klaxon (k)\n10. mattress (m)\n11. tomography (t, o)\n12. townsmen (t, o)\n * Comparing tomography and townsmen: to-m vs to-w. 'm' comes before 'w'. So, tomography comes before townsmen.\n\nFinal sorted list:\n1. alleviate\n2. benelux\n3. buoyant\n4. duopoly\n5. felice\n6. gland\n7. gunk\n8. hardbound\n9. klaxon\n10. mattress\n11. tomography\n12. townsmen\n\nAnswer: alleviate, benelux, buoyant, duopoly, felice, gland, gunk, hardbound, klaxon, mattress, tomography, townsmen\n\nAnswer: alleviate, benelux, buoyant, duopoly, felice, gland, gunk", + "schema_version": 6, + "seq": 153, + "span_id": "m1.model_call.282", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:29:46.532349+00:00" + }, + { + "analyzer": "selfcheck_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 24, + "case_id": "bbh_word_sorting-0000", + "cycle": 0, + "duration_sec": 2.4128, + "event": "model_call", + "event_seq": 283, + "inputs": { + "prompt": "Sort the following words alphabetically: List: alleviate duopoly mattress gland benelux townsmen buoyant klaxon hardbound tomography felice gunk\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: alleviate, duopoly, mattress, gland, benelux, townsmen, buoyant, klaxon, hardbound, tomography, felice, gunk.\n\nHere is the alphabetical sorting process:\n\n1. **A**lleviate\n2. **B**enelux, **B**uoyant\n * Benelux comes before Buoyant (e comes before u)\n3. **D**uopoly\n4. **F**elice\n5. **G**land, **G**unk\n * Gland comes before Gunk (l comes before u)\n6. **H**ardbound\n7. **K**laxon\n8. **M**attress\n9. **T**ownsmen, **T**omography\n * Townsmen comes before Tomography (w comes before o in the second position, but sorting by the full word: townsmen vs tomography. 'o' in tomography comes before 'w' in townsmen if we consider the second letter, but 't' is common. Let's compare closely: t-o-w-n-s-m-e-n vs t-o-m-o-g-r-a-p-h-y. 'm' comes before 'w'.)\n * Tomography vs Townsmen: To-m... vs To-w... $\\rightarrow$ Tomography comes before Townsmen.\n10. **G**unk (Wait, I already placed G terms. Let's re-list and sort properly.)\n\n**Original List:** alleviate, duopoly, mattress, gland, benelux, townsmen, buoyant, klaxon, hardbound, tomography, felice, gunk\n\n**Alphabetical Sort:**\n\n1. alleviate (A)\n2. benelux (B)\n3. buoyant (B)\n4. duopoly (D)\n5. felice (F)\n6. gland (G)\n7. gunk (G)\n8. hardbound (H)\n9. klaxon (K)\n10. mattress (M)\n11. tomography (T)\n12. townsmen (T)\n\n**Refining the G words:**\ngland (g-l...)\ngunk (g-u...)\ngland comes before gunk.\n\n**Refining the T words:**\ntomography", + "schema_version": 6, + "seq": 154, + "span_id": "m1.model_call.283", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:29:48.957824+00:00" + }, + { + "analyzer": "selfcheck_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 25, + "case_id": "bbh_word_sorting-0063", + "cycle": 0, + "duration_sec": 2.203, + "event": "model_call", + "event_seq": 284, + "inputs": { + "prompt": "Sort the following words alphabetically: List: vegetate artillery harm fda doris prosody bainite incongruous monkey vivian\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: vegetate, artillery, harm, fda, doris, prosody, bainite, incongruous, monkey, vivian.\n\nHere is the step-by-step alphabetical sorting:\n\n1. **Analyze the starting letters:**\n - v (vegetate, vivian)\n - a (artillery)\n - h (harm)\n - f (fda)\n - d (doris)\n - p (prosody)\n - b (bainite)\n - i (incongruous)\n - m (monkey)\n\n2. **Sort based on the first letter:**\n - a: artillery\n - b: bainite\n - d: doris\n - f: fda\n - h: harm\n - i: incongruous\n - m: monkey\n - p: prosody\n - v: vegetate, vivian\n\n3. **Sort the 'v' words:**\n - vegetate\n - vivian (v-e comes before v-i)\n\n4. **Final Sorted Order:**\n 1. artillery\n 2. bainite\n 3. doris\n 4. fda\n 5. harm\n 6. incongruous\n 7. monkey\n 8. prosody\n 9. vegetate\n 10. vivian\n\nLet's re-check the order based on standard alphabetical sorting:\n* artillery (a)\n* bainite (b)\n* doris (d)\n* fda (f)\n* harm (h)\n* incongruous (i)\n* monkey (m)\n* prosody (p)\n* vegetate (v-e)\n* vivian (v-i)\n\nCorrect alphabetical order for the 'v' words: vegetate, vivian.\n\nFinal sorted list: artillery, bainite, doris, fda, harm, incongruous, monkey, prosody, vegetate, vivian.\n\nAnswer: artillery, bainite, doris, fda, harm, incongruous, monkey, prosody, vegetate, vivian", + "schema_version": 6, + "seq": 155, + "span_id": "m1.model_call.284", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:29:51.185016+00:00" + }, + { + "analyzer": "selfcheck_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 26, + "case_id": "bbh_word_sorting-0063", + "cycle": 0, + "duration_sec": 1.6527, + "event": "model_call", + "event_seq": 285, + "inputs": { + "prompt": "Sort the following words alphabetically: List: vegetate artillery harm fda doris prosody bainite incongruous monkey vivian\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words to sort: vegetate, artillery, harm, fda, doris, prosody, bainite, incongruous, monkey, vivian.\n\nLet's sort them alphabetically:\n\n1. bainite\n2. artillery\n3. doris\n4. fda\n5. harm\n6. incongruous\n7. monkey\n8. prosody\n9. vegetate\n10. vivian\n\nDetailed comparison:\n* **b**ainite\n* **a**rtillery\n* **d**oris\n* **f**da\n* **h**arm\n* **i**ncongruous\n* **m**onkey\n* **p**rosody\n* **v**egetate (starts with V)\n* **v**ivian (v-e comes before v-i) $\\rightarrow$ vivian comes before vegetate.\n\nLet's recheck V words:\n* vegetate (v-e)\n* vivian (v-i)\n'e' comes before 'i'. So, vegetate comes before vivian.\n\nCorrect alphabetical order:\n1. bainite\n2. artillery\n3. doris\n4. fda\n5. harm\n6. incongruous\n7. monkey\n8. prosody\n9. vegetate\n10. vivian\n\nAnswer: bainite, artillery, doris, fda, harm, incongruous, monkey, prosody, vegetate, vivian\n\nAnswer: bainite, artillery, doris, fda, harm, incongruous, monkey, prosody, vegetate, vivian", + "schema_version": 6, + "seq": 156, + "span_id": "m1.model_call.285", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:29:52.844169+00:00" + }, + { + "analyzer": "selfcheck_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 27, + "case_id": "bbh_word_sorting-0063", + "cycle": 0, + "duration_sec": 2.3834, + "event": "model_call", + "event_seq": 286, + "inputs": { + "prompt": "Sort the following words alphabetically: List: vegetate artillery harm fda doris prosody bainite incongruous monkey vivian\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: vegetate, artillery, harm, fda, doris, prosody, bainite, incongruous, monkey, vivian.\n\nHere is the alphabetical sorting process:\n\n1. **Compare by the first letter:**\n * bainite (b)\n * doris (d)\n * fda (f)\n * harm (h)\n * incongruous (i)\n * monkey (m)\n * prosody (p)\n * artillery (a)\n * vegetate (v)\n * vivian (v)\n\n2. **Sort based on the first letter:**\n * **a**: artillery\n * **b**: bainite\n * **d**: doris\n * **f**: fda\n * **h**: harm\n * **i**: incongruous\n * **m**: monkey\n * **p**: prosody\n * **v**: vegetate, vivian\n\n3. **Sort the 'v' words:**\n * vivian\n * vegetate (vivi comes before vegetate, but alphabetically, 'e' comes before 'e' in the second pair if we look closer. 'vivian' comes before 'vegetate' because 'i' comes before 'e' is false. Let's check the full words: v**i**vian vs v**e**getate. 'e' comes before 'i'.)\n * vivian (v i v i a n)\n * vegetate (v e g e t a t e)\n * Therefore, 'vegetate' comes before 'vivian'.\n * Correct order for 'v' words: vegetate, vivian.\n\n4. **Final sorted list:**\n 1. artillery\n 2. bainite\n 3. doris\n 4. fda\n 5. harm\n 6. incongruous\n 7. monkey\n 8. prosody\n 9. vegetate\n 10. vivian\n\nLet's re-check the order for words starting with 'v':\nvivian (v i...)\nvegetate (v e...)\nSince 'e' comes before 'i', 'vegetate' comes before 'vivian'.\n\nCorrected order for 'v' words: vegetate, vivian.\n\nFinal consolidated list:\nart", + "schema_version": 6, + "seq": 157, + "span_id": "m1.model_call.286", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:29:55.234194+00:00" + }, + { + "analyzer": "selfcheck_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 28, + "case_id": "bbh_word_sorting-0063", + "cycle": 0, + "duration_sec": 1.755, + "event": "model_call", + "event_seq": 287, + "inputs": { + "prompt": "Sort the following words alphabetically: List: vegetate artillery harm fda doris prosody bainite incongruous monkey vivian\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: vegetate, artillery, harm, fda, doris, prosody, bainite, incongruous, monkey, vivian.\n\nTo sort these words alphabetically, we compare them letter by letter.\n\n1. **artillery** (A)\n2. **bainite** (B)\n3. **doris** (D)\n4. **fda** (F)\n5. **harm** (H)\n6. **incongruous** (I)\n7. **monkey** (M)\n8. **prosody** (P)\n9. **vegetate** (V)\n10. **vivian** (V)\n\nLet's refine the order, paying attention to words starting with the same letter:\n\n* **A:** artillery\n* **B:** bainite\n* **D:** doris\n* **F:** fda\n* **H:** harm\n* **I:** incongruous\n* **M:** monkey\n* **P:** prosody\n* **V:** vegetate, vivian (Comparing 'vegetate' and 'vivian': 'e' comes before 'i', so vegetate comes before vivian.)\n\nAlphabetical order:\n1. artillery\n2. bainite\n3. doris\n4. fda\n5. harm\n6. incongruous\n7. monkey\n8. prosody\n9. vegetate\n10. vivian\n\nAnswer: artillery, bainite, doris, fda, harm, incongruous, monkey, prosody, vegetate, vivian\n\nAnswer: artillery, bainite, doris, fda, harm, incongruous, monkey, prosody, vegetate, vivian", + "schema_version": 6, + "seq": 158, + "span_id": "m1.model_call.287", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:29:56.996118+00:00" + }, + { + "analyzer": "selfcheck_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 29, + "case_id": "bbh_word_sorting-0101", + "cycle": 0, + "duration_sec": 1.1838, + "event": "model_call", + "event_seq": 288, + "inputs": { + "prompt": "Sort the following words alphabetically: List: regret starlight wallboard cotyledon more pepperoni\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: regret, starlight, wallboard, cotyledon, more, pepperoni.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **cotyledon** (Starts with 'c')\n2. **more** (Starts with 'm')\n3. **pepperoni** (Starts with 'p')\n4. **regret** (Starts with 'r')\n5. **starlight** (Starts with 's')\n6. **wallboard** (Starts with 'w')\n\nLet's re-sort based on the first letter:\nC: cotyledon\nM: more\nP: pepperoni\nR: regret\nS: starlight\nW: wallboard\n\nAlphabetical order:\n1. cotyledon\n2. more\n3. pepperoni\n4. regret\n5. starlight\n6. wallboard\n\nAnswer: cotyledon, more, pepperoni, regret, starlight, wallboard\n\nAnswer: cotyledon, more, pepperoni, regret, starlight, wallboard", + "schema_version": 6, + "seq": 159, + "span_id": "m1.model_call.288", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:29:58.189951+00:00" + }, + { + "analyzer": "selfcheck_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 30, + "case_id": "bbh_word_sorting-0101", + "cycle": 0, + "duration_sec": 0.9262, + "event": "model_call", + "event_seq": 289, + "inputs": { + "prompt": "Sort the following words alphabetically: List: regret starlight wallboard cotyledon more pepperoni\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: regret, starlight, wallboard, cotyledon, more, pepperoni.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **cotyledon**\n2. **more**\n3. **pepperoni**\n4. **regret**\n5. **starlight**\n6. **wallboard**\n\nLet's verify the order:\n* **c**otyledon\n* **m**ore\n* **p**epperoni\n* **r**egret\n* **s**tarlight\n* **w**allboard\n\nThis order is correct alphabetically.\n\nAnswer: cotyledon, more, pepperoni, regret, starlight, wallboard\n\nAnswer: cotyledon, more, pepperoni, regret, starlight, wallboard", + "schema_version": 6, + "seq": 160, + "span_id": "m1.model_call.289", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:29:59.124795+00:00" + }, + { + "analyzer": "selfcheck_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 31, + "case_id": "bbh_word_sorting-0101", + "cycle": 0, + "duration_sec": 1.5276, + "event": "model_call", + "event_seq": 290, + "inputs": { + "prompt": "Sort the following words alphabetically: List: regret starlight wallboard cotyledon more pepperoni\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: regret, starlight, wallboard, cotyledon, more, pepperoni.\n\nTo sort the words alphabetically, we compare them letter by letter.\n\n1. **cotyledon** (starts with c)\n2. **more** (starts with m)\n3. **pepperoni** (starts with p)\n4. **regret** (starts with r)\n5. **starlight** (starts with s)\n6. **wallboard** (starts with w)\n\nLet's re-sort them based on the first letter: C, M, P, R, S, W.\n\n1. **cotyledon**\n2. **more**\n3. **pepperoni**\n4. **regret**\n5. **starlight**\n6. **wallboard**\n\nLet's do a final check:\nC (cotyledon) comes before M (more).\nM (more) comes before P (pepperoni).\nP (pepperoni) comes before R (regret).\nR (regret) comes before S (starlight).\nS (starlight) comes before W (wallboard).\n\nThe alphabetical order is: cotyledon, more, pepperoni, regret, starlight, wallboard.\n\nAnswer: cotyledon, more, pepperoni, regret, starlight, wallboard\nAnswer: cotyledon, more, pepperoni, regret, starlight, wallboard", + "schema_version": 6, + "seq": 161, + "span_id": "m1.model_call.290", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:30:00.662827+00:00" + }, + { + "analyzer": "selfcheck_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 32, + "case_id": "bbh_word_sorting-0101", + "cycle": 0, + "duration_sec": 1.1001, + "event": "model_call", + "event_seq": 291, + "inputs": { + "prompt": "Sort the following words alphabetically: List: regret starlight wallboard cotyledon more pepperoni\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: regret, starlight, wallboard, cotyledon, more, pepperoni.\n\nTo sort these words alphabetically, we compare them letter by letter.\n\n1. **cotyledon** (c...)\n2. **more** (m...)\n3. **pepperoni** (p...)\n4. **regret** (r...)\n5. **starlight** (s...)\n6. **wallboard** (w...)\n\nLet's re-sort them carefully:\n\n* **c**otyledon\n* **m**ore\n* **p**epperoni\n* **r**egret\n* **s**tarlight\n* **w**allboard\n\nAlphabetical order:\n1. cotyledon\n2. more\n3. pepperoni\n4. regret\n5. starlight\n6. wallboard\n\nAnswer: cotyledon, more, pepperoni, regret, starlight, wallboard\n\nAnswer: cotyledon, more, pepperoni, regret, starlight, wallboard", + "schema_version": 6, + "seq": 162, + "span_id": "m1.model_call.291", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:30:01.769452+00:00" + }, + { + "analyzer": "self_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 1, + "case_id": "bbh_word_sorting-0006", + "cycle": 0, + "duration_sec": 137.8821, + "event": "model_call", + "event_seq": 292, + "inputs": { + "prompt": "Sort the following words alphabetically: List: torpedo phosphorescent pristine decadent shrunk dey administer gradate littleneck thrown jacky coachman aeneid verdict tasting sinh delhi systemwide grim\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. gradate\n7. grim\n8. jacky\n9. littleneck\n10. phosphorescent\n11. pristine\n12. sinh\n13. systemwide\n14. tasting\n15. torpedo\n16. thrown\n17. verdict\n18. dey\n19. shrunk\n\nLet's re-sort them carefully:\n\n* administer\n* aeneid\n* decadent\n* coachman\n* delhi\n* grim\n* gradate\n* jacky\n* littleneck\n* phosphorescent\n* pristine\n* sinh\n* systemwide\n* tasting\n* torpedo\n* thrown\n* verdict\n* dey\n* shrunk\n\nChecking for correct alphabetical order:\nA: administer, aeneid\nC: decadent, coachman\nD: delhi, dey\nG: grim, gradate\nJ: jacky\nL: littleneck\nP: phosphorescent, pristine\nS: sinh, systemwide, shrunk\nT: tasting, torpedo, thrown\nV: verdict\n\nCorrected Sorted List:\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. dey\n7. grim\n8. gradate\n9. jacky\n10. littleneck\n11. phosphorescent\n12. pristine\n13. sinh\n14. shrunk\n15. systemwide\n16. tasting\n17. torpedo\n18. thrown\n19. verdict\n\n(Comparing D words: delhi, dey. D-e-l-h-i vs D-e-y. 'l' comes before 'y', so delhi comes before dey.)\n\nFinal check on the complete list:\nadminister (A)\naeneid (A)\ndecadent (D)\ncoachman (C) -> Should be C before D. Let's restart the alphabetical sort strictly.\n\nList: torpedo, phosphorescent, pristine, decadent, shrunk, dey, administer, gradate, littleneck, thrown, jacky, coachman, aeneid, verdict, tasting, sinh, delhi, systemwide, grim\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6", + "schema_version": 6, + "seq": 163, + "span_id": "m1.model_call.292", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:32:19.659487+00:00" + }, + { + "analyzer": "self_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 2, + "case_id": "bbh_word_sorting-0006", + "cycle": 0, + "duration_sec": 2.3315, + "event": "model_call", + "event_seq": 293, + "inputs": { + "prompt": "Sort the following words alphabetically: List: torpedo phosphorescent pristine decadent shrunk dey administer gradate littleneck thrown jacky coachman aeneid verdict tasting sinh delhi systemwide grim\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. gradate\n7. grim\n8. jacky\n9. littleneck\n10. phosphorescent\n11. pristine\n12. sinh\n13. systemwide\n14. tasting\n15. torpedo\n16. thrown\n17. verdict\n18. dey\n19. shrunk\n\nLet's re-sort them carefully:\n\nA: administer, aeneid\nC: decadent, coachman\nD: delhi, dey\nG: grim, gradate\nJ: jacky\nL: littleneck\nP: phosphorescent, pristine\nS: sinh, systemwide, shrunk\nT: tasting, torpedo, thrown\nV: verdict\n\n**Step-by-step alphabetical sorting:**\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. dey\n7. grim\n8. gradate\n9. jacky\n10. littleneck\n11. phosphorescent\n12. pristine\n13. sinh\n14. shrunk\n15. systemwide\n16. tasting\n17. torpedo\n18. thrown\n19. verdict\n\nAnswer: administer, aeneid, decadent, coachman, delhi, dey, grim, gradate, jacky, littleneck, phosphorescent, pristine, sinh, shrunk, systemwide, tasting, torpedo, thrown, verdict", + "schema_version": 6, + "seq": 164, + "span_id": "m1.model_call.293", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:32:22.001559+00:00" + }, + { + "analyzer": "self_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 3, + "case_id": "bbh_word_sorting-0006", + "cycle": 0, + "duration_sec": 3.0119, + "event": "model_call", + "event_seq": 294, + "inputs": { + "prompt": "Sort the following words alphabetically: List: torpedo phosphorescent pristine decadent shrunk dey administer gradate littleneck thrown jacky coachman aeneid verdict tasting sinh delhi systemwide grim\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words from the list sorted alphabetically:\n\nList: torpedo phosphorescent pristine decadent shrunk dey administer gradate littleneck thrown jacky coachman aeneid verdict tasting sinh delhi systemwide grim\n\n1. aeneid\n2. administer\n3. decadent\n4. delhi\n5. gradate\n6. grim\n7. jacky\n8. littleneck\n9. phosphorescent\n10. pristine\n11. sinh\n12. systemwide\n13. tasting\n14. torpedo\n15. thrown\n16. verdict\n\nLet's re-sort them carefully:\n\n* aeneid\n* administer\n* decadent\n* delhi\n* gradate\n* grim\n* jacky\n* littleneck\n* phosphorescent\n* pristine\n* sinh\n* systemwide\n* tasting\n* torpedo\n* thrown\n* verdict\n\nComparing the 'a' words: aeneid, administer. (aeneid comes before administer)\nComparing 'd' words: decadent, delhi. (decadent comes before delhi)\nComparing 'g' words: gradate, grim. (gradate comes before grim)\nComparing 'l' words: littleneck.\nComparing 'p' words: phosphorescent, pristine. (phosphorescent comes before pristine)\nComparing 's' words: sinh, systemwide, tasting. (sinh, systemwide, tasting)\nComparing 't' words: torpedo, thrown, verdict. (torpedo, thrown, verdict)\n\nCorrect alphabetical order:\n\n1. aeneid\n2. administer\n3. decadent\n4. delhi\n5. gradate\n6. grim\n7. jacky\n8. littleneck\n9. phosphorescent\n10. pristine\n11. sinh\n12. systemwide\n13. tasting\n14. torpedo\n15. thrown\n16. verdict\n\nAnswer: aeneid, administer, decadent, delhi, gradate, grim, jacky, littleneck, phosphorescent, pristine, sinh, systemwide, tasting, torpedo, thrown, verdict", + "schema_version": 6, + "seq": 165, + "span_id": "m1.model_call.294", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:32:25.024225+00:00" + }, + { + "analyzer": "self_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 4, + "case_id": "bbh_word_sorting-0006", + "cycle": 0, + "duration_sec": 3.5492, + "event": "model_call", + "event_seq": 295, + "inputs": { + "prompt": "Sort the following words alphabetically: List: torpedo phosphorescent pristine decadent shrunk dey administer gradate littleneck thrown jacky coachman aeneid verdict tasting sinh delhi systemwide grim\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. gradate\n7. grim\n8. jacky\n9. littleneck\n10. phosphorescent\n11. pristine\n12. sinh\n13. systemwide\n14. tasting\n15. torpedo\n16. thrown\n17. verdict\n18. dey\n19. shrunk\n\nLet's re-sort them carefully:\n\n* administer\n* aeneid\n* decadent\n* coachman\n* delhi\n* gradate\n* grim\n* jacky\n* littleneck\n* phosphorescent\n* pristine\n* sinh\n* systemwide\n* tasting\n* torpedo\n* thrown\n* verdict\n* dey\n* shrunk\n\nComparing the list again:\naeneid\ndecadent\ndelhi\nadminister\ncoachman\ngrim\ngradate\njacky\nlittleneck\nphosphorescent\npristine\nshrunk\nsinh\nsystemwide\ntasting\ntorpedo\nthrown\nverdict\ndey\n\nLet's use standard alphabetical order:\n\n1. aeneid\n2. decadent\n3. administer\n4. coachman\n5. delhi\n6. dey\n7. grim\n8. gradate\n9. jacky\n10. littleneck\n11. phosphorescent\n12. pristine\n13. sinh\n14. shrunk\n15. systemwide\n16. tasting\n17. torpedo\n18. thrown\n19. verdict\n\n(Note: For words starting with 's', 'shrunk' comes before 'systemwide', 'sinh' comes after 'pristine' if sorted strictly by character sequence, but typically 's' words are grouped. Checking 'sinh' vs 'shrunk': 'sinh' vs 'shrunk' -> 'i' before 'r', so sinh before shrunk. Re-evaluating 'sinh' and 'shrunk'.)\n\nCorrected 'S' ordering:\n* sinh\n* shrunk\n* systemwide\n\nFinal Sorted List:\n1. aeneid\n2. decadent\n3. administer\n4. coachman\n5. delhi\n6. dey\n7. grim\n8. gradate\n9. jacky\n10. littleneck\n11. phosphorescent\n12. pristine", + "schema_version": 6, + "seq": 166, + "span_id": "m1.model_call.295", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:32:28.585416+00:00" + }, + { + "analyzer": "self_consistency", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 5, + "case_id": "bbh_word_sorting-0006", + "cycle": 0, + "duration_sec": 3.5342, + "event": "model_call", + "event_seq": 296, + "inputs": { + "prompt": "Sort the following words alphabetically: List: torpedo phosphorescent pristine decadent shrunk dey administer gradate littleneck thrown jacky coachman aeneid verdict tasting sinh delhi systemwide grim\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. gradate\n7. grim\n8. jacky\n9. littleneck\n10. phosphorescent\n11. pristine\n12. sinh\n13. systemwide\n14. tasting\n15. torpedo\n16. thrown\n17. verdict\n18. dey\n19. shrunk\n\nLet's re-sort them carefully:\n\n* **aeneid** (A)\n* **decadent** (D)\n* **delhi** (D) - *delhi comes before decadent if we look at the next letters, but D-E-L-H-I vs D-E-C-A-D-E-N-T. Decadent comes first.*\n* **administer** (A) - *aeneid vs administer. aeneid before administer.*\n* **coachman** (C)\n* **grim** (G)\n* **gradate** (G) - *grim vs gradate. grim before gradate.*\n* **jacky** (J)\n* **littleneck** (L)\n* **phosphorescent** (P)\n* **pristine** (P) - *phosphorescent vs pristine. phosphorescent before pristine.*\n* **sinh** (S)\n* **systemwide** (S) - *sinh vs systemwide. sinh before systemwide.*\n* **tasting** (T)\n* **torpedo** (T) - *tasting vs torpedo. tasting before torpedo.*\n* **thrown** (T) - *thrown vs tasting/torpedo. thrown comes after tasting.*\n* **verdict** (V)\n* **dey** (D) - *dey comes after decadent/delhi.*\n* **shrunk** (S)\n\nLet's list them alphabetically based on the first letter:\nA: administer, aeneid\nC: coachman\nD: decadent, delhi, dey\nG: grim, gradate\nJ: jacky\nL: littleneck\nP: phosphorescent, pristine\nS: sinh, shrunk, systemwide, tasting\nT: torpedo, thrown, tasting\nV: verdict\n\nRefining the sort:\n\n1. **aeneid**\n2.", + "schema_version": 6, + "seq": 167, + "span_id": "m1.model_call.296", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:32:32.128494+00:00" + }, + { + "analyzer": "calibration", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 1, + "case_id": "bbh_word_sorting-0006", + "cycle": 0, + "duration_sec": 102.5087, + "event": "model_call", + "event_seq": 297, + "inputs": { + "prompt": "Sort the following words alphabetically: List: torpedo phosphorescent pristine decadent shrunk dey administer gradate littleneck thrown jacky coachman aeneid verdict tasting sinh delhi systemwide grim\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "logprobs", + "n_batch_cases": 8, + "output": [ + { + "logprob": -0.0743, + "token": "Here" + }, + { + "logprob": -0.0124, + "token": " are" + }, + { + "logprob": 0, + "token": " the" + }, + { + "logprob": -0.0134, + "token": " words" + }, + { + "logprob": -0.0872, + "token": " sorted" + }, + { + "logprob": 0, + "token": " alphabetically" + }, + { + "logprob": 0, + "token": ":" + }, + { + "logprob": -0.0003, + "token": "\n\n" + }, + { + "logprob": -0.0004, + "token": "1" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": -0.1908, + "token": " administer" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "2" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": -0.011, + "token": " a" + }, + { + "logprob": 0, + "token": "ene" + }, + { + "logprob": 0, + "token": "id" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "3" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " decadent" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "4" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": -0.0151, + "token": " coach" + }, + { + "logprob": 0, + "token": "man" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "5" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " delhi" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "6" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": -0.0028, + "token": " grad" + }, + { + "logprob": 0, + "token": "ate" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "7" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " grim" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "8" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " jack" + }, + { + "logprob": 0, + "token": "y" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "9" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " litt" + }, + { + "logprob": 0, + "token": "leneck" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "1" + }, + { + "logprob": 0, + "token": "0" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " phosph" + }, + { + "logprob": 0, + "token": "orescent" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "1" + }, + { + "logprob": 0, + "token": "1" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " pristine" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "1" + }, + { + "logprob": 0, + "token": "2" + }, + { + "logprob": 0, + "token": "." + } + ], + "schema_version": 6, + "seq": 168, + "span_id": "m1.model_call.297", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:34:14.651626+00:00" + }, + { + "analyzer": "calibration", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 2, + "case_id": null, + "cycle": 0, + "duration_sec": 2.5982, + "event": "model_call", + "event_seq": 298, + "inputs": { + "prompt": "Sort the following words alphabetically: List: torpedo phosphorescent pristine decadent shrunk dey administer gradate littleneck thrown jacky coachman aeneid verdict tasting sinh delhi systemwide grim\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAfter answering, state your confidence on a 0-100 scale on its own line as: Confidence: " + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: torpedo phosphorescent pristine decadent shrunk dey administer gradate littleneck thrown jacky coachman aeneid verdict tasting sinh delhi systemwide grim\n\nHere is the alphabetical sorting process:\n\n1. **aeneid**\n2. **administer**\n3. **decadent**\n4. **delhi**\n5. **gradate**\n6. **grim**\n7. **littleneck**\n8. **phosphorescent**\n9. **pristine**\n10. **systemwide**\n11. **tasting**\n12. **torpedo**\n13. **verdict**\n14. **shrunk**\n15. **sinh** (Assuming this is a word, though it might be a name or less common)\n16. **coachman**\n17. **jacky**\n18. **dye** (Assuming 'dey' is a typo for 'dye' or sorting 'dey' as is) - Sorting 'dey' based on 'd'\n19. **thrown**\n\nLet's re-sort carefully, treating all entries as provided:\n\nOriginal list: torpedo phosphorescent pristine decadent shrunk dey administer gradate littleneck thrown jacky coachman aeneid verdict tasting sinh delhi systemwide grim\n\nSorting by the first letter:\nA: aeneid, administer\nC: coachman\nD: decadent, delhi, dey\nG: gradate, grim\nJ: jacky\nL: littleneck\nP: phosphorescent, pristine\nS: shrunk, sinh, systemwide\nT: torpedo, tasting\nV: verdict\nTh: thrown\n\nDetailed sorting:\n\n1. **aeneid**\n2. **administer**\n3. **coachman**\n4. **decadent**\n5. **delhi**\n6. **dey**\n7. **gradate**\n8. **grim**\n9. **jacky**\n10. **littleneck**\n11. **phosphorescent**\n12. **pristine**\n13. **systemwide**\n14. **sinh**\n15. **tasting**\n16. **torpedo**\n17. **verdict**\n18. **thrown**\n19. **shrunk**\n\nLet's re-check the 'D' words: decadent", + "schema_version": 6, + "seq": 169, + "span_id": "m1.model_call.298", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:34:17.257534+00:00" + }, + { + "analyzer": "calibration", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 3, + "case_id": "bbh_word_sorting-0025", + "cycle": 0, + "duration_sec": 0.6419, + "event": "model_call", + "event_seq": 299, + "inputs": { + "prompt": "Sort the following words alphabetically: List: linchpin arrear eavesdropping fasciculate shin postdoctoral maritime ibn henry hermaphrodite sticky jane brookside incorrigible vehicular herodotus\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "logprobs", + "n_batch_cases": 8, + "output": [ + { + "logprob": -0.2015, + "token": "Here" + }, + { + "logprob": -0.0142, + "token": " are" + }, + { + "logprob": 0, + "token": " the" + }, + { + "logprob": -0.0317, + "token": " words" + }, + { + "logprob": -0.0812, + "token": " sorted" + }, + { + "logprob": 0, + "token": " alphabetically" + }, + { + "logprob": 0, + "token": ":" + }, + { + "logprob": -0.0004, + "token": "\n\n" + }, + { + "logprob": -0.0003, + "token": "1" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " arre" + }, + { + "logprob": 0, + "token": "ar" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "2" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": -0.0407, + "token": " eaves" + }, + { + "logprob": 0, + "token": "dropping" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "3" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": -0.0001, + "token": " fasc" + }, + { + "logprob": 0, + "token": "iculate" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "4" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": -0.004, + "token": " her" + }, + { + "logprob": 0, + "token": "odot" + }, + { + "logprob": 0, + "token": "us" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "5" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": -0.0001, + "token": " hermaph" + }, + { + "logprob": 0, + "token": "rodite" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "6" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": -0.0962, + "token": " ibn" + }, + { + "logprob": -0.0015, + "token": " henry" + }, + { + "logprob": -0.0013, + "token": "\n" + }, + { + "logprob": 0, + "token": "7" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " incor" + }, + { + "logprob": 0, + "token": "rig" + }, + { + "logprob": 0, + "token": "ible" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "8" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " jane" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "9" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " lin" + }, + { + "logprob": 0, + "token": "ch" + }, + { + "logprob": 0, + "token": "pin" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "1" + }, + { + "logprob": 0, + "token": "0" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " maritime" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "1" + }, + { + "logprob": 0, + "token": "1" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": -0.0001, + "token": " postdoctoral" + } + ], + "schema_version": 6, + "seq": 170, + "span_id": "m1.model_call.299", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:34:17.906651+00:00" + }, + { + "analyzer": "calibration", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 4, + "case_id": null, + "cycle": 0, + "duration_sec": 2.3678, + "event": "model_call", + "event_seq": 300, + "inputs": { + "prompt": "Sort the following words alphabetically: List: linchpin arrear eavesdropping fasciculate shin postdoctoral maritime ibn henry hermaphrodite sticky jane brookside incorrigible vehicular herodotus\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAfter answering, state your confidence on a 0-100 scale on its own line as: Confidence: " + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: linchpin, arrear, eavesdropping, fasciculate, shin, postdoctoral, maritime, ibn henry, hermaphrodite, sticky, jane, brookside, incorrigible, vehicular, herodotus.\n\nHere is the alphabetical sorting process:\n\n1. **A**rrear\n2. **E**avesdropping\n3. **F**asciculate\n4. **H**erodotus\n5. **H**ermafrodite\n6. **I**bn henry\n7. **I**ncorrigible\n8. **J**ane\n9. **L**inchpin\n10. **M**aritime\n11. **P**ostdoctoral\n12. **S**hin\n13. **S**ticky\n14. **B**rookside (Wait, B comes before S. Let's re-sort based on the first letter.)\n\nLet's list the words and sort them strictly alphabetically:\n\n* arrear (A)\n* brookside (B)\n* eavesdropping (E)\n* fasciculate (F)\n* hermaphrodite (H)\n* herodotus (H)\n* ibn henry (I)\n* incorrigible (I)\n* jane (J)\n* linchpin (L)\n* maritime (M)\n* postdoctoral (P)\n* shin (S)\n* sticky (S)\n* vehicular (V)\n\nNow, let's handle ties:\n\n**H words:**\n* hermaphrodite\n* herodotus\n(hermafrodite comes before herodotus)\n\n**I words:**\n* ibn henry\n* incorrigible\n(ibn henry comes before incorrigible)\n\n**S words:**\n* shin\n* sticky\n(shin comes before sticky)\n\nThe final sorted list:\n1. arrear\n2. brookside\n3. eavesdropping\n4. fasciculate\n5. hermaphrodite\n6. herodotus\n7. ibn henry\n8. incorrigible\n9. jane\n10. linchpin\n11. maritime\n12. postdoctoral\n13. shin\n14. sticky\n15. vehicular\n\nAnswer: arrear, brookside, eavesdropping, fasciculate, hermaphrodite, herodotus, ibn henry, incorrigible, jane, linchpin, maritime, postdoctoral", + "schema_version": 6, + "seq": 171, + "span_id": "m1.model_call.300", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:34:20.281837+00:00" + }, + { + "analyzer": "calibration", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 5, + "case_id": "bbh_word_sorting-0045", + "cycle": 0, + "duration_sec": 0.944, + "event": "model_call", + "event_seq": 301, + "inputs": { + "prompt": "Sort the following words alphabetically: List: spongy separate gabriel jackson quail hendrickson washy backslide shelter calvert anastomosis variety commando pizzicato sticktight syndicate hollister\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "logprobs", + "n_batch_cases": 8, + "output": [ + { + "logprob": -0.0405, + "token": "Here" + }, + { + "logprob": -0.0077, + "token": " are" + }, + { + "logprob": 0, + "token": " the" + }, + { + "logprob": -0.0092, + "token": " words" + }, + { + "logprob": -0.0044, + "token": " sorted" + }, + { + "logprob": 0, + "token": " alphabetically" + }, + { + "logprob": 0, + "token": ":" + }, + { + "logprob": -0.0005, + "token": "\n\n" + }, + { + "logprob": -0.0002, + "token": "1" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " back" + }, + { + "logprob": 0, + "token": "slide" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "2" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": -0.0707, + "token": " cal" + }, + { + "logprob": 0, + "token": "vert" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "3" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": -0.0219, + "token": " gab" + }, + { + "logprob": 0, + "token": "riel" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "4" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": -0.1019, + "token": " hend" + }, + { + "logprob": 0, + "token": "rick" + }, + { + "logprob": 0, + "token": "son" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "5" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " holl" + }, + { + "logprob": 0, + "token": "ister" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "6" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " jack" + }, + { + "logprob": 0, + "token": "son" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "7" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": -0.0001, + "token": " pizz" + }, + { + "logprob": 0, + "token": "icato" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "8" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " quail" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "9" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " separate" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "1" + }, + { + "logprob": 0, + "token": "0" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": -0.2486, + "token": " shel" + }, + { + "logprob": -0.1045, + "token": "ter" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "1" + }, + { + "logprob": 0, + "token": "1" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": -0.0002, + "token": " stick" + }, + { + "logprob": 0, + "token": "tight" + }, + { + "logprob": 0, + "token": "\n" + } + ], + "schema_version": 6, + "seq": 172, + "span_id": "m1.model_call.301", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:34:21.233524+00:00" + }, + { + "analyzer": "calibration", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 6, + "case_id": null, + "cycle": 0, + "duration_sec": 2.5835, + "event": "model_call", + "event_seq": 302, + "inputs": { + "prompt": "Sort the following words alphabetically: List: spongy separate gabriel jackson quail hendrickson washy backslide shelter calvert anastomosis variety commando pizzicato sticktight syndicate hollister\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAfter answering, state your confidence on a 0-100 scale on its own line as: Confidence: " + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: spongy, separate, gabriel, jackson, quail, hendrickson, washy, backslide, shelter, calvert, anastomosis, variety, commando, pizzicato, sticktight, syndicate, hollister.\n\nHere is the alphabetical sorting process:\n\n1. **Identify the words:** spongy, separate, gabriel, jackson, quail, hendrickson, washy, backslide, shelter, calvert, anastomosis, variety, commando, pizzicato, sticktight, syndicate, hollister.\n\n2. **Sort by the first letter:**\n * b: backslide\n * c: calvert, commando\n * g: gabriel\n * h: hendrickson, hollister\n * j: jackson\n * p: pizzicato\n * q: quail\n * s: spongy, separate, shelter, sticktight, syndicate\n * v: variety\n * w: washy\n\n3. **Sort within groups (A-Z):**\n\n * **B:** backslide\n * **C:** calvert, commando (calvert comes before commando)\n * **G:** gabriel\n * **H:** hendrickson, hollister (hendrickson comes before hollister)\n * **J:** jackson\n * **P:** pizzicato\n * **Q:** quail\n * **S:** spongy, separate, shelter, sticktight, syndicate (spongy, separate, shelter, sticktight, syndicate)\n * spongy\n * separate\n * shelter\n * sticktight\n * syndicate\n * **V:** variety\n * **W:** washy\n\n4. **Final Sorted List:**\n backslide, calvert, commando, gabriel, hendrickson, hollister, jackson, pizzicato, quail, spongy, separate, shelter, sticktight, syndicate, variety, washy\n\nLet's re-verify the 'S' words:\nspongy\nseparate\nshelter\nsticktight\nsyndicate\n(s-p-o, s-e-p, s-h-e, s-t-i, s-y-n)\nspongy (o)\nseparate (e)\nshelter (h)\nsticktight (t)\nsyndicate (y)\nOrder: separate", + "schema_version": 6, + "seq": 173, + "span_id": "m1.model_call.302", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:34:23.824978+00:00" + }, + { + "analyzer": "calibration", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 7, + "case_id": "bbh_word_sorting-0076", + "cycle": 0, + "duration_sec": 33.5442, + "event": "model_call", + "event_seq": 303, + "inputs": { + "prompt": "Sort the following words alphabetically: List: whisper eyesight titmice mirage fletch cry biennial creedal miltonic fraudulent j\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "logprobs", + "n_batch_cases": 8, + "output": [ + { + "logprob": -0.1806, + "token": "The" + }, + { + "logprob": -0.0062, + "token": " given" + }, + { + "logprob": -0.0004, + "token": " list" + }, + { + "logprob": 0, + "token": " of" + }, + { + "logprob": 0, + "token": " words" + }, + { + "logprob": 0, + "token": " is" + }, + { + "logprob": 0, + "token": ":" + }, + { + "logprob": 0, + "token": " whisper" + }, + { + "logprob": -0.0019, + "token": "," + }, + { + "logprob": 0, + "token": " eyesight" + }, + { + "logprob": 0, + "token": "," + }, + { + "logprob": 0, + "token": " tit" + }, + { + "logprob": 0, + "token": "mice" + }, + { + "logprob": 0, + "token": "," + }, + { + "logprob": 0, + "token": " mir" + }, + { + "logprob": 0, + "token": "age" + }, + { + "logprob": 0, + "token": "," + }, + { + "logprob": 0, + "token": " f" + }, + { + "logprob": 0, + "token": "let" + }, + { + "logprob": 0, + "token": "ch" + }, + { + "logprob": 0, + "token": "," + }, + { + "logprob": 0, + "token": " cry" + }, + { + "logprob": 0, + "token": "," + }, + { + "logprob": 0, + "token": " biennial" + }, + { + "logprob": 0, + "token": "," + }, + { + "logprob": 0, + "token": " creed" + }, + { + "logprob": 0, + "token": "al" + }, + { + "logprob": 0, + "token": "," + }, + { + "logprob": 0, + "token": " mil" + }, + { + "logprob": 0, + "token": "tonic" + }, + { + "logprob": 0, + "token": "," + }, + { + "logprob": 0, + "token": " fraudulent" + }, + { + "logprob": 0, + "token": "," + }, + { + "logprob": 0, + "token": " j" + }, + { + "logprob": -0.2254, + "token": "." + }, + { + "logprob": 0, + "token": "\n\n" + }, + { + "logprob": -0.3965, + "token": "Here" + }, + { + "logprob": -0.1198, + "token": " is" + }, + { + "logprob": 0, + "token": " the" + }, + { + "logprob": -0.3098, + "token": " alphabetical" + }, + { + "logprob": 0, + "token": " sorting" + }, + { + "logprob": -0.0231, + "token": " process" + }, + { + "logprob": -0.0001, + "token": ":" + }, + { + "logprob": -0.0001, + "token": "\n\n" + }, + { + "logprob": 0, + "token": "1" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": -0.0002, + "token": " **" + }, + { + "logprob": -0.0335, + "token": "Compare" + }, + { + "logprob": -0.2332, + "token": " the" + }, + { + "logprob": -0.0061, + "token": " first" + }, + { + "logprob": -0.5759, + "token": " letter" + }, + { + "logprob": -0.2813, + "token": ":**" + }, + { + "logprob": -0.0025, + "token": "\n" + }, + { + "logprob": -0.6326, + "token": " " + }, + { + "logprob": -0.3874, + "token": "*" + }, + { + "logprob": -0.1427, + "token": " f" + }, + { + "logprob": -0.0088, + "token": "let" + }, + { + "logprob": 0, + "token": "ch" + }, + { + "logprob": -0.0041, + "token": " (" + }, + { + "logprob": -0.0205, + "token": "f" + }, + { + "logprob": 0, + "token": ")" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": " " + }, + { + "logprob": 0, + "token": "*" + } + ], + "schema_version": 6, + "seq": 174, + "span_id": "m1.model_call.303", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:34:57.377487+00:00" + }, + { + "analyzer": "calibration", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 8, + "case_id": null, + "cycle": 0, + "duration_sec": 2.7703, + "event": "model_call", + "event_seq": 304, + "inputs": { + "prompt": "Sort the following words alphabetically: List: whisper eyesight titmice mirage fletch cry biennial creedal miltonic fraudulent j\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAfter answering, state your confidence on a 0-100 scale on its own line as: Confidence: " + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: whisper, eyesight, titmice, mirage, fletch, cry, biennial, creedal, miltonic, fraudulent, j.\n\nTo sort these words alphabetically, we compare them letter by letter.\n\n1. **b**iennial\n2. **c**reedal\n3. **c**ry\n4. **f**letch\n5. **f**raudulent\n6. **i**sight\n7. **j**\n8. **m**iltonic\n9. **m**irage\n10. **t**itmice\n11. **w**hisper\n\nLet's perform a more systematic sort:\n\n* **b**: biennial\n* **c**: creedal, cry (creedal comes before cry)\n* **f**: fletch, fraudulent (fletch comes before fraudulent)\n* **i**: eyesight\n* **j**: j\n* **m**: miltonic, mirage (miltonic comes before mirage)\n* **t**: titmice\n* **w**: whisper\n\nSorting the words:\n1. biennial\n2. creedal\n3. cry\n4. fletch\n5. fraudulent\n6. eyesight\n7. j\n8. miltonic\n9. mirage\n10. titmice\n11. whisper\n\nLet's re-check the 'c' words: creedal vs cry.\nc-r-e-e-d-a-l\nc-r-y\n'e' comes before 'y', so creedal comes before cry. (Correct)\n\nLet's re-check the 'f' words: fletch vs fraudulent.\nf-l-e-t-c-h\nf-r-a-u-d-e-n-t\n'l' comes before 'r', so fletch comes before fraudulent. (Correct)\n\nLet's re-check the 'm' words: miltonic vs mirage.\nm-i-l-t-o-n-i-c\nm-i-r-a-g-e\n'l' comes before 'r', so miltonic comes before mirage. (Correct)\n\nThe final alphabetical order is:\nbiennial, creedal, cry, fletch, fraudulent, eyesight, j, miltonic, mirage, titmice,", + "schema_version": 6, + "seq": 175, + "span_id": "m1.model_call.304", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:35:00.163126+00:00" + }, + { + "analyzer": "calibration", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 9, + "case_id": "bbh_word_sorting-0094", + "cycle": 0, + "duration_sec": 1.027, + "event": "model_call", + "event_seq": 305, + "inputs": { + "prompt": "Sort the following words alphabetically: List: crossbill bushland capacitive meager ricochet battery enigma wysiwyg wallet lipton jane wacke contingent\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "logprobs", + "n_batch_cases": 8, + "output": [ + { + "logprob": -0.2387, + "token": "Here" + }, + { + "logprob": -0.0153, + "token": " are" + }, + { + "logprob": 0, + "token": " the" + }, + { + "logprob": -0.0074, + "token": " words" + }, + { + "logprob": -0.3913, + "token": " sorted" + }, + { + "logprob": 0, + "token": " alphabetically" + }, + { + "logprob": 0, + "token": ":" + }, + { + "logprob": -0.0022, + "token": "\n\n" + }, + { + "logprob": -0.0015, + "token": "1" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " battery" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "2" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " bush" + }, + { + "logprob": 0, + "token": "land" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "3" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " capacitive" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "4" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": -0.268, + "token": " contingent" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "5" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " cross" + }, + { + "logprob": 0, + "token": "bill" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "6" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " enigma" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "7" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " jane" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "8" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": -0.0278, + "token": " lip" + }, + { + "logprob": 0, + "token": "ton" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "9" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " meager" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "1" + }, + { + "logprob": 0, + "token": "0" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " ric" + }, + { + "logprob": 0, + "token": "ochet" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "1" + }, + { + "logprob": 0, + "token": "1" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": -0.3685, + "token": " wallet" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "1" + }, + { + "logprob": 0, + "token": "2" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " w" + }, + { + "logprob": 0, + "token": "ack" + }, + { + "logprob": 0, + "token": "e" + } + ], + "schema_version": 6, + "seq": 176, + "span_id": "m1.model_call.305", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:35:01.200233+00:00" + }, + { + "analyzer": "calibration", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 10, + "case_id": null, + "cycle": 0, + "duration_sec": 2.7106, + "event": "model_call", + "event_seq": 306, + "inputs": { + "prompt": "Sort the following words alphabetically: List: crossbill bushland capacitive meager ricochet battery enigma wysiwyg wallet lipton jane wacke contingent\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAfter answering, state your confidence on a 0-100 scale on its own line as: Confidence: " + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: crossbill, bushland, capacitive, meager, ricochet, battery, enigma, wysiwyg, wallet, lipton, jane, wacke, contingent.\n\nHere is the alphabetical sorting process:\n\n1. **b**attery\n2. **b**ushland\n3. **c**apacitive\n4. **c**rossbill\n5. **c**ontingent\n6. **e**nigma\n7. **j**ane\n8. **l**ipton\n9. **m**eager\n10. **r**icochet\n11. **w**acke\n12. **w**allet\n13. **w**ysiwyg\n\nLet's refine the sorting, especially for words starting with the same letter:\n\n* **B:** battery, bushland (battery comes before bushland)\n* **C:** capacitive, crossbill, contingent (capacitive, crossbill, contingent)\n * capacitive\n * crossbill\n * contingent\n* **E:** enigma\n* **J:** jane\n* **L:** lipton\n* **M:** meager\n* **R:** ricochet\n* **W:** wacke, wallet, wysiwyg (wacke, wallet, wysiwyg)\n * wacke\n * wallet\n * wysiwyg\n\nLet's re-sort based on standard alphabetical order:\n\n1. battery\n2. bushland\n3. capacitive\n4. crossbill\n5. contingent\n6. enigma\n7. jane\n8. lipton\n9. meager\n10. ricochet\n11. wacke\n12. wallet\n13. wysiwyg\n\nChecking the initial list again: crossbill, bushland, capacitive, meager, ricochet, battery, enigma, wysiwyg, wallet, lipton, jane, wacke, contingent.\n\nCorrect alphabetical order:\n1. battery (B)\n2. bushland (B)\n3. capacitive (C)\n4. crossbill (C)\n5. contingent (C)\n6. enigma (E)\n7. jane (J)\n8. lipton (L)\n9. meager (M)\n10. ricochet (R)\n11. wacke (W)\n12. wallet (W)", + "schema_version": 6, + "seq": 177, + "span_id": "m1.model_call.306", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:35:03.920580+00:00" + }, + { + "analyzer": "calibration", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 11, + "case_id": "bbh_word_sorting-0000", + "cycle": 0, + "duration_sec": 0.9496, + "event": "model_call", + "event_seq": 307, + "inputs": { + "prompt": "Sort the following words alphabetically: List: alleviate duopoly mattress gland benelux townsmen buoyant klaxon hardbound tomography felice gunk\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "logprobs", + "n_batch_cases": 8, + "output": [ + { + "logprob": -0.8262, + "token": "Here" + }, + { + "logprob": -0.0298, + "token": " are" + }, + { + "logprob": 0, + "token": " the" + }, + { + "logprob": -0.0117, + "token": " words" + }, + { + "logprob": -0.5821, + "token": " to" + }, + { + "logprob": -0.3672, + "token": " sort" + }, + { + "logprob": -0.3485, + "token": " alphabetically" + }, + { + "logprob": 0, + "token": ":" + }, + { + "logprob": -0.6932, + "token": " alleviate" + }, + { + "logprob": 0, + "token": "," + }, + { + "logprob": 0, + "token": " du" + }, + { + "logprob": 0, + "token": "opoly" + }, + { + "logprob": 0, + "token": "," + }, + { + "logprob": 0, + "token": " mattress" + }, + { + "logprob": 0, + "token": "," + }, + { + "logprob": 0, + "token": " gland" + }, + { + "logprob": 0, + "token": "," + }, + { + "logprob": 0, + "token": " ben" + }, + { + "logprob": 0, + "token": "el" + }, + { + "logprob": 0, + "token": "ux" + }, + { + "logprob": 0, + "token": "," + }, + { + "logprob": 0, + "token": " towns" + }, + { + "logprob": -0.0026, + "token": "men" + }, + { + "logprob": 0, + "token": "," + }, + { + "logprob": 0, + "token": " buoyant" + }, + { + "logprob": 0, + "token": "," + }, + { + "logprob": 0, + "token": " kl" + }, + { + "logprob": 0, + "token": "axon" + }, + { + "logprob": 0, + "token": "," + }, + { + "logprob": 0, + "token": " hard" + }, + { + "logprob": 0, + "token": "bound" + }, + { + "logprob": 0, + "token": "," + }, + { + "logprob": 0, + "token": " tomography" + }, + { + "logprob": 0, + "token": "," + }, + { + "logprob": 0, + "token": " fel" + }, + { + "logprob": 0, + "token": "ice" + }, + { + "logprob": 0, + "token": "," + }, + { + "logprob": 0, + "token": " g" + }, + { + "logprob": 0, + "token": "unk" + }, + { + "logprob": -0.043, + "token": "." + }, + { + "logprob": 0, + "token": "\n\n" + }, + { + "logprob": -0.0729, + "token": "1" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": -0.0017, + "token": " **" + }, + { + "logprob": -0.0049, + "token": "al" + }, + { + "logprob": 0, + "token": "lev" + }, + { + "logprob": 0, + "token": "iate" + }, + { + "logprob": 0, + "token": "**" + }, + { + "logprob": -0.016, + "token": "\n" + }, + { + "logprob": 0, + "token": "2" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " **" + }, + { + "logprob": 0, + "token": "ben" + }, + { + "logprob": 0, + "token": "el" + }, + { + "logprob": 0, + "token": "ux" + }, + { + "logprob": 0, + "token": "**" + }, + { + "logprob": -0.0007, + "token": "\n" + }, + { + "logprob": 0, + "token": "3" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " **" + }, + { + "logprob": 0, + "token": "bu" + }, + { + "logprob": 0, + "token": "oyant" + }, + { + "logprob": 0, + "token": "**" + }, + { + "logprob": 0, + "token": "\n" + } + ], + "schema_version": 6, + "seq": 178, + "span_id": "m1.model_call.307", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:35:04.878594+00:00" + }, + { + "analyzer": "calibration", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 12, + "case_id": null, + "cycle": 0, + "duration_sec": 1.989, + "event": "model_call", + "event_seq": 308, + "inputs": { + "prompt": "Sort the following words alphabetically: List: alleviate duopoly mattress gland benelux townsmen buoyant klaxon hardbound tomography felice gunk\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAfter answering, state your confidence on a 0-100 scale on its own line as: Confidence: " + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: alleviate, duopoly, mattress, gland, benelux, townsmen, buoyant, klaxon, hardbound, tomography, felice, gunk.\n\nTo sort them alphabetically, we compare them letter by letter:\n\n1. **alleviate** (A)\n2. **benelux** (B)\n3. **buoyant** (B)\n4. **duopoly** (D)\n5. **felice** (F)\n6. **gland** (G)\n7. **gunk** (G)\n8. **hardbound** (H)\n9. **klaxon** (K)\n10. **mattress** (M)\n11. **tomography** (T)\n12. **townsmen** (T)\n\nLet's refine the order:\n\n**A:** alleviate\n**B:** benelux, buoyant (benelux comes before buoyant)\n**D:** duopoly\n**F:** felice\n**G:** gland, gunk (gland comes before gunk)\n**H:** hardbound\n**K:** klaxon\n**M:** mattress\n**T:** tomography, townsmen (tomography comes before townsmen)\n\nThe sorted list is:\n1. alleviate\n2. benelux\n3. buoyant\n4. duopoly\n5. felice\n6. gland\n7. gunk\n8. hardbound\n9. klaxon\n10. mattress\n11. tomography\n12. townsmen\n\nAnswer: alleviate, benelux, buoyant, duopoly, felice, gland, gunk, hardbound, klaxon, mattress, tomography, townsmen\n\nConfidence: 100", + "schema_version": 6, + "seq": 179, + "span_id": "m1.model_call.308", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:35:06.876214+00:00" + }, + { + "analyzer": "calibration", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 13, + "case_id": "bbh_word_sorting-0063", + "cycle": 0, + "duration_sec": 0.8134, + "event": "model_call", + "event_seq": 309, + "inputs": { + "prompt": "Sort the following words alphabetically: List: vegetate artillery harm fda doris prosody bainite incongruous monkey vivian\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "logprobs", + "n_batch_cases": 8, + "output": [ + { + "logprob": -0.5237, + "token": "The" + }, + { + "logprob": -0.0044, + "token": " given" + }, + { + "logprob": -0.0017, + "token": " list" + }, + { + "logprob": 0, + "token": " of" + }, + { + "logprob": 0, + "token": " words" + }, + { + "logprob": 0, + "token": " is" + }, + { + "logprob": 0, + "token": ":" + }, + { + "logprob": 0, + "token": " veget" + }, + { + "logprob": 0, + "token": "ate" + }, + { + "logprob": -0.0022, + "token": "," + }, + { + "logprob": 0, + "token": " artillery" + }, + { + "logprob": 0, + "token": "," + }, + { + "logprob": 0, + "token": " harm" + }, + { + "logprob": 0, + "token": "," + }, + { + "logprob": 0, + "token": " f" + }, + { + "logprob": 0, + "token": "da" + }, + { + "logprob": 0, + "token": "," + }, + { + "logprob": 0, + "token": " dor" + }, + { + "logprob": 0, + "token": "is" + }, + { + "logprob": 0, + "token": "," + }, + { + "logprob": 0, + "token": " pros" + }, + { + "logprob": 0, + "token": "ody" + }, + { + "logprob": 0, + "token": "," + }, + { + "logprob": 0, + "token": " bain" + }, + { + "logprob": 0, + "token": "ite" + }, + { + "logprob": 0, + "token": "," + }, + { + "logprob": 0, + "token": " incongru" + }, + { + "logprob": 0, + "token": "ous" + }, + { + "logprob": 0, + "token": "," + }, + { + "logprob": 0, + "token": " monkey" + }, + { + "logprob": 0, + "token": "," + }, + { + "logprob": 0, + "token": " viv" + }, + { + "logprob": 0, + "token": "ian" + }, + { + "logprob": -0.0001, + "token": "." + }, + { + "logprob": 0, + "token": "\n\n" + }, + { + "logprob": -0.4349, + "token": "Here" + }, + { + "logprob": -0.0867, + "token": " is" + }, + { + "logprob": 0, + "token": " the" + }, + { + "logprob": -0.3157, + "token": " alphabetical" + }, + { + "logprob": 0, + "token": " sorting" + }, + { + "logprob": -0.0125, + "token": " process" + }, + { + "logprob": -0.0002, + "token": ":" + }, + { + "logprob": -0.0001, + "token": "\n\n" + }, + { + "logprob": 0, + "token": "1" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": -0.0002, + "token": " **" + }, + { + "logprob": -0.018, + "token": "Compare" + }, + { + "logprob": -0.3152, + "token": " the" + }, + { + "logprob": -0.0027, + "token": " first" + }, + { + "logprob": -0.4074, + "token": " letter" + }, + { + "logprob": -0.1129, + "token": ":**" + }, + { + "logprob": -0.0008, + "token": "\n" + }, + { + "logprob": -0.4741, + "token": " " + }, + { + "logprob": 0, + "token": "*" + }, + { + "logprob": -0.0048, + "token": " bain" + }, + { + "logprob": 0, + "token": "ite" + }, + { + "logprob": -0.001, + "token": " (" + }, + { + "logprob": -0.0041, + "token": "b" + }, + { + "logprob": 0, + "token": ")" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": " " + }, + { + "logprob": 0, + "token": "*" + }, + { + "logprob": -0.0002, + "token": " dor" + }, + { + "logprob": 0, + "token": "is" + } + ], + "schema_version": 6, + "seq": 180, + "span_id": "m1.model_call.309", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:35:07.706495+00:00" + }, + { + "analyzer": "calibration", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 14, + "case_id": null, + "cycle": 0, + "duration_sec": 2.47, + "event": "model_call", + "event_seq": 310, + "inputs": { + "prompt": "Sort the following words alphabetically: List: vegetate artillery harm fda doris prosody bainite incongruous monkey vivian\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAfter answering, state your confidence on a 0-100 scale on its own line as: Confidence: " + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: vegetate, artillery, harm, fda, doris, prosody, bainite, incongruous, monkey, vivian.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **bainite** (starts with b)\n2. **doris** (starts with d)\n3. **fda** (starts with f)\n4. **harm** (starts with h)\n5. **incongruous** (starts with i)\n6. **monkey** (starts with m)\n7. **prosody** (starts with p)\n8. **artillery** (starts with a) - Wait, 'artillery' starts with 'a'. Let's re-sort based on the first letter.\n\nLet's list the words and their starting letters:\n* vegetate (v)\n* artillery (a)\n* harm (h)\n* fda (f)\n* doris (d)\n* prosody (p)\n* bainite (b)\n* incongruous (i)\n* monkey (m)\n* vivian (v)\n\nSorting by the first letter (A, B, D, F, H, I, M, P, V):\n\n1. **artillery** (a)\n2. **bainite** (b)\n3. **doris** (d)\n4. **fda** (f)\n5. **harm** (h)\n6. **incongruous** (i)\n7. **monkey** (m)\n8. **prosody** (p)\n9. **vegetate** (v)\n10. **vivian** (v)\n\nNow we need to resolve the tie between 'vegetate' and 'vivian'.\n* vegetate (v e g ...)\n* vivian (v i v ...)\n'e' comes before 'i', so 'vegetate' comes before 'vivian'.\n\nThe final sorted list is:\n1. artillery\n2. bainite\n3. doris\n4. fda\n5. harm\n6. incongruous\n7. monkey\n8. prosody\n9. vegetate\n10. vivian\n\nAnswer: artillery, bainite, doris, fda, harm, incongruous, monkey, prosody, vegetate, viv", + "schema_version": 6, + "seq": 181, + "span_id": "m1.model_call.310", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:35:10.201014+00:00" + }, + { + "analyzer": "calibration", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 15, + "case_id": "bbh_word_sorting-0101", + "cycle": 0, + "duration_sec": 0.9302, + "event": "model_call", + "event_seq": 311, + "inputs": { + "prompt": "Sort the following words alphabetically: List: regret starlight wallboard cotyledon more pepperoni\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "logprobs", + "n_batch_cases": 8, + "output": [ + { + "logprob": -0.0491, + "token": "The" + }, + { + "logprob": -0.0051, + "token": " given" + }, + { + "logprob": -0.0063, + "token": " list" + }, + { + "logprob": 0, + "token": " of" + }, + { + "logprob": 0, + "token": " words" + }, + { + "logprob": 0, + "token": " is" + }, + { + "logprob": 0, + "token": ":" + }, + { + "logprob": 0, + "token": " regret" + }, + { + "logprob": 0, + "token": "," + }, + { + "logprob": 0, + "token": " st" + }, + { + "logprob": 0, + "token": "arlight" + }, + { + "logprob": 0, + "token": "," + }, + { + "logprob": 0, + "token": " wall" + }, + { + "logprob": 0, + "token": "board" + }, + { + "logprob": 0, + "token": "," + }, + { + "logprob": 0, + "token": " cot" + }, + { + "logprob": 0, + "token": "yled" + }, + { + "logprob": 0, + "token": "on" + }, + { + "logprob": 0, + "token": "," + }, + { + "logprob": 0, + "token": " more" + }, + { + "logprob": 0, + "token": "," + }, + { + "logprob": 0, + "token": " pepperoni" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": -0.0003, + "token": "\n\n" + }, + { + "logprob": -0.1304, + "token": "To" + }, + { + "logprob": 0, + "token": " sort" + }, + { + "logprob": -0.3062, + "token": " these" + }, + { + "logprob": 0, + "token": " words" + }, + { + "logprob": 0, + "token": " alphabetically" + }, + { + "logprob": 0, + "token": "," + }, + { + "logprob": 0, + "token": " we" + }, + { + "logprob": -0.0001, + "token": " compare" + }, + { + "logprob": 0, + "token": " them" + }, + { + "logprob": -0.0001, + "token": " letter" + }, + { + "logprob": 0, + "token": " by" + }, + { + "logprob": 0, + "token": " letter" + }, + { + "logprob": -0.3789, + "token": ":" + }, + { + "logprob": 0, + "token": "\n\n" + }, + { + "logprob": 0, + "token": "1" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": -0.0003, + "token": " **" + }, + { + "logprob": -0.0704, + "token": "cot" + }, + { + "logprob": 0, + "token": "yled" + }, + { + "logprob": 0, + "token": "on" + }, + { + "logprob": -0.0081, + "token": "**" + }, + { + "logprob": -0.0012, + "token": " (" + }, + { + "logprob": -0.9434, + "token": "Starts" + }, + { + "logprob": 0, + "token": " with" + }, + { + "logprob": -0.1621, + "token": " '" + }, + { + "logprob": 0, + "token": "c" + }, + { + "logprob": 0, + "token": "')" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "2" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " **" + }, + { + "logprob": 0, + "token": "more" + }, + { + "logprob": 0, + "token": "**" + }, + { + "logprob": 0, + "token": " (" + }, + { + "logprob": 0, + "token": "Starts" + }, + { + "logprob": 0, + "token": " with" + }, + { + "logprob": 0, + "token": " '" + }, + { + "logprob": 0, + "token": "m" + }, + { + "logprob": -0.0003, + "token": "')" + }, + { + "logprob": -0.0006, + "token": "\n" + } + ], + "schema_version": 6, + "seq": 182, + "span_id": "m1.model_call.311", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:35:11.141250+00:00" + }, + { + "analyzer": "calibration", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 16, + "case_id": null, + "cycle": 0, + "duration_sec": 1.3951, + "event": "model_call", + "event_seq": 312, + "inputs": { + "prompt": "Sort the following words alphabetically: List: regret starlight wallboard cotyledon more pepperoni\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAfter answering, state your confidence on a 0-100 scale on its own line as: Confidence: " + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: regret, starlight, wallboard, cotyledon, more, pepperoni.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **cotyledon** (c)\n2. **more** (m)\n3. **pepperoni** (p)\n4. **regret** (r)\n5. **starlight** (s)\n6. **wallboard** (w)\n\nLet's verify the order:\n* c (cotyledon) comes before m, p, r, s, w.\n* m (more) comes before p, r, s, w.\n* p (pepperoni) comes before r, s, w.\n* r (regret) comes before s, w.\n* s (starlight) comes before w.\n* w (wallboard) is last.\n\nThe alphabetical order is: cotyledon, more, pepperoni, regret, starlight, wallboard.\n\nAnswer: cotyledon, more, pepperoni, regret, starlight, wallboard\n\nConfidence: 100", + "schema_version": 6, + "seq": 183, + "span_id": "m1.model_call.312", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:35:12.545798+00:00" + }, + { + "analyzer": "logprob_entropy", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 1, + "case_id": "bbh_word_sorting-0006", + "cycle": 0, + "duration_sec": 0.6313, + "event": "model_call", + "event_seq": 313, + "inputs": { + "prompt": "Sort the following words alphabetically: List: torpedo phosphorescent pristine decadent shrunk dey administer gradate littleneck thrown jacky coachman aeneid verdict tasting sinh delhi systemwide grim\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "logprobs", + "n_batch_cases": 8, + "output": [ + { + "logprob": -0.0743, + "token": "Here" + }, + { + "logprob": -0.0124, + "token": " are" + }, + { + "logprob": 0, + "token": " the" + }, + { + "logprob": -0.0134, + "token": " words" + }, + { + "logprob": -0.0872, + "token": " sorted" + }, + { + "logprob": 0, + "token": " alphabetically" + }, + { + "logprob": 0, + "token": ":" + }, + { + "logprob": -0.0003, + "token": "\n\n" + }, + { + "logprob": -0.0004, + "token": "1" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": -0.1908, + "token": " administer" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "2" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": -0.011, + "token": " a" + }, + { + "logprob": 0, + "token": "ene" + }, + { + "logprob": 0, + "token": "id" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "3" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " decadent" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "4" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": -0.0151, + "token": " coach" + }, + { + "logprob": 0, + "token": "man" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "5" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " delhi" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "6" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": -0.0028, + "token": " grad" + }, + { + "logprob": 0, + "token": "ate" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "7" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " grim" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "8" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " jack" + }, + { + "logprob": 0, + "token": "y" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "9" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " litt" + }, + { + "logprob": 0, + "token": "leneck" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "1" + }, + { + "logprob": 0, + "token": "0" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " phosph" + }, + { + "logprob": 0, + "token": "orescent" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "1" + }, + { + "logprob": 0, + "token": "1" + }, + { + "logprob": 0, + "token": "." + }, + { + "logprob": 0, + "token": " pristine" + }, + { + "logprob": 0, + "token": "\n" + }, + { + "logprob": 0, + "token": "1" + }, + { + "logprob": 0, + "token": "2" + }, + { + "logprob": 0, + "token": "." + } + ], + "schema_version": 6, + "seq": 184, + "span_id": "m1.model_call.313", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:35:13.186422+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 1, + "case_id": "bbh_word_sorting-0006", + "cycle": 0, + "duration_sec": 2.9673, + "event": "model_call", + "event_seq": 314, + "inputs": { + "prompt": "Sort the following words alphabetically: List: torpedo phosphorescent pristine decadent shrunk dey administer gradate littleneck thrown jacky coachman aeneid verdict tasting sinh delhi systemwide grim\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. gradate\n7. grim\n8. jacky\n9. littleneck\n10. phosphorescent\n11. pristine\n12. sinh\n13. systemwide\n14. tasting\n15. torpedo\n16. thrown\n17. verdict\n18. dey\n19. shrunk\n\nLet's re-sort them carefully:\n\n* **aeneid**\n* **decadent**\n* **delhi**\n* **administer**\n* **coachman**\n* **gradate**\n* **grim**\n* **jacky**\n* **littleneck**\n* **phosphorescent**\n* **pristine**\n* **sinh**\n* **shrunk**\n* **systemwide**\n* **tasting**\n* **torpedo**\n* **verdict**\n* **dey**\n* **thrown**\n\nLet's check the ordering again (focusing on the first letter):\nA: aeneid, administer\nD: decadent, delhi, dey\nG: gradate, grim\nJ: jacky\nL: littleneck\nP: phosphorescent, pristine\nS: sinh, shrunk, systemwide\nT: tasting, torpedo, thrown\nV: verdict\n\n**Alphabetical Sort:**\n1. aeneid\n2. administer\n3. decadent\n4. delhi\n5. dey\n6. gradate\n7. grim\n8. jacky\n9. littleneck\n10. phosphorescent\n11. pristine\n12. sinh\n13. shrunk\n14. systemwide\n15. tasting\n16. torpedo\n17. thrown\n18. verdict\n\nAnswer: aeneid, administer, decadent, delhi, dey, gradate, grim, jacky, littleneck, phosphorescent, pristine, sinh, shrunk, systemwide, tasting, torpedo, thrown, verdict", + "schema_version": 6, + "seq": 185, + "span_id": "m1.model_call.314", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:35:16.163649+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 2, + "case_id": "bbh_word_sorting-0006", + "cycle": 0, + "duration_sec": 3.5026, + "event": "model_call", + "event_seq": 315, + "inputs": { + "prompt": "Sort the following words alphabetically: List: torpedo phosphorescent pristine decadent shrunk dey administer gradate littleneck thrown jacky coachman aeneid verdict tasting sinh delhi systemwide grim\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. gradate\n7. grim\n8. jacky\n9. littleneck\n10. phosphorescent\n11. pristine\n12. sinh\n13. systemwide\n14. tasting\n15. torpedo\n16. thrown\n17. verdict\n18. dey\n19. shrunk\n\nLet's re-sort them carefully:\n\n* **aeneid**\n* **decadent**\n* **delhi**\n* **administer**\n* **coachman**\n* **gradate**\n* **grim**\n* **jacky**\n* **littleneck**\n* **phosphorescent**\n* **pristine**\n* **shrunk**\n* **systemwide**\n* **tasting**\n* **torpedo**\n* **thrown**\n* **verdict**\n* **dey**\n* **sinh**\n\nLet's check the sorting again, paying close attention to 'sinh' vs others and 'dey' vs others.\n\nOriginal list: torpedo phosphorescent pristine decadent shrunk dey administer gradate littleneck thrown jacky coachman aeneid verdict tasting sinh delhi systemwide grim\n\nAlphabetical order:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. grim\n7. gradate\n8. jacky\n9. littleneck\n10. phosphorescent\n11. pristine\n12. sinh\n13. shrunk\n14. systemwide\n15. tasting\n16. torpedo\n17. thrown\n18. verdict\n19. dey\n\nWait, 'sinh' comes before 'shrunk' and 'systemwide'. Let's list all the words and sort:\n\n1. administer (A)\n2. aeneid (A)\n3. decadent (D)\n4. delhi (D)\n5. grim (G)\n6. gradate (G)\n7. jacky (J)\n8. littleneck (L)\n9. phosphorescent (P)\n10. pristine (P)\n11. sinh (S)\n12. shrunk (S)\n13. systemwide (S)\n14.", + "schema_version": 6, + "seq": 186, + "span_id": "m1.model_call.315", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:35:19.681813+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 3, + "case_id": "bbh_word_sorting-0006", + "cycle": 0, + "duration_sec": 3.4303, + "event": "model_call", + "event_seq": 316, + "inputs": { + "prompt": "Sort the following words alphabetically: List: torpedo phosphorescent pristine decadent shrunk dey administer gradate littleneck thrown jacky coachman aeneid verdict tasting sinh delhi systemwide grim\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. gradate\n7. grim\n8. jacky\n9. littleneck\n10. phosphorescent\n11. pristine\n12. sinh\n13. systemwide\n14. tasting\n15. torpedo\n16. thrown\n17. verdict\n18. dey\n19. shrunk\n\nLet's re-sort them carefully:\n\n* administer\n* aeneid\n* decadent\n* coachman\n* delhi\n* grim\n* gradate\n* jacky\n* littleneck\n* phosphorescent\n* pristine\n* sinh\n* systemwide\n* tasting\n* torpedo\n* thrown\n* verdict\n* dey\n* shrunk\n\nChecking the order:\nA: administer, aeneid\nC: decadent, coachman\nD: delhi, dey\nG: grim, gradate\nJ: jacky\nL: littleneck\nP: phosphorescent, pristine\nS: sinh, systemwide, shrunk\nT: tasting, torpedo, thrown\nV: verdict\n\nRefined Alphabetical Order:\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. dey\n7. grim\n8. gradate\n9. jacky\n10. littleneck\n11. phosphorescent\n12. pristine\n13. sinh\n14. shrunk\n15. systemwide\n16. tasting\n17. torpedo\n18. thrown\n19. verdict\n\n(Comparing 'dey' and 'delhi': 'dey' comes before 'delhi' if we consider the 'd' followed by 'e' vs 'e' followed by 'l', but standard alphabetical order places 'delhi' before 'dey'. Let's stick to strict alphabetical sorting.)\n\nStandard Alphabetical Sorting:\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. dey\n7. grim\n8. gradate\n9. jacky\n10. littleneck\n11. phosphorescent\n12. pristine\n13. sinh\n14. shrunk\n15. systemwide\n16. tasting\n17. torpedo\n18. thrown\n19. verdict\n\n", + "schema_version": 6, + "seq": 187, + "span_id": "m1.model_call.316", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:35:23.121137+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 4, + "case_id": "bbh_word_sorting-0006", + "cycle": 0, + "duration_sec": 3.4931, + "event": "model_call", + "event_seq": 317, + "inputs": { + "prompt": "Sort the following words alphabetically: List: torpedo phosphorescent pristine decadent shrunk dey administer gradate littleneck thrown jacky coachman aeneid verdict tasting sinh delhi systemwide grim\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. aeneid\n2. decadent\n3. administer\n4. delhi\n5. gradate\n6. grim\n7. jacky\n8. littleneck\n9. phosphorescent\n10. pristine\n11. sinh\n12. systemwide\n13. tasting\n14. torpedo\n15. thrown\n16. verdict\n\nLet's re-sort them carefully:\n\n* administer\n* aeneid\n* decadent\n* delhi\n* gradate\n* grim\n* jacky\n* littleneck\n* phosphorescent\n* pristine\n* sinh\n* systemwide\n* tasting\n* torpedo\n* thrown\n* verdict\n\nComparing 'aeneid' and 'administer': 'a' comes before 'a', 'e' comes before 'd'. Wait, 'aeneid' starts with 'a', 'administer' starts with 'a'. 'aeneid' (ae...) comes before 'administer' (ad...).\n\nCorrect alphabetical order:\n\n1. aeneid\n2. decadent\n3. administer\n4. delhi\n5. gradate\n6. grim\n7. jacky\n8. littleneck\n9. phosphorescent\n10. pristine\n11. sinh\n12. systemwide\n13. tasting\n14. torpedo\n15. thrown\n16. verdict\n\nLet's verify the sorting based on the initial list: torpedo, phosphorescent, pristine, decadent, shrunk, dey, administer, gradate, littleneck, thrown, jacky, coachman, aeneid, verdict, tasting, sinh, delhi, systemwide, grim. (I noticed 'shrunk' and 'coachman' were missing from the list I was working with initially, let's use the provided list exactly.)\n\n**Provided List:** torpedo, phosphorescent, pristine, decadent, shrunk, dey, administer, gradate, littleneck, thrown, jacky, coachman, aeneid, verdict, tasting, sinh, delhi, systemwide, grim\n\n**Sorted List:**\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. grim\n7. jacky\n8. littleneck\n9. phosphorescent\n10. pristine\n11. sinh\n12. shrunk\n13. systemwide\n14.", + "schema_version": 6, + "seq": 188, + "span_id": "m1.model_call.317", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:35:26.627816+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 5, + "case_id": "bbh_word_sorting-0006", + "cycle": 0, + "duration_sec": 3.4958, + "event": "model_call", + "event_seq": 318, + "inputs": { + "prompt": "Sort the following words alphabetically: List: torpedo phosphorescent pristine decadent shrunk dey administer gradate littleneck thrown jacky coachman aeneid verdict tasting sinh delhi systemwide grim\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. gradate\n7. grim\n8. jacky\n9. littleneck\n10. phosphorescent\n11. pristine\n12. sinh\n13. systemwide\n14. tasting\n15. torpedo\n16. thrown\n17. verdict\n18. dey\n19. shrunk\n\nLet's re-sort them carefully:\n\n* administer\n* aeneid\n* decadent\n* coachman\n* delhi\n* grim\n* gradate\n* jacky\n* littleneck\n* phosphorescent\n* pristine\n* sinh\n* systemwide\n* tasting\n* torpedo\n* thrown\n* verdict\n* dey\n* shrunk\n\n**Alphabetical Order Check:**\nA: administer, aeneid\nC: coachman\nD: decadent, delhi, dey\nG: grim, gradate\nJ: jacky\nL: littleneck\nP: phosphorescent, pristine\nS: sinh, systemwide, shrunk\nT: tasting, torpedo, thrown\nV: verdict\n\n**Final Sorted List:**\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. dey\n7. grim\n8. gradate\n9. jacky\n10. littleneck\n11. phosphorescent\n12. pristine\n13. sinh\n14. shrunk\n15. systemwide\n16. tasting\n17. torpedo\n18. thrown\n19. verdict\n\n(Note: \"dey\" comes before \"delhi\" if we strictly follow 'd' followed by 'e', but usually, we look at the whole word. D-E-Y vs D-E-L-H-I. In standard alphabetical sorting, 'delhi' comes before 'dey'.)\n\nLet's re-sort D words: decadent, delhi, dey.\nDecadent (decad...)\nDelhi (delh...)\nDey (dey...)\nOrder: decadent, delhi, dey.\n\nLet's re-sort S words: sinh, shrunk, systemwide.\nSinh (sin...)\nShrunk (shr...)\nSystemwide (sys...)\nOrder: sinh, shrunk, systemwide.\n\nLet's re-sort T", + "schema_version": 6, + "seq": 189, + "span_id": "m1.model_call.318", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:35:30.135090+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 6, + "case_id": "bbh_word_sorting-0025", + "cycle": 0, + "duration_sec": 3.1407, + "event": "model_call", + "event_seq": 319, + "inputs": { + "prompt": "Sort the following words alphabetically: List: linchpin arrear eavesdropping fasciculate shin postdoctoral maritime ibn henry hermaphrodite sticky jane brookside incorrigible vehicular herodotus\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. arrear\n2. eavesdropping\n3. fasciculate\n4. herodotus\n5. hermaphrodite\n6. ibn henry\n7. incorrigible\n8. jane\n9. linchpin\n10. maritime\n11. postdoctoral\n12. shin\n13. sticky\n14. vehicular\n15. brookside\n\nLet's re-sort them carefully:\n\n* arrear\n* eavesdropping\n* fasciculate\n* herodotus\n* hermaphrodite\n* ibn henry\n* incorrigible\n* jane\n* linchpin\n* maritime\n* postdoctoral\n* shin\n* sticky\n* vehicular\n* brookside\n\nComparing 'herodotus' and 'hermaphrodite':\n* herodotus (h e r o d t u s)\n* hermaphrodite (h e r m a p h r o d i t e)\n'herodotus' comes before 'hermaphrodite' because 'o' comes before 'm'. (Wait, checking standard alphabetical order: d vs m. d comes before m). Correct.\n\nComparing 'ibn henry' and 'incorrigible':\n* ibn henry (i b n h e n r y)\n* incorrigible (i n c o r r i g i b l e)\n'ibn henry' comes before 'incorrigible' because 'b' comes before 'n'.\n\nThe final sorted list is:\n\n1. arrear\n2. eavesdropping\n3. fasciculate\n4. herodotus\n5. hermaphrodite\n6. ibn henry\n7. incorrigible\n8. jane\n9. linchpin\n10. maritime\n11. postdoctoral\n12. shin\n13. sticky\n14. vehicular\n15. brookside\n\nAnswer: arrear, eavesdropping, fasciculate, herodotus, hermaphrodite, ibn henry, incorrigible, jane, linchpin, maritime, postdoctoral, shin, sticky, vehicular, brookside", + "schema_version": 6, + "seq": 190, + "span_id": "m1.model_call.319", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:35:33.285671+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 7, + "case_id": "bbh_word_sorting-0025", + "cycle": 0, + "duration_sec": 1.8585, + "event": "model_call", + "event_seq": 320, + "inputs": { + "prompt": "Sort the following words alphabetically: List: linchpin arrear eavesdropping fasciculate shin postdoctoral maritime ibn henry hermaphrodite sticky jane brookside incorrigible vehicular herodotus\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. arrear\n2. eavesdropping\n3. fasciculate\n4. herodotus\n5. hermaphrodite\n6. ibn henry\n7. incorrigible\n8. jane\n9. linchpin\n10. maritime\n11. postdoctoral\n12. shin\n13. sticky\n14. brookside\n15. vehicular\n\nLet's re-sort them carefully:\n\nOriginal List: linchpin, arrear, eavesdropping, fasciculate, shin, postdoctoral, maritime, ibn henry, hermaphrodite, sticky, jane, brookside, incorrigible, vehicular, herodotus\n\nAlphabetical Order:\n\n1. arrear\n2. brookside\n3. eavesdropping\n4. fasciculate\n5. hermaphrodite\n6. herodotus\n7. ibn henry\n8. incorrigible\n9. jane\n10. linchpin\n11. maritime\n12. postdoctoral\n13. shin\n14. sticky\n15. vehicular\n\nAnswer: arrear, brookside, eavesdropping, fasciculate, hermaphrodite, herodotus, ibn henry, incorrigible, jane, linchpin, maritime, postdoctoral, shin, sticky, vehicular", + "schema_version": 6, + "seq": 191, + "span_id": "m1.model_call.320", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:35:35.153351+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 8, + "case_id": "bbh_word_sorting-0025", + "cycle": 0, + "duration_sec": 3.4924, + "event": "model_call", + "event_seq": 321, + "inputs": { + "prompt": "Sort the following words alphabetically: List: linchpin arrear eavesdropping fasciculate shin postdoctoral maritime ibn henry hermaphrodite sticky jane brookside incorrigible vehicular herodotus\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. arrear\n2. eavesdropping\n3. fasciculate\n4. herodotus\n5. hermaphrodite\n6. ibn henry\n7. incorrigible\n8. jane\n9. linchpin\n10. maritime\n11. postdoctoral\n12. shin\n13. sticky\n14. vehicular\n15. brookside\n\nLet's re-sort carefully:\n\n* **a**rrear\n* **e**avesdropping\n* **f**asciculate\n* **h**erodotus\n* **h**ermaphrodite (H before I)\n* **i**bn henry\n* **i**ncorrigible (I before J)\n* **j**ane\n* **l**inchpin\n* **m**aritime\n* **p**ostdoctoral\n* **s**hin\n* **s**ticky (Shin before Sticky)\n* **v**ehicular\n* **b**rookside (B comes before L, M, P, S, V)\n\nLet's do a full alphabetical sort:\n\n1. arrear\n2. eavesdropping\n3. fasciculate\n4. herodotus\n5. hermaphrodite\n6. ibn henry\n7. incorrigible\n8. jane\n9. linchpin\n10. maritime\n11. postdoctoral\n12. shin\n13. sticky\n14. vehicular\n15. brookside\n\nWait, checking the list again for proper alphabetical order:\n\nA: arrear\nE: eavesdropping\nF: fasciculate\nH: hermaphrodite, herodotus (H e r m a p h r o d i t e vs H e r o d o t u s -> hermaphrodite comes before herodotus)\nI: ibn henry, incorrigible (i b n h e n r y vs i n c o r r i g i b l e -> ibn henry comes before incorrigible)\nJ: jane\nL: linchpin\nM: maritime\nP: postdoctoral\nS: shin, sticky (shin before sticky)\nV: vehicular\nB: brookside\n\nCorrect order:\n\n1. arrear\n2. eavesdropping\n3. fasciculate\n4. hermaphrodite\n5. herodotus\n6. ibn henry\n7. incorrigible\n8. jane\n", + "schema_version": 6, + "seq": 192, + "span_id": "m1.model_call.321", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:35:38.660470+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 9, + "case_id": "bbh_word_sorting-0025", + "cycle": 0, + "duration_sec": 2.3911, + "event": "model_call", + "event_seq": 322, + "inputs": { + "prompt": "Sort the following words alphabetically: List: linchpin arrear eavesdropping fasciculate shin postdoctoral maritime ibn henry hermaphrodite sticky jane brookside incorrigible vehicular herodotus\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. arrear\n2. eavesdropping\n3. fasciculate\n4. herodotus\n5. hermaphrodite\n6. ibn henry\n7. incorrigible\n8. jane\n9. linchpin\n10. maritime\n11. postdoctoral\n12. shin\n13. sticky\n14. brookside\n15. vehicular\n\nLet's re-sort them carefully:\n\n* **a**: arrear\n* **e**: eavesdropping\n* **f**: fasciculate\n* **h**: herodotus, hermaphrodite\n * herodotus\n * hermaphrodite\n* **i**: ibn henry, incorrigible\n * ibn henry\n * incorrigible\n* **j**: jane\n* **l**: linchpin\n* **m**: maritime\n* **p**: postdoctoral\n* **s**: shin, sticky\n * shin\n * sticky\n* **b**: brookside\n* **v**: vehicular\n\n**Alphabetical Order Check:**\n\n1. arrear\n2. eavesdropping\n3. fasciculate\n4. herodotus\n5. hermaphrodite\n6. ibn henry\n7. incorrigible\n8. jane\n9. linchpin\n10. maritime\n11. postdoctoral\n12. shin\n13. sticky\n14. brookside\n15. vehicular\n\nAnswer: arrear, eavesdropping, fasciculate, herodotus, hermaphrodite, ibn henry, incorrigible, jane, linchpin, maritime, postdoctoral, shin, sticky, brookside, vehicular", + "schema_version": 6, + "seq": 193, + "span_id": "m1.model_call.322", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:35:41.063431+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 10, + "case_id": "bbh_word_sorting-0025", + "cycle": 0, + "duration_sec": 3.4722, + "event": "model_call", + "event_seq": 323, + "inputs": { + "prompt": "Sort the following words alphabetically: List: linchpin arrear eavesdropping fasciculate shin postdoctoral maritime ibn henry hermaphrodite sticky jane brookside incorrigible vehicular herodotus\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. arrear\n2. eavesdropping\n3. fasciculate\n4. herodotus\n5. hermaphrodite\n6. ibn henry\n7. incorrigible\n8. jane\n9. linchpin\n10. maritime\n11. postdoctoral\n12. shin\n13. sticky\n14. vehicular\n15. brookside\n\nLet's re-sort them carefully:\n\n* **a**rrear\n* **e**avesdropping\n* **f**asciculate\n* **h**erodotus\n* **h**ermaphrodite (H-e-r-m vs H-e-r-o. Hermaphrodite comes before Herodotus)\n* **i**bn henry\n* **i**ncorrigible (I-b vs I-n. Ibn Henry comes before Incorrigible)\n* **j**ane\n* **l**inchpin\n* **m**aritime\n* **p**ostdoctoral\n* **s**hin\n* **s**ticky\n* **v**ehicular\n* **b**rookside (Wait, B comes before S and V. I need to start over with proper alphabetical order.)\n\nLet's list them and sort:\n1. arrear\n2. brookside\n3. eavesdropping\n4. fasciculate\n5. hermaphrodite\n6. herodotus\n7. ibn henry\n8. incorrigible\n9. jane\n10. linchpin\n11. maritime\n12. postdoctoral\n13. shin\n14. sticky\n15. vehicular\n\nRe-sorting based on standard alphabetical order:\n\n* **a**rrear\n* **b**rookside\n* **e**avesdropping\n* **f**asciculate\n* **h**ermaphrodite\n* **h**erodotus (h e r m vs h e r o $\\rightarrow$ Hermaphrodite before Herodotus)\n* **i**bn henry\n* **i**ncorrigible (i b vs i n $\\rightarrow$ Ibn Henry before Incorrigible)\n* **j**ane\n* **l**inchpin\n* **m**aritime\n* **p**ostdoctoral\n* **s**hin\n* **s**ticky\n* **v**ehicular\n\n", + "schema_version": 6, + "seq": 194, + "span_id": "m1.model_call.323", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:35:44.552584+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 11, + "case_id": "bbh_word_sorting-0045", + "cycle": 0, + "duration_sec": 3.4282, + "event": "model_call", + "event_seq": 324, + "inputs": { + "prompt": "Sort the following words alphabetically: List: spongy separate gabriel jackson quail hendrickson washy backslide shelter calvert anastomosis variety commando pizzicato sticktight syndicate hollister\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: spongy, separate, gabriel, jackson, quail, hendrickson, washy, backslide, shelter, calvert, anastomosis, variety, commando, pizzicato, sticktight, syndicate, hollister.\n\nHere is the step-by-step alphabetical sorting:\n\n1. **backslide**\n2. **calvert**\n3. **commando**\n4. **gabriel**\n5. **hendrickson**\n6. **hollister**\n7. **jackson**\n8. **pizzicato**\n9. **quail**\n10. **separate**\n11. **shelter**\n12. **spongy**\n13. **sticktight**\n14. **syndicate**\n15. **variety**\n16. **washy**\n17. **anastomosis** (This word starts with 'a', so it should come earlier than 'variety' and 'washy'. Let's re-sort based on the first letter.)\n\n**Re-sorting based on the first letter:**\n\n* **a**: anastomosis\n* **b**: backslide\n* **c**: calvert, commando\n* **g**: gabriel\n* **h**: hendrickson, hollister\n* **j**: jackson\n* **p**: pizzicato\n* **q**: quail\n* **s**: spongy, separate, shelter, sticktight, syndicate\n* **v**: variety\n* **w**: washy\n\n**Detailed sorting within groups:**\n\n1. **anastomosis**\n2. **backslide**\n3. **calvert**\n4. **commando**\n5. **gabriel**\n6. **hendrickson**\n7. **hollister**\n8. **jackson**\n9. **pizzicato**\n10. **quail**\n11. **separate**\n12. **shelter**\n13. **spongy** (S words: spongy, separate, shelter, sticktight, syndicate)\n * sticktight\n * syndicate\n * separate\n * shelter\n * spongy\n14. **sticktight**\n15. **syndicate**\n16. **separate** (Wait, S words re-sort: separate, shelter, sticktight, spongy,", + "schema_version": 6, + "seq": 195, + "span_id": "m1.model_call.324", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:35:47.990565+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 12, + "case_id": "bbh_word_sorting-0045", + "cycle": 0, + "duration_sec": 3.4162, + "event": "model_call", + "event_seq": 325, + "inputs": { + "prompt": "Sort the following words alphabetically: List: spongy separate gabriel jackson quail hendrickson washy backslide shelter calvert anastomosis variety commando pizzicato sticktight syndicate hollister\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. backslide\n2. calvert\n3. gabriel\n4. hendrickson\n5. hollister\n6. jackson\n7. pizzicato\n8. quail\n9. separate\n10. shelter\n11. sticktight\n12. spongy\n13. syndicate\n14. variety\n15. washy\n16. commando\n17. anastomosis\n\nLet's re-sort them carefully:\n\nOriginal List: spongy, separate, gabriel, jackson, quail, hendrickson, washy, backslide, shelter, calvert, anastomosis, variety, commando, pizzicato, sticktight, syndicate, hollister\n\nSorted List:\n1. anastomosis\n2. backslide\n3. calvert\n4. commando\n5. gabriel\n6. hendrickson\n7. hollister\n8. jackson\n9. pizzicato\n10. quail\n11. separate\n12. shelter\n13. sticktight\n14. spongy\n15. syndicate\n16. variety\n17. washy\n\n(Checking: A, B, C, C, G, H, H, J, P, Q, S, S, S, S, S, V, W)\n*Anastomosis (A)*\n*Backslide (B)*\n*Calvert (C)*\n*Commando (C)*\n*Gabriel (G)*\n*Hendrickson (H)*\n*Hollister (H)*\n*Jackson (J)*\n*Pizzicato (P)*\n*Quail (Q)*\n*Separate (S)*\n*Shelter (S)*\n*Sticktight (S)*\n*Spongy (S)*\n*Syndicate (S)*\n*Variety (V)*\n*Washy (W)*\n\nLet's refine the S words:\nSeparate (S-e)\nShelter (S-h-e)\nSticktight (S-t-i)\nSpongy (S-p)\nSyndicate (S-y)\n\nCorrect S order: Separate, Shelter, Sticktight, Spongy, Syndicate\n\nFinal check of the sorted list:\n1. anastomosis\n2. backslide\n3. calvert\n4. commando\n5. gabriel\n6.", + "schema_version": 6, + "seq": 196, + "span_id": "m1.model_call.325", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:35:51.416139+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 13, + "case_id": "bbh_word_sorting-0045", + "cycle": 0, + "duration_sec": 2.6216, + "event": "model_call", + "event_seq": 326, + "inputs": { + "prompt": "Sort the following words alphabetically: List: spongy separate gabriel jackson quail hendrickson washy backslide shelter calvert anastomosis variety commando pizzicato sticktight syndicate hollister\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. backslide\n2. calvert\n3. gabriel\n4. hollister\n5. jackson\n6. hendrickson\n7. pizzicato\n8. quail\n9. separate\n10. shelter\n11. sticktight\n12. spongy\n13. syndicate\n14. variety\n15. washy\n16. commando\n17. anastomosis\n\nLet's re-sort them carefully:\n\n* backslide\n* calvert\n* commando\n* gabriel\n* hendrickson\n* hollister\n* jackson\n* pizzicato\n* quail\n* separate\n* shelter\n* sticktight\n* spongy\n* syndicate\n* variety\n* washy\n* anastomosis\n\nLet's re-check the order based on the first letter:\nB: backslide\nC: calvert, commando\nG: gabriel\nH: hendrickson, hollister\nJ: jackson\nP: pizzicato\nQ: quail\nS: separate, shelter, sticktight, spongy, syndicate\nV: variety\nW: washy\nA: anastomosis\n\nAlphabetical sorting:\n1. backslide\n2. calvert\n3. commando\n4. gabriel\n5. hendrickson\n6. hollister\n7. jackson\n8. anastomosis\n9. pizzicato\n10. quail\n11. separate\n12. shelter\n13. sticktight\n14. spongy\n15. syndicate\n16. variety\n17. washy\n\nAnswer: backslide, calvert, commando, gabriel, hendrickson, hollister, jackson, anastomosis, pizzicato, quail, separate, shelter, sticktight, spongy, syndicate, variety, washy", + "schema_version": 6, + "seq": 197, + "span_id": "m1.model_call.326", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:35:54.047081+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 14, + "case_id": "bbh_word_sorting-0045", + "cycle": 0, + "duration_sec": 2.6496, + "event": "model_call", + "event_seq": 327, + "inputs": { + "prompt": "Sort the following words alphabetically: List: spongy separate gabriel jackson quail hendrickson washy backslide shelter calvert anastomosis variety commando pizzicato sticktight syndicate hollister\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. backslide\n2. gabriel\n3. hollister\n4. jackson\n5. pizzicato\n6. quail\n7. separate\n8. shelter\n9. spongy\n10. sticktight\n11. syndicate\n12. variety\n13. washy\n14. calvert\n15. anastomosis\n16. commando\n17. hendrickson\n\nLet's re-sort carefully:\n\n1. backslide\n2. calvert\n3. commando\n4. gabriel\n5. hendrickson\n6. hollister\n7. jackson\n8. pizzicato\n9. quail\n10. separate\n11. shelter\n12. spongy\n13. sticktight\n14. syndicate\n15. variety\n16. washy\n\n(Checking the words again: spongy, separate, gabriel, jackson, quail, hendrickson, washy, backslide, shelter, calvert, anastomosis, variety, commando, pizzicato, sticktight, syndicate, hollister)\n\nCorrect alphabetical order:\n1. backslide\n2. calvert\n3. commando\n4. gabriel\n5. hendrickson\n6. hollister\n7. jackson\n8. anastomosis\n9. pizzicato\n10. quail\n11. separate\n12. shelter\n13. spongy\n14. sticktight\n15. syndicate\n16. variety\n17. washy\n\nAnswer: backslide, calvert, commando, gabriel, hendrickson, hollister, jackson, anastomosis, pizzicato, quail, separate, shelter, spongy, sticktight, syndicate, variety, washy", + "schema_version": 6, + "seq": 198, + "span_id": "m1.model_call.327", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:35:56.712989+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 15, + "case_id": "bbh_word_sorting-0045", + "cycle": 0, + "duration_sec": 3.4234, + "event": "model_call", + "event_seq": 328, + "inputs": { + "prompt": "Sort the following words alphabetically: List: spongy separate gabriel jackson quail hendrickson washy backslide shelter calvert anastomosis variety commando pizzicato sticktight syndicate hollister\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. backslide\n2. gabriel\n3. hollowister\n4. jackson\n5. hendrickson\n6. pizzicato\n7. quail\n8. separate\n9. shelter\n10. spongy\n11. sticktight\n12. syndicate\n13. variety\n14. washy\n15. calvert\n16. anastomosis\n17. commando\n\nLet's re-sort them carefully:\n\n* **b**ackslide\n* **c**alvert, commando\n* **g**abriel\n* **h**endrickson, hollister\n* **j**ackson\n* **p**izzicato\n* **q**uail\n* **s**eparate, spongy, shelter, sticktight, syndicate\n* **v**ariety, washy\n\n**Detailed Sorting:**\n\n1. backslide\n2. calvert\n3. commando\n4. gabriel\n5. hendrickson\n6. hollister\n7. jackson\n8. pizzicato\n9. quail\n10. separate\n11. shelter\n12. spongy\n13. sticktight\n14. syndicate\n15. variety\n16. washy\n17. anastomosis (A comes before C, but we are sorting the list provided. Let's check the original list again.)\n\nOriginal List: spongy, separate, gabriel, jackson, quail, hendrickson, washy, backslide, shelter, calvert, anastomosis, variety, commando, pizzicato, sticktight, syndicate, hollister\n\n**Alphabetical Order:**\n\n1. anastomosis (A)\n2. backslide (B)\n3. calvert (C)\n4. commando (C)\n5. gabriel (G)\n6. hendrickson (H)\n7. hollister (H)\n8. jackson (J)\n9. pizzicato (P)\n10. quail (Q)\n11. separate (S)\n12. shelter (S)\n13. sticktight (S)\n14. spongy (S)\n15. syndicate (S)\n16. variety (V)\n17. washy (W)\n\n*Tie-breaking for C: calvert vs commando (a comes before o)", + "schema_version": 6, + "seq": 199, + "span_id": "m1.model_call.328", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:36:00.145992+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 16, + "case_id": "bbh_word_sorting-0076", + "cycle": 0, + "duration_sec": 2.9528, + "event": "model_call", + "event_seq": 329, + "inputs": { + "prompt": "Sort the following words alphabetically: List: whisper eyesight titmice mirage fletch cry biennial creedal miltonic fraudulent j\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words to be sorted alphabetically:\nwhisper, eyesight, titmice, mirage, fletch, cry, biennial, creedal, miltonic, fraudulent, j\n\nLet's sort them alphabetically:\n\n1. biennial\n2. creedal\n3. cry\n4. fletch\n5. fraudulent\n6. eyesight\n7. j (Since 'j' is a letter, it usually comes before letters that start with other sounds, but when sorting words, we consider the first letter. If we treat 'j' as a word, it comes early.)\n8. miltonic\n9. mirage\n10. whisper\n11. titmice\n\nLet's re-sort carefully, focusing on the first letter:\n\n* **b**iennial\n* **c**reedal\n* **c**ry\n* **f**letch\n* **f**raudulent\n* **i**sight\n* **j** (If 'j' is treated as a word/entry, it comes before 'm')\n* **m**iltonic\n* **m**irage\n* **w**hisper\n* **t**itmice\n\nSorting the 'c' words: creedal, cry (creedal comes before cry)\nSorting the 'f' words: fletch, fraudulent (fletch comes before fraudulent)\nSorting the 'm' words: miltonic, mirage (miltonic comes before mirage)\n\nRevised Alphabetical Order:\n\n1. biennial\n2. creedal\n3. cry\n4. fletch\n5. fraudulent\n6. eyesight\n7. j\n8. miltonic\n9. mirage\n10. whisper\n11. titmice\n\n(Note: The placement of 'j' depends on the sorting convention. In standard alphabetical sorting of strings, 'j' comes before 'm'.)\n\nAnswer: biennial, creedal, cry, fletch, fraudulent, eyesight, j, miltonic, mirage, whisper, titmice", + "schema_version": 6, + "seq": 200, + "span_id": "m1.model_call.329", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:36:03.113948+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 17, + "case_id": "bbh_word_sorting-0076", + "cycle": 0, + "duration_sec": 3.5275, + "event": "model_call", + "event_seq": 330, + "inputs": { + "prompt": "Sort the following words alphabetically: List: whisper eyesight titmice mirage fletch cry biennial creedal miltonic fraudulent j\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words to sort: whisper, eyesight, titmice, mirage, fletch, cry, biennial, creedal, miltonic, fraudulent, j\n\nLet's sort them alphabetically:\n\n1. biennial\n2. creedal\n3. cry\n4. fletch\n5. fraudulent\n6. eyesight\n7. j (Assuming 'j' is treated as a single word for sorting purposes, typically before letters)\n8. miltonic\n9. mirage\n10. whisper\n11. titmice\n\nLet's re-check the sorting based on standard alphabetical order:\n\n* **b**iennial\n* **c**reedal\n* **c**ry (comes after creedal)\n* **f**letch\n* **f**raudulent (fletch comes before fraudulent)\n* **e**yesight\n* **j**\n* **m**iltonic\n* **m**irage (miltonic comes before mirage)\n* **w**hisper\n* **t**itmice\n\nLet's refine the list:\n\n1. biennial\n2. creedal\n3. cry\n4. fletch\n5. fraudulent\n6. eyesight\n7. j\n8. miltonic\n9. mirage\n10. whisper\n11. titmice\n\nWait, let's check 'fletch' and 'fraudulent' again:\nfletch (fl...)\nfraudulent (fr...)\nfletch comes before fraudulent.\n\nLet's check 'j'. In standard alphabetical sorting, letters come before words starting with letters. If 'j' is treated as the word \"j\", it would come after letters like 'i' and before 'k'. However, typically in lists of words, we sort based on the sequence of characters.\n\nStandard alphabetical order check:\nb (biennial)\nc (creedal, cry) -> creedal, cry\nf (fletch, fraudulent) -> fletch, fraudulent\ne (eyesight)\nj (j)\nm (miltonic, mirage) -> miltonic, mirage\nw (whisper)\nt (titmice)\n\nFinal Sorted List:\n1. biennial\n2. creedal\n3. cry\n4. fletch\n5. fraudulent\n6. eyesight\n7. j\n8. miltonic\n9. mir", + "schema_version": 6, + "seq": 201, + "span_id": "m1.model_call.330", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:36:06.655464+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 18, + "case_id": "bbh_word_sorting-0076", + "cycle": 0, + "duration_sec": 3.5154, + "event": "model_call", + "event_seq": 331, + "inputs": { + "prompt": "Sort the following words alphabetically: List: whisper eyesight titmice mirage fletch cry biennial creedal miltonic fraudulent j\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: whisper, eyesight, titmice, mirage, fletch, cry, biennial, creedal, miltonic, fraudulent, j.\n\nHere is the step-by-step alphabetical sorting:\n\n1. **Compare the first letter:**\n * fletch (f)\n * fraudulent (f)\n * j (j)\n * miltonic (m)\n * mirage (m)\n * creedal (c)\n * biennial (b)\n * cry (c)\n * whisper (w)\n * eyesight (e)\n * titmice (t)\n\n2. **Sort by the first letter:**\n * **b:** biennial\n * **c:** creedal, cry\n * **e:** eyesight\n * **f:** fletch, fraudulent\n * **j:** j\n * **m:** miltonic, mirage\n * **t:** titmice\n * **w:** whisper\n\n3. **Sort within groups:**\n\n * **c:** creedal, cry (creedal comes before cry) $\\rightarrow$ creedal, cry\n * **f:** fletch, fraudulent (fletch comes before fraudulent) $\\rightarrow$ fletch, fraudulent\n * **m:** miltonic, mirage (miltonic comes before mirage) $\\rightarrow$ miltonic, mirage\n\n4. **Combine the sorted lists:**\n * biennial\n * creedal\n * cry\n * eyesight\n * fletch\n * fraudulent\n * j\n * miltonic\n * mirage\n * titmice\n * whisper\n\nLet's re-verify the order based on standard alphabetical sorting:\n\n* **b**iennial\n* **c**reedal\n* **c**ry\n* **e**yesight\n* **f**letch\n* **f**raudulent\n* **j**\n* **m**iltonic\n* **m**irage\n* **t**itmice\n* **w**hisper\n\nChecking the 'c' words: creedal vs cry. 'creedal' comes before 'cry'.\nChecking the 'f' words: fletch vs fraudulent. 'fletch' comes before 'fraudulent'.\nChecking the 'm' words: miltonic vs mirage.", + "schema_version": 6, + "seq": 202, + "span_id": "m1.model_call.331", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:36:10.183630+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 19, + "case_id": "bbh_word_sorting-0076", + "cycle": 0, + "duration_sec": 3.4491, + "event": "model_call", + "event_seq": 332, + "inputs": { + "prompt": "Sort the following words alphabetically: List: whisper eyesight titmice mirage fletch cry biennial creedal miltonic fraudulent j\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: whisper, eyesight, titmice, mirage, fletch, cry, biennial, creedal, miltonic, fraudulent, j.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter:**\n * fletch (f)\n * fraudulent (f)\n * j (j)\n * miltonic (m)\n * mirage (m)\n * creedal (c)\n * biennial (b)\n * cry (c)\n * whisper (w)\n * eyesight (e)\n * titmice (t)\n\n2. **Sort by the first letter:**\n * **b**: biennial\n * **c**: creedal, cry\n * **e**: eyesight\n * **f**: fletch, fraudulent\n * **j**: j\n * **m**: miltonic, mirage\n * **t**: titmice\n * **w**: whisper\n\n3. **Resolve ties within groups:**\n\n * **C words (creedal, cry):**\n * creedal (cr...)\n * cry (cr...)\n * Comparing the third letter: creedal (e), cry (y). 'e' comes before 'y'.\n * Order: creedal, cry\n\n * **F words (fletch, fraudulent):**\n * fletch (fl...)\n * fraudulent (fr...)\n * 'l' comes before 'r'.\n * Order: fletch, fraudulent\n\n * **M words (miltonic, mirage):**\n * miltonic (mi...)\n * mirage (mi...)\n * Comparing the third letter: miltonic (l), mirage (r). 'l' comes before 'r'.\n * Order: miltonic, mirage\n\n4. **Assemble the final sorted list:**\n * biennial (b)\n * creedal (c)\n * cry (c)\n * eyesight (e)\n * fletch (f)\n * fraudulent (f)\n * j (j)\n * miltonic (m)\n * mirage (m)\n * titmice (t)\n * whisper (w)\n\nLet's re-check the C words:\ncreedal vs cry\nc", + "schema_version": 6, + "seq": 203, + "span_id": "m1.model_call.332", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:36:13.642516+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 20, + "case_id": "bbh_word_sorting-0076", + "cycle": 0, + "duration_sec": 3.0966, + "event": "model_call", + "event_seq": 333, + "inputs": { + "prompt": "Sort the following words alphabetically: List: whisper eyesight titmice mirage fletch cry biennial creedal miltonic fraudulent j\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: whisper, eyesight, titmice, mirage, fletch, cry, biennial, creedal, miltonic, fraudulent, j\n\nHere are the words sorted alphabetically:\n\n1. biennial\n2. creedal\n3. cry\n4. fletch\n5. fraudulent\n6. eyesight\n7. j (Assuming 'j' is treated as a word or character)\n8. miltonic\n9. mirage\n10. whisper\n11. titmice\n\nLet's re-examine the list and sort them carefully:\n\nOriginal List: whisper, eyesight, titmice, mirage, fletch, cry, biennial, creedal, miltonic, fraudulent, j\n\nSorting based on the first letter:\nb: biennial\nc: creedal, cry\nf: fletch, fraudulent\ni: eyesight\nj: j\nm: mirage, miltonic\nw: whisper\nt: titmice\n\nDetailed sorting:\n1. biennial\n2. creedal\n3. cry\n4. fletch\n5. fraudulent\n6. eyesight\n7. j\n8. miltonic\n9. mirage\n10. whisper\n11. titmice\n\nLet's check the 'c' words: creedal vs cry. 'c'r vs 'c'r. creedal comes before cry.\n\nLet's check the 'f' words: fletch vs fraudulent. fletch comes before fraudulent.\n\nLet's check the 'm' words: miltonic vs mirage. miltonic comes before mirage.\n\nCorrected Alphabetical Order:\n1. biennial\n2. creedal\n3. cry\n4. fletch\n5. fraudulent\n6. eyesight\n7. j\n8. miltonic\n9. mirage\n10. whisper\n11. titmice\n\nAnswer: biennial, creedal, cry, fletch, fraudulent, eyesight, j, miltonic, mirage, whisper, titmice\n\nAnswer: biennial, creedal, cry, fletch, fraudulent, eyesight, j, miltonic, mirage, whisper, titmice", + "schema_version": 6, + "seq": 204, + "span_id": "m1.model_call.333", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:36:16.748691+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 21, + "case_id": "bbh_word_sorting-0094", + "cycle": 0, + "duration_sec": 2.4746, + "event": "model_call", + "event_seq": 334, + "inputs": { + "prompt": "Sort the following words alphabetically: List: crossbill bushland capacitive meager ricochet battery enigma wysiwyg wallet lipton jane wacke contingent\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. battery\n2. bushland\n3. capacitive\n4. contingent\n5. crossbill\n6. enigma\n7. jane\n8. lipton\n9. meager\n10. ricochet\n11. wallet\n12. wacke\n13. wysiwyg\n\nLet's re-sort carefully:\n\n* **b**attery\n* **b**ushland\n* **c**apacitive\n* **c**ontingent\n* **c**rossbill\n* **e**nigma\n* **j**ane\n* **l**ipton\n* **m**eager\n* **r**icochet\n* **w**allet\n* **w**acke\n* **w**ysiwyg\n\nComparing the words starting with 'c':\n* capacitive\n* contingent\n* crossbill\n\nAlphabetical order for 'c' words: capacitive, contingent, crossbill.\n\nComparing the words starting with 'w':\n* wallet\n* wacke\n* wysiwyg\n\nAlphabetical order for 'w' words: wallet, wacke, wysiwyg.\n\nThe final sorted list is:\n1. battery\n2. bushland\n3. capacitive\n4. contingent\n5. crossbill\n6. enigma\n7. jane\n8. lipton\n9. meager\n10. ricochet\n11. wallet\n12. wacke\n13. wysiwyg\n\nAnswer: battery, bushland, capacitive, contingent, crossbill, enigma, jane, lipton, meager, ricochet, wallet, wacke, wysiwyg", + "schema_version": 6, + "seq": 205, + "span_id": "m1.model_call.334", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:36:19.236141+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 22, + "case_id": "bbh_word_sorting-0094", + "cycle": 0, + "duration_sec": 3.4304, + "event": "model_call", + "event_seq": 335, + "inputs": { + "prompt": "Sort the following words alphabetically: List: crossbill bushland capacitive meager ricochet battery enigma wysiwyg wallet lipton jane wacke contingent\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words to sort alphabetically:\ncrossbill, bushland, capacitive, meager, ricochet, battery, enigma, wysiwyg, wallet, lipton, jane, wacke, contingent\n\nLet's sort them:\n\n1. battery\n2. bushland\n3. capacitive\n4. contingent\n5. crossbill\n6. enigma\n7. jane\n8. lipton\n9. meager\n10. ricochet\n11. wallet\n12. wacke\n13. wysiwyg\n\nLet's re-sort carefully, focusing on the first letter and then subsequent letters:\n\n* **b**: battery, bushland\n * battery (ba...)\n * bushland (bu...) -> battery comes before bushland.\n* **c**: capacitive, contingent, crossbill\n * capacitive (ca...)\n * contingent (co...)\n * crossbill (cr...) -> capacitive, contingent, crossbill.\n* **e**: enigma\n* **j**: jane\n* **l**: lipton\n* **m**: meager\n* **r**: ricochet\n* **w**: wacke, wallet, wysiwyg\n\n**Detailed Alphabetical Ordering:**\n\n1. **battery** (b-a)\n2. **bushland** (b-u)\n3. **capacitive** (c-a-p)\n4. **contingent** (c-o)\n5. **crossbill** (c-r)\n6. **enigma** (e)\n7. **jane** (j)\n8. **lipton** (l)\n9. **meager** (m)\n10. **ricochet** (r)\n11. **wallet** (w-a)\n12. **wacke** (w-a-c-k)\n13. **wysiwyg** (w-y)\n\n*Comparing 'wallet', 'wacke', and 'wysiwyg':*\n* wallet (wa...)\n* wacke (wa...) -> wallet vs wacke: l vs c. 'c' comes before 'l'. So, wacke comes before wallet.\n* wysiwyg (wy...) -> wy comes after wa.\n\nLet's re-examine the 'w' words:\n* wacke\n* wallet\n* wysiw", + "schema_version": 6, + "seq": 206, + "span_id": "m1.model_call.335", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:36:22.676183+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 23, + "case_id": "bbh_word_sorting-0094", + "cycle": 0, + "duration_sec": 2.4514, + "event": "model_call", + "event_seq": 336, + "inputs": { + "prompt": "Sort the following words alphabetically: List: crossbill bushland capacitive meager ricochet battery enigma wysiwyg wallet lipton jane wacke contingent\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. battery\n2. bushland\n3. capacitive\n4. contingent\n5. crossbill\n6. enigma\n7. jane\n8. lipton\n9. meager\n10. ricochet\n11. wallet\n12. wacke\n13. wysiwyg\n\nLet's re-sort them carefully:\n\n* **b**: battery, bushland\n* **c**: capacitive, contingent, crossbill\n* **e**: enigma\n* **j**: jane\n* **l**: lipton\n* **m**: meager\n* **r**: ricochet\n* **w**: wacke, wallet, wysiwyg\n\n**Sorting within the groups:**\n\n* **b**: battery, bushland (battery comes before bushland)\n* **c**: capacitive, contingent, crossbill (capacitive, contingent, crossbill)\n* **e**: enigma\n* **j**: jane\n* **l**: lipton\n* **m**: meager\n* **r**: ricochet\n* **w**: wacke, wallet, wysiwyg (wacke, wallet, wysiwyg)\n\n**Final Alphabetical Order:**\n\n1. battery\n2. bushland\n3. capacitive\n4. contingent\n5. crossbill\n6. enigma\n7. jane\n8. lipton\n9. meager\n10. ricochet\n11. wacke\n12. wallet\n13. wysiwyg\n\nAnswer: battery, bushland, capacitive, contingent, crossbill, enigma, jane, lipton, meager, ricochet, wacke, wallet, wysiwyg", + "schema_version": 6, + "seq": 207, + "span_id": "m1.model_call.336", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:36:25.138825+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 24, + "case_id": "bbh_word_sorting-0094", + "cycle": 0, + "duration_sec": 2.7168, + "event": "model_call", + "event_seq": 337, + "inputs": { + "prompt": "Sort the following words alphabetically: List: crossbill bushland capacitive meager ricochet battery enigma wysiwyg wallet lipton jane wacke contingent\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. battery\n2. bushland\n3. capacitive\n4. contingent\n5. crossbill\n6. enigma\n7. jane\n8. lipton\n9. meager\n10. ricochet\n11. wallet\n12. wacke\n13. wysiwyg\n\nLet's re-sort them carefully:\n\n* **b**attery\n* **b**ushland\n* **c**apacitive\n* **c**ontingent\n* **c**rossbill\n* **e**nigma\n* **j**ane\n* **l**ipton\n* **m**eager\n* **r**icochet\n* **w**allet\n* **w**acke\n* **w**ysiwyg\n\nSorting by the first letter:\nB: battery, bushland\nC: capacitive, contingent, crossbill\nE: enigma\nJ: jane\nL: lipton\nM: meager\nR: ricochet\nW: wallet, wacke, wysiwyg\n\nSorting within the groups:\nB: battery, bushland (Battery comes before bushland)\nC: capacitive, contingent, crossbill (Capacitive, contingent, crossbill)\nW: wallet, wacke, wysiwyg (Wallet, wacke, wysiwyg)\n\nLet's use standard dictionary order:\n\n1. battery\n2. bushland\n3. capacitive\n4. contingent\n5. crossbill\n6. enigma\n7. jane\n8. lipton\n9. meager\n10. ricochet\n11. wallet\n12. wacke\n13. wysiwyg\n\nAnswer: battery, bushland, capacitive, contingent, crossbill, enigma, jane, lipton, meager, ricochet, wallet, wacke, wysiwyg", + "schema_version": 6, + "seq": 208, + "span_id": "m1.model_call.337", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:36:27.865243+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 25, + "case_id": "bbh_word_sorting-0094", + "cycle": 0, + "duration_sec": 3.5216, + "event": "model_call", + "event_seq": 338, + "inputs": { + "prompt": "Sort the following words alphabetically: List: crossbill bushland capacitive meager ricochet battery enigma wysiwyg wallet lipton jane wacke contingent\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words to be sorted alphabetically:\ncrossbill, bushland, capacitive, meager, ricochet, battery, enigma, wysiwyg, wallet, lipton, jane, wacke, contingent\n\nLet's sort them:\n\n1. **battery**\n2. **bushland**\n3. **capacitive**\n4. **contingent**\n5. **crossbill**\n6. **enigma**\n7. **jane**\n8. **lipton**\n9. **meager**\n10. **ricochet**\n11. **wacke**\n12. **wallet**\n13. **wysiwyg**\n\nLet's re-check the order carefully, especially for words starting with the same letter or similar structures:\n\n* **b**: battery, bushland\n * battery (b-a-t)\n * bushland (b-u-s) -> battery comes before bushland.\n* **c**: capacitive, contingent, crossbill\n * capacitive (c-a-p)\n * contingent (c-o-n)\n * crossbill (c-r-o)\n * capacitive comes first.\n * comparing contingent and crossbill: contingent comes before crossbill.\n* **e**: enigma\n* **j**: jane\n* **l**: lipton\n* **m**: meager\n* **r**: ricochet\n* **w**: wacke, wallet, wysiwyg\n * wacke (w-a-c-k)\n * wallet (w-a-l)\n * wysiwyg (w-y-s)\n * wacke comes before wallet (c before l).\n * wacke comes before wysiwyg (a before y).\n * wallet vs wysiwyg: wallet comes before wysiwyg (l before y).\n\nLet's perform a full alphabetical sort:\n\n1. battery\n2. bushland\n3. capacitive\n4. contingent\n5. crossbill\n6. enigma\n7. jane\n8. lipton\n9. meager\n10. ricochet\n11. wacke\n12. wallet\n13. wysiwyg\n\n(Checking W words again: wacke, wallet, wysiwyg. wacke, wallet, wysiwyg. Correct order", + "schema_version": 6, + "seq": 209, + "span_id": "m1.model_call.338", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:36:31.397162+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 26, + "case_id": "bbh_word_sorting-0000", + "cycle": 0, + "duration_sec": 2.246, + "event": "model_call", + "event_seq": 339, + "inputs": { + "prompt": "Sort the following words alphabetically: List: alleviate duopoly mattress gland benelux townsmen buoyant klaxon hardbound tomography felice gunk\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. alleviate\n2. benelux\n3. buoyant\n4. gunk\n5. felice\n6. gland\n7. hardbound\n8. klaxon\n9. mattress\n10. tomography\n11. townsmen\n12. duopoly\n\nLet's re-sort them carefully:\n\n* alleviate (a)\n* benelux (b)\n* buoyant (b) - buoyant comes before benelux\n* gland (g)\n* felice (f)\n* hardbound (h)\n* klaxon (k)\n* mattress (m)\n* tomography (t)\n* townsmen (t)\n* duopoly (d) - duopoly comes before gland\n\nLet's list them again and sort:\nalleviate\nbenelux\nbuoyant\ngland\nfelice\nhardbound\nklaxon\nmattress\ntomography\ntownsmen\nduopoly\ngunk\n\nCorrect alphabetical order:\n\n1. alleviate\n2. benelux\n3. buoyant\n4. duopoly\n5. felice\n6. gland\n7. gunk\n8. hardbound\n9. klaxon\n10. mattress\n11. tomography\n12. townsmen\n\nAnswer: alleviate, benelux, buoyant, duopoly, felice, gland, gunk, hardbound, klaxon, mattress, tomography, townsmen", + "schema_version": 6, + "seq": 210, + "span_id": "m1.model_call.339", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:36:33.653407+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 27, + "case_id": "bbh_word_sorting-0000", + "cycle": 0, + "duration_sec": 3.5108, + "event": "model_call", + "event_seq": 340, + "inputs": { + "prompt": "Sort the following words alphabetically: List: alleviate duopoly mattress gland benelux townsmen buoyant klaxon hardbound tomography felice gunk\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: alleviate, duopoly, mattress, gland, benelux, townsmen, buoyant, klaxon, hardbound, tomography, felice, gunk.\n\nHere is the alphabetical sorting process:\n\n1. **Alleviate** (A)\n2. **Benelux** (B)\n3. **Buoyant** (B) - Comes after Benelux (Bu vs Be) - Wait, B-e comes before B-u. Let's re-evaluate B words: Benelux, Buoyant. Benelux comes before Buoyant.\n4. **Duopoly** (D)\n5. **Felice** (F)\n6. **Gland** (G)\n7. **Hardbound** (H)\n8. **Klaxon** (K)\n9. **Mattress** (M)\n10. **Tomography** (T)\n11. **Townsmen** (T)\n12. **Gunk** (G) - Should be placed with other G words.\n\nLet's list them clearly and sort:\n\nWords: alleviate, duopoly, mattress, gland, benelux, townsmen, buoyant, klaxon, hardbound, tomography, felice, gunk\n\n1. **alleviate** (A)\n2. **benelux** (B)\n3. **buoyant** (B)\n4. **duopoly** (D)\n5. **felice** (F)\n6. **gland** (G)\n7. **gunk** (G) (Gland before Gunk)\n8. **hardbound** (H)\n9. **klaxon** (K)\n10. **mattress** (M)\n11. **tomography** (T)\n12. **townsmen** (T) (Tomography before Townsmen)\n\nChecking T words: tomography vs townsmen. Tomography comes before Townsmen.\n\nFinal sorted list:\n1. alleviate\n2. benelux\n3. buoyant\n4. duopoly\n5. felice\n6. gland\n7. gunk\n8. hardbound\n9. klaxon\n10. mattress\n11. tomography\n12. townsmen\n\nAnswer: alleviate, benelux, buoyant, duopoly, felice, gland", + "schema_version": 6, + "seq": 211, + "span_id": "m1.model_call.340", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:36:37.174791+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 28, + "case_id": "bbh_word_sorting-0000", + "cycle": 0, + "duration_sec": 3.2478, + "event": "model_call", + "event_seq": 341, + "inputs": { + "prompt": "Sort the following words alphabetically: List: alleviate duopoly mattress gland benelux townsmen buoyant klaxon hardbound tomography felice gunk\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words to sort alphabetically:\nalleviate, duopoly, mattress, gland, benelux, townsmen, buoyant, klaxon, hardbound, tomography, felice, gunk\n\nSorting these words alphabetically:\n\n1. alleviate\n2. benelux\n3. buoyant\n4. duopoly\n5. felice\n6. gland\n7. gunk\n8. hardbound\n9. klaxon\n10. mattress\n11. tomography\n12. townsmen\n\nLet's re-sort carefully, comparing letter by letter:\n\n* **a**: alleviate\n* **b**: benelux, buoyant\n * benelux comes before buoyant (e before u)\n* **d**: duopoly\n* **f**: felice\n* **g**: gland, gunk\n * gland comes before gunk (l before u)\n* **h**: hardbound\n* **k**: klaxon\n* **m**: mattress\n* **t**: tomography, townsmen\n\n**Step-by-step alphabetical comparison:**\n\n1. alleviate (a...)\n2. benelux (b, e...)\n3. buoyant (b, u...)\n4. duopoly (d...)\n5. felice (f...)\n6. gland (g, l...)\n7. gunk (g, u...)\n8. hardbound (h...)\n9. klaxon (k...)\n10. mattress (m...)\n11. tomography (t, o...)\n12. townsmen (t, o...)\n\nComparing tomography and townsmen:\nto-m...\nto-w...\n'm' comes before 'w', so tomography comes before townsmen.\n\n**Final Sorted List:**\n1. alleviate\n2. benelux\n3. buoyant\n4. duopoly\n5. felice\n6. gland\n7. gunk\n8. hardbound\n9. klaxon\n10. mattress\n11. tomography\n12. townsmen\n\nAnswer: alleviate, benelux, buoyant, duopoly, felice, gland, gunk, hardbound, klaxon, mattress, tomography, townsmen", + "schema_version": 6, + "seq": 212, + "span_id": "m1.model_call.341", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:36:40.440815+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 29, + "case_id": "bbh_word_sorting-0000", + "cycle": 0, + "duration_sec": 3.369, + "event": "model_call", + "event_seq": 342, + "inputs": { + "prompt": "Sort the following words alphabetically: List: alleviate duopoly mattress gland benelux townsmen buoyant klaxon hardbound tomography felice gunk\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: alleviate, duopoly, mattress, gland, benelux, townsmen, buoyant, klaxon, hardbound, tomography, felice, gunk.\n\nHere is the alphabetical sorting process:\n\n1. **Alleviate** (A)\n2. **Benelux** (B)\n3. **Buoyant** (B) - Comes after Benelux (Bu > Be)\n4. **Duopoly** (D)\n5. **Felice** (F)\n6. **Gland** (G)\n7. **Hardbound** (H)\n8. **Klaxon** (K)\n9. **Mattress** (M)\n10. **Tomography** (T)\n11. **Townsmen** (T)\n12. **Gunk** (G) - Wait, 'Gland' and 'Gunk' need to be ordered. 'Gland' comes before 'Gunk'.\n\nLet's re-sort carefully:\n\nWords starting with A:\n* alleviate\n\nWords starting with B:\n* benelux\n* buoyant (B-u-a vs B-e-n) $\\rightarrow$ benelux comes before buoyant.\n\nWords starting with D:\n* duopoly\n\nWords starting with F:\n* felice\n\nWords starting with G:\n* gland\n* gunk (gland comes before gunk)\n\nWords starting with H:\n* hardbound\n\nWords starting with K:\n* klaxon\n\nWords starting with M:\n* mattress\n\nWords starting with T:\n* tomography\n* townsmen (tomography comes before townsmen)\n\nFinal sorted list:\n1. alleviate\n2. benelux\n3. buoyant\n4. duopoly\n5. felice\n6. gland\n7. gunk\n8. hardbound\n9. klaxon\n10. mattress\n11. tomography\n12. townsmen\n\nAnswer: alleviate, benelux, buoyant, duopoly, felice, gland, gunk, hardbound, klaxon, mattress, tomography, townsmen\n\nAnswer: alleviate, benelux, buoyant, duopoly, felice, gland, gunk, hardbound, klaxon, mattress, tomography, townsmen", + "schema_version": 6, + "seq": 213, + "span_id": "m1.model_call.342", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:36:43.823411+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 30, + "case_id": "bbh_word_sorting-0000", + "cycle": 0, + "duration_sec": 2.0398, + "event": "model_call", + "event_seq": 343, + "inputs": { + "prompt": "Sort the following words alphabetically: List: alleviate duopoly mattress gland benelux townsmen buoyant klaxon hardbound tomography felice gunk\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. alleviate\n2. benelux\n3. buoyant\n4. duopoly\n5. felice\n6. gland\n7. gunk\n8. hardbound\n9. klaxon\n10. mattress\n11. tomography\n12. townsmen\n\nLet's re-sort carefully:\n\n* **a**lleviate\n* **b**enelux, **b**uoyant\n * benelux\n * buoyant\n* **d**uopoly\n* **f**elice\n* **g**land, **g**unk\n * gland\n * gunk\n* **h**ardbound\n* **k**laxon\n* **m**attress\n* **t**omography, **t**ownsmen\n * tomography\n * townsmen\n\n**Final Alphabetical Order:**\n1. alleviate\n2. benelux\n3. buoyant\n4. duopoly\n5. felice\n6. gland\n7. gunk\n8. hardbound\n9. klaxon\n10. mattress\n11. tomography\n12. townsmen\n\nAnswer: alleviate, benelux, buoyant, duopoly, felice, gland, gunk, hardbound, klaxon, mattress, tomography, townsmen", + "schema_version": 6, + "seq": 214, + "span_id": "m1.model_call.343", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:36:45.884973+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 31, + "case_id": "bbh_word_sorting-0063", + "cycle": 0, + "duration_sec": 1.8262, + "event": "model_call", + "event_seq": 344, + "inputs": { + "prompt": "Sort the following words alphabetically: List: vegetate artillery harm fda doris prosody bainite incongruous monkey vivian\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words to sort alphabetically:\nvegetate, artillery, harm, fda, doris, prosody, bainite, incongruous, monkey, vivian\n\n1. **artillery** (a)\n2. **bainite** (b)\n3. **doris** (d)\n4. **fda** (f)\n5. **harm** (h)\n6. **incongruous** (i)\n7. **monkey** (m)\n8. **prosody** (p)\n9. **vegetate** (v)\n10. **vivian** (v)\n\nNow, let's compare the words starting with 'v':\n* vegetate\n* vivian\n'v'e comes before 'v'i, so 'vegetate' comes before 'vivian'.\n\nThe sorted list is:\nartillery, bainite, doris, fda, harm, incongruous, monkey, prosody, vegetate, vivian\n\nAnswer: artillery, bainite, doris, fda, harm, incongruous, monkey, prosody, vegetate, vivian", + "schema_version": 6, + "seq": 215, + "span_id": "m1.model_call.344", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:36:47.724567+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 32, + "case_id": "bbh_word_sorting-0063", + "cycle": 0, + "duration_sec": 2.5935, + "event": "model_call", + "event_seq": 345, + "inputs": { + "prompt": "Sort the following words alphabetically: List: vegetate artillery harm fda doris prosody bainite incongruous monkey vivian\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: vegetate, artillery, harm, fda, doris, prosody, bainite, incongruous, monkey, vivian.\n\nHere is the alphabetical sorting process:\n\n1. **Compare by the first letter:**\n * bainite (b)\n * doris (d)\n * fda (f)\n * harm (h)\n * incongruous (i)\n * monkey (m)\n * prosody (p)\n * artillery (a) - *Wait, 'artillery' starts with 'a'. Let's re-sort based on the first letter.*\n * vegetate (v)\n * vivian (v)\n\n2. **Re-sorting based on the first letter (A to Z):**\n * **a**: artillery\n * **b**: bainite\n * **d**: doris\n * **f**: fda\n * **h**: harm\n * **i**: incongruous\n * **m**: monkey\n * **p**: prosody\n * **v**: vegetate, vivian\n\n3. **Sorting words starting with 'v':**\n * vivian\n * vegetate\n * (vivi comes before vege)\n\n4. **Final Sorted List:**\n 1. artillery\n 2. bainite\n 3. doris\n 4. fda\n 5. harm\n 6. incongruous\n 7. monkey\n 8. prosody\n 9. vivian\n 10. vegetate\n\nAnswer: artillery, bainite, doris, fda, harm, incongruous, monkey, prosody, vivian, vegetate", + "schema_version": 6, + "seq": 216, + "span_id": "m1.model_call.345", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:36:50.341572+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 33, + "case_id": "bbh_word_sorting-0063", + "cycle": 0, + "duration_sec": 2.3974, + "event": "model_call", + "event_seq": 346, + "inputs": { + "prompt": "Sort the following words alphabetically: List: vegetate artillery harm fda doris prosody bainite incongruous monkey vivian\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: vegetate, artillery, harm, fda, doris, prosody, bainite, incongruous, monkey, vivian.\n\nHere is the alphabetized list:\n\n1. **artillery** (A)\n2. **bainite** (B)\n3. **doris** (D)\n4. **fda** (F)\n5. **harm** (H)\n6. **incongruous** (I)\n7. **monkey** (M)\n8. **prosody** (P)\n9. **vegetate** (V)\n10. **vivian** (V)\n\nNow we need to sort the words that start with 'V': vegetate and vivian.\nComparing 'vegetate' and 'vivian':\nv e g e t a t e\nv i v i a n\n'e' comes before 'i', so 'vegetate' comes before 'vivian'.\n\nLet's re-sort based on the first letter:\nA: artillery\nB: bainite\nD: doris\nF: fda\nH: harm\nI: incongruous\nM: monkey\nP: prosody\nV: vegetate, vivian\n\nSorting the V words:\nvegetate\nvivian\n\nThe final sorted list is:\nartillery, bainite, doris, fda, harm, incongruous, monkey, prosody, vegetate, vivian\n\nAnswer: artillery, bainite, doris, fda, harm, incongruous, monkey, prosody, vegetate, vivian", + "schema_version": 6, + "seq": 217, + "span_id": "m1.model_call.346", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:36:52.753932+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 34, + "case_id": "bbh_word_sorting-0063", + "cycle": 0, + "duration_sec": 3.1577, + "event": "model_call", + "event_seq": 347, + "inputs": { + "prompt": "Sort the following words alphabetically: List: vegetate artillery harm fda doris prosody bainite incongruous monkey vivian\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: vegetate, artillery, harm, fda, doris, prosody, bainite, incongruous, monkey, vivian.\n\nTo sort them alphabetically, we compare them letter by letter:\n\n1. **bainite**\n2. **artillery**\n3. **doris**\n4. **fda**\n5. **harm**\n6. **incongruous**\n7. **monkey**\n8. **prosody**\n9. **vegetate**\n10. **vivian**\n\nLet's re-sort them carefully:\n\n* **b**ainite\n* **a**rtillery\n* **d**oris\n* **f**da\n* **h**arm\n* **i**ncongruous\n* **m**onkey\n* **p**rosody\n* **v**egetate, **v**ivian (v-e comes before v-i) -> vegetate, vivian\n\nLet's check the initial letters: b, a, d, f, h, i, m, p, v, v.\n\n1. **b**ainite\n2. **a**rtillery\n3. **d**oris\n4. **f**da\n5. **h**arm\n6. **i**ncongruous\n7. **m**onkey\n8. **p**rosody\n9. **v**egetate (v-e)\n10. **v**ivian (v-i)\n\nComparing the 'v' words: vegetate vs vivian.\nv-e-g...\nv-i-v...\n'e' comes before 'i', so vegetate comes before vivian.\n\nThe sorted list is: bainite, artillery, doris, fda, harm, incongruous, monkey, prosody, vegetate, vivian.\n\nAnswer: bainite, artillery, doris, fda, harm, incongruous, monkey, prosody, vegetate, vivian\nAnswer: bainite, artillery, doris, fda, harm, incongruous, monkey, prosody, vegetate, vivian", + "schema_version": 6, + "seq": 218, + "span_id": "m1.model_call.347", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:36:55.921608+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 35, + "case_id": "bbh_word_sorting-0063", + "cycle": 0, + "duration_sec": 3.0796, + "event": "model_call", + "event_seq": 348, + "inputs": { + "prompt": "Sort the following words alphabetically: List: vegetate artillery harm fda doris prosody bainite incongruous monkey vivian\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: vegetate, artillery, harm, fda, doris, prosody, bainite, incongruous, monkey, vivian.\n\nWe need to sort these words alphabetically.\n\nLet's list them and compare them:\n1. bainite\n2. artillery\n3. doris\n4. fda\n5. harm\n6. incongruous\n7. monkey\n8. prosody\n9. vegetate\n10. vivian\n\nComparing them letter by letter:\n- **b**ainite\n- **a**rtillery\n- **d**oris\n- **f**da\n- **h**arm\n- **i**ncongruous\n- **m**onkey\n- **p**rosody\n- **v**egetate\n- **v**ivian\n\nSorting the initial letters: a, b, d, f, h, i, m, p, v, v.\n\n1. **artillery** (A)\n2. **bainite** (B)\n3. **doris** (D)\n4. **fda** (F)\n5. **harm** (H)\n6. **incongruous** (I)\n7. **monkey** (M)\n8. **prosody** (P)\n9. **vegetate** (V)\n10. **vivian** (V)\n\nComparing the words starting with 'V':\n- vegetate\n- vivian\n'v' is the same. The next letter is 'e' in vegetate and 'i' in vivian. Since 'e' comes before 'i', 'vegetate' comes before 'vivian'.\n\nThe final sorted list is:\n1. artillery\n2. bainite\n3. doris\n4. fda\n5. harm\n6. incongruous\n7. monkey\n8. prosody\n9. vegetate\n10. vivian\n\nAnswer: artillery, bainite, doris, fda, harm, incongruous, monkey, prosody, vegetate, vivian", + "schema_version": 6, + "seq": 219, + "span_id": "m1.model_call.348", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:36:59.017799+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 36, + "case_id": "bbh_word_sorting-0101", + "cycle": 0, + "duration_sec": 1.6023, + "event": "model_call", + "event_seq": 349, + "inputs": { + "prompt": "Sort the following words alphabetically: List: regret starlight wallboard cotyledon more pepperoni\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: regret, starlight, wallboard, cotyledon, more, pepperoni.\n\nTo sort them alphabetically, we compare them letter by letter:\n\n1. **cotyledon** (Starts with 'c')\n2. **more** (Starts with 'm')\n3. **pepperoni** (Starts with 'p')\n4. **regret** (Starts with 'r')\n5. **starlight** (Starts with 's')\n6. **wallboard** (Starts with 'w')\n\nLet's check the order of the starting letters: c, m, p, r, s, w.\n\n1. cotyledon (c)\n2. more (m)\n3. pepperoni (p)\n4. regret (r)\n5. starlight (s)\n6. wallboard (w)\n\nThe correct alphabetical order is: cotyledon, more, pepperoni, regret, starlight, wallboard.\n\nAnswer: cotyledon, more, pepperoni, regret, starlight, wallboard", + "schema_version": 6, + "seq": 220, + "span_id": "m1.model_call.349", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:37:00.630662+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 37, + "case_id": "bbh_word_sorting-0101", + "cycle": 0, + "duration_sec": 1.5448, + "event": "model_call", + "event_seq": 350, + "inputs": { + "prompt": "Sort the following words alphabetically: List: regret starlight wallboard cotyledon more pepperoni\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: regret, starlight, wallboard, cotyledon, more, pepperoni.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **cotyledon** (c)\n2. **more** (m)\n3. **pepperoni** (p)\n4. **regret** (r)\n5. **starlight** (s)\n6. **wallboard** (w)\n\nLet's verify the alphabetical order:\n* C comes before M, P, R, S, W.\n* M comes before P, R, S, W.\n* P comes before R, S, W.\n* R comes before S, W.\n* S comes before W.\n\nThe sorted order is: cotyledon, more, pepperoni, regret, starlight, wallboard.\n\nAnswer: cotyledon, more, pepperoni, regret, starlight, wallboard\n\nAnswer: cotyledon, more, pepperoni, regret, starlight, wallboard", + "schema_version": 6, + "seq": 221, + "span_id": "m1.model_call.350", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:37:02.185639+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 38, + "case_id": "bbh_word_sorting-0101", + "cycle": 0, + "duration_sec": 1.2045, + "event": "model_call", + "event_seq": 351, + "inputs": { + "prompt": "Sort the following words alphabetically: List: regret starlight wallboard cotyledon more pepperoni\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: regret, starlight, wallboard, cotyledon, more, pepperoni.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **cotyledon** (c...)\n2. **more** (m...)\n3. **pepperoni** (p...)\n4. **regret** (r...)\n5. **starlight** (s...)\n6. **wallboard** (w...)\n\nLet's check the order of the starting letters: c, m, p, r, s, w. This order is alphabetical.\n\nThe sorted list is: cotyledon, more, pepperoni, regret, starlight, wallboard.\n\nAnswer: cotyledon, more, pepperoni, regret, starlight, wallboard", + "schema_version": 6, + "seq": 222, + "span_id": "m1.model_call.351", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:37:03.400312+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 39, + "case_id": "bbh_word_sorting-0101", + "cycle": 0, + "duration_sec": 1.6094, + "event": "model_call", + "event_seq": 352, + "inputs": { + "prompt": "Sort the following words alphabetically: List: regret starlight wallboard cotyledon more pepperoni\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: regret, starlight, wallboard, cotyledon, more, pepperoni.\n\nTo sort the words alphabetically, we compare them letter by letter.\n\n1. **cotyledon** (starts with c)\n2. **more** (starts with m)\n3. **pepperoni** (starts with p)\n4. **regret** (starts with r)\n5. **starlight** (starts with s)\n6. **wallboard** (starts with w)\n\nLet's re-sort based on the first letter:\nC: cotyledon\nM: more\nP: pepperoni\nR: regret\nS: starlight\nW: wallboard\n\nAlphabetical order of the first letters: C, M, P, R, S, W.\n\nThe sorted list is: cotyledon, more, pepperoni, regret, starlight, wallboard.\n\nAnswer: cotyledon, more, pepperoni, regret, starlight, wallboard\nAnswer: cotyledon, more, pepperoni, regret, starlight, wallboard", + "schema_version": 6, + "seq": 223, + "span_id": "m1.model_call.352", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:37:05.019897+00:00" + }, + { + "analyzer": "coverage_verification_gap", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 40, + "case_id": "bbh_word_sorting-0101", + "cycle": 0, + "duration_sec": 1.6862, + "event": "model_call", + "event_seq": 353, + "inputs": { + "prompt": "Sort the following words alphabetically: List: regret starlight wallboard cotyledon more pepperoni\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": { + "temperature": "1.0", + "top_k": "20", + "top_p": "0.95" + }, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: regret, starlight, wallboard, cotyledon, more, pepperoni.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **cotyledon** (starts with c)\n2. **more** (starts with m)\n3. **pepperoni** (starts with p)\n4. **regret** (starts with r)\n5. **starlight** (starts with s)\n6. **wallboard** (starts with w)\n\nLet's re-sort them carefully:\n\n* **c**: cotyledon\n* **m**: more\n* **p**: pepperoni\n* **r**: regret\n* **s**: starlight\n* **w**: wallboard\n\nThe alphabetical order is:\n1. cotyledon\n2. more\n3. pepperoni\n4. regret\n5. starlight\n6. wallboard\n\nAnswer: cotyledon, more, pepperoni, regret, starlight, wallboard\n\nAnswer: cotyledon, more, pepperoni, regret, starlight, wallboard", + "schema_version": 6, + "seq": 224, + "span_id": "m1.model_call.353", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:37:06.722931+00:00" + }, + { + "analyzer": "perturbation_battery", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 1, + "case_id": "bbh_word_sorting-0006", + "cycle": 0, + "duration_sec": 1.6292, + "event": "model_call", + "event_seq": 354, + "inputs": { + "prompt": "Sort the following words alphabetically: List: torpedo phosphorescent pristine decadent shrunk dey administer gradate littleneck thrown jacky coachman aeneid verdict tasting sinh delhi systemwide grim\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. gradate\n7. grim\n8. jacky\n9. littleneck\n10. phosphorescent\n11. pristine\n12. sinh\n13. systemwide\n14. tasting\n15. torpedo\n16. thrown\n17. verdict\n18. dey\n19. shrunk\n\nLet's re-sort them carefully:\n\n* administer\n* aeneid\n* decadent\n* coachman\n* delhi\n* grim\n* gradate\n* jacky\n* littleneck\n* phosphorescent\n* pristine\n* sinh\n* systemwide\n* tasting\n* torpedo\n* thrown\n* verdict\n* dey\n* shrunk\n\n**Alphabetical Sorting:**\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. dey\n7. grim\n8. gradate\n9. jacky\n10. littleneck\n11. phosphorescent\n12. pristine\n13. sinh\n14. shrunk\n15. systemwide\n16. tasting\n17. torpedo\n18. thrown\n19. verdict\n\nAnswer: administer, aeneid, decadent, coachman, delhi, dey, grim, gradate, jacky, littleneck, phosphorescent, pristine, sinh, shrunk, systemwide, tasting, torpedo, thrown, verdict", + "schema_version": 6, + "seq": 225, + "span_id": "m1.model_call.354", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:37:08.366593+00:00" + }, + { + "analyzer": "perturbation_battery", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 2, + "case_id": null, + "cycle": 0, + "duration_sec": 2.4202, + "event": "model_call", + "event_seq": 355, + "inputs": { + "prompt": "Sort the following words alphabetically: Kavi: torpedo phosphorescent pristine decadent shrunk dey administer gradate littleneck thrown jacky coachman aeneid verdict tasting sinh delhi systemwide grim\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words to sort alphabetically:\nKavi, torpedo, phosphorescent, pristine, decadent, shrunk, dey, administer, gradate, littleneck, thrown, jacky, coachman, aeneid, verdict, tasting, sinh, delhi, systemwide, grim\n\nLet's sort them:\n\n1. administer\n2. decadent\n3. aeneid\n4. delhi\n5. gradate\n6. grim\n7. jacky\n8. Kavi\n9. littleneck\n10. phosphorescent\n11. pristine\n12. sinh\n13. systemwide\n14. tasting\n15. torpedo\n16. thrown\n17. verdict\n18. dey\n\nLet's re-sort carefully, paying attention to capitalization (assuming standard alphabetical sorting where case doesn't strictly matter unless specified, but we'll treat them as provided):\n\nA: aeneid, administer\nD: decadent, delhi, dey\nG: gradate, grim\nJ: jacky\nK: Kavi\nL: littleneck\nP: phosphorescent, pristine\nS: sinh, systemwide\nT: tasting, torpedo, thrown\nV: verdict\n\nSorting within groups:\n\nA: aeneid, administer (aeneid before administer)\nD: decadent, delhi, dey (decadent, delhi, dey)\nG: gradate, grim (gradate before grim)\nJ: jacky\nK: Kavi\nL: littleneck\nP: phosphorescent, pristine (phosphorescent before pristine)\nS: sinh, systemwide (sinh before systemwide)\nT: tasting, torpedo, thrown (tasting, torpedo, thrown)\nV: verdict\n\nFinal sorted list:\n1. aeneid\n2. administer\n3. decadent\n4. delhi\n5. dey\n6. gradate\n7. grim\n8. jacky\n9. Kavi\n10. littleneck\n11. phosphorescent\n12. pristine\n13. sinh\n14. systemwide\n15. tasting\n16. torpedo\n17. thrown\n18. verdict\n\nAnswer: aeneid, administer, decadent, delhi, dey, gradate, grim, jacky, Kavi, littleneck, phosphorescent, pristine, sinh, systemwide, tasting, torpedo, thrown, verdict", + "schema_version": 6, + "seq": 226, + "span_id": "m1.model_call.355", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:37:10.804086+00:00" + }, + { + "analyzer": "perturbation_battery", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 3, + "case_id": null, + "cycle": 0, + "duration_sec": 2.4141, + "event": "model_call", + "event_seq": 356, + "inputs": { + "prompt": "Read the problem carefully, then solve it.\n\nSort the following words alphabetically: List: torpedo phosphorescent pristine decadent shrunk dey administer gradate littleneck thrown jacky coachman aeneid verdict tasting sinh delhi systemwide grim\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "Here is the list of words to be sorted alphabetically:\ntorpedo, phosphorescent, pristine, decadent, shrunk, dey, administer, gradate, littleneck, thrown, jacky, coachman, aeneid, verdict, tasting, sinh, delhi, systemwide, grim\n\nWe will sort these words alphabetically:\n\n1. **aeneid**\n2. **administer**\n3. **decadent**\n4. **delhi**\n5. **grim**\n6. **gradate**\n7. **jacky**\n8. **littleneck**\n9. **phosphorescent**\n10. **pristine**\n11. **sinh**\n12. **systemwide**\n13. **tasting**\n14. **torpedo**\n15. **verdict**\n16. **coachman**\n17. **dey**\n18. **shrunk**\n19. **thrown**\n\nLet's re-sort carefully, paying attention to the first letter and subsequent letters:\n\nA: aeneid\nD: decadent, delhi\nG: gradate, grim\nJ: jacky\nL: littleneck\nP: phosphorescent, pristine\nS: sinh, shrunk, systemwide\nT: tasting, torpedo\nV: verdict\nC: coachman\nD: dey\nT: thrown\n\nLet's list them systematically:\n\n1. aeneid\n2. administer\n3. decadent\n4. delhi\n5. dey\n6. grim\n7. gradate\n8. jacky\n9. littleneck\n10. phosphorescent\n11. pristine\n12. sinh\n13. shrunk\n14. systemwide\n15. tasting\n16. torpedo\n17. thrown\n18. verdict\n19. coachman\n\nLet's check the order of words starting with 'D': decadent, delhi, dey.\nDecadent (deca...)\nDelhi (delh...)\nDey (dey...)\nOrder: decadent, delhi, dey. (Correct)\n\nLet's check words starting with 'G': grim, gradate.\nGrim (gri...)\nGradate (gra...)\nOrder: gradate, grim. (Wait, G-R-A vs G-R-I. 'a' comes before 'i').\nCorrect order: grad", + "schema_version": 6, + "seq": 227, + "span_id": "m1.model_call.356", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:37:13.229561+00:00" + }, + { + "analyzer": "perturbation_battery", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 4, + "case_id": null, + "cycle": 0, + "duration_sec": 2.3866, + "event": "model_call", + "event_seq": 357, + "inputs": { + "prompt": "Sort the following words alphabetically: List: torpedo phosphorescent pristine decadent shrunk dey administer gradate littleneck thrown jacky coachman aeneid verdict tasting sinh delhi systemwide grim\n Note that last year the same quantities were recorded in a different notebook, which has since been archived.\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. gradate\n7. grim\n8. jacky\n9. littleneck\n10. phosphorescent\n11. pristine\n12. sinh\n13. systemwide\n14. tasting\n15. torpedo\n16. thrown\n17. verdict\n18. dey\n\nLet's re-sort them carefully:\n\n* administer\n* aeneid\n* decadent\n* coachman\n* delhi\n* grim\n* gradate\n* jacky\n* littleneck\n* phosphorescent\n* pristine\n* sinh\n* systemwide\n* tasting\n* torpedo\n* thrown\n* verdict\n* dey\n\nComparing the initial letters:\nA: administer, aeneid\nC: coachman\nD: decadent, delhi, dey\nG: grim, gradate\nJ: jacky\nL: littleneck\nP: phosphorescent, pristine\nS: sinh, systemwide, tasting\nT: torpedo, thrown\nV: verdict\n\nDetailed sorting:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. dey\n7. grim\n8. gradate\n9. jacky\n10. littleneck\n11. phosphorescent\n12. pristine\n13. sinh\n14. systemwide\n15. tasting\n16. torpedo\n17. thrown\n18. verdict\n\n(Checking D words: decadent, delhi, dey. Decadent, delhi, dey)\n(Checking P words: phosphorescent, pristine. Phosphorescent comes before pristine)\n(Checking S words: sinh, systemwide, tasting. Sinh, systemwide, tasting)\n\nFinal sorted list:\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. dey\n7. grim\n8. gradate\n9. jacky\n10. littleneck\n11. phosphorescent\n12. pristine\n13. sinh\n14. systemwide\n15. tasting\n16. torpedo\n17. thrown\n18. verdict\n\nAnswer: administer, aeneid, decadent, coachman, delhi, dey, grim, gradate, jacky, littleneck, phosph", + "schema_version": 6, + "seq": 228, + "span_id": "m1.model_call.357", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:37:15.626478+00:00" + }, + { + "analyzer": "perturbation_battery", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 5, + "case_id": "bbh_word_sorting-0025", + "cycle": 0, + "duration_sec": 2.4508, + "event": "model_call", + "event_seq": 358, + "inputs": { + "prompt": "Sort the following words alphabetically: List: linchpin arrear eavesdropping fasciculate shin postdoctoral maritime ibn henry hermaphrodite sticky jane brookside incorrigible vehicular herodotus\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. arrear\n2. eavesdropping\n3. fasciculate\n4. herodotus\n5. hermaphrodite\n6. ibn henry\n7. incorrigible\n8. jane\n9. linchpin\n10. maritime\n11. postdoctoral\n12. shin\n13. sticky\n14. brookside\n15. vehicular\n\nLet's re-sort them carefully:\n\n* **a**rrear\n* **e**avesdropping\n* **f**asciculate\n* **h**erodotus\n* **h**ermaphrodite (H before I)\n* **i**bn henry\n* **i**ncorrigible (I before L)\n* **j**ane\n* **l**inchpin\n* **m**aritime\n* **p**ostdoctoral\n* **s**hin\n* **s**ticky\n* **b**rookside (B comes before S)\n* **v**ehicular\n\nLet's do a full alphabetical sort:\n\n1. arrear\n2. brookside\n3. eavesdropping\n4. fasciculate\n5. herodotus\n6. hermaphrodite\n7. ibn henry\n8. incorrigible\n9. jane\n10. linchpin\n11. maritime\n12. postdoctoral\n13. shin\n14. sticky\n15. vehicular\n\nWait, let's check the 'H' words: hermaphrodite and herodotus.\nH-e-r-m-a-p-h-r-o-d-i-t-e\nH-e-r-o-d-t-u-s\n'herm' comes before 'hero'. So, hermaphrodite comes before herodotus.\n\nLet's re-verify the full list:\narrear\nbrookside\neavesdropping\nfasciculate\nhermaphrodite\nherodotus\nibn henry\nincorrigible\njane\nlinchpin\nmaritime\npostdoctoral\nshin\nsticky\nvehicular\n\nFinal check on the order:\nA: arrear\nB: brookside\nE: eavesdropping\nF: fasciculate\nH: hermaphrodite, herodotus (herm before hero)\nI: ibn henry, incorrigible (ibn before incorrigible)\nJ:", + "schema_version": 6, + "seq": 229, + "span_id": "m1.model_call.358", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:37:18.093864+00:00" + }, + { + "analyzer": "perturbation_battery", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 6, + "case_id": null, + "cycle": 0, + "duration_sec": 2.3898, + "event": "model_call", + "event_seq": 359, + "inputs": { + "prompt": "Sort the following words alphabetically: Kavi: linchpin arrear eavesdropping fasciculate shin postdoctoral maritime ibn henry hermaphrodite sticky jane brookside incorrigible vehicular herodotus\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "The given words are: Kavi, linchpin, arrear, eavesdropping, fasciculate, shin, postdoctoral, maritime, ibn henry, hermaphrodite, sticky, jane, brookside, incorrigible, vehicular, herodotus.\n\nHere is the alphabetical sorting process:\n\n1. **Compare by the first letter:**\n * arrear (A)\n * brookside (B)\n * eavesdropping (E)\n * fasciculate (F)\n * hermaphrodite (H)\n * herodotus (H)\n * ibn henry (I)\n * incorrigible (I)\n * jane (J)\n * Kavi (K)\n * linchpin (L)\n * maritime (M)\n * postdoctoral (P)\n * shin (S)\n * sticky (S)\n * vehicular (V)\n\n2. **Sort words starting with the same letter:**\n\n * **H:** hermaphrodite, herodotus (herodotus comes before hermaphrodite)\n * **I:** ibn henry, incorrigible (ibn henry comes before incorrigible)\n * **S:** shin, sticky (shin comes before sticky)\n\n3. **Final Sorted List:**\n\n * arrear\n * brookside\n * eavesdropping\n * fasciculate\n * herodotus\n * hermaphrodite\n * ibn henry\n * incorrigible\n * jane\n * Kavi\n * linchpin\n * maritime\n * postdoctoral\n * shin\n * sticky\n * vehicular\n\nLet's re-verify the order, especially for H and I:\nH: hermaphrodite, herodotus (h-e vs h-e-r) -> hermaphrodite, herodotus (Wait, 'her' vs 'herm'. 'her' comes before 'herm' if we compare character by character. Let's check standard alphabetical order.)\nhermophrodite\nherodotus\n'herm' vs 'herod'. 'm' comes before 'o'. So, hermaphrodite comes before herodotus.\n\nI: ibn henry, incorrigible (i-b vs i-n) -> ibn henry, incorrigible.\n\nS: shin, sticky (s-h vs s-t) -> shin, sticky.\n\n**Revised", + "schema_version": 6, + "seq": 230, + "span_id": "m1.model_call.359", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:37:20.494088+00:00" + }, + { + "analyzer": "perturbation_battery", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 7, + "case_id": null, + "cycle": 0, + "duration_sec": 2.4565, + "event": "model_call", + "event_seq": 360, + "inputs": { + "prompt": "Read the problem carefully, then solve it.\n\nSort the following words alphabetically: List: linchpin arrear eavesdropping fasciculate shin postdoctoral maritime ibn henry hermaphrodite sticky jane brookside incorrigible vehicular herodotus\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "Here is the list of words to be sorted alphabetically:\nlinchpin, arrear, eavesdropping, fasciculate, shin, postdoctoral, maritime, ibn henry, hermaphrodite, sticky, jane, brookside, incorrigible, vehicular, herodotus\n\nWe will sort these words alphabetically:\n\n1. **arrear** (A)\n2. **eavesdropping** (E)\n3. **herodotus** (H)\n4. **hermaphrodite** (H) - 'herodotus' comes before 'hermaphrodite' if we compare the third letter (r vs m). Wait, let's recheck. H-e-r-o-d-t-u-s vs H-e-r-m-a-p-h-r-o-d-i-t-e. 'm' comes before 'o'. So, hermaphrodite comes before herodotus.\n * Corrected H words: hermaphrodite, herodotus\n5. **incorrrigible** (I)\n6. **i**bn henry (I) - 'i'bn henry comes before incorrigible.\n * Corrected I words: ibn henry, incorrigible\n7. **jane** (J)\n8. **linchpin** (L)\n9. **maritime** (M)\n10. **postdoctoral** (P)\n11. **shin** (S)\n12. **sticky** (S) - 'shin' comes before 'sticky'.\n * Corrected S words: shin, sticky\n13. **brookside** (B)\n14. **fasciculate** (F)\n15. **vehicular** (V)\n\nLet's perform a full alphabetical sort:\n\nA: arrear\nB: brookside\nE: eavesdropping\nF: fasciculate\nH: hermaphrodite, herodotus (H-e-r-m vs H-e-r-o) -> hermaphrodite, herodotus\nI: ibn henry, incorrigible (i-b vs i-n) -> ibn henry, incorrigible\nJ: jane\nL: linchpin\nM: maritime\nP: postdoctoral\nS: shin, sticky (s-h vs s-t) -> shin, sticky\nV: vehicular\n\nLet's re-verify the", + "schema_version": 6, + "seq": 231, + "span_id": "m1.model_call.360", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:37:22.972975+00:00" + }, + { + "analyzer": "perturbation_battery", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 8, + "case_id": null, + "cycle": 0, + "duration_sec": 2.3854, + "event": "model_call", + "event_seq": 361, + "inputs": { + "prompt": "Sort the following words alphabetically: List: linchpin arrear eavesdropping fasciculate shin postdoctoral maritime ibn henry hermaphrodite sticky jane brookside incorrigible vehicular herodotus\n Note that last year the same quantities were recorded in a different notebook, which has since been archived.\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. arrear\n2. eavesdropping\n3. fasciculate\n4. herodotus\n5. hermaphrodite\n6. ibn henry\n7. incorrigible\n8. jane\n9. linchpin\n10. maritime\n11. postdoctoral\n12. shin\n13. sticky\n14. brookside\n15. vehicular\n\nLet's re-sort them carefully:\n\n* **a**rrear\n* **e**avesdropping\n* **f**asciculate\n* **h**erodotus\n* **h**ermaphrodite (H before I)\n* **i**bn henry\n* **i**ncorrigible (I before L)\n* **j**ane\n* **l**inchpin\n* **m**aritime\n* **p**ostdoctoral\n* **s**hin\n* **s**ticky\n* **b**rookside (B comes before S)\n* **v**ehicular\n\nLet's do a full alphabetical sort:\n\n1. arrear\n2. brookside\n3. eavesdropping\n4. fasciculate\n5. herodotus\n6. hermaphrodite\n7. ibn henry\n8. incorrigible\n9. jane\n10. linchpin\n11. maritime\n12. postdoctoral\n13. shin\n14. sticky\n15. vehicular\n\nWait, let's check the 'H' words: hermaphrodite and herodotus.\nH-e-r-m-a-p-h-r-o-d-i-t-e\nH-e-r-o-d-t-u-s\n'herm' comes before 'hero'. So, hermaphrodite comes before herodotus.\n\nLet's re-verify the full list:\n* arrear (A)\n* brookside (B)\n* eavesdropping (E)\n* fasciculate (F)\n* hermaphrodite (H)\n* herodotus (H)\n* ibn henry (I)\n* incorrigible (I)\n* jane (J)\n* linchpin (L)\n* maritime (M)\n* postdoctoral (P)\n* shin (S)\n* sticky (S)\n* vehicular (V)\n\nSorting H words:\nhermaph", + "schema_version": 6, + "seq": 232, + "span_id": "m1.model_call.361", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:37:25.368807+00:00" + }, + { + "analyzer": "perturbation_battery", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 9, + "case_id": "bbh_word_sorting-0045", + "cycle": 0, + "duration_sec": 1.8094, + "event": "model_call", + "event_seq": 362, + "inputs": { + "prompt": "Sort the following words alphabetically: List: spongy separate gabriel jackson quail hendrickson washy backslide shelter calvert anastomosis variety commando pizzicato sticktight syndicate hollister\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. backslide\n2. calvert\n3. gabriel\n4. hendrickson\n5. hollister\n6. jackson\n7. pizzicato\n8. quail\n9. separate\n10. shelter\n11. sticktight\n12. spongy\n13. syndicate\n14. variety\n15. washy\n16. commando\n17. anastomosis\n\nLet's re-sort them carefully:\n\n* backslide\n* calvert\n* gabriel\n* hendrickson\n* hollister\n* jackson\n* pizzicato\n* quail\n* separate\n* shelter\n* sticktight\n* spongy\n* syndicate\n* variety\n* washy\n* commando\n* anastomosis\n\nComparing the words:\nB: backslide\nC: calvert, commando\nG: gabriel\nH: hendrickson, hollister\nJ: jackson\nP: pizzicato\nQ: quail\nS: separate, shelter, spongy, sticktight, syndicate\nV: variety\nW: washy\nA: anastomosis\n\nDetailed alphabetical sort:\n1. backslide\n2. calvert\n3. commando\n4. gabriel\n5. hendrickson\n6. hollister\n7. jackson\n8. anastomosis\n9. pizzicato\n10. quail\n11. separate\n12. shelter\n13. sticktight\n14. spongy\n15. syndicate\n16. variety\n17. washy\n\nAnswer: backslide, calvert, commando, gabriel, hendrickson, hollister, jackson, anastomosis, pizzicato, quail, separate, shelter, sticktight, spongy, syndicate, variety, washy", + "schema_version": 6, + "seq": 233, + "span_id": "m1.model_call.362", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:37:27.188579+00:00" + }, + { + "analyzer": "perturbation_battery", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 10, + "case_id": null, + "cycle": 0, + "duration_sec": 2.4318, + "event": "model_call", + "event_seq": 363, + "inputs": { + "prompt": "Sort the following words alphabetically: Kavi: spongy separate gabriel jackson quail hendrickson washy backslide shelter calvert anastomosis variety commando pizzicato sticktight syndicate hollister\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. backslide\n2. calvert\n3. gabriel\n4. hollister\n5. jackson\n6. Kavi\n7. hendrickson\n8. pizzicato\n9. quail\n10. separate\n11. shelter\n12. spongy\n13. sticktight\n14. syndicate\n15. variety\n16. washy\n17. commando\n18. anastomosis\n\nLet's re-sort them carefully:\n\n* backslide\n* calvert\n* commando\n* gabriel\n* hendrickson\n* hollister\n* jackson\n* Kavi\n* anastomosis\n* pizzicato\n* quail\n* separate\n* shelter\n* sticktight\n* spongy\n* syndicate\n* variety\n* washy\n\n**Alphabetical Order Check:**\nB (backslide)\nC (calvert, commando) -> calvert, commando\nG (gabriel)\nH (hendrickson, hollister) -> hendrickson, hollister\nJ (jackson)\nK (Kavi)\nA (anastomosis) - Wait, 'anastomosis' starts with A. It should come before 'gabriel'.\n\nLet's restart the sorting based on the first letter:\n\n* **a**: anastomosis\n* **b**: backslide\n* **c**: calvert, commando\n* **g**: gabriel\n* **h**: hendrickson, hollister\n* **j**: jackson\n* **k**: Kavi\n* **p**: pizzicato\n* **q**: quail\n* **s**: separate, shelter, spongy, sticktight, syndicate\n* **v**: variety\n* **w**: washy\n\n**Detailed Sorting:**\n\n1. anastomosis (A)\n2. backslide (B)\n3. calvert (C)\n4. commando (C)\n5. gabriel (G)\n6. hendrickson (H)\n7. hollister (H)\n8. jackson (J)\n9. Kavi (K)\n10. pizzicato (P)\n11. quail (Q)\n12. separate (S)\n13. shelter (S)\n14. spongy (S)\n15. sticktight (S)\n16. syndicate (S", + "schema_version": 6, + "seq": 234, + "span_id": "m1.model_call.363", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:37:29.631092+00:00" + }, + { + "analyzer": "perturbation_battery", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 11, + "case_id": null, + "cycle": 0, + "duration_sec": 2.5466, + "event": "model_call", + "event_seq": 364, + "inputs": { + "prompt": "Read the problem carefully, then solve it.\n\nSort the following words alphabetically: List: spongy separate gabriel jackson quail hendrickson washy backslide shelter calvert anastomosis variety commando pizzicato sticktight syndicate hollister\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "Here is the list of words to be sorted alphabetically:\nspongy, separate, gabriel, jackson, quail, hendrickson, washy, backslide, shelter, calvert, anastomosis, variety, commando, pizzicato, sticktight, syndicate, hollister\n\nWe will sort them alphabetically:\n\n1. **anastomosis**\n2. **backslide**\n3. **calvert**\n4. **commando**\n5. **gabriel**\n6. **hendrickson**\n7. **hollister**\n8. **jackson**\n9. **pizzicato**\n10. **quail**\n11. **separate**\n12. **shelter**\n13. **sticktight**\n14. **spongy**\n15. **syndicate**\n16. **variety**\n17. **washy**\n\nLet's re-verify the sorting, especially for words starting with similar letters:\n\n* A: anastomosis\n* B: backslide\n* C: calvert, commando\n * calvert comes before commando.\n* G: gabriel\n* H: hendrickson, hollister\n * hendrickson comes before hollister.\n* J: jackson\n* P: pizzicato\n* Q: quail\n* S: separate, shelter, sticktight, spongy, syndicate\n * separate\n * shelter\n * sticktight\n * spongy\n * syndicate\n* V: variety\n* W: washy\n\n**Final Sorted List:**\n1. anastomosis\n2. backslide\n3. calvert\n4. commando\n5. gabriel\n6. hendrickson\n7. hollister\n8. jackson\n9. pizzicato\n10. quail\n11. separate\n12. shelter\n13. sticktight\n14. spongy\n15. syndicate\n16. variety\n17. washy\n\nAnswer: anastomosis, backslide, calvert, commando, gabriel, hendrickson, hollister, jackson, pizzicato, quail, separate, shelter, sticktight, spongy, syndicate, variety, washy", + "schema_version": 6, + "seq": 235, + "span_id": "m1.model_call.364", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:37:32.188158+00:00" + }, + { + "analyzer": "perturbation_battery", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 12, + "case_id": null, + "cycle": 0, + "duration_sec": 2.7809, + "event": "model_call", + "event_seq": 365, + "inputs": { + "prompt": "Sort the following words alphabetically: List: spongy separate gabriel jackson quail hendrickson washy backslide shelter calvert anastomosis variety commando pizzicato sticktight syndicate hollister\n Note that last year the same quantities were recorded in a different notebook, which has since been archived.\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. backslide\n2. gabriel\n3. hollowister\n4. jackson\n5. hendrickson\n6. pizzicato\n7. quail\n8. separate\n9. shelter\n10. spongy\n11. sticktight\n12. variety\n13. washy\n14. commando\n15. calvert\n16. anastomosis\n17. syndicate\n\nLet's re-sort them carefully:\n\n* backslide\n* calvert\n* commando\n* gabriel\n* hendrickson\n* hollister\n* jackson\n* pizzicato\n* quail\n* separate\n* shelter\n* spongy\n* sticktight\n* variety\n* washy\n\nLet's check the order again:\n\n1. backslide\n2. calvert\n3. commando\n4. gabriel\n5. hendrickson\n6. hollister\n7. jackson\n8. pizzicato\n9. quail\n10. separate\n11. shelter\n12. spongy\n13. sticktight\n14. variety\n15. washy\n16. anastomosis (A comes before S in anastomosis, but it should be placed based on the first letter)\n17. syndicate\n\nLet's use the standard alphabetical order:\n\n1. backslide\n2. calvert\n3. commando\n4. gabriel\n5. hendrickson\n6. hollister\n7. jackson\n8. pizzicato\n9. quail\n10. separate\n11. shelter\n12. spongy\n13. sticktight\n14. syndicate\n15. variety\n16. washy\n17. anastomosis\n\nWait, let's check 'anastomosis' vs 'syndicate' and 'variety'.\n\nA: anastomosis\nB: backslide\nC: calvert\nC: commando\nG: gabriel\nH: hendrickson\nH: hollister\nJ: jackson\nP: pizzicato\nQ: quail\nS: separate\nS: shelter\nS: spongy\nS: sticktight\nS: syndicate\nV: variety\nW: washy\n\nCorrect alphabetical order:\n\n1. anastomosis\n2. backslide\n3. calvert\n4. commando\n5. gabriel\n6. hendrickson\n7. holl", + "schema_version": 6, + "seq": 236, + "span_id": "m1.model_call.365", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:37:34.994688+00:00" + }, + { + "analyzer": "perturbation_battery", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 13, + "case_id": "bbh_word_sorting-0076", + "cycle": 0, + "duration_sec": 2.3951, + "event": "model_call", + "event_seq": 366, + "inputs": { + "prompt": "Sort the following words alphabetically: List: whisper eyesight titmice mirage fletch cry biennial creedal miltonic fraudulent j\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: whisper, eyesight, titmice, mirage, fletch, cry, biennial, creedal, miltonic, fraudulent, j.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter:**\n * fletch (f)\n * fraudulent (f)\n * j (j)\n * miltonic (m)\n * mirage (m)\n * creedal (c)\n * biennial (b)\n * cry (c)\n * whisper (w)\n * eyesight (e)\n * titmice (t)\n\n2. **Sort by the first letter:**\n * **b:** biennial\n * **c:** creedal, cry\n * **e:** eyesight\n * **f:** fletch, fraudulent\n * **j:** j\n * **m:** miltonic, mirage\n * **t:** titmice\n * **w:** whisper\n\n3. **Sort within groups:**\n\n * **c:** creedal, cry (creedal comes before cry)\n * **f:** fletch, fraudulent (fletch comes before fraudulent)\n * **m:** miltonic, mirage (miltonic comes before mirage)\n\n4. **Final ordered list:**\n * biennial (b)\n * creedal (c)\n * cry (c)\n * eyesight (e)\n * fletch (f)\n * fraudulent (f)\n * j (j)\n * miltonic (m)\n * mirage (m)\n * titmice (t)\n * whisper (w)\n\nLet's re-verify the order based on standard alphabetical rules:\n\n1. biennial (b)\n2. creedal (c)\n3. cry (c)\n4. eyesight (e)\n5. fletch (f)\n6. fraudulent (f)\n7. j (j)\n8. miltonic (m)\n9. mirage (m)\n10. titmice (t)\n11. whisper (w)\n\n(Note on 'j': In alphabetical sorting, 'j' comes after 'f' and before 'm'.)\n\nThe correct alphabetical order is: biennial, creedal, cry, eyesight, fletch,", + "schema_version": 6, + "seq": 237, + "span_id": "m1.model_call.366", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:37:37.400053+00:00" + }, + { + "analyzer": "perturbation_battery", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 14, + "case_id": null, + "cycle": 0, + "duration_sec": 2.3936, + "event": "model_call", + "event_seq": 367, + "inputs": { + "prompt": "Sort the following words alphabetically: Kavi: whisper eyesight titmice mirage fletch cry biennial creedal miltonic fraudulent j\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "The given words are: Kavi, whisper, eyesight, titmice, mirage, fletch, cry, biennial, creedal, miltonic, fraudulent, j.\n\nHere is the alphabetical sorting process:\n\n1. **Compare by the first letter:**\n * creedal (c)\n * biennial (b)\n * fletch (f)\n * fraudulent (f)\n * eyesight (e)\n * j (j)\n * Kavi (K)\n * miltonic (m)\n * mirage (m)\n * cry (c)\n * whisper (w)\n * titmice (t)\n\n2. **Sort words starting with 'c':**\n * creedal\n * cry\n * (Comparing 'creedal' and 'cry': 'creedal' comes before 'cry')\n\n3. **Sort words starting with 'f':**\n * fletch\n * fraudulent\n * (Comparing 'fletch' and 'fraudulent': 'fletch' comes before 'fraudulent')\n\n4. **Sort words starting with 'm':**\n * miltonic\n * mirage\n * (Comparing 'miltonic' and 'mirage': 'miltonic' comes before 'mirage')\n\n5. **Remaining words in order of first letter:**\n * biennial (b)\n * eyesight (e)\n * j (j)\n * Kavi (K)\n * whisper (w)\n * titmice (t)\n\n**Putting it all together alphabetically:**\n\n1. biennial\n2. creedal\n3. cry\n4. fletch\n5. fraudulent\n6. eyesight\n7. j\n8. Kavi\n9. miltonic\n10. mirage\n11. whisper\n12. titmice\n\nLet's re-verify the full list:\n* biennial (b)\n* creedal (c)\n* cry (c)\n* fletch (f)\n* fraudulent (f)\n* eyesight (e)\n* j (j)\n* Kavi (K)\n* miltonic (m)\n* mirage (m)\n* whisper (w)\n* titmice (t)\n\n**Final Alphabetical Order:**\nbiennial, creedal, cry, flet", + "schema_version": 6, + "seq": 238, + "span_id": "m1.model_call.367", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:37:39.804037+00:00" + }, + { + "analyzer": "perturbation_battery", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 15, + "case_id": null, + "cycle": 0, + "duration_sec": 2.392, + "event": "model_call", + "event_seq": 368, + "inputs": { + "prompt": "Read the problem carefully, then solve it.\n\nSort the following words alphabetically: List: whisper eyesight titmice mirage fletch cry biennial creedal miltonic fraudulent j\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: whisper, eyesight, titmice, mirage, fletch, cry, biennial, creedal, miltonic, fraudulent, j.\n\nWe need to sort these words alphabetically.\n\nLet's list the words and compare them letter by letter:\n1. whisper\n2. eyesight\n3. titmice\n4. mirage\n5. fletch\n6. cry\n7. biennial\n8. creedal\n9. miltonic\n10. fraudulent\n11. j\n\nSorting based on the first letter:\n'b': biennial\n'c': creedal\n'f': fletch\n'i': eyesight\n'j': j\n'm': miltonic, mirage\n'c': cry\n'w': whisper\n\nLet's refine the sorting:\n\nWords starting with 'b':\n* biennial\n\nWords starting with 'c':\n* creedal\n* cry\n\nWords starting with 'f':\n* fletch\n\nWords starting with 'i':\n* eyesight\n\nWords starting with 'j':\n* j\n\nWords starting with 'm':\n* miltonic\n* mirage (m-i-l vs m-i-r. 'l' comes before 'r')\n * miltonic\n * mirage\n\nWords starting with 'w':\n* whisper\n\nNow let's combine and sort the 'c' words:\ncreedal vs cry. 'c-r-e' vs 'c-r-y'. 'e' comes before 'y'.\n* creedal\n* cry\n\nThe full sorted list based on the first letter:\n1. biennial (b)\n2. creedal (c)\n3. cry (c)\n4. fletch (f)\n5. eyesight (i)\n6. j (j)\n7. miltonic (m)\n8. mirage (m)\n9. whisper (w)\n10. fraudulent (f) - Wait, 'f' comes before 'i'. Let's re-sort based on the first letter order: b, c, f, i, j, m, w.\n\nLet's re-list and sort systematically:\n\nWords: whisper, eyesight, titmice, mirage, fletch, cry, biennial, creedal, miltonic, fraudulent, j\n\n1. **biennial** (b)\n", + "schema_version": 6, + "seq": 239, + "span_id": "m1.model_call.368", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:37:42.206354+00:00" + }, + { + "analyzer": "perturbation_battery", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 16, + "case_id": null, + "cycle": 0, + "duration_sec": 2.3882, + "event": "model_call", + "event_seq": 369, + "inputs": { + "prompt": "Sort the following words alphabetically: List: whisper eyesight titmice mirage fletch cry biennial creedal miltonic fraudulent j\n Note that last year the same quantities were recorded in a different notebook, which has since been archived.\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: whisper, eyesight, titmice, mirage, fletch, cry, biennial, creedal, miltonic, fraudulent, j.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter:**\n * fletch (f)\n * fraudulent (f)\n * j (j)\n * miltonic (m)\n * mirage (m)\n * creedal (c)\n * biennial (b)\n * cry (c)\n * whisper (w)\n * eyesight (e)\n * titmice (t)\n\n2. **Sort by the first letter:**\n * **b:** biennial\n * **c:** creedal, cry\n * **e:** eyesight\n * **f:** fletch, fraudulent\n * **j:** j\n * **m:** miltonic, mirage\n * **t:** titmice\n * **w:** whisper\n\n3. **Resolve ties:**\n\n * **c:** creedal vs. cry\n * creedal (cr...) comes before cry (cr**y**)\n * **f:** fletch vs. fraudulent\n * fletch (fl...) comes before fraudulent (fr...)\n * **m:** miltonic vs. mirage\n * miltonic (mi**l**) comes before mirage (mi**r**)\n\n4. **Final Sorted Order:**\n 1. biennial (b)\n 2. creedal (c)\n 3. cry (c)\n 4. eyesight (e)\n 5. fletch (f)\n 6. fraudulent (f)\n 7. j (j)\n 8. miltonic (m)\n 9. mirage (m)\n 10. titmice (t)\n 11. whisper (w)\n\nLet's re-check the sorting carefully:\n\nWords: whisper, eyesight, titmice, mirage, fletch, cry, biennial, creedal, miltonic, fraudulent, j\n\nA-Z order:\n1. biennial (b)\n2. creedal (c)\n3. cry (c)\n4. eyesight (e)\n5. fletch (f)\n6. fraudulent (f)\n7. j (j)\n8. mil", + "schema_version": 6, + "seq": 240, + "span_id": "m1.model_call.369", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:37:44.604962+00:00" + }, + { + "analyzer": "perturbation_battery", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 17, + "case_id": "bbh_word_sorting-0094", + "cycle": 0, + "duration_sec": 1.6544, + "event": "model_call", + "event_seq": 370, + "inputs": { + "prompt": "Sort the following words alphabetically: List: crossbill bushland capacitive meager ricochet battery enigma wysiwyg wallet lipton jane wacke contingent\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. battery\n2. bushland\n3. capacitive\n4. contingent\n5. crossbill\n6. enigma\n7. jane\n8. lipton\n9. meager\n10. ricochet\n11. wallet\n12. wacke\n13. wysiwyg\n\nLet's re-sort them carefully:\n\n* **b**attery\n* **b**ushland\n* **c**apacitive\n* **c**ontingent\n* **c**rossbill\n* **e**nigma\n* **j**ane\n* **l**ipton\n* **m**eager\n* **r**icochet\n* **w**allet\n* **w**acke\n* **w**ysiwyg\n\nComparing the 'c' words:\n* capacitive\n* contingent\n* crossbill\nAlphabetical order: capacitive, contingent, crossbill.\n\nComparing the 'w' words:\n* wallet\n* wacke\n* wysiwyg\nAlphabetical order: wallet, wacke, wysiwyg.\n\nThe final sorted list is:\n1. battery\n2. bushland\n3. capacitive\n4. contingent\n5. crossbill\n6. enigma\n7. jane\n8. lipton\n9. meager\n10. ricochet\n11. wallet\n12. wacke\n13. wysiwyg\n\nAnswer: battery, bushland, capacitive, contingent, crossbill, enigma, jane, lipton, meager, ricochet, wallet, wacke, wysiwyg", + "schema_version": 6, + "seq": 241, + "span_id": "m1.model_call.370", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:37:46.269770+00:00" + }, + { + "analyzer": "perturbation_battery", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 18, + "case_id": null, + "cycle": 0, + "duration_sec": 2.3753, + "event": "model_call", + "event_seq": 371, + "inputs": { + "prompt": "Sort the following words alphabetically: Kavi: crossbill bushland capacitive meager ricochet battery enigma wysiwyg wallet lipton jane wacke contingent\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "The given words are: Kavi, crossbill, bushland, capacitive, meager, ricochet, battery, enigma, wysiwyg, wallet, lipton, jane, wacke, contingent.\n\nHere is the alphabetical sorting process:\n\n1. **Compare by the first letter:**\n * battery (b)\n * bushland (b)\n * capacitive (c)\n * contingent (c)\n * crossbill (c)\n * enigma (e)\n * jane (j)\n * Kavi (K)\n * lipton (l)\n * meager (m)\n * ricochet (r)\n * wallet (w)\n * wacke (w)\n * wysiwyg (w)\n\n2. **Sort words starting with 'b':**\n * battery\n * bushland\n * (battery comes before bushland)\n\n3. **Sort words starting with 'c':**\n * capacitive\n * contingent\n * crossbill\n * (capacitive, contingent, crossbill)\n\n4. **Sort words starting with 'e':**\n * enigma\n\n5. **Sort words starting with 'j':**\n * jane\n\n6. **Sort words starting with 'K':**\n * Kavi\n\n7. **Sort words starting with 'l':**\n * lipton\n\n8. **Sort words starting with 'm':**\n * meager\n\n9. **Sort words starting with 'r':**\n * ricochet\n\n10. **Sort words starting with 'w':**\n * wallet\n * wacke\n * wysiwyg\n * (wallet, wacke, wysiwyg)\n\n**Detailed comparison for ties:**\n\n* **'b' words:** battery, bushland (a comes before u) $\\rightarrow$ battery, bushland\n* **'c' words:** capacitive, contingent, crossbill\n * capacitive (c a p...)\n * contingent (c o n...)\n * crossbill (c r o...)\n * Order: capacitive, contingent, crossbill\n* **'w' words:** wallet, wacke, wysiwyg\n * wallet (wa l...)\n * wacke (wa c...)\n * wysiwyg (wy s...)\n * Comparing '", + "schema_version": 6, + "seq": 242, + "span_id": "m1.model_call.371", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:37:48.655479+00:00" + }, + { + "analyzer": "perturbation_battery", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 19, + "case_id": null, + "cycle": 0, + "duration_sec": 2.4457, + "event": "model_call", + "event_seq": 372, + "inputs": { + "prompt": "Read the problem carefully, then solve it.\n\nSort the following words alphabetically: List: crossbill bushland capacitive meager ricochet battery enigma wysiwyg wallet lipton jane wacke contingent\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: crossbill, bushland, capacitive, meager, ricochet, battery, enigma, wysiwyg, wallet, lipton, jane, wacke, contingent.\n\nWe need to sort these words alphabetically.\n\nLet's list the words and compare them letter by letter:\n1. battery\n2. bushland\n3. capacitive\n4. contingent\n5. crossbill\n6. enigma\n7. jane\n8. lipton\n9. meager\n10. ricochet\n11. wallet\n12. wacke\n13. wysiwyg\n\nSorting process:\n\n**Starting with B:**\n* battery\n* bushland\n\n**Starting with C:**\n* capacitive\n* contingent\n* crossbill\n\n**Starting with E:**\n* enigma\n\n**Starting with J:**\n* jane\n\n**Starting with L:**\n* lipton\n\n**Starting with M:**\n* meager\n\n**Starting with R:**\n* ricochet\n\n**Starting with W:**\n* wacke\n* wallet\n* wysiwyg\n\nNow let's refine the order within groups:\n\n**B:** battery, bushland (b-a vs b-u. 'a' comes before 'u') $\\rightarrow$ battery, bushland\n\n**C:** capacitive, contingent, crossbill\n * capacitive (ca...)\n * contingent (co...)\n * crossbill (cr...)\n * Order: capacitive, contingent, crossbill\n\n**E:** enigma\n\n**J:** jane\n\n**L:** lipton\n\n**M:** meager\n\n**R:** ricochet\n\n**W:** wacke, wallet, wysiwyg\n * wacke (wa...)\n * wallet (wa...)\n * wysiwyg (ws...)\n * Comparing wacke and wallet: wacke vs wallet. 'a' vs 'l'. 'a' comes before 'l'. $\\rightarrow$ wacke, wallet\n * wysiwyg comes last among W words.\n * Order: wacke, wallet, wysiwyg\n\n**Combining the sorted groups:**\n1. battery\n2. bushland\n3. capacitive\n4. contingent\n5. crossbill\n6. enigma\n7. jane\n8. lipton\n9. meager\n10. ricochet\n11. wacke\n12. wallet\n13. wys", + "schema_version": 6, + "seq": 243, + "span_id": "m1.model_call.372", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:37:51.113471+00:00" + }, + { + "analyzer": "perturbation_battery", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 20, + "case_id": null, + "cycle": 0, + "duration_sec": 1.8636, + "event": "model_call", + "event_seq": 373, + "inputs": { + "prompt": "Sort the following words alphabetically: List: crossbill bushland capacitive meager ricochet battery enigma wysiwyg wallet lipton jane wacke contingent\n Note that last year the same quantities were recorded in a different notebook, which has since been archived.\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. battery\n2. bushland\n3. capacitive\n4. contingent\n5. crossbill\n6. enigma\n7. jane\n8. lipton\n9. meager\n10. ricochet\n11. wallet\n12. wacke\n13. wysiwyg\n\nLet's re-sort them carefully:\n\n* **b**attery\n* **b**ushland\n* **c**apacitive\n* **c**ontingent\n* **c**rossbill\n* **e**nigma\n* **j**ane\n* **l**ipton\n* **m**eager\n* **r**icochet\n* **w**allet\n* **w**acke\n* **w**ysiwyg\n\nComparing the 'c' words:\n* capacitive\n* contingent\n* crossbill\nAlphabetical order: capacitive, contingent, crossbill.\n\nComparing the 'w' words:\n* wallet\n* wacke\n* wysiwyg\nAlphabetical order: wallet, wacke, wysiwyg.\n\nThe final sorted list is:\n1. battery\n2. bushland\n3. capacitive\n4. contingent\n5. crossbill\n6. enigma\n7. jane\n8. lipton\n9. meager\n10. ricochet\n11. wallet\n12. wacke\n13. wysiwyg\n\nAnswer: battery, bushland, capacitive, contingent, crossbill, enigma, jane, lipton, meager, ricochet, wallet, wacke, wysiwyg", + "schema_version": 6, + "seq": 244, + "span_id": "m1.model_call.373", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:37:52.988220+00:00" + }, + { + "analyzer": "perturbation_battery", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 21, + "case_id": "bbh_word_sorting-0000", + "cycle": 0, + "duration_sec": 2.3427, + "event": "model_call", + "event_seq": 374, + "inputs": { + "prompt": "Sort the following words alphabetically: List: alleviate duopoly mattress gland benelux townsmen buoyant klaxon hardbound tomography felice gunk\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words to sort alphabetically: alleviate, duopoly, mattress, gland, benelux, townsmen, buoyant, klaxon, hardbound, tomography, felice, gunk.\n\n1. **alleviate**\n2. **benelux**\n3. **buoyant**\n4. **duopoly**\n5. **felice**\n6. **gland**\n7. **hardbound**\n8. **klaxon**\n9. **mattress**\n10. **tomography**\n11. **townsmen**\n\nLet's re-sort them carefully:\n\n* **a**lleviate\n* **b**enelux, **b**uoyant (benelux before buoyant)\n* **d**uopoly\n* **f**elice\n* **g**land, **g**unk (gland before gunk)\n* **h**ardbound\n* **k**laxon\n* **m**attress\n* **t**omography, **t**ownsmen (tomography before townsmen)\n\n**Final Sorted List:**\n1. alleviate\n2. benelux\n3. buoyant\n4. duopoly\n5. felice\n6. gland\n7. gunk\n8. hardbound\n9. klaxon\n10. mattress\n11. tomography\n12. townsmen\n\nWait, let's re-check the 'g' words: gland, gunk. Gland comes before Gunk.\n\nLet's re-check the 'b' words: benelux, buoyant. Benelux comes before Buoyant.\n\nLet's re-check the 't' words: tomography, townsmen. Tomography comes before Townsmen.\n\n**Final Alphabetical Order:**\n1. alleviate\n2. benelux\n3. buoyant\n4. duopoly\n5. felice\n6. gland\n7. gunk\n8. hardbound\n9. klaxon\n10. mattress\n11. tomography\n12. townsmen\n\nAnswer: alleviate, benelux, buoyant, duopoly, felice, gland, gunk, hardbound, klaxon, mattress, tomography, townsmen", + "schema_version": 6, + "seq": 245, + "span_id": "m1.model_call.374", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:37:55.345030+00:00" + }, + { + "analyzer": "perturbation_battery", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 22, + "case_id": null, + "cycle": 0, + "duration_sec": 2.1816, + "event": "model_call", + "event_seq": 375, + "inputs": { + "prompt": "Sort the following words alphabetically: Kavi: alleviate duopoly mattress gland benelux townsmen buoyant klaxon hardbound tomography felice gunk\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "The given words are: Kavi, alleviate, duopoly, mattress, gland, benelux, townsmen, buoyant, klaxon, hardbound, tomography, felice, gunk.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter:**\n * alleviate (a)\n * benelux (b)\n * buoyant (b)\n * duopoly (d)\n * felice (f)\n * gland (g)\n * gunk (g)\n * hardbound (h)\n * klaxon (k)\n * Kavi (K) - Treating 'K' as uppercase for initial sorting, or 'k' if case-insensitive. Assuming standard alphabetical order where 'K' comes before lowercase letters, or treating all as lowercase for simplicity. Let's treat them all as lowercase for standard sorting: kavi.\n * mattress (m)\n * tomography (t)\n * townsmen (t)\n\n2. **Sorting within groups:**\n\n * **A:** alleviate\n * **B:** benelux, buoyant (benelux comes before buoyant)\n * **D:** duopoly\n * **F:** felice\n * **G:** gland, gunk (gland comes before gunk)\n * **H:** hardbound\n * **K:** kavi\n * **M:** mattress\n * **T:** tomography, townsmen (tomography comes before townsmen)\n\n3. **Final Sorted List (Alphabetical Order):**\n 1. alleviate\n 2. benelux\n 3. buoyant\n 4. duopoly\n 5. felice\n 6. gland\n 7. gunk\n 8. hardbound\n 9. kavi\n 10. mattress\n 11. tomography\n 12. townsmen\n\nAnswer: alleviate, benelux, buoyant, duopoly, felice, gland, gunk, hardbound, kavi, mattress, tomography, townsmen", + "schema_version": 6, + "seq": 246, + "span_id": "m1.model_call.375", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:37:57.538193+00:00" + }, + { + "analyzer": "perturbation_battery", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 23, + "case_id": null, + "cycle": 0, + "duration_sec": 2.4663, + "event": "model_call", + "event_seq": 376, + "inputs": { + "prompt": "Read the problem carefully, then solve it.\n\nSort the following words alphabetically: List: alleviate duopoly mattress gland benelux townsmen buoyant klaxon hardbound tomography felice gunk\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: alleviate, duopoly, mattress, gland, benelux, townsmen, buoyant, klaxon, hardbound, tomography, felice, gunk.\n\nWe need to sort these words alphabetically.\n\nLet's list the words and compare them letter by letter:\n1. alleviate\n2. duopoly\n3. mattress\n4. gland\n5. benelux\n6. townsmen\n7. buoyant\n8. klaxon\n9. hardbound\n10. tomography\n11. felice\n12. gunk\n\nComparing the first letters:\nA: alleviate\nB: benelux, buoyant\nD: duopoly\nF: felice\nG: gland, gunk\nH: hardbound\nK: klaxon\nM: mattress\nT: townsmen, tomography\n\nSorting based on the first letter:\nA: alleviate\nB: benelux, buoyant (benelux comes before buoyant)\nD: duopoly\nF: felice\nG: gland, gunk (gland comes before gunk)\nH: hardbound\nK: klaxon\nM: mattress\nT: townsmen, tomography (townsmen comes before tomography)\n\nLet's refine the order within the groups:\n\nGroup B: benelux, buoyant (b-e vs b-u, 'e' comes before 'u') $\\rightarrow$ benelux, buoyant\nGroup G: gland, gunk (g-l vs g-u, 'l' comes before 'u') $\\rightarrow$ gland, gunk\nGroup T: townsmen, tomography (t-o vs t-o, 'w' vs 'o'. Wait, T-o-w-n-s-m-e-n vs T-o-m-o-g-r-a-p-h-y. 'w' comes after 'm' in the second position if we compare the full words. Let's compare them carefully:\ntownsmen\ntomography\nt-o-w...\nt-o-m...\n'w' comes after 'm'. So, tomography comes before townsmen.\nCorrect order for T: tomography, townsmen\n\nLet's re-sort the entire list:\n\n1. alleviate (A)\n2. benelux (B)\n3. buoyant (B)\n4. duopoly (", + "schema_version": 6, + "seq": 247, + "span_id": "m1.model_call.376", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:38:00.015746+00:00" + }, + { + "analyzer": "perturbation_battery", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 24, + "case_id": null, + "cycle": 0, + "duration_sec": 1.6314, + "event": "model_call", + "event_seq": 377, + "inputs": { + "prompt": "Sort the following words alphabetically: List: alleviate duopoly mattress gland benelux townsmen buoyant klaxon hardbound tomography felice gunk\n Note that last year the same quantities were recorded in a different notebook, which has since been archived.\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "Here are the words sorted alphabetically:\n\n1. alleviate\n2. benelux\n3. buoyant\n4. duopoly\n5. felice\n6. gland\n7. gunk\n8. hardbound\n9. klaxon\n10. mattress\n11. tomography\n12. townsmen\n\nLet's re-sort them carefully:\n\n* **a**lleviate\n* **b**enelux, **b**uoyant\n* **d**uopoly\n* **f**elice\n* **g**land, **g**unk\n* **h**ardbound\n* **k**laxon\n* **m**attress\n* **t**omography, **t**ownsmen\n\nSorting the 'b' words: benelux, buoyant\nSorting the 'g' words: gland, gunk\nSorting the 't' words: tomography, townsmen\n\nThe final alphabetical order is:\n\n1. alleviate\n2. benelux\n3. buoyant\n4. duopoly\n5. felice\n6. gland\n7. gunk\n8. hardbound\n9. klaxon\n10. mattress\n11. tomography\n12. townsmen\n\nAnswer: alleviate, benelux, buoyant, duopoly, felice, gland, gunk, hardbound, klaxon, mattress, tomography, townsmen", + "schema_version": 6, + "seq": 248, + "span_id": "m1.model_call.377", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:38:01.657544+00:00" + }, + { + "analyzer": "perturbation_battery", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 25, + "case_id": "bbh_word_sorting-0063", + "cycle": 0, + "duration_sec": 1.8291, + "event": "model_call", + "event_seq": 378, + "inputs": { + "prompt": "Sort the following words alphabetically: List: vegetate artillery harm fda doris prosody bainite incongruous monkey vivian\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: vegetate, artillery, harm, fda, doris, prosody, bainite, incongruous, monkey, vivian.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter:**\n * bainite (b)\n * doris (d)\n * fda (f)\n * harm (h)\n * incongruous (i)\n * monkey (m)\n * prosody (p)\n * artillery (a)\n * vegetate (v)\n * vivian (v)\n\n2. **Sort based on the first letter:**\n * **a:** artillery\n * **b:** bainite\n * **d:** doris\n * **f:** fda\n * **h:** harm\n * **i:** incongruous\n * **m:** monkey\n * **p:** prosody\n * **v:** vegetate, vivian\n\n3. **Sort the 'v' words:**\n * vegetate\n * vivian\n * Comparing the second letter: 'e' in vegetate, 'i' in vivian. 'e' comes before 'i'.\n * So, vegetate comes before vivian.\n\n4. **Final sorted order:**\n 1. artillery\n 2. bainite\n 3. doris\n 4. fda\n 5. harm\n 6. incongruous\n 7. monkey\n 8. prosody\n 9. vegetate\n 10. vivian\n\nAnswer: artillery, bainite, doris, fda, harm, incongruous, monkey, prosody, vegetate, vivian", + "schema_version": 6, + "seq": 249, + "span_id": "m1.model_call.378", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:38:03.499524+00:00" + }, + { + "analyzer": "perturbation_battery", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 26, + "case_id": null, + "cycle": 0, + "duration_sec": 1.9715, + "event": "model_call", + "event_seq": 379, + "inputs": { + "prompt": "Sort the following words alphabetically: Kavi: vegetate artillery harm fda doris prosody bainite incongruous monkey vivian\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "The given words are: Kavi, vegetate, artillery, harm, fda, doris, prosody, bainite, incongruous, monkey, vivian.\n\nHere is the alphabetical sorting process:\n\n1. **Compare by the first letter:**\n * bainite (b)\n * doris (d)\n * fda (f)\n * harm (h)\n * incongruous (i)\n * Kavi (K)\n * monkey (m)\n * prosody (p)\n * artillery (a)\n * vegetate (v)\n * vivian (v)\n\n2. **Sort based on the first letter order (A, B, D, F, H, I, K, M, P, V):**\n\n * **A:** artillery\n * **B:** bainite\n * **D:** doris\n * **F:** fda\n * **H:** harm\n * **I:** incongruous\n * **K:** Kavi\n * **M:** monkey\n * **P:** prosody\n * **V:** vegetate, vivian (Need to compare these two)\n\n3. **Compare words starting with 'V':**\n * vegetate\n * vivian\n * 'v'i' comes before 'v'e. So, vivian comes before vegetate.\n * Order: vivian, vegetate\n\n4. **Final Sorted List:**\n 1. artillery\n 2. bainite\n 3. doris\n 4. fda\n 5. harm\n 6. incongruous\n 7. Kavi\n 8. monkey\n 9. prosody\n 10. vivian\n 11. vegetate\n\nAnswer: artillery, bainite, doris, fda, harm, incongruous, Kavi, monkey, prosody, vivian, vegetate", + "schema_version": 6, + "seq": 250, + "span_id": "m1.model_call.379", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:38:05.481476+00:00" + }, + { + "analyzer": "perturbation_battery", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 27, + "case_id": null, + "cycle": 0, + "duration_sec": 2.3284, + "event": "model_call", + "event_seq": 380, + "inputs": { + "prompt": "Read the problem carefully, then solve it.\n\nSort the following words alphabetically: List: vegetate artillery harm fda doris prosody bainite incongruous monkey vivian\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: vegetate, artillery, harm, fda, doris, prosody, bainite, incongruous, monkey, vivian.\n\nWe need to sort these words alphabetically. We will compare them letter by letter.\n\n1. **bainite** (Starts with 'b')\n2. **doris** (Starts with 'd')\n3. **fda** (Starts with 'f')\n4. **harm** (Starts with 'h')\n5. **incongruous** (Starts with 'i')\n6. **monkey** (Starts with 'm')\n7. **prosody** (Starts with 'p')\n8. **vegetate** (Starts with 'v')\n9. **vivian** (Starts with 'v')\n10. **artillery** (Starts with 'a')\n\nLet's re-sort based on the first letter:\n\nA: artillery\nB: bainite\nD: doris\nF: fda\nH: harm\nI: incongruous\nM: monkey\nP: prosody\nV: vegetate, vivian\n\nNow compare the words starting with 'V':\n* vegetate\n* vivian\n'v' is the same.\n'e' in vegetate, 'i' in vivian. 'e' comes before 'i'.\nSo, vegetate comes before vivian.\n\nThe sorted order is:\n1. artillery\n2. bainite\n3. doris\n4. fda\n5. harm\n6. incongruous\n7. monkey\n8. prosody\n9. vegetate\n10. vivian\n\nLet's double-check the alphabetical order:\nartillery (a)\nbainite (b)\ndoris (d)\nfda (f)\nharm (h)\nincongruous (i)\nmonkey (m)\nprosody (p)\nvegetate (v, e)\nvivian (v, i)\n\nThe order seems correct.\n\nAnswer: artillery, bainite, doris, fda, harm, incongruous, monkey, prosody, vegetate, vivian\n\nAnswer: artillery, bainite, doris, fda, harm, incongruous, monkey, prosody, vegetate, vivian", + "schema_version": 6, + "seq": 251, + "span_id": "m1.model_call.380", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:38:07.826434+00:00" + }, + { + "analyzer": "perturbation_battery", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 28, + "case_id": null, + "cycle": 0, + "duration_sec": 2.3802, + "event": "model_call", + "event_seq": 381, + "inputs": { + "prompt": "Sort the following words alphabetically: List: vegetate artillery harm fda doris prosody bainite incongruous monkey vivian\n Note that last year the same quantities were recorded in a different notebook, which has since been archived.\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: vegetate, artillery, harm, fda, doris, prosody, bainite, incongruous, monkey, vivian.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **bainite** (starts with b)\n2. **doris** (starts with d)\n3. **fda** (starts with f)\n4. **harm** (starts with h)\n5. **incongruous** (starts with i)\n6. **monkey** (starts with m)\n7. **prosody** (starts with p)\n8. **artillery** (starts with a) - Wait, 'artillery' starts with 'a'. Let's re-sort based on the first letter.\n\nLet's list the words and their starting letters:\n* vegetate (v)\n* artillery (a)\n* harm (h)\n* fda (f)\n* doris (d)\n* prosody (p)\n* bainite (b)\n* incongruous (i)\n* monkey (m)\n* vivian (v)\n\nSorting by the first letter (A, B, D, F, H, I, M, P, V):\n\n1. **artillery** (a)\n2. **bainite** (b)\n3. **doris** (d)\n4. **fda** (f)\n5. **harm** (h)\n6. **incongruous** (i)\n7. **monkey** (m)\n8. **prosody** (p)\n9. **vegetate** (v)\n10. **vivian** (v)\n\nNow we need to resolve the tie between 'vegetate' and 'vivian'.\n* vegetate\n* vivian\n'e' comes before 'i', so 'vegetate' comes before 'vivian'.\n\nThe final sorted list is:\n1. artillery\n2. bainite\n3. doris\n4. fda\n5. harm\n6. incongruous\n7. monkey\n8. prosody\n9. vegetate\n10. vivian\n\nAnswer: artillery, bainite, doris, fda, harm, incongruous, monkey, prosody, vegetate, vivian\n\nAnswer: artillery, bainite, dor", + "schema_version": 6, + "seq": 252, + "span_id": "m1.model_call.381", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:38:10.217069+00:00" + }, + { + "analyzer": "perturbation_battery", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 29, + "case_id": "bbh_word_sorting-0101", + "cycle": 0, + "duration_sec": 1.1416, + "event": "model_call", + "event_seq": 382, + "inputs": { + "prompt": "Sort the following words alphabetically: List: regret starlight wallboard cotyledon more pepperoni\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: regret, starlight, wallboard, cotyledon, more, pepperoni.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **cotyledon** (Starts with 'c')\n2. **more** (Starts with 'm')\n3. **pepperoni** (Starts with 'p')\n4. **regret** (Starts with 'r')\n5. **starlight** (Starts with 's')\n6. **wallboard** (Starts with 'w')\n\nLet's verify the order:\n* c (cotyledon)\n* m (more)\n* p (pepperoni)\n* r (regret)\n* s (starlight)\n* w (wallboard)\n\nThe alphabetical order is: cotyledon, more, pepperoni, regret, starlight, wallboard.\n\nAnswer: cotyledon, more, pepperoni, regret, starlight, wallboard\n\nAnswer: cotyledon, more, pepperoni, regret, starlight, wallboard", + "schema_version": 6, + "seq": 253, + "span_id": "m1.model_call.382", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:38:11.375855+00:00" + }, + { + "analyzer": "perturbation_battery", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 30, + "case_id": null, + "cycle": 0, + "duration_sec": 1.1984, + "event": "model_call", + "event_seq": 383, + "inputs": { + "prompt": "Sort the following words alphabetically: Kavi: regret starlight wallboard cotyledon more pepperoni\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "The given words are: Kavi, regret, starlight, wallboard, cotyledon, more, pepperoni.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter of each word:**\n * Kavi (K)\n * regret (r)\n * starlight (s)\n * wallboard (w)\n * cotyledon (c)\n * more (m)\n * pepperoni (p)\n\n2. **Order the first letters alphabetically:**\n * c (cotyledon)\n * K (Kavi)\n * m (more)\n * p (pepperoni)\n * r (regret)\n * s (starlight)\n * w (wallboard)\n\n3. **The sorted order is:**\n * cotyledon\n * Kavi\n * more\n * pepperoni\n * regret\n * starlight\n * wallboard\n\nAnswer: cotyledon, Kavi, more, pepperoni, regret, starlight, wallboard", + "schema_version": 6, + "seq": 254, + "span_id": "m1.model_call.383", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:38:12.591869+00:00" + }, + { + "analyzer": "perturbation_battery", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 31, + "case_id": null, + "cycle": 0, + "duration_sec": 1.9178, + "event": "model_call", + "event_seq": 384, + "inputs": { + "prompt": "Read the problem carefully, then solve it.\n\nSort the following words alphabetically: List: regret starlight wallboard cotyledon more pepperoni\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: regret, starlight, wallboard, cotyledon, more, pepperoni.\n\nWe need to sort these words alphabetically. We compare them letter by letter from the beginning.\n\n1. **c**otyledon\n2. **m**ore\n3. **p**epperoni\n4. **r**egret\n5. **r**egret (Wait, there is only one 'regret'. Let's re-list and compare carefully.)\n\nWords:\n1. regret\n2. starlight\n3. wallboard\n4. cotyledon\n5. more\n6. pepperoni\n\nComparing the first letters:\nC: cotyledon\nM: more\nP: pepperoni\nR: regret, (and we have two 'r' words if we count 'regret' twice, but the list only contains 'regret' once).\nS: starlight\nW: wallboard\n\nOrder based on the first letter: C, M, P, R, S, W.\n\n1. **cotyledon** (C)\n2. **more** (M)\n3. **pepperoni** (P)\n4. **regret** (R)\n5. **starlight** (S)\n6. **wallboard** (W)\n\nLet's verify the order of words starting with 'R' and 'S' if there were any ties, but here the first letters are distinct.\n\nC (cotyledon)\nM (more)\nP (pepperoni)\nR (regret)\nS (starlight)\nW (wallboard)\n\nAlphabetical order: cotyledon, more, pepperoni, regret, starlight, wallboard.\n\nAnswer: cotyledon, more, pepperoni, regret, starlight, wallboard\n\nAnswer: cotyledon, more, pepperoni, regret, starlight, wallboard", + "schema_version": 6, + "seq": 255, + "span_id": "m1.model_call.384", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:38:14.520280+00:00" + }, + { + "analyzer": "perturbation_battery", + "batch_case_ids": [ + "bbh_word_sorting-0006", + "bbh_word_sorting-0025", + "bbh_word_sorting-0045", + "bbh_word_sorting-0076", + "bbh_word_sorting-0094", + "bbh_word_sorting-0000", + "bbh_word_sorting-0063", + "bbh_word_sorting-0101" + ], + "call_index": 32, + "case_id": null, + "cycle": 0, + "duration_sec": 1.1096, + "event": "model_call", + "event_seq": 385, + "inputs": { + "prompt": "Sort the following words alphabetically: List: regret starlight wallboard cotyledon more pepperoni\n Note that last year the same quantities were recorded in a different notebook, which has since been archived.\nSolve the problem. Put the final answer on its own last line as 'Answer: '." + }, + "kwargs": {}, + "method": "generate", + "n_batch_cases": 8, + "output": "The given list of words is: regret, starlight, wallboard, cotyledon, more, pepperoni.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **cotyledon** (starts with c)\n2. **more** (starts with m)\n3. **pepperoni** (starts with p)\n4. **regret** (starts with r)\n5. **starlight** (starts with s)\n6. **wallboard** (starts with w)\n\nLet's re-sort them based on the first letter:\nC: cotyledon\nM: more\nP: pepperoni\nR: regret\nS: starlight\nW: wallboard\n\nAlphabetical order:\n1. cotyledon\n2. more\n3. pepperoni\n4. regret\n5. starlight\n6. wallboard\n\nAnswer: cotyledon, more, pepperoni, regret, starlight, wallboard\n\nAnswer: cotyledon, more, pepperoni, regret, starlight, wallboard", + "schema_version": 6, + "seq": 256, + "span_id": "m1.model_call.385", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:38:15.640676+00:00" + } + ], + "probe": [ + { + "analyzers": [ + "answer_extraction_audit", + "termination_audit", + "selfcheck_consistency", + "self_consistency", + "calibration", + "logprob_entropy", + "coverage_verification_gap", + "perturbation_battery" + ], + "artifact_paths": {}, + "artifacts": { + "logprob_entropy/token_logprobs": [ + "TokenLogprob(token='Here', logprob=-0.07431311905384064, top={'Here': -0.07431311905384064, 'The': -2.636813163757324, 'List': -10.605563163757324, 'To': -12.808688163757324, '<|channel>': -13.058688163757324})", + "TokenLogprob(token=' are', logprob=-0.012359222397208214, top={' are': -0.012359222397208214, ' is': -4.512359142303467, \"'\": -6.637359142303467, ' list': -16.512359619140625, ' the': -20.887359619140625})", + "TokenLogprob(token=' the', logprob=0.0, top={' the': 0.0, ' a': -23.0625, '-': -23.5625, ' my': -25.5625, ' our': -25.8125})", + "TokenLogprob(token=' words', logprob=-0.013370612636208534, top={' words': -0.013370612636208534, ' steps': -4.325870513916016, ' given': -9.794620513916016, ' letters': -12.982120513916016, ' provided': -13.966495513916016})", + "TokenLogprob(token=' sorted', logprob=-0.08724380284547806, top={' sorted': -0.08724380284547806, ' from': -2.7122437953948975, ' to': -4.212244033813477, ' in': -6.274744033813477, ' provided': -8.462244033813477})", + "TokenLogprob(token=' alphabetically', logprob=0.0, top={' alphabetically': 0.0, ' in': -20.4765625, 'alphabet': -23.4375, ' alph': -26.09375, ' alphabet': -26.46875})", + "TokenLogprob(token=':', logprob=-3.5404529626248404e-05, top={':': -3.5404529626248404e-05, ' from': -10.250035285949707, '.': -18.984411239624023, ',': -19.546911239624023, ' in': -19.968786239624023})", + "TokenLogprob(token='\\n\\n', logprob=-0.0002612726530060172, top={'\\n\\n': -0.0002612726530060172, '\\n': -8.250261306762695, ' \"': -20.469011306762695, ' Add': -20.562761306762695, ' add': -20.844011306762695})", + "TokenLogprob(token='1', logprob=-0.00036936139804311097, top={'1': -0.00036936139804311097, 'List': -8.00036907196045, 'Original': -10.93786907196045, '*': -11.68786907196045, 'Adams': -13.50036907196045})", + "TokenLogprob(token='.', logprob=0.0, top={'.': 0.0, ')': -21.90625, ',': -22.890625, '\\\\.': -23.3125, '-': -23.359375})", + "TokenLogprob(token=' administer', logprob=-0.19084125757217407, top={' administer': -0.19084125757217407, ' a': -1.7533411979675293, ' decadent': -7.565841197967529, ' admin': -10.659591674804688, ' administers': -15.003341674804688})", + "TokenLogprob(token='\\n', logprob=-2.3841855067985307e-07, top={'\\n': -2.3841855067985307e-07, ' (': -15.3125, ' ($\\\\': -20.625, ' ($': -22.755859375, ',': -25.34375})", + "TokenLogprob(token='2', logprob=0.0, top={'2': 0.0, '1': -19.125, '*': -23.0, '4': -24.15625, ' ': -24.453125})", + "TokenLogprob(token='.', logprob=0.0, top={'.': 0.0, ',': -32.9375, '-': -32.9375, '0': -33.1875, '1': -33.6875})", + "TokenLogprob(token=' a', logprob=-0.011047860607504845, top={' a': -0.011047860607504845, ' decadent': -4.511047840118408, ' aet': -16.65948486328125, ' aDecoder': -18.77667236328125, ' decad': -19.47979736328125})", + "TokenLogprob(token='ene', logprob=0.0, top={'ene': 0.0, 'ened': -29.3125, 'en': -30.0625, 'ere': -30.75, 'enes': -33.375})", + "TokenLogprob(token='id', logprob=0.0, top={'id': 0.0, 'ide': -34.5625, 'ad': -35.1875, '\\n': -35.3125, 'ids': -35.4375})", + "TokenLogprob(token='\\n', logprob=0.0, top={'\\n': 0.0, ' (': -20.171875, ' ': -25.625, ' ($\\\\': -26.234375, '_': -26.4375})", + "TokenLogprob(token='3', logprob=0.0, top={'3': 0.0, '4': -24.5625, '1': -25.0, '2': -26.0, '5': -27.75})", + "TokenLogprob(token='.', logprob=0.0, top={'.': 0.0, '-': -27.625, '0': -28.625, ',': -28.625, '1': -29.375})", + "TokenLogprob(token=' decadent', logprob=-1.2993727978027891e-05, top={' decadent': -1.2993727978027891e-05, ' coach': -11.25001335144043, ' decad': -22.26563835144043, ' candid': -24.00001335144043, ' dec': -24.75001335144043})", + "TokenLogprob(token='\\n', logprob=0.0, top={'\\n': 0.0, ' (': -20.046875, ' ': -25.6953125, ',': -26.859375, ' ($\\\\': -26.90625})", + "TokenLogprob(token='4', logprob=0.0, top={'4': 0.0, '3': -22.8125, '1': -25.1875, '5': -26.4375, '6': -26.8125})", + "TokenLogprob(token='.', logprob=0.0, top={'.': 0.0, '-': -29.75, ',': -30.75, '0': -30.75, '1': -31.125})", + "TokenLogprob(token=' coach', logprob=-0.01507262047380209, top={' coach': -0.01507262047380209, ' delhi': -4.202572822570801, ' co': -13.4525728225708, ' coaching': -14.7260103225708, ' coeff': -15.5853853225708})", + "TokenLogprob(token='man', logprob=0.0, top={'man': 0.0, 'men': -32.375, 'mann': -33.5, '\\n': -33.875, 'm': -33.875})", + "TokenLogprob(token='\\n', logprob=0.0, top={'\\n': 0.0, ' (': -17.375, '(': -23.2890625, ' $\\\\': -23.701171875, ' ($\\\\': -23.8876953125})", + "TokenLogprob(token='5', logprob=0.0, top={'5': 0.0, '4': -17.671875, '1': -20.6875, '6': -21.0, '3': -22.15625})", + "TokenLogprob(token='.', logprob=0.0, top={'.': 0.0, '-': -27.3125, '0': -27.3125, '1': -28.0625, ',': -28.3125})", + "TokenLogprob(token=' delhi', logprob=-2.2411095415009186e-05, top={' delhi': -2.2411095415009186e-05, ' del': -11.671896934509277, ' deli': -12.109396934509277, ' delta': -12.515646934509277, ' delight': -13.121115684509277})", + "TokenLogprob(token='\\n', logprob=-1.6689285757820471e-06, top={'\\n': -1.6689285757820471e-06, ' (': -13.312501907348633, ' ($\\\\': -20.484376907348633, ' ': -21.109376907348633, ' $\\\\': -21.921876907348633})", + "TokenLogprob(token='6', logprob=0.0, top={'6': 0.0, '4': -22.4375, '5': -22.6875, '1': -22.75, '7': -24.3125})", + "TokenLogprob(token='.', logprob=0.0, top={'.': 0.0, '-': -24.875, ',': -26.5, '0': -26.625, '1': -27.0})", + "TokenLogprob(token=' grad', logprob=-0.002804872114211321, top={' grad': -0.002804872114211321, ' grim': -5.877804756164551, ' gradual': -18.752805709838867, ' dim': -19.674680709838867, ' grade': -20.721555709838867})", + "TokenLogprob(token='ate', logprob=0.0, top={'ate': 0.0, 'iate': -31.0, 'are': -31.875, 'ates': -32.0, 'nate': -32.125})", + "TokenLogprob(token='\\n', logprob=-1.5735502529423684e-05, top={'\\n': -1.5735502529423684e-05, ' (': -11.062515258789062, ' ($\\\\': -18.562515258789062, ' ($': -20.265640258789062, ' $\\\\': -21.015640258789062})", + "TokenLogprob(token='7', logprob=0.0, top={'7': 0.0, '6': -23.25, '1': -23.9375, '4': -25.0625, '8': -25.8125})", + "TokenLogprob(token='.', logprob=0.0, top={'.': 0.0, '-': -26.8125, '0': -28.0625, 't': -28.3125, ',': -28.4375})", + "TokenLogprob(token=' grim', logprob=-3.5404529626248404e-05, top={' grim': -3.5404529626248404e-05, ' jack': -10.250035285949707, ' germ': -20.324254989624023, ' grin': -24.046911239624023, ' jacks': -25.656286239624023})", + "TokenLogprob(token='\\n', logprob=0.0, top={'\\n': 0.0, ' (': -18.1875, 's': -23.171875, ',': -23.1796875, '\\n\\n': -23.2421875})", + "TokenLogprob(token='8', logprob=0.0, top={'8': 0.0, '1': -21.875, '7': -22.5625, '6': -23.9375, '9': -24.1875})", + "TokenLogprob(token='.', logprob=0.0, top={'.': 0.0, '-': -25.4375, ')': -26.6875, ',': -26.9375, 't': -27.0625})", + "TokenLogprob(token=' jack', logprob=-8.940655789047014e-06, top={' jack': -8.940655789047014e-06, ' jacks': -11.625008583068848, ' litt': -18.484384536743164, ' hissed': -19.265634536743164, ' jer': -22.531259536743164})", + "TokenLogprob(token='y', logprob=0.0, top={'y': 0.0, ' y': -32.125, 'ty': -32.125, 'ly': -32.75, 'ย': -32.75})", + "TokenLogprob(token='\\n', logprob=-4.768370445162873e-07, top={'\\n': -4.768370445162873e-07, ' (': -14.65625, 'y': -17.78125, '(': -21.427734375, '_': -21.6259765625})", + "TokenLogprob(token='9', logprob=0.0, top={'9': 0.0, '1': -18.1875, '8': -22.0625, '7': -23.4375, '4': -23.6875})", + "TokenLogprob(token='.', logprob=0.0, top={'.': 0.0, '-': -25.375, '0': -26.625, 't': -26.625, ')': -27.125})", + "TokenLogprob(token=' litt', logprob=-2.264974000354414e-06, top={' litt': -2.264974000354414e-06, ' litter': -13.015626907348633, ' literal': -16.500001907348633, ' lit': -17.812501907348633, ' little': -19.187501907348633})", + "TokenLogprob(token='leneck', logprob=0.0, top={'leneck': 0.0, 'lene': -31.0, 'en': -33.125, ' bottleneck': -33.75, 'l': -34.125})", + "TokenLogprob(token='\\n', logprob=0.0, top={'\\n': 0.0, ' (': -18.78125, ',': -22.3359375, ' ($\\\\': -24.04345703125, ' $\\\\': -24.435546875})", + "TokenLogprob(token='1', logprob=0.0, top={'1': 0.0, '2': -23.25, ' ': -23.375, '9': -23.375, '*': -23.5})", + "TokenLogprob(token='0', logprob=0.0, top={'0': 0.0, '.': -18.84375, '1': -19.984375, '2': -22.79296875, '3': -23.328125})", + "TokenLogprob(token='.', logprob=0.0, top={'.': 0.0, ')': -25.5625, '-': -25.625, ',': -26.0625, ']': -26.5625})", + "TokenLogprob(token=' phosph', logprob=-2.014616438827943e-05, top={' phosph': -2.014616438827943e-05, ' pristine': -10.812520027160645, ' phosphorylated': -18.60939598083496, 'phosph': -19.60939598083496, ' phospho': -22.02736473083496})", + "TokenLogprob(token='orescent', logprob=0.0, top={'orescent': 0.0, 'ores': -31.5, 'escent': -34.25, ' fluorescent': -35.5, 'rescent': -35.875})", + "TokenLogprob(token='\\n', logprob=0.0, top={'\\n': 0.0, ' (': -17.15625, ',': -22.796875, ' ($\\\\': -23.27734375, ' $\\\\': -23.876953125})", + "TokenLogprob(token='1', logprob=0.0, top={'1': 0.0, '2': -28.6875, '(': -30.3125, '*': -30.5625, '6': -31.125})", + "TokenLogprob(token='1', logprob=0.0, top={'1': 0.0, '.': -19.30859375, '0': -20.5078125, '2': -22.578125, '4': -24.0})", + "TokenLogprob(token='.', logprob=0.0, top={'.': 0.0, '-': -26.375, ',': -26.375, ')': -27.375, '0': -27.6875})", + "TokenLogprob(token=' pristine', logprob=0.0, top={' pristine': 0.0, ' prist': -22.9921875, ' pram': -27.234375, ' prickly': -27.90625, ' preconceived': -28.84375})", + "TokenLogprob(token='\\n', logprob=0.0, top={'\\n': 0.0, ' (': -20.09375, ',': -22.3984375, ' ($\\\\': -24.40625, ' $\\\\': -24.796875})", + "TokenLogprob(token='1', logprob=0.0, top={'1': 0.0, '2': -28.9375, '*': -29.9375, ' ': -30.875, '\\n': -31.375})", + "TokenLogprob(token='2', logprob=0.0, top={'2': 0.0, '.': -20.3125, '3': -20.96875, '1': -21.0625, '0': -22.34375})", + "TokenLogprob(token='.', logprob=0.0, top={'.': 0.0, '-': -25.75, ',': -26.6875, '0': -27.3125, ')': -27.375})" + ], + "self_consistency/samples": [ + "Here are the words sorted alphabetically:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. gradate\n7. grim\n8. jacky\n9. littleneck\n10. phosphorescent\n11. pristine\n12. sinh\n13. systemwide\n14. tasting\n15. torpedo\n16. thrown\n17. verdict\n18. dey\n19. shrunk\n\nLet's re-sort them carefully:\n\n* administer\n* aeneid\n* decadent\n* coachman\n* delhi\n* grim\n* gradate\n* jacky\n* littleneck\n* phosphorescent\n* pristine\n* sinh\n* systemwide\n* tasting\n* torpedo\n* thrown\n* verdict\n* dey\n* shrunk\n\nChecking for correct alphabetical order:\nA: administer, aeneid\nC: decadent, coachman\nD: delhi, dey\nG: grim, gradate\nJ: jacky\nL: littleneck\nP: phosphorescent, pristine\nS: sinh, systemwide, shrunk\nT: tasting, torpedo, thrown\nV: verdict\n\nCorrected Sorted List:\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. dey\n7. grim\n8. gradate\n9. jacky\n10. littleneck\n11. phosphorescent\n12. pristine\n13. sinh\n14. shrunk\n15. systemwide\n16. tasting\n17. torpedo\n18. thrown\n19. verdict\n\n(Comparing D words: delhi, dey. D-e-l-h-i vs D-e-y. 'l' comes before 'y', so delhi comes before dey.)\n\nFinal check on the complete list:\nadminister (A)\naeneid (A)\ndecadent (D)\ncoachman (C) -> Should be C before D. Let's restart the alphabetical sort strictly.\n\nList: torpedo, phosphorescent, pristine, decadent, shrunk, dey, administer, gradate, littleneck, thrown, jacky, coachman, aeneid, verdict, tasting, sinh, delhi, systemwide, grim\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6", + "Here are the words sorted alphabetically:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. gradate\n7. grim\n8. jacky\n9. littleneck\n10. phosphorescent\n11. pristine\n12. sinh\n13. systemwide\n14. tasting\n15. torpedo\n16. thrown\n17. verdict\n18. dey\n19. shrunk\n\nLet's re-sort them carefully:\n\nA: administer, aeneid\nC: decadent, coachman\nD: delhi, dey\nG: grim, gradate\nJ: jacky\nL: littleneck\nP: phosphorescent, pristine\nS: sinh, systemwide, shrunk\nT: tasting, torpedo, thrown\nV: verdict\n\n**Step-by-step alphabetical sorting:**\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. dey\n7. grim\n8. gradate\n9. jacky\n10. littleneck\n11. phosphorescent\n12. pristine\n13. sinh\n14. shrunk\n15. systemwide\n16. tasting\n17. torpedo\n18. thrown\n19. verdict\n\nAnswer: administer, aeneid, decadent, coachman, delhi, dey, grim, gradate, jacky, littleneck, phosphorescent, pristine, sinh, shrunk, systemwide, tasting, torpedo, thrown, verdict", + "Here are the words from the list sorted alphabetically:\n\nList: torpedo phosphorescent pristine decadent shrunk dey administer gradate littleneck thrown jacky coachman aeneid verdict tasting sinh delhi systemwide grim\n\n1. aeneid\n2. administer\n3. decadent\n4. delhi\n5. gradate\n6. grim\n7. jacky\n8. littleneck\n9. phosphorescent\n10. pristine\n11. sinh\n12. systemwide\n13. tasting\n14. torpedo\n15. thrown\n16. verdict\n\nLet's re-sort them carefully:\n\n* aeneid\n* administer\n* decadent\n* delhi\n* gradate\n* grim\n* jacky\n* littleneck\n* phosphorescent\n* pristine\n* sinh\n* systemwide\n* tasting\n* torpedo\n* thrown\n* verdict\n\nComparing the 'a' words: aeneid, administer. (aeneid comes before administer)\nComparing 'd' words: decadent, delhi. (decadent comes before delhi)\nComparing 'g' words: gradate, grim. (gradate comes before grim)\nComparing 'l' words: littleneck.\nComparing 'p' words: phosphorescent, pristine. (phosphorescent comes before pristine)\nComparing 's' words: sinh, systemwide, tasting. (sinh, systemwide, tasting)\nComparing 't' words: torpedo, thrown, verdict. (torpedo, thrown, verdict)\n\nCorrect alphabetical order:\n\n1. aeneid\n2. administer\n3. decadent\n4. delhi\n5. gradate\n6. grim\n7. jacky\n8. littleneck\n9. phosphorescent\n10. pristine\n11. sinh\n12. systemwide\n13. tasting\n14. torpedo\n15. thrown\n16. verdict\n\nAnswer: aeneid, administer, decadent, delhi, gradate, grim, jacky, littleneck, phosphorescent, pristine, sinh, systemwide, tasting, torpedo, thrown, verdict", + "Here are the words sorted alphabetically:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. gradate\n7. grim\n8. jacky\n9. littleneck\n10. phosphorescent\n11. pristine\n12. sinh\n13. systemwide\n14. tasting\n15. torpedo\n16. thrown\n17. verdict\n18. dey\n19. shrunk\n\nLet's re-sort them carefully:\n\n* administer\n* aeneid\n* decadent\n* coachman\n* delhi\n* gradate\n* grim\n* jacky\n* littleneck\n* phosphorescent\n* pristine\n* sinh\n* systemwide\n* tasting\n* torpedo\n* thrown\n* verdict\n* dey\n* shrunk\n\nComparing the list again:\naeneid\ndecadent\ndelhi\nadminister\ncoachman\ngrim\ngradate\njacky\nlittleneck\nphosphorescent\npristine\nshrunk\nsinh\nsystemwide\ntasting\ntorpedo\nthrown\nverdict\ndey\n\nLet's use standard alphabetical order:\n\n1. aeneid\n2. decadent\n3. administer\n4. coachman\n5. delhi\n6. dey\n7. grim\n8. gradate\n9. jacky\n10. littleneck\n11. phosphorescent\n12. pristine\n13. sinh\n14. shrunk\n15. systemwide\n16. tasting\n17. torpedo\n18. thrown\n19. verdict\n\n(Note: For words starting with 's', 'shrunk' comes before 'systemwide', 'sinh' comes after 'pristine' if sorted strictly by character sequence, but typically 's' words are grouped. Checking 'sinh' vs 'shrunk': 'sinh' vs 'shrunk' -> 'i' before 'r', so sinh before shrunk. Re-evaluating 'sinh' and 'shrunk'.)\n\nCorrected 'S' ordering:\n* sinh\n* shrunk\n* systemwide\n\nFinal Sorted List:\n1. aeneid\n2. decadent\n3. administer\n4. coachman\n5. delhi\n6. dey\n7. grim\n8. gradate\n9. jacky\n10. littleneck\n11. phosphorescent\n12. pristine", + "Here are the words sorted alphabetically:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. gradate\n7. grim\n8. jacky\n9. littleneck\n10. phosphorescent\n11. pristine\n12. sinh\n13. systemwide\n14. tasting\n15. torpedo\n16. thrown\n17. verdict\n18. dey\n19. shrunk\n\nLet's re-sort them carefully:\n\n* **aeneid** (A)\n* **decadent** (D)\n* **delhi** (D) - *delhi comes before decadent if we look at the next letters, but D-E-L-H-I vs D-E-C-A-D-E-N-T. Decadent comes first.*\n* **administer** (A) - *aeneid vs administer. aeneid before administer.*\n* **coachman** (C)\n* **grim** (G)\n* **gradate** (G) - *grim vs gradate. grim before gradate.*\n* **jacky** (J)\n* **littleneck** (L)\n* **phosphorescent** (P)\n* **pristine** (P) - *phosphorescent vs pristine. phosphorescent before pristine.*\n* **sinh** (S)\n* **systemwide** (S) - *sinh vs systemwide. sinh before systemwide.*\n* **tasting** (T)\n* **torpedo** (T) - *tasting vs torpedo. tasting before torpedo.*\n* **thrown** (T) - *thrown vs tasting/torpedo. thrown comes after tasting.*\n* **verdict** (V)\n* **dey** (D) - *dey comes after decadent/delhi.*\n* **shrunk** (S)\n\nLet's list them alphabetically based on the first letter:\nA: administer, aeneid\nC: coachman\nD: decadent, delhi, dey\nG: grim, gradate\nJ: jacky\nL: littleneck\nP: phosphorescent, pristine\nS: sinh, shrunk, systemwide, tasting\nT: torpedo, thrown, tasting\nV: verdict\n\nRefining the sort:\n\n1. **aeneid**\n2." + ] + }, + "cycle": 0, + "duration_sec": 771.456, + "event": "probe", + "event_seq": 386, + "examples": [ + { + "baseline_output": "Here are the words sorted alphabetically:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. gradate\n7. grim\n8. jacky\n9. littleneck\n10. phosphorescent\n11. pristine\n12. sinh\n13. systemwide\n14. tasting\n15. torpedo\n16. thrown\n17. verdict\n18. dey\n19. shrunk\n\nLet's re-sort them carefully:\n\n* administer\n* aeneid\n* decadent\n* coachman\n* delhi\n* grim\n* gradate\n* jacky\n* littleneck\n* phosphorescent\n* pristine\n* sinh\n* systemwide\n* tasting\n* torpedo\n* thrown\n* verdict\n* dey\n* shrunk\n\n**Alphabetical Sorting:**\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. dey\n7. grim\n8. gradate\n9. jacky\n10. littleneck\n11. phosphorescent\n12. pristine\n13. sinh\n14. shrunk\n15. systemwide\n16. tasting\n17. torpedo\n18. thrown\n19. verdict\n\nAnswer: administer, aeneid, decadent, coachman, delhi, dey, grim, gradate, jacky, littleneck, phosphorescent, pristine, sinh, shrunk, systemwide, tasting, torpedo, thrown, verdict", + "case_id": "bbh_word_sorting-0006", + "check_result": { + "extracted answer": "administer, aeneid, decadent, coachman, delhi, dey, grim, gradate, jacky, littleneck, phosphorescent, pristine, sinh, sh", + "extraction point miss": 0, + "extraction suspect": 0, + "gave up": 0, + "gold in answer region": 0, + "gold in output": 0, + "has answer tag": 1, + "has output": 1, + "label disagrees": 0, + "labelled fail": 1, + "output chars": 917, + "output truncated": 0, + "strict match": 0 + }, + "evidence_scope": "one recorded case within M1", + "expected": "administer aeneid coachman decadent delhi dey gradate grim jacky littleneck phosphorescent pristine shrunk sinh systemwide tasting thrown torpedo verdict", + "id": "bbh_word_sorting-0006", + "input": "Sort the following words alphabetically: List: torpedo phosphorescent pristine decadent shrunk dey administer gradate littleneck thrown jacky coachman aeneid verdict tasting sinh delhi systemwide grim\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "kind": "case_measurement", + "outcome": "fail", + "plain_reading": "This one case illustrates the recorded check. The aggregate M1 result uses all measured cases.", + "probe_title": "Answer Extraction Audit" + }, + { + "baseline_output": "Here are the words sorted alphabetically:\n\n1. arrear\n2. eavesdropping\n3. fasciculate\n4. herodotus\n5. hermaphrodite\n6. ibn henry\n7. incorrigible\n8. jane\n9. linchpin\n10. maritime\n11. postdoctoral\n12. shin\n13. sticky\n14. brookside\n15. vehicular\n\nLet's re-sort them carefully:\n\n* **a**rrear\n* **e**avesdropping\n* **f**asciculate\n* **h**erodotus\n* **h**ermaphrodite (H before I)\n* **i**bn henry\n* **i**ncorrigible (I before L)\n* **j**ane\n* **l**inchpin\n* **m**aritime\n* **p**ostdoctoral\n* **s**hin\n* **s**ticky\n* **b**rookside (B comes before S)\n* **v**ehicular\n\nLet's do a full alphabetical sort:\n\n1. arrear\n2. brookside\n3. eavesdropping\n4. fasciculate\n5. herodotus\n6. hermaphrodite\n7. ibn henry\n8. incorrigible\n9. jane\n10. linchpin\n11. maritime\n12. postdoctoral\n13. shin\n14. sticky\n15. vehicular\n\nWait, let's check the 'H' words: hermaphrodite and herodotus.\nH-e-r-m-a-p-h-r-o-d-i-t-e\nH-e-r-o-d-t-u-s\n'herm' comes before 'hero'. So, hermaphrodite comes before herodotus.\n\nLet's re-verify the full list:\narrear\nbrookside\neavesdropping\nfasciculate\nhermaphrodite\nherodotus\nibn henry\nincorrigible\njane\nlinchpin\nmaritime\npostdoctoral\nshin\nsticky\nvehicular\n\nFinal check on the order:\nA: arrear\nB: brookside\nE: eavesdropping\nF: fasciculate\nH: hermaphrodite, herodotus (herm before hero)\nI: ibn henry, incorrigible (ibn before incorrigible)\nJ:", + "case_id": "bbh_word_sorting-0025", + "check_result": { + "continuation chars": 1217, + "continuation correct": 0, + "continuation has answer": 1, + "gave up": 0, + "has answer tag": 0, + "looks truncated": 1, + "matches output contract": 0, + "output chars": 1332, + "output words": 187, + "recovered by continuation": 0, + "repetition score": 0, + "termination class": "truncated" + }, + "evidence_scope": "one recorded case within M1", + "expected": "arrear brookside eavesdropping fasciculate henry hermaphrodite herodotus ibn incorrigible jane linchpin maritime postdoctoral shin sticky vehicular", + "id": "bbh_word_sorting-0025", + "input": "Sort the following words alphabetically: List: linchpin arrear eavesdropping fasciculate shin postdoctoral maritime ibn henry hermaphrodite sticky jane brookside incorrigible vehicular herodotus\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "kind": "case_measurement", + "outcome": "fail", + "plain_reading": "This one case illustrates the recorded check. The aggregate M1 result uses all measured cases.", + "probe_title": "Termination Audit" + } + ], + "findings": { + "answer_extraction_audit": { + "_caveat": "suspect_rate is the fraction of FAIL cases whose gold answer sits in the ANSWER REGION of the output (tail window or a tagged/boxed span) — HARNESS failures wearing a model-failure label. gold_in_output is the loose upper bound and over-counts: it fires on a gold that only appears as an intermediate quantity, so never read it as the suspect rate. Above ~5% the labels feeding M2 are contaminated and every other probe's correlations should be re-read after fixing extraction (or after dropping extraction_suspect==1 cases). This probe is OBSERVATIONAL by default: it re-reads stored outputs and introduces no new sampling. With reask=True the reask_* columns are interventional and must be re-run for held-out confirmation. On yes/no (true/false) tasks answered_yes and gold_yes are the answer's and the gold's DIRECTION, two separate marginals (not re-grades): a directional hypothesis is tested on them, e.g. answered_yes HIGHER on failing cases.", + "missing_tag_rate": 0.25, + "n_cases": 8, + "n_extraction_point_miss": 0, + "n_extraction_suspect": 0, + "n_gradable": 8, + "n_labelled_fail": 5, + "n_reask_confirmed": null, + "n_reasked": 0, + "per_case": [ + { + "extracted_answer": "administer, aeneid, decadent, coachman, delhi, dey, grim, gradate, jacky, littleneck, phosphorescent, pristine, sinh, sh", + "extraction_point_miss": 0, + "extraction_suspect": 0, + "gave_up": 0, + "gold_in_answer_region": 0, + "gold_in_output": 0, + "has_answer_tag": 1, + "has_output": 1, + "label_disagrees": 0, + "labelled_fail": 1, + "output_chars": 917, + "output_truncated": 0, + "sample_id": "bbh_word_sorting-0006", + "strict_match": 0 + }, + { + "extracted_answer": "J:", + "extraction_point_miss": 0, + "extraction_suspect": 0, + "gave_up": 0, + "gold_in_answer_region": 0, + "gold_in_output": 0, + "has_answer_tag": 0, + "has_output": 1, + "label_disagrees": 0, + "labelled_fail": 1, + "output_chars": 1332, + "output_truncated": 1, + "sample_id": "bbh_word_sorting-0025", + "strict_match": 0 + }, + { + "extracted_answer": "backslide, calvert, commando, gabriel, hendrickson, hollister, jackson, anastomosis, pizzicato, quail, separate, shelter", + "extraction_point_miss": 0, + "extraction_suspect": 0, + "gave_up": 0, + "gold_in_answer_region": 0, + "gold_in_output": 0, + "has_answer_tag": 1, + "has_output": 1, + "label_disagrees": 0, + "labelled_fail": 1, + "output_chars": 1101, + "output_truncated": 0, + "sample_id": "bbh_word_sorting-0045", + "strict_match": 0 + }, + { + "extracted_answer": "The correct alphabetical order is: biennial, creedal, cry, eyesight, fletch,", + "extraction_point_miss": 0, + "extraction_suspect": 0, + "gave_up": 0, + "gold_in_answer_region": 0, + "gold_in_output": 0, + "has_answer_tag": 0, + "has_output": 1, + "label_disagrees": 0, + "labelled_fail": 1, + "output_chars": 1389, + "output_truncated": 1, + "sample_id": "bbh_word_sorting-0076", + "strict_match": 0 + }, + { + "extracted_answer": "battery, bushland, capacitive, contingent, crossbill, enigma, jane, lipton, meager, ricochet, wallet, wacke, wysiwyg", + "extraction_point_miss": 0, + "extraction_suspect": 0, + "gave_up": 0, + "gold_in_answer_region": 0, + "gold_in_output": 0, + "has_answer_tag": 1, + "has_output": 1, + "label_disagrees": 0, + "labelled_fail": 1, + "output_chars": 922, + "output_truncated": 0, + "sample_id": "bbh_word_sorting-0094", + "strict_match": 0 + }, + { + "extracted_answer": "alleviate, benelux, buoyant, duopoly, felice, gland, gunk, hardbound, klaxon, mattress, tomography, townsmen", + "extraction_point_miss": 0, + "extraction_suspect": 0, + "gave_up": 0, + "gold_in_answer_region": 1, + "gold_in_output": 1, + "has_answer_tag": 1, + "has_output": 1, + "label_disagrees": 0, + "labelled_fail": 0, + "output_chars": 1286, + "output_truncated": 0, + "sample_id": "bbh_word_sorting-0000", + "strict_match": 1 + }, + { + "extracted_answer": "artillery, bainite, doris, fda, harm, incongruous, monkey, prosody, vegetate, vivian", + "extraction_point_miss": 0, + "extraction_suspect": 0, + "gave_up": 0, + "gold_in_answer_region": 1, + "gold_in_output": 1, + "has_answer_tag": 1, + "has_output": 1, + "label_disagrees": 0, + "labelled_fail": 0, + "output_chars": 1054, + "output_truncated": 0, + "sample_id": "bbh_word_sorting-0063", + "strict_match": 1 + }, + { + "extracted_answer": "cotyledon, more, pepperoni, regret, starlight, wallboard", + "extraction_point_miss": 0, + "extraction_suspect": 0, + "gave_up": 0, + "gold_in_answer_region": 1, + "gold_in_output": 1, + "has_answer_tag": 1, + "has_output": 1, + "label_disagrees": 0, + "labelled_fail": 0, + "output_chars": 690, + "output_truncated": 0, + "sample_id": "bbh_word_sorting-0101", + "strict_match": 1 + } + ], + "suspect_rate": 0 + }, + "calibration": { + "_caveat": "Needs PASS/FAIL labels — unlabeled cases are skipped, and ECE on few labelled cases is unstable (aim for >= ~50). conf_logprob is the geometric-mean token probability of the model's own continuation — a proxy, not an answer probability; on API backends it reflects only the returned top-k, and a model without LOGPROBS reports only the verbalized channel. A positive overconfidence_gap on FAIL-heavy slices argues for abstention/recalibration fixes rather than capability fixes.", + "logprob_channel": { + "brier": 0.6015, + "ece": 0.5978, + "n": 8, + "overconfidence_gap": 0.5978 + }, + "logprob_channel_available": true, + "n_bins": 10, + "n_cases": 8, + "n_unlabelled_skipped": 0, + "per_case": [ + { + "conf_logprob": 0.9936, + "correct": 0, + "sample_id": "bbh_word_sorting-0006" + }, + { + "conf_logprob": 0.9926, + "correct": 0, + "sample_id": "bbh_word_sorting-0025" + }, + { + "conf_logprob": 0.9905, + "correct": 0, + "sample_id": "bbh_word_sorting-0045" + }, + { + "conf_logprob": 0.9454, + "correct": 0, + "sample_id": "bbh_word_sorting-0076" + }, + { + "conf_logprob": 0.9796, + "correct": 0, + "sample_id": "bbh_word_sorting-0094" + }, + { + "conf_logprob": 0.9542, + "conf_verbal": 1, + "correct": 1, + "sample_id": "bbh_word_sorting-0000" + }, + { + "conf_logprob": 0.9583, + "correct": 1, + "sample_id": "bbh_word_sorting-0063" + }, + { + "conf_logprob": 0.9683, + "conf_verbal": 1, + "correct": 1, + "sample_id": "bbh_word_sorting-0101" + } + ], + "verbalized_channel": { + "brier": 0, + "ece": 0, + "n": 2, + "overconfidence_gap": 0 + } + }, + "coverage_verification_gap": { + "_caveat": "coverage_gap = 'the answer was in the pool, the vote missed it' ⇒ a selection defect, fixable at L1/L2 with a verifier or reranker. no_coverage_rate = 'never produced once in k' ⇒ a capability defect no scaffold reaches; do not let a headline pass@k hide which of the two dominates. REQUIRES sampling: degenerate_sampling=true means every sample was identical (temperature 0 or a deterministic backend) and the gap is structurally zero, not measured. pass_at_k here uses the unbiased estimator over exactly k draws, so with k small its variance is large per case — read the batch rate. INTERVENTIONAL: held-out confirmation must re-sample.", + "coverage_gap_rate": 0.125, + "degenerate_sampling": false, + "gen_kwargs": { + "temperature": 1, + "top_k": 20, + "top_p": 0.95 + }, + "k": 5, + "mean_majority_correct": 0.375, + "mean_pass_at_k": 0.5, + "mean_verify_correct": null, + "n_cases": 8, + "n_scored": 8, + "no_coverage_rate": 0.5, + "per_case": [ + { + "any_correct": 0, + "coverage_gap": 0, + "majority_correct": 0, + "majority_share": 0.4, + "modal_answer": "14", + "n_correct": 0, + "n_graded": 5, + "n_samples": 5, + "n_unique": 4, + "pass_at_k": 0, + "sample_id": "bbh_word_sorting-0006" + }, + { + "any_correct": 0, + "coverage_gap": 0, + "majority_correct": 0, + "majority_share": 0.2, + "modal_answer": "arrear eavesdropping fasciculate herodotus hermaphrodite ibn henry incorrigible ", + "n_correct": 0, + "n_graded": 5, + "n_samples": 5, + "n_unique": 5, + "pass_at_k": 0, + "sample_id": "bbh_word_sorting-0025" + }, + { + "any_correct": 0, + "coverage_gap": 0, + "majority_correct": 0, + "majority_share": 0.2, + "modal_answer": "16. separate wait s words re-sort: separate shelter sticktight spongy", + "n_correct": 0, + "n_graded": 5, + "n_samples": 5, + "n_unique": 5, + "pass_at_k": 0, + "sample_id": "bbh_word_sorting-0045" + }, + { + "any_correct": 0, + "coverage_gap": 0, + "majority_correct": 0, + "majority_share": 0.4, + "modal_answer": "biennial creedal cry fletch fraudulent eyesight j miltonic mirage whisper titmic", + "n_correct": 0, + "n_graded": 5, + "n_samples": 5, + "n_unique": 4, + "pass_at_k": 0, + "sample_id": "bbh_word_sorting-0076" + }, + { + "any_correct": 1, + "coverage_gap": 1, + "majority_correct": 0, + "majority_share": 0.4, + "modal_answer": "battery bushland capacitive contingent crossbill enigma jane lipton meager ricoc", + "n_correct": 1, + "n_graded": 5, + "n_samples": 5, + "n_unique": 4, + "pass_at_k": 1, + "sample_id": "bbh_word_sorting-0094" + }, + { + "any_correct": 1, + "coverage_gap": 0, + "majority_correct": 1, + "majority_share": 0.8, + "modal_answer": "alleviate benelux buoyant duopoly felice gland gunk hardbound klaxon mattress to", + "n_correct": 4, + "n_graded": 5, + "n_samples": 5, + "n_unique": 2, + "pass_at_k": 1, + "sample_id": "bbh_word_sorting-0000" + }, + { + "any_correct": 1, + "coverage_gap": 0, + "majority_correct": 1, + "majority_share": 0.6, + "modal_answer": "artillery bainite doris fda harm incongruous monkey prosody vegetate vivian", + "n_correct": 3, + "n_graded": 5, + "n_samples": 5, + "n_unique": 3, + "pass_at_k": 1, + "sample_id": "bbh_word_sorting-0063" + }, + { + "any_correct": 1, + "coverage_gap": 0, + "majority_correct": 1, + "majority_share": 1, + "modal_answer": "cotyledon more pepperoni regret starlight wallboard", + "n_correct": 5, + "n_graded": 5, + "n_samples": 5, + "n_unique": 1, + "pass_at_k": 1, + "sample_id": "bbh_word_sorting-0101" + } + ] + }, + "logprob_entropy": { + "mean_logprob": -0.0064, + "mean_top_entropy": 0.0213, + "min_token_logprob": -0.1908, + "n_tokens": 64, + "perplexity": 1.0064 + }, + "perturbation_battery": { + "_caveat": "Two DIFFERENT defects share this probe and must not be summed: invariance_break_rate should be 0 (a preserving edit moved the answer) while sensitivity_rate should be 1 (an altering edit must move it). sensitivity_rate == 0 with a correct baseline is the memorisation signature — cross-check contamination_score before calling it reasoning. Every rate is measured against the model's OWN baseline answer, not the gold, so a case that was already wrong still contributes: read these as stability, never as accuracy. Under sampling, a 'flip' can be decoding noise — set temperature 0 or repeat. INTERVENTIONAL: re-run to confirm.", + "applied_perturbations": [ + "noop_clause", + "rename_entities", + "restate_question" + ], + "gen_kwargs": {}, + "mean_invariance_break_rate": 0.7917, + "mean_sensitivity_rate": null, + "n_cases": 8, + "n_memorization_suspect": 0, + "n_scored": 8, + "noop_break_rate": 0.625, + "per_case": [ + { + "applied": [ + "rename_entities", + "restate_question", + "noop_clause" + ], + "baseline_answer": "administer aeneid decadent coachman delhi dey grim gradate jacky littleneck phos", + "invariance_break_rate": 1, + "n_altering": 0, + "n_preserving": 3, + "noop_clause_flipped": 1, + "rename_entities_flipped": 1, + "restate_question_flipped": 1, + "sample_id": "bbh_word_sorting-0006", + "sensitivity_rate": null + }, + { + "applied": [ + "rename_entities", + "restate_question", + "noop_clause" + ], + "baseline_answer": "j:", + "invariance_break_rate": 1, + "n_altering": 0, + "n_preserving": 3, + "noop_clause_flipped": 1, + "rename_entities_flipped": 1, + "restate_question_flipped": 1, + "sample_id": "bbh_word_sorting-0025", + "sensitivity_rate": null + }, + { + "applied": [ + "rename_entities", + "restate_question", + "noop_clause" + ], + "baseline_answer": "backslide calvert commando gabriel hendrickson hollister jackson anastomosis piz", + "invariance_break_rate": 1, + "n_altering": 0, + "n_preserving": 3, + "noop_clause_flipped": 1, + "rename_entities_flipped": 1, + "restate_question_flipped": 1, + "sample_id": "bbh_word_sorting-0045", + "sensitivity_rate": null + }, + { + "applied": [ + "rename_entities", + "restate_question", + "noop_clause" + ], + "baseline_answer": "correct alphabetical order is: biennial creedal cry eyesight fletch", + "invariance_break_rate": 1, + "n_altering": 0, + "n_preserving": 3, + "noop_clause_flipped": 1, + "rename_entities_flipped": 1, + "restate_question_flipped": 1, + "sample_id": "bbh_word_sorting-0076", + "sensitivity_rate": null + }, + { + "applied": [ + "rename_entities", + "restate_question", + "noop_clause" + ], + "baseline_answer": "battery bushland capacitive contingent crossbill enigma jane lipton meager ricoc", + "invariance_break_rate": 0.6667, + "n_altering": 0, + "n_preserving": 3, + "noop_clause_flipped": 0, + "rename_entities_flipped": 1, + "restate_question_flipped": 1, + "sample_id": "bbh_word_sorting-0094", + "sensitivity_rate": null + }, + { + "applied": [ + "rename_entities", + "restate_question", + "noop_clause" + ], + "baseline_answer": "alleviate benelux buoyant duopoly felice gland gunk hardbound klaxon mattress to", + "invariance_break_rate": 0.6667, + "n_altering": 0, + "n_preserving": 3, + "noop_clause_flipped": 0, + "rename_entities_flipped": 1, + "restate_question_flipped": 1, + "sample_id": "bbh_word_sorting-0000", + "sensitivity_rate": null + }, + { + "applied": [ + "rename_entities", + "restate_question", + "noop_clause" + ], + "baseline_answer": "artillery bainite doris fda harm incongruous monkey prosody vegetate vivian", + "invariance_break_rate": 0.6667, + "n_altering": 0, + "n_preserving": 3, + "noop_clause_flipped": 1, + "rename_entities_flipped": 1, + "restate_question_flipped": 0, + "sample_id": "bbh_word_sorting-0063", + "sensitivity_rate": null + }, + { + "applied": [ + "rename_entities", + "restate_question", + "noop_clause" + ], + "baseline_answer": "cotyledon more pepperoni regret starlight wallboard", + "invariance_break_rate": 0.3333, + "n_altering": 0, + "n_preserving": 3, + "noop_clause_flipped": 0, + "rename_entities_flipped": 1, + "restate_question_flipped": 0, + "sample_id": "bbh_word_sorting-0101", + "sensitivity_rate": null + } + ], + "perturbations": [ + "rename_entities", + "restate_question", + "noop_clause", + "perturb_numbers" + ] + }, + "self_consistency": { + "_caveat": "semantic_* uses the SAME samples as consistency — it costs no extra generations, and the two are meant to be read together: consistency << semantic_consistency means the disagreement was only wording. With the default lexical_proxy, clustering is content-word overlap, NOT entailment: it merges answers that share vocabulary while contradicting each other ('the claim holds' / 'the claim does not hold') and splits correct paraphrases with no shared words. Inject an NLI entailment_fn before reading semantic_entropy as the Farquhar et al. metric. At temperature 0 every sample is identical and both scores are 1 by construction, not by measurement.", + "cluster_representatives": [ + "Here are the words sorted alphabetically:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. gradate\n7. grim\n8", + "Here are the words sorted alphabetically:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. gradate\n7. grim\n8", + "Here are the words sorted alphabetically:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. gradate\n7. grim\n8" + ], + "cluster_sizes": [ + 2, + 2, + 1 + ], + "compared_on": "raw_text", + "consistency": 0.2, + "entailment": "lexical_proxy", + "gen_kwargs": { + "temperature": 1, + "top_k": 20, + "top_p": 0.95 + }, + "modal_answer": "Here are the words sorted alphabetically:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. gradate\n7. grim\n8. jacky\n9. littleneck\n10. phosphorescent\n11. pristine\n12. sinh\n13. systemwide\n14. tasting\n15. torpedo\n16. thrown\n17. verdict\n18. dey\n19. shrunk\n\nLet's re-sort them carefully:\n\n* administer\n* aeneid\n* decadent\n* coachman\n* delhi\n* grim\n* gradate\n* jacky\n* littleneck\n* phosphorescent\n* pristine\n* sinh\n* systemwide\n* tasting\n* torpedo\n* thrown\n* verdict\n* dey\n* shrunk\n\nChecking for correct alphabetical order:\nA: administer, aeneid\nC: decadent, coachman\nD: delhi, dey\nG: grim, gradate\nJ: jacky\nL: littleneck\nP: phosphorescent, pristine\nS: sinh, systemwide, shrunk\nT: tasting, torpedo, thrown\nV: verdict\n\nCorrected Sorted List:\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. dey\n7. grim\n8. gradate\n9. jacky\n10. littleneck\n11. phosphorescent\n12. pristine\n13. sinh\n14. shrunk\n15. systemwide\n16. tasting\n17. torpedo\n18. thrown\n19. verdict\n\n(Comparing D words: delhi, dey. D-e-l-h-i vs D-e-y. 'l' comes before 'y', so delhi comes before dey.)\n\nFinal check on the complete list:\nadminister (A)\naeneid (A)\ndecadent (D)\ncoachman (C) -> Should be C before D. Let's restart the alphabetical sort strictly.\n\nList: torpedo, phosphorescent, pristine, decadent, shrunk, dey, administer, gradate, littleneck, thrown, jacky, coachman, aeneid, verdict, tasting, sinh, delhi, systemwide, grim\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6", + "n_samples": 5, + "n_semantic_clusters": 3, + "n_unique": 5, + "normalized_semantic_entropy": 0.6554, + "raw_text_consistency": 0.2, + "semantic_consistency": 0.4, + "semantic_entropy": 1.0549 + }, + "selfcheck_consistency": { + "_caveat": "Unigram-containment proxy for SelfCheckGPT (no NLI model): paraphrases inflate the score, verbatim repetition of a wrong fact deflates it. Requires stochastic sampling — with temperature 0 the resamples repeat one greedy generation and the column degenerates to baseline-vs-greedy disagreement (degenerate_sampling flags this). selfcheck_inconsistency / selfcheck_worst_sentence are numeric M2 columns; treat them as signals to correlate with the outcome label, never as a hallucination verdict.", + "degenerate_sampling": false, + "gen_kwargs": { + "temperature": 1 + }, + "mean_inconsistency": 0.015, + "n_cases": 8, + "n_samples": 4, + "per_case": [ + { + "baseline_from": "observed", + "n_sentences": 19, + "sample_id": "bbh_word_sorting-0006", + "selfcheck_inconsistency": 0, + "selfcheck_worst_sentence": 0, + "worst_sentence_text": "Here are the words sorted alphabetically:" + }, + { + "baseline_from": "observed", + "n_sentences": 45, + "sample_id": "bbh_word_sorting-0025", + "selfcheck_inconsistency": 0.037, + "selfcheck_worst_sentence": 0.5, + "worst_sentence_text": "* **v**ehicular" + }, + { + "baseline_from": "observed", + "n_sentences": 29, + "sample_id": "bbh_word_sorting-0045", + "selfcheck_inconsistency": 0.023, + "selfcheck_worst_sentence": 0.3333, + "worst_sentence_text": "Comparing the words:" + }, + { + "baseline_from": "observed", + "n_sentences": 47, + "sample_id": "bbh_word_sorting-0076", + "selfcheck_inconsistency": 0.0376, + "selfcheck_worst_sentence": 0.6667, + "worst_sentence_text": "**Final ordered list:**" + }, + { + "baseline_from": "observed", + "n_sentences": 28, + "sample_id": "bbh_word_sorting-0094", + "selfcheck_inconsistency": 0.0168, + "selfcheck_worst_sentence": 0.4, + "worst_sentence_text": "The final sorted list is:" + }, + { + "baseline_from": "observed", + "n_sentences": 32, + "sample_id": "bbh_word_sorting-0000", + "selfcheck_inconsistency": 0.0016, + "selfcheck_worst_sentence": 0.0526, + "worst_sentence_text": "Here are the words to sort alphabetically: alleviate, duopoly, mattress, gland, benelux, townsmen, buoyant, klaxon, hardbound, tomography, felice, gunk." + }, + { + "baseline_from": "observed", + "n_sentences": 30, + "sample_id": "bbh_word_sorting-0063", + "selfcheck_inconsistency": 0.0037, + "selfcheck_worst_sentence": 0.1111, + "worst_sentence_text": "* Comparing the second letter: 'e' in vegetate, 'i' in vivian." + }, + { + "baseline_from": "observed", + "n_sentences": 18, + "sample_id": "bbh_word_sorting-0101", + "selfcheck_inconsistency": 0, + "selfcheck_worst_sentence": 0, + "worst_sentence_text": "The given list of words is: regret, starlight, wallboard, cotyledon, more, pepperoni." + } + ] + }, + "termination_audit": { + "_caveat": "CONFOUND CONTROL — read this before any other probe. Cases with termination_class != 'clean' have short, tag-less outputs, the same signature every other probe scores as 'bad'; leaving them pooled makes M2 attribute a decoding-budget or repetition problem to whichever mechanism is under test. Stratify on termination_class (or exclude non-clean cases) first. A high recovered_by_continuation rate means the budget is the defect: raise max_tokens and re-collect before diagnosing anything else. The continuation columns are INTERVENTIONAL — held-out verification must re-run them.", + "class_counts": { + "clean": 6, + "degenerate": 0, + "gave_up": 0, + "no_answer": 0, + "truncated": 2 + }, + "clean_rate": 0.75, + "degenerate_rate": 0, + "giveup_rate": 0, + "n_cases": 8, + "non_clean_rate": 0.25, + "per_case": [ + { + "gave_up": 0, + "has_answer_tag": 1, + "looks_truncated": 0, + "matches_output_contract": 0, + "output_chars": 917, + "output_words": 146, + "repetition_score": 0.0288, + "sample_id": "bbh_word_sorting-0006", + "termination_class": "clean" + }, + { + "continuation_chars": 1217, + "continuation_correct": 0, + "continuation_has_answer": 1, + "gave_up": 0, + "has_answer_tag": 0, + "looks_truncated": 1, + "matches_output_contract": 0, + "output_chars": 1332, + "output_words": 187, + "recovered_by_continuation": 0, + "repetition_score": 0, + "sample_id": "bbh_word_sorting-0025", + "termination_class": "truncated" + }, + { + "gave_up": 0, + "has_answer_tag": 1, + "looks_truncated": 0, + "matches_output_contract": 0, + "output_chars": 1101, + "output_words": 164, + "repetition_score": 0, + "sample_id": "bbh_word_sorting-0045", + "termination_class": "clean" + }, + { + "continuation_chars": 1472, + "continuation_correct": 0, + "continuation_has_answer": 0, + "gave_up": 0, + "has_answer_tag": 0, + "looks_truncated": 1, + "matches_output_contract": 0, + "output_chars": 1389, + "output_words": 224, + "recovered_by_continuation": 0, + "repetition_score": 0.0415, + "sample_id": "bbh_word_sorting-0076", + "termination_class": "truncated" + }, + { + "gave_up": 0, + "has_answer_tag": 1, + "looks_truncated": 0, + "matches_output_contract": 0, + "output_chars": 922, + "output_words": 137, + "repetition_score": 0.1462, + "sample_id": "bbh_word_sorting-0094", + "termination_class": "clean" + }, + { + "gave_up": 0, + "has_answer_tag": 1, + "looks_truncated": 0, + "matches_output_contract": 0, + "output_chars": 1286, + "output_words": 176, + "repetition_score": 0.1006, + "sample_id": "bbh_word_sorting-0000", + "termination_class": "clean" + }, + { + "gave_up": 0, + "has_answer_tag": 1, + "looks_truncated": 0, + "matches_output_contract": 0, + "output_chars": 1054, + "output_words": 157, + "repetition_score": 0, + "sample_id": "bbh_word_sorting-0063", + "termination_class": "clean" + }, + { + "gave_up": 0, + "has_answer_tag": 1, + "looks_truncated": 0, + "matches_output_contract": 0, + "output_chars": 690, + "output_words": 99, + "repetition_score": 0, + "sample_id": "bbh_word_sorting-0101", + "termination_class": "clean" + } + ], + "recovered_rate": 0, + "truncation_rate": 0.25 + } + }, + "n_model_calls": 256, + "results": { + "answer_extraction_audit": { + "analyzer": "answer_extraction_audit", + "findings": { + "_caveat": "suspect_rate is the fraction of FAIL cases whose gold answer sits in the ANSWER REGION of the output (tail window or a tagged/boxed span) — HARNESS failures wearing a model-failure label. gold_in_output is the loose upper bound and over-counts: it fires on a gold that only appears as an intermediate quantity, so never read it as the suspect rate. Above ~5% the labels feeding M2 are contaminated and every other probe's correlations should be re-read after fixing extraction (or after dropping extraction_suspect==1 cases). This probe is OBSERVATIONAL by default: it re-reads stored outputs and introduces no new sampling. With reask=True the reask_* columns are interventional and must be re-run for held-out confirmation. On yes/no (true/false) tasks answered_yes and gold_yes are the answer's and the gold's DIRECTION, two separate marginals (not re-grades): a directional hypothesis is tested on them, e.g. answered_yes HIGHER on failing cases.", + "missing_tag_rate": 0.25, + "n_cases": 8, + "n_extraction_point_miss": 0, + "n_extraction_suspect": 0, + "n_gradable": 8, + "n_labelled_fail": 5, + "n_reask_confirmed": null, + "n_reasked": 0, + "per_case": [ + { + "extracted_answer": "administer, aeneid, decadent, coachman, delhi, dey, grim, gradate, jacky, littleneck, phosphorescent, pristine, sinh, sh", + "extraction_point_miss": 0, + "extraction_suspect": 0, + "gave_up": 0, + "gold_in_answer_region": 0, + "gold_in_output": 0, + "has_answer_tag": 1, + "has_output": 1, + "label_disagrees": 0, + "labelled_fail": 1, + "output_chars": 917, + "output_truncated": 0, + "sample_id": "bbh_word_sorting-0006", + "strict_match": 0 + }, + { + "extracted_answer": "J:", + "extraction_point_miss": 0, + "extraction_suspect": 0, + "gave_up": 0, + "gold_in_answer_region": 0, + "gold_in_output": 0, + "has_answer_tag": 0, + "has_output": 1, + "label_disagrees": 0, + "labelled_fail": 1, + "output_chars": 1332, + "output_truncated": 1, + "sample_id": "bbh_word_sorting-0025", + "strict_match": 0 + }, + { + "extracted_answer": "backslide, calvert, commando, gabriel, hendrickson, hollister, jackson, anastomosis, pizzicato, quail, separate, shelter", + "extraction_point_miss": 0, + "extraction_suspect": 0, + "gave_up": 0, + "gold_in_answer_region": 0, + "gold_in_output": 0, + "has_answer_tag": 1, + "has_output": 1, + "label_disagrees": 0, + "labelled_fail": 1, + "output_chars": 1101, + "output_truncated": 0, + "sample_id": "bbh_word_sorting-0045", + "strict_match": 0 + }, + { + "extracted_answer": "The correct alphabetical order is: biennial, creedal, cry, eyesight, fletch,", + "extraction_point_miss": 0, + "extraction_suspect": 0, + "gave_up": 0, + "gold_in_answer_region": 0, + "gold_in_output": 0, + "has_answer_tag": 0, + "has_output": 1, + "label_disagrees": 0, + "labelled_fail": 1, + "output_chars": 1389, + "output_truncated": 1, + "sample_id": "bbh_word_sorting-0076", + "strict_match": 0 + }, + { + "extracted_answer": "battery, bushland, capacitive, contingent, crossbill, enigma, jane, lipton, meager, ricochet, wallet, wacke, wysiwyg", + "extraction_point_miss": 0, + "extraction_suspect": 0, + "gave_up": 0, + "gold_in_answer_region": 0, + "gold_in_output": 0, + "has_answer_tag": 1, + "has_output": 1, + "label_disagrees": 0, + "labelled_fail": 1, + "output_chars": 922, + "output_truncated": 0, + "sample_id": "bbh_word_sorting-0094", + "strict_match": 0 + }, + { + "extracted_answer": "alleviate, benelux, buoyant, duopoly, felice, gland, gunk, hardbound, klaxon, mattress, tomography, townsmen", + "extraction_point_miss": 0, + "extraction_suspect": 0, + "gave_up": 0, + "gold_in_answer_region": 1, + "gold_in_output": 1, + "has_answer_tag": 1, + "has_output": 1, + "label_disagrees": 0, + "labelled_fail": 0, + "output_chars": 1286, + "output_truncated": 0, + "sample_id": "bbh_word_sorting-0000", + "strict_match": 1 + }, + { + "extracted_answer": "artillery, bainite, doris, fda, harm, incongruous, monkey, prosody, vegetate, vivian", + "extraction_point_miss": 0, + "extraction_suspect": 0, + "gave_up": 0, + "gold_in_answer_region": 1, + "gold_in_output": 1, + "has_answer_tag": 1, + "has_output": 1, + "label_disagrees": 0, + "labelled_fail": 0, + "output_chars": 1054, + "output_truncated": 0, + "sample_id": "bbh_word_sorting-0063", + "strict_match": 1 + }, + { + "extracted_answer": "cotyledon, more, pepperoni, regret, starlight, wallboard", + "extraction_point_miss": 0, + "extraction_suspect": 0, + "gave_up": 0, + "gold_in_answer_region": 1, + "gold_in_output": 1, + "has_answer_tag": 1, + "has_output": 1, + "label_disagrees": 0, + "labelled_fail": 0, + "output_chars": 690, + "output_truncated": 0, + "sample_id": "bbh_word_sorting-0101", + "strict_match": 1 + } + ], + "suspect_rate": 0 + }, + "metadata": { + "experiment": {}, + "fingerprint": "92a33b74cf5cbbb0" + }, + "model": "JaxLocalModel(key='gemma-4-e2b-it', adapter=GemmaJaxAdapter, loaded)", + "n_cases": 8, + "summary": "[answer_extraction_audit] on JaxLocalModel(key='gemma-4-e2b-it', adapter=GemmaJaxAdapter, loaded)\n n_cases: 8\n n_gradable: 8\n n_labelled_fail: 5\n n_extraction_suspect: 0\n suspect_rate: 0.0\n n_reasked: 0\n n_reask_confirmed: None\n n_extraction_point_miss: 0\n missing_tag_rate: 0.25\n per_case: [{'sample_id': 'bbh_word_sorting-0006', 'has_output': 1, 'has_answer_tag': 1, 'output_truncated': 0, 'gave_up': 0, 'output_chars': 917, 'labelled_fail': 1, 'strict_match': 0, 'gold_in_output': 0, 'gold_in_answer_region': 0, 'extracted_answer': 'administer, aeneid, decadent, coachman, delhi, dey, grim, gradate, jacky, littleneck, phosphorescent, pristine, sinh, sh', 'extraction_suspect': 0, 'extraction_point_miss': 0, 'label_disagrees': 0}, {'sample_id': 'bbh_word_sorting-0025', 'has_output': 1, 'has_answer_tag': 0, 'output_truncated': 1, 'gave_up': 0, 'output_chars': 1332, 'labelled_fail': 1, 'strict_match': 0, 'gold_in_output': 0, 'gold_in_answer_region': 0, 'extracted_answer': 'J:', 'extraction_suspect': 0, 'extraction_point_miss': 0, 'label_disagrees': 0}, {'sample_id': 'bbh_word_sorting-0045', 'has_output': 1, 'has_answer_tag': 1, 'output_truncated': 0, 'gave_up': 0, 'output_chars': 1101, 'labelled_fail': 1, 'strict_match': 0, 'gold_in_output': 0, 'gold_in_answer_region': 0, 'extracted_answer': 'backslide, calvert, commando, gabriel, hendrickson, hollister, jackson, anastomosis, pizzicato, quail, separate, shelter', 'extraction_suspect': 0, 'extraction_point_miss': 0, 'label_disagrees': 0}, {'sample_id': 'bbh_word_sorting-0076', 'has_output': 1, 'has_answer_tag': 0, 'output_truncated': 1, 'gave_up': 0, 'output_chars': 1389, 'labelled_fail': 1, 'strict_match': 0, 'gold_in_output': 0, 'gold_in_answer_region': 0, 'extracted_answer': 'The correct alphabetical order is: biennial, creedal, cry, eyesight, fletch,', 'extraction_suspect': 0, 'extraction_point_miss': 0, 'label_disagrees': 0}, {'sample_id': 'bbh_word_sorting-0094', 'has_output': 1, 'has_answer_tag': 1, 'output_truncated': 0, 'gave_up': 0, 'output_chars': 922, 'labelled_fail': 1, 'strict_match': 0, 'gold_in_output': 0, 'gold_in_answer_region': 0, 'extracted_answer': 'battery, bushland, capacitive, contingent, crossbill, enigma, jane, lipton, meager, ricochet, wallet, wacke, wysiwyg', 'extraction_suspect': 0, 'extraction_point_miss': 0, 'label_disagrees': 0}, {'sample_id': 'bbh_word_sorting-0000', 'has_output': 1, 'has_answer_tag': 1, 'output_truncated': 0, 'gave_up': 0, 'output_chars': 1286, 'labelled_fail': 0, 'strict_match': 1, 'gold_in_output': 1, 'gold_in_answer_region': 1, 'extracted_answer': 'alleviate, benelux, buoyant, duopoly, felice, gland, gunk, hardbound, klaxon, mattress, tomography, townsmen', 'extraction_suspect': 0, 'extraction_point_miss': 0, 'label_disagrees': 0}, {'sample_id': 'bbh_word_sorting-0063', 'has_output': 1, 'has_answer_tag': 1, 'output_truncated': 0, 'gave_up': 0, 'output_chars': 1054, 'labelled_fail': 0, 'strict_match': 1, 'gold_in_output': 1, 'gold_in_answer_region': 1, 'extracted_answer': 'artillery, bainite, doris, fda, harm, incongruous, monkey, prosody, vegetate, vivian', 'extraction_suspect': 0, 'extraction_point_miss': 0, 'label_disagrees': 0}, {'sample_id': 'bbh_word_sorting-0101', 'has_output': 1, 'has_answer_tag': 1, 'output_truncated': 0, 'gave_up': 0, 'output_chars': 690, 'labelled_fail': 0, 'strict_match': 1, 'gold_in_output': 1, 'gold_in_answer_region': 1, 'extracted_answer': 'cotyledon, more, pepperoni, regret, starlight, wallboard', 'extraction_suspect': 0, 'extraction_point_miss': 0, 'label_disagrees': 0}]\n _caveat: suspect_rate is the fraction of FAIL cases whose gold answer sits in the ANSWER REGION of the output (tail window or a tagged/boxed span) — HARNESS failures wearing a model-failure label. gold_in_output is the loose upper bound and over-counts: it fires on a gold that only appears as an intermediate quantity, so never read it as the suspect rate. Above ~5% the labels feeding M2 are contaminated and every other probe's correlations should be re-read after fixing extraction (or after dropping extraction_suspect==1 cases). This probe is OBSERVATIONAL by default: it re-reads stored outputs and introduces no new sampling. With reask=True the reask_* columns are interventional and must be re-run for held-out confirmation. On yes/no (true/false) tasks answered_yes and gold_yes are the answer's and the gold's DIRECTION, two separate marginals (not re-grades): a directional hypothesis is tested on them, e.g. answered_yes HIGHER on failing cases." + }, + "calibration": { + "analyzer": "calibration", + "findings": { + "_caveat": "Needs PASS/FAIL labels — unlabeled cases are skipped, and ECE on few labelled cases is unstable (aim for >= ~50). conf_logprob is the geometric-mean token probability of the model's own continuation — a proxy, not an answer probability; on API backends it reflects only the returned top-k, and a model without LOGPROBS reports only the verbalized channel. A positive overconfidence_gap on FAIL-heavy slices argues for abstention/recalibration fixes rather than capability fixes.", + "logprob_channel": { + "brier": 0.6015, + "ece": 0.5978, + "n": 8, + "overconfidence_gap": 0.5978 + }, + "logprob_channel_available": true, + "n_bins": 10, + "n_cases": 8, + "n_unlabelled_skipped": 0, + "per_case": [ + { + "conf_logprob": 0.9936, + "correct": 0, + "sample_id": "bbh_word_sorting-0006" + }, + { + "conf_logprob": 0.9926, + "correct": 0, + "sample_id": "bbh_word_sorting-0025" + }, + { + "conf_logprob": 0.9905, + "correct": 0, + "sample_id": "bbh_word_sorting-0045" + }, + { + "conf_logprob": 0.9454, + "correct": 0, + "sample_id": "bbh_word_sorting-0076" + }, + { + "conf_logprob": 0.9796, + "correct": 0, + "sample_id": "bbh_word_sorting-0094" + }, + { + "conf_logprob": 0.9542, + "conf_verbal": 1, + "correct": 1, + "sample_id": "bbh_word_sorting-0000" + }, + { + "conf_logprob": 0.9583, + "correct": 1, + "sample_id": "bbh_word_sorting-0063" + }, + { + "conf_logprob": 0.9683, + "conf_verbal": 1, + "correct": 1, + "sample_id": "bbh_word_sorting-0101" + } + ], + "verbalized_channel": { + "brier": 0, + "ece": 0, + "n": 2, + "overconfidence_gap": 0 + } + }, + "metadata": { + "experiment": {}, + "fingerprint": "0856a0cb22c7c481" + }, + "model": "JaxLocalModel(key='gemma-4-e2b-it', adapter=GemmaJaxAdapter, loaded)", + "n_cases": 8, + "summary": "[calibration] on JaxLocalModel(key='gemma-4-e2b-it', adapter=GemmaJaxAdapter, loaded)\n n_cases: 8\n n_unlabelled_skipped: 0\n n_bins: 10\n logprob_channel_available: True\n logprob_channel: {'n': 8, 'ece': 0.5978, 'brier': 0.6015, 'overconfidence_gap': 0.5978}\n verbalized_channel: {'n': 2, 'ece': 0.0, 'brier': 0.0, 'overconfidence_gap': 0.0}\n per_case: [{'sample_id': 'bbh_word_sorting-0006', 'correct': 0, 'conf_logprob': 0.9936}, {'sample_id': 'bbh_word_sorting-0025', 'correct': 0, 'conf_logprob': 0.9926}, {'sample_id': 'bbh_word_sorting-0045', 'correct': 0, 'conf_logprob': 0.9905}, {'sample_id': 'bbh_word_sorting-0076', 'correct': 0, 'conf_logprob': 0.9454}, {'sample_id': 'bbh_word_sorting-0094', 'correct': 0, 'conf_logprob': 0.9796}, {'sample_id': 'bbh_word_sorting-0000', 'correct': 1, 'conf_logprob': 0.9542, 'conf_verbal': 1.0}, {'sample_id': 'bbh_word_sorting-0063', 'correct': 1, 'conf_logprob': 0.9583}, {'sample_id': 'bbh_word_sorting-0101', 'correct': 1, 'conf_logprob': 0.9683, 'conf_verbal': 1.0}]\n _caveat: Needs PASS/FAIL labels — unlabeled cases are skipped, and ECE on few labelled cases is unstable (aim for >= ~50). conf_logprob is the geometric-mean token probability of the model's own continuation — a proxy, not an answer probability; on API backends it reflects only the returned top-k, and a model without LOGPROBS reports only the verbalized channel. A positive overconfidence_gap on FAIL-heavy slices argues for abstention/recalibration fixes rather than capability fixes." + }, + "coverage_verification_gap": { + "analyzer": "coverage_verification_gap", + "findings": { + "_caveat": "coverage_gap = 'the answer was in the pool, the vote missed it' ⇒ a selection defect, fixable at L1/L2 with a verifier or reranker. no_coverage_rate = 'never produced once in k' ⇒ a capability defect no scaffold reaches; do not let a headline pass@k hide which of the two dominates. REQUIRES sampling: degenerate_sampling=true means every sample was identical (temperature 0 or a deterministic backend) and the gap is structurally zero, not measured. pass_at_k here uses the unbiased estimator over exactly k draws, so with k small its variance is large per case — read the batch rate. INTERVENTIONAL: held-out confirmation must re-sample.", + "coverage_gap_rate": 0.125, + "degenerate_sampling": false, + "gen_kwargs": { + "temperature": 1, + "top_k": 20, + "top_p": 0.95 + }, + "k": 5, + "mean_majority_correct": 0.375, + "mean_pass_at_k": 0.5, + "mean_verify_correct": null, + "n_cases": 8, + "n_scored": 8, + "no_coverage_rate": 0.5, + "per_case": [ + { + "any_correct": 0, + "coverage_gap": 0, + "majority_correct": 0, + "majority_share": 0.4, + "modal_answer": "14", + "n_correct": 0, + "n_graded": 5, + "n_samples": 5, + "n_unique": 4, + "pass_at_k": 0, + "sample_id": "bbh_word_sorting-0006" + }, + { + "any_correct": 0, + "coverage_gap": 0, + "majority_correct": 0, + "majority_share": 0.2, + "modal_answer": "arrear eavesdropping fasciculate herodotus hermaphrodite ibn henry incorrigible ", + "n_correct": 0, + "n_graded": 5, + "n_samples": 5, + "n_unique": 5, + "pass_at_k": 0, + "sample_id": "bbh_word_sorting-0025" + }, + { + "any_correct": 0, + "coverage_gap": 0, + "majority_correct": 0, + "majority_share": 0.2, + "modal_answer": "16. separate wait s words re-sort: separate shelter sticktight spongy", + "n_correct": 0, + "n_graded": 5, + "n_samples": 5, + "n_unique": 5, + "pass_at_k": 0, + "sample_id": "bbh_word_sorting-0045" + }, + { + "any_correct": 0, + "coverage_gap": 0, + "majority_correct": 0, + "majority_share": 0.4, + "modal_answer": "biennial creedal cry fletch fraudulent eyesight j miltonic mirage whisper titmic", + "n_correct": 0, + "n_graded": 5, + "n_samples": 5, + "n_unique": 4, + "pass_at_k": 0, + "sample_id": "bbh_word_sorting-0076" + }, + { + "any_correct": 1, + "coverage_gap": 1, + "majority_correct": 0, + "majority_share": 0.4, + "modal_answer": "battery bushland capacitive contingent crossbill enigma jane lipton meager ricoc", + "n_correct": 1, + "n_graded": 5, + "n_samples": 5, + "n_unique": 4, + "pass_at_k": 1, + "sample_id": "bbh_word_sorting-0094" + }, + { + "any_correct": 1, + "coverage_gap": 0, + "majority_correct": 1, + "majority_share": 0.8, + "modal_answer": "alleviate benelux buoyant duopoly felice gland gunk hardbound klaxon mattress to", + "n_correct": 4, + "n_graded": 5, + "n_samples": 5, + "n_unique": 2, + "pass_at_k": 1, + "sample_id": "bbh_word_sorting-0000" + }, + { + "any_correct": 1, + "coverage_gap": 0, + "majority_correct": 1, + "majority_share": 0.6, + "modal_answer": "artillery bainite doris fda harm incongruous monkey prosody vegetate vivian", + "n_correct": 3, + "n_graded": 5, + "n_samples": 5, + "n_unique": 3, + "pass_at_k": 1, + "sample_id": "bbh_word_sorting-0063" + }, + { + "any_correct": 1, + "coverage_gap": 0, + "majority_correct": 1, + "majority_share": 1, + "modal_answer": "cotyledon more pepperoni regret starlight wallboard", + "n_correct": 5, + "n_graded": 5, + "n_samples": 5, + "n_unique": 1, + "pass_at_k": 1, + "sample_id": "bbh_word_sorting-0101" + } + ] + }, + "metadata": { + "experiment": {}, + "fingerprint": "8bf070740570e619" + }, + "model": "JaxLocalModel(key='gemma-4-e2b-it', adapter=GemmaJaxAdapter, loaded)", + "n_cases": 8, + "summary": "[coverage_verification_gap] on JaxLocalModel(key='gemma-4-e2b-it', adapter=GemmaJaxAdapter, loaded)\n n_cases: 8\n n_scored: 8\n k: 5\n gen_kwargs: {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20}\n mean_pass_at_k: 0.5\n mean_majority_correct: 0.375\n coverage_gap_rate: 0.125\n mean_verify_correct: None\n no_coverage_rate: 0.5\n degenerate_sampling: False\n per_case: [{'sample_id': 'bbh_word_sorting-0006', 'n_samples': 5, 'n_graded': 5, 'n_correct': 0, 'n_unique': 4, 'pass_at_k': 0.0, 'any_correct': 0, 'majority_correct': 0, 'majority_share': 0.4, 'coverage_gap': 0, 'modal_answer': '14'}, {'sample_id': 'bbh_word_sorting-0025', 'n_samples': 5, 'n_graded': 5, 'n_correct': 0, 'n_unique': 5, 'pass_at_k': 0.0, 'any_correct': 0, 'majority_correct': 0, 'majority_share': 0.2, 'coverage_gap': 0, 'modal_answer': 'arrear eavesdropping fasciculate herodotus hermaphrodite ibn henry incorrigible '}, {'sample_id': 'bbh_word_sorting-0045', 'n_samples': 5, 'n_graded': 5, 'n_correct': 0, 'n_unique': 5, 'pass_at_k': 0.0, 'any_correct': 0, 'majority_correct': 0, 'majority_share': 0.2, 'coverage_gap': 0, 'modal_answer': '16. separate wait s words re-sort: separate shelter sticktight spongy'}, {'sample_id': 'bbh_word_sorting-0076', 'n_samples': 5, 'n_graded': 5, 'n_correct': 0, 'n_unique': 4, 'pass_at_k': 0.0, 'any_correct': 0, 'majority_correct': 0, 'majority_share': 0.4, 'coverage_gap': 0, 'modal_answer': 'biennial creedal cry fletch fraudulent eyesight j miltonic mirage whisper titmic'}, {'sample_id': 'bbh_word_sorting-0094', 'n_samples': 5, 'n_graded': 5, 'n_correct': 1, 'n_unique': 4, 'pass_at_k': 1.0, 'any_correct': 1, 'majority_correct': 0, 'majority_share': 0.4, 'coverage_gap': 1, 'modal_answer': 'battery bushland capacitive contingent crossbill enigma jane lipton meager ricoc'}, {'sample_id': 'bbh_word_sorting-0000', 'n_samples': 5, 'n_graded': 5, 'n_correct': 4, 'n_unique': 2, 'pass_at_k': 1.0, 'any_correct': 1, 'majority_correct': 1, 'majority_share': 0.8, 'coverage_gap': 0, 'modal_answer': 'alleviate benelux buoyant duopoly felice gland gunk hardbound klaxon mattress to'}, {'sample_id': 'bbh_word_sorting-0063', 'n_samples': 5, 'n_graded': 5, 'n_correct': 3, 'n_unique': 3, 'pass_at_k': 1.0, 'any_correct': 1, 'majority_correct': 1, 'majority_share': 0.6, 'coverage_gap': 0, 'modal_answer': 'artillery bainite doris fda harm incongruous monkey prosody vegetate vivian'}, {'sample_id': 'bbh_word_sorting-0101', 'n_samples': 5, 'n_graded': 5, 'n_correct': 5, 'n_unique': 1, 'pass_at_k': 1.0, 'any_correct': 1, 'majority_correct': 1, 'majority_share': 1.0, 'coverage_gap': 0, 'modal_answer': 'cotyledon more pepperoni regret starlight wallboard'}]\n _caveat: coverage_gap = 'the answer was in the pool, the vote missed it' ⇒ a selection defect, fixable at L1/L2 with a verifier or reranker. no_coverage_rate = 'never produced once in k' ⇒ a capability defect no scaffold reaches; do not let a headline pass@k hide which of the two dominates. REQUIRES sampling: degenerate_sampling=true means every sample was identical (temperature 0 or a deterministic backend) and the gap is structurally zero, not measured. pass_at_k here uses the unbiased estimator over exactly k draws, so with k small its variance is large per case — read the batch rate. INTERVENTIONAL: held-out confirmation must re-sample." + }, + "logprob_entropy": { + "analyzer": "logprob_entropy", + "findings": { + "mean_logprob": -0.0064, + "mean_top_entropy": 0.0213, + "min_token_logprob": -0.1908, + "n_tokens": 64, + "perplexity": 1.0064 + }, + "metadata": { + "experiment": {}, + "fingerprint": "7477010b006f9347" + }, + "model": "JaxLocalModel(key='gemma-4-e2b-it', adapter=GemmaJaxAdapter, loaded)", + "n_cases": 8, + "summary": "[logprob_entropy] on JaxLocalModel(key='gemma-4-e2b-it', adapter=GemmaJaxAdapter, loaded)\n n_tokens: 64\n mean_logprob: -0.0064\n perplexity: 1.0064\n min_token_logprob: -0.1908\n mean_top_entropy: 0.0213" + }, + "perturbation_battery": { + "analyzer": "perturbation_battery", + "findings": { + "_caveat": "Two DIFFERENT defects share this probe and must not be summed: invariance_break_rate should be 0 (a preserving edit moved the answer) while sensitivity_rate should be 1 (an altering edit must move it). sensitivity_rate == 0 with a correct baseline is the memorisation signature — cross-check contamination_score before calling it reasoning. Every rate is measured against the model's OWN baseline answer, not the gold, so a case that was already wrong still contributes: read these as stability, never as accuracy. Under sampling, a 'flip' can be decoding noise — set temperature 0 or repeat. INTERVENTIONAL: re-run to confirm.", + "applied_perturbations": [ + "noop_clause", + "rename_entities", + "restate_question" + ], + "gen_kwargs": {}, + "mean_invariance_break_rate": 0.7917, + "mean_sensitivity_rate": null, + "n_cases": 8, + "n_memorization_suspect": 0, + "n_scored": 8, + "noop_break_rate": 0.625, + "per_case": [ + { + "applied": [ + "rename_entities", + "restate_question", + "noop_clause" + ], + "baseline_answer": "administer aeneid decadent coachman delhi dey grim gradate jacky littleneck phos", + "invariance_break_rate": 1, + "n_altering": 0, + "n_preserving": 3, + "noop_clause_flipped": 1, + "rename_entities_flipped": 1, + "restate_question_flipped": 1, + "sample_id": "bbh_word_sorting-0006", + "sensitivity_rate": null + }, + { + "applied": [ + "rename_entities", + "restate_question", + "noop_clause" + ], + "baseline_answer": "j:", + "invariance_break_rate": 1, + "n_altering": 0, + "n_preserving": 3, + "noop_clause_flipped": 1, + "rename_entities_flipped": 1, + "restate_question_flipped": 1, + "sample_id": "bbh_word_sorting-0025", + "sensitivity_rate": null + }, + { + "applied": [ + "rename_entities", + "restate_question", + "noop_clause" + ], + "baseline_answer": "backslide calvert commando gabriel hendrickson hollister jackson anastomosis piz", + "invariance_break_rate": 1, + "n_altering": 0, + "n_preserving": 3, + "noop_clause_flipped": 1, + "rename_entities_flipped": 1, + "restate_question_flipped": 1, + "sample_id": "bbh_word_sorting-0045", + "sensitivity_rate": null + }, + { + "applied": [ + "rename_entities", + "restate_question", + "noop_clause" + ], + "baseline_answer": "correct alphabetical order is: biennial creedal cry eyesight fletch", + "invariance_break_rate": 1, + "n_altering": 0, + "n_preserving": 3, + "noop_clause_flipped": 1, + "rename_entities_flipped": 1, + "restate_question_flipped": 1, + "sample_id": "bbh_word_sorting-0076", + "sensitivity_rate": null + }, + { + "applied": [ + "rename_entities", + "restate_question", + "noop_clause" + ], + "baseline_answer": "battery bushland capacitive contingent crossbill enigma jane lipton meager ricoc", + "invariance_break_rate": 0.6667, + "n_altering": 0, + "n_preserving": 3, + "noop_clause_flipped": 0, + "rename_entities_flipped": 1, + "restate_question_flipped": 1, + "sample_id": "bbh_word_sorting-0094", + "sensitivity_rate": null + }, + { + "applied": [ + "rename_entities", + "restate_question", + "noop_clause" + ], + "baseline_answer": "alleviate benelux buoyant duopoly felice gland gunk hardbound klaxon mattress to", + "invariance_break_rate": 0.6667, + "n_altering": 0, + "n_preserving": 3, + "noop_clause_flipped": 0, + "rename_entities_flipped": 1, + "restate_question_flipped": 1, + "sample_id": "bbh_word_sorting-0000", + "sensitivity_rate": null + }, + { + "applied": [ + "rename_entities", + "restate_question", + "noop_clause" + ], + "baseline_answer": "artillery bainite doris fda harm incongruous monkey prosody vegetate vivian", + "invariance_break_rate": 0.6667, + "n_altering": 0, + "n_preserving": 3, + "noop_clause_flipped": 1, + "rename_entities_flipped": 1, + "restate_question_flipped": 0, + "sample_id": "bbh_word_sorting-0063", + "sensitivity_rate": null + }, + { + "applied": [ + "rename_entities", + "restate_question", + "noop_clause" + ], + "baseline_answer": "cotyledon more pepperoni regret starlight wallboard", + "invariance_break_rate": 0.3333, + "n_altering": 0, + "n_preserving": 3, + "noop_clause_flipped": 0, + "rename_entities_flipped": 1, + "restate_question_flipped": 0, + "sample_id": "bbh_word_sorting-0101", + "sensitivity_rate": null + } + ], + "perturbations": [ + "rename_entities", + "restate_question", + "noop_clause", + "perturb_numbers" + ] + }, + "metadata": { + "experiment": {}, + "fingerprint": "21f01ca6f2ebd563" + }, + "model": "JaxLocalModel(key='gemma-4-e2b-it', adapter=GemmaJaxAdapter, loaded)", + "n_cases": 8, + "summary": "[perturbation_battery] on JaxLocalModel(key='gemma-4-e2b-it', adapter=GemmaJaxAdapter, loaded)\n n_cases: 8\n n_scored: 8\n perturbations: ['rename_entities', 'restate_question', 'noop_clause', 'perturb_numbers']\n applied_perturbations: ['noop_clause', 'rename_entities', 'restate_question']\n gen_kwargs: {}\n mean_invariance_break_rate: 0.7917\n noop_break_rate: 0.625\n mean_sensitivity_rate: None\n n_memorization_suspect: 0\n per_case: [{'sample_id': 'bbh_word_sorting-0006', 'baseline_answer': 'administer aeneid decadent coachman delhi dey grim gradate jacky littleneck phos', 'rename_entities_flipped': 1, 'restate_question_flipped': 1, 'noop_clause_flipped': 1, 'applied': ['rename_entities', 'restate_question', 'noop_clause'], 'invariance_break_rate': 1.0, 'n_preserving': 3, 'sensitivity_rate': None, 'n_altering': 0}, {'sample_id': 'bbh_word_sorting-0025', 'baseline_answer': 'j:', 'rename_entities_flipped': 1, 'restate_question_flipped': 1, 'noop_clause_flipped': 1, 'applied': ['rename_entities', 'restate_question', 'noop_clause'], 'invariance_break_rate': 1.0, 'n_preserving': 3, 'sensitivity_rate': None, 'n_altering': 0}, {'sample_id': 'bbh_word_sorting-0045', 'baseline_answer': 'backslide calvert commando gabriel hendrickson hollister jackson anastomosis piz', 'rename_entities_flipped': 1, 'restate_question_flipped': 1, 'noop_clause_flipped': 1, 'applied': ['rename_entities', 'restate_question', 'noop_clause'], 'invariance_break_rate': 1.0, 'n_preserving': 3, 'sensitivity_rate': None, 'n_altering': 0}, {'sample_id': 'bbh_word_sorting-0076', 'baseline_answer': 'correct alphabetical order is: biennial creedal cry eyesight fletch', 'rename_entities_flipped': 1, 'restate_question_flipped': 1, 'noop_clause_flipped': 1, 'applied': ['rename_entities', 'restate_question', 'noop_clause'], 'invariance_break_rate': 1.0, 'n_preserving': 3, 'sensitivity_rate': None, 'n_altering': 0}, {'sample_id': 'bbh_word_sorting-0094', 'baseline_answer': 'battery bushland capacitive contingent crossbill enigma jane lipton meager ricoc', 'rename_entities_flipped': 1, 'restate_question_flipped': 1, 'noop_clause_flipped': 0, 'applied': ['rename_entities', 'restate_question', 'noop_clause'], 'invariance_break_rate': 0.6667, 'n_preserving': 3, 'sensitivity_rate': None, 'n_altering': 0}, {'sample_id': 'bbh_word_sorting-0000', 'baseline_answer': 'alleviate benelux buoyant duopoly felice gland gunk hardbound klaxon mattress to', 'rename_entities_flipped': 1, 'restate_question_flipped': 1, 'noop_clause_flipped': 0, 'applied': ['rename_entities', 'restate_question', 'noop_clause'], 'invariance_break_rate': 0.6667, 'n_preserving': 3, 'sensitivity_rate': None, 'n_altering': 0}, {'sample_id': 'bbh_word_sorting-0063', 'baseline_answer': 'artillery bainite doris fda harm incongruous monkey prosody vegetate vivian', 'rename_entities_flipped': 1, 'restate_question_flipped': 0, 'noop_clause_flipped': 1, 'applied': ['rename_entities', 'restate_question', 'noop_clause'], 'invariance_break_rate': 0.6667, 'n_preserving': 3, 'sensitivity_rate': None, 'n_altering': 0}, {'sample_id': 'bbh_word_sorting-0101', 'baseline_answer': 'cotyledon more pepperoni regret starlight wallboard', 'rename_entities_flipped': 1, 'restate_question_flipped': 0, 'noop_clause_flipped': 0, 'applied': ['rename_entities', 'restate_question', 'noop_clause'], 'invariance_break_rate': 0.3333, 'n_preserving': 3, 'sensitivity_rate': None, 'n_altering': 0}]\n _caveat: Two DIFFERENT defects share this probe and must not be summed: invariance_break_rate should be 0 (a preserving edit moved the answer) while sensitivity_rate should be 1 (an altering edit must move it). sensitivity_rate == 0 with a correct baseline is the memorisation signature — cross-check contamination_score before calling it reasoning. Every rate is measured against the model's OWN baseline answer, not the gold, so a case that was already wrong still contributes: read these as stability, never as accuracy. Under sampling, a 'flip' can be decoding noise — set temperature 0 or repeat. INTERVENTIONAL: re-run to confirm." + }, + "self_consistency": { + "analyzer": "self_consistency", + "findings": { + "_caveat": "semantic_* uses the SAME samples as consistency — it costs no extra generations, and the two are meant to be read together: consistency << semantic_consistency means the disagreement was only wording. With the default lexical_proxy, clustering is content-word overlap, NOT entailment: it merges answers that share vocabulary while contradicting each other ('the claim holds' / 'the claim does not hold') and splits correct paraphrases with no shared words. Inject an NLI entailment_fn before reading semantic_entropy as the Farquhar et al. metric. At temperature 0 every sample is identical and both scores are 1 by construction, not by measurement.", + "cluster_representatives": [ + "Here are the words sorted alphabetically:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. gradate\n7. grim\n8", + "Here are the words sorted alphabetically:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. gradate\n7. grim\n8", + "Here are the words sorted alphabetically:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. gradate\n7. grim\n8" + ], + "cluster_sizes": [ + 2, + 2, + 1 + ], + "compared_on": "raw_text", + "consistency": 0.2, + "entailment": "lexical_proxy", + "gen_kwargs": { + "temperature": 1, + "top_k": 20, + "top_p": 0.95 + }, + "modal_answer": "Here are the words sorted alphabetically:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. gradate\n7. grim\n8. jacky\n9. littleneck\n10. phosphorescent\n11. pristine\n12. sinh\n13. systemwide\n14. tasting\n15. torpedo\n16. thrown\n17. verdict\n18. dey\n19. shrunk\n\nLet's re-sort them carefully:\n\n* administer\n* aeneid\n* decadent\n* coachman\n* delhi\n* grim\n* gradate\n* jacky\n* littleneck\n* phosphorescent\n* pristine\n* sinh\n* systemwide\n* tasting\n* torpedo\n* thrown\n* verdict\n* dey\n* shrunk\n\nChecking for correct alphabetical order:\nA: administer, aeneid\nC: decadent, coachman\nD: delhi, dey\nG: grim, gradate\nJ: jacky\nL: littleneck\nP: phosphorescent, pristine\nS: sinh, systemwide, shrunk\nT: tasting, torpedo, thrown\nV: verdict\n\nCorrected Sorted List:\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. dey\n7. grim\n8. gradate\n9. jacky\n10. littleneck\n11. phosphorescent\n12. pristine\n13. sinh\n14. shrunk\n15. systemwide\n16. tasting\n17. torpedo\n18. thrown\n19. verdict\n\n(Comparing D words: delhi, dey. D-e-l-h-i vs D-e-y. 'l' comes before 'y', so delhi comes before dey.)\n\nFinal check on the complete list:\nadminister (A)\naeneid (A)\ndecadent (D)\ncoachman (C) -> Should be C before D. Let's restart the alphabetical sort strictly.\n\nList: torpedo, phosphorescent, pristine, decadent, shrunk, dey, administer, gradate, littleneck, thrown, jacky, coachman, aeneid, verdict, tasting, sinh, delhi, systemwide, grim\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6", + "n_samples": 5, + "n_semantic_clusters": 3, + "n_unique": 5, + "normalized_semantic_entropy": 0.6554, + "raw_text_consistency": 0.2, + "semantic_consistency": 0.4, + "semantic_entropy": 1.0549 + }, + "metadata": { + "experiment": {}, + "fingerprint": "0830814a73d489d3" + }, + "model": "JaxLocalModel(key='gemma-4-e2b-it', adapter=GemmaJaxAdapter, loaded)", + "n_cases": 8, + "summary": "[self_consistency] on JaxLocalModel(key='gemma-4-e2b-it', adapter=GemmaJaxAdapter, loaded)\n n_samples: 5\n consistency: 0.2\n n_unique: 5\n compared_on: raw_text\n raw_text_consistency: 0.2\n modal_answer: Here are the words sorted alphabetically:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. gradate\n7. grim\n8. jacky\n9. littleneck\n10. phosphorescent\n11. pristine\n12. sinh\n13. systemwide\n14. tasting\n15. torpedo\n16. thrown\n17. verdict\n18. dey\n19. shrunk\n\nLet's re-sort them carefully:\n\n* administer\n* aeneid\n* decadent\n* coachman\n* delhi\n* grim\n* gradate\n* jacky\n* littleneck\n* phosphorescent\n* pristine\n* sinh\n* systemwide\n* tasting\n* torpedo\n* thrown\n* verdict\n* dey\n* shrunk\n\nChecking for correct alphabetical order:\nA: administer, aeneid\nC: decadent, coachman\nD: delhi, dey\nG: grim, gradate\nJ: jacky\nL: littleneck\nP: phosphorescent, pristine\nS: sinh, systemwide, shrunk\nT: tasting, torpedo, thrown\nV: verdict\n\nCorrected Sorted List:\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. dey\n7. grim\n8. gradate\n9. jacky\n10. littleneck\n11. phosphorescent\n12. pristine\n13. sinh\n14. shrunk\n15. systemwide\n16. tasting\n17. torpedo\n18. thrown\n19. verdict\n\n(Comparing D words: delhi, dey. D-e-l-h-i vs D-e-y. 'l' comes before 'y', so delhi comes before dey.)\n\nFinal check on the complete list:\nadminister (A)\naeneid (A)\ndecadent (D)\ncoachman (C) -> Should be C before D. Let's restart the alphabetical sort strictly.\n\nList: torpedo, phosphorescent, pristine, decadent, shrunk, dey, administer, gradate, littleneck, thrown, jacky, coachman, aeneid, verdict, tasting, sinh, delhi, systemwide, grim\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6\n gen_kwargs: {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20}\n n_semantic_clusters: 3\n semantic_entropy: 1.0549\n normalized_semantic_entropy: 0.6554\n semantic_consistency: 0.4\n cluster_sizes: [2, 2, 1]\n cluster_representatives: ['Here are the words sorted alphabetically:\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. gradate\\n7. grim\\n8', 'Here are the words sorted alphabetically:\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. gradate\\n7. grim\\n8', 'Here are the words sorted alphabetically:\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. gradate\\n7. grim\\n8']\n entailment: lexical_proxy\n _caveat: semantic_* uses the SAME samples as consistency — it costs no extra generations, and the two are meant to be read together: consistency << semantic_consistency means the disagreement was only wording. With the default lexical_proxy, clustering is content-word overlap, NOT entailment: it merges answers that share vocabulary while contradicting each other ('the claim holds' / 'the claim does not hold') and splits correct paraphrases with no shared words. Inject an NLI entailment_fn before reading semantic_entropy as the Farquhar et al. metric. At temperature 0 every sample is identical and both scores are 1 by construction, not by measurement." + }, + "selfcheck_consistency": { + "analyzer": "selfcheck_consistency", + "findings": { + "_caveat": "Unigram-containment proxy for SelfCheckGPT (no NLI model): paraphrases inflate the score, verbatim repetition of a wrong fact deflates it. Requires stochastic sampling — with temperature 0 the resamples repeat one greedy generation and the column degenerates to baseline-vs-greedy disagreement (degenerate_sampling flags this). selfcheck_inconsistency / selfcheck_worst_sentence are numeric M2 columns; treat them as signals to correlate with the outcome label, never as a hallucination verdict.", + "degenerate_sampling": false, + "gen_kwargs": { + "temperature": 1 + }, + "mean_inconsistency": 0.015, + "n_cases": 8, + "n_samples": 4, + "per_case": [ + { + "baseline_from": "observed", + "n_sentences": 19, + "sample_id": "bbh_word_sorting-0006", + "selfcheck_inconsistency": 0, + "selfcheck_worst_sentence": 0, + "worst_sentence_text": "Here are the words sorted alphabetically:" + }, + { + "baseline_from": "observed", + "n_sentences": 45, + "sample_id": "bbh_word_sorting-0025", + "selfcheck_inconsistency": 0.037, + "selfcheck_worst_sentence": 0.5, + "worst_sentence_text": "* **v**ehicular" + }, + { + "baseline_from": "observed", + "n_sentences": 29, + "sample_id": "bbh_word_sorting-0045", + "selfcheck_inconsistency": 0.023, + "selfcheck_worst_sentence": 0.3333, + "worst_sentence_text": "Comparing the words:" + }, + { + "baseline_from": "observed", + "n_sentences": 47, + "sample_id": "bbh_word_sorting-0076", + "selfcheck_inconsistency": 0.0376, + "selfcheck_worst_sentence": 0.6667, + "worst_sentence_text": "**Final ordered list:**" + }, + { + "baseline_from": "observed", + "n_sentences": 28, + "sample_id": "bbh_word_sorting-0094", + "selfcheck_inconsistency": 0.0168, + "selfcheck_worst_sentence": 0.4, + "worst_sentence_text": "The final sorted list is:" + }, + { + "baseline_from": "observed", + "n_sentences": 32, + "sample_id": "bbh_word_sorting-0000", + "selfcheck_inconsistency": 0.0016, + "selfcheck_worst_sentence": 0.0526, + "worst_sentence_text": "Here are the words to sort alphabetically: alleviate, duopoly, mattress, gland, benelux, townsmen, buoyant, klaxon, hardbound, tomography, felice, gunk." + }, + { + "baseline_from": "observed", + "n_sentences": 30, + "sample_id": "bbh_word_sorting-0063", + "selfcheck_inconsistency": 0.0037, + "selfcheck_worst_sentence": 0.1111, + "worst_sentence_text": "* Comparing the second letter: 'e' in vegetate, 'i' in vivian." + }, + { + "baseline_from": "observed", + "n_sentences": 18, + "sample_id": "bbh_word_sorting-0101", + "selfcheck_inconsistency": 0, + "selfcheck_worst_sentence": 0, + "worst_sentence_text": "The given list of words is: regret, starlight, wallboard, cotyledon, more, pepperoni." + } + ] + }, + "metadata": { + "experiment": {}, + "fingerprint": "6ffb9af8a87bbc95" + }, + "model": "JaxLocalModel(key='gemma-4-e2b-it', adapter=GemmaJaxAdapter, loaded)", + "n_cases": 8, + "summary": "[selfcheck_consistency] on JaxLocalModel(key='gemma-4-e2b-it', adapter=GemmaJaxAdapter, loaded)\n n_cases: 8\n n_samples: 4\n degenerate_sampling: False\n gen_kwargs: {'temperature': 1.0}\n mean_inconsistency: 0.015\n per_case: [{'sample_id': 'bbh_word_sorting-0006', 'baseline_from': 'observed', 'n_sentences': 19, 'selfcheck_inconsistency': 0.0, 'selfcheck_worst_sentence': 0.0, 'worst_sentence_text': 'Here are the words sorted alphabetically:'}, {'sample_id': 'bbh_word_sorting-0025', 'baseline_from': 'observed', 'n_sentences': 45, 'selfcheck_inconsistency': 0.037, 'selfcheck_worst_sentence': 0.5, 'worst_sentence_text': '* **v**ehicular'}, {'sample_id': 'bbh_word_sorting-0045', 'baseline_from': 'observed', 'n_sentences': 29, 'selfcheck_inconsistency': 0.023, 'selfcheck_worst_sentence': 0.3333, 'worst_sentence_text': 'Comparing the words:'}, {'sample_id': 'bbh_word_sorting-0076', 'baseline_from': 'observed', 'n_sentences': 47, 'selfcheck_inconsistency': 0.0376, 'selfcheck_worst_sentence': 0.6667, 'worst_sentence_text': '**Final ordered list:**'}, {'sample_id': 'bbh_word_sorting-0094', 'baseline_from': 'observed', 'n_sentences': 28, 'selfcheck_inconsistency': 0.0168, 'selfcheck_worst_sentence': 0.4, 'worst_sentence_text': 'The final sorted list is:'}, {'sample_id': 'bbh_word_sorting-0000', 'baseline_from': 'observed', 'n_sentences': 32, 'selfcheck_inconsistency': 0.0016, 'selfcheck_worst_sentence': 0.0526, 'worst_sentence_text': 'Here are the words to sort alphabetically: alleviate, duopoly, mattress, gland, benelux, townsmen, buoyant, klaxon, hardbound, tomography, felice, gunk.'}, {'sample_id': 'bbh_word_sorting-0063', 'baseline_from': 'observed', 'n_sentences': 30, 'selfcheck_inconsistency': 0.0037, 'selfcheck_worst_sentence': 0.1111, 'worst_sentence_text': \"* Comparing the second letter: 'e' in vegetate, 'i' in vivian.\"}, {'sample_id': 'bbh_word_sorting-0101', 'baseline_from': 'observed', 'n_sentences': 18, 'selfcheck_inconsistency': 0.0, 'selfcheck_worst_sentence': 0.0, 'worst_sentence_text': 'The given list of words is: regret, starlight, wallboard, cotyledon, more, pepperoni.'}]\n _caveat: Unigram-containment proxy for SelfCheckGPT (no NLI model): paraphrases inflate the score, verbatim repetition of a wrong fact deflates it. Requires stochastic sampling — with temperature 0 the resamples repeat one greedy generation and the column degenerates to baseline-vs-greedy disagreement (degenerate_sampling flags this). selfcheck_inconsistency / selfcheck_worst_sentence are numeric M2 columns; treat them as signals to correlate with the outcome label, never as a hallucination verdict." + }, + "termination_audit": { + "analyzer": "termination_audit", + "findings": { + "_caveat": "CONFOUND CONTROL — read this before any other probe. Cases with termination_class != 'clean' have short, tag-less outputs, the same signature every other probe scores as 'bad'; leaving them pooled makes M2 attribute a decoding-budget or repetition problem to whichever mechanism is under test. Stratify on termination_class (or exclude non-clean cases) first. A high recovered_by_continuation rate means the budget is the defect: raise max_tokens and re-collect before diagnosing anything else. The continuation columns are INTERVENTIONAL — held-out verification must re-run them.", + "class_counts": { + "clean": 6, + "degenerate": 0, + "gave_up": 0, + "no_answer": 0, + "truncated": 2 + }, + "clean_rate": 0.75, + "degenerate_rate": 0, + "giveup_rate": 0, + "n_cases": 8, + "non_clean_rate": 0.25, + "per_case": [ + { + "gave_up": 0, + "has_answer_tag": 1, + "looks_truncated": 0, + "matches_output_contract": 0, + "output_chars": 917, + "output_words": 146, + "repetition_score": 0.0288, + "sample_id": "bbh_word_sorting-0006", + "termination_class": "clean" + }, + { + "continuation_chars": 1217, + "continuation_correct": 0, + "continuation_has_answer": 1, + "gave_up": 0, + "has_answer_tag": 0, + "looks_truncated": 1, + "matches_output_contract": 0, + "output_chars": 1332, + "output_words": 187, + "recovered_by_continuation": 0, + "repetition_score": 0, + "sample_id": "bbh_word_sorting-0025", + "termination_class": "truncated" + }, + { + "gave_up": 0, + "has_answer_tag": 1, + "looks_truncated": 0, + "matches_output_contract": 0, + "output_chars": 1101, + "output_words": 164, + "repetition_score": 0, + "sample_id": "bbh_word_sorting-0045", + "termination_class": "clean" + }, + { + "continuation_chars": 1472, + "continuation_correct": 0, + "continuation_has_answer": 0, + "gave_up": 0, + "has_answer_tag": 0, + "looks_truncated": 1, + "matches_output_contract": 0, + "output_chars": 1389, + "output_words": 224, + "recovered_by_continuation": 0, + "repetition_score": 0.0415, + "sample_id": "bbh_word_sorting-0076", + "termination_class": "truncated" + }, + { + "gave_up": 0, + "has_answer_tag": 1, + "looks_truncated": 0, + "matches_output_contract": 0, + "output_chars": 922, + "output_words": 137, + "repetition_score": 0.1462, + "sample_id": "bbh_word_sorting-0094", + "termination_class": "clean" + }, + { + "gave_up": 0, + "has_answer_tag": 1, + "looks_truncated": 0, + "matches_output_contract": 0, + "output_chars": 1286, + "output_words": 176, + "repetition_score": 0.1006, + "sample_id": "bbh_word_sorting-0000", + "termination_class": "clean" + }, + { + "gave_up": 0, + "has_answer_tag": 1, + "looks_truncated": 0, + "matches_output_contract": 0, + "output_chars": 1054, + "output_words": 157, + "repetition_score": 0, + "sample_id": "bbh_word_sorting-0063", + "termination_class": "clean" + }, + { + "gave_up": 0, + "has_answer_tag": 1, + "looks_truncated": 0, + "matches_output_contract": 0, + "output_chars": 690, + "output_words": 99, + "repetition_score": 0, + "sample_id": "bbh_word_sorting-0101", + "termination_class": "clean" + } + ], + "recovered_rate": 0, + "truncation_rate": 0.25 + }, + "metadata": { + "experiment": {}, + "fingerprint": "3539f2e41b11f79c" + }, + "model": "JaxLocalModel(key='gemma-4-e2b-it', adapter=GemmaJaxAdapter, loaded)", + "n_cases": 8, + "summary": "[termination_audit] on JaxLocalModel(key='gemma-4-e2b-it', adapter=GemmaJaxAdapter, loaded)\n n_cases: 8\n class_counts: {'clean': 6, 'truncated': 2, 'degenerate': 0, 'gave_up': 0, 'no_answer': 0}\n clean_rate: 0.75\n non_clean_rate: 0.25\n truncation_rate: 0.25\n degenerate_rate: 0.0\n giveup_rate: 0.0\n recovered_rate: 0.0\n per_case: [{'sample_id': 'bbh_word_sorting-0006', 'termination_class': 'clean', 'repetition_score': 0.0288, 'has_answer_tag': 1, 'looks_truncated': 0, 'gave_up': 0, 'matches_output_contract': 0, 'output_chars': 917, 'output_words': 146}, {'sample_id': 'bbh_word_sorting-0025', 'termination_class': 'truncated', 'repetition_score': 0.0, 'has_answer_tag': 0, 'looks_truncated': 1, 'gave_up': 0, 'matches_output_contract': 0, 'output_chars': 1332, 'output_words': 187, 'continuation_has_answer': 1, 'continuation_chars': 1217, 'recovered_by_continuation': 0, 'continuation_correct': 0}, {'sample_id': 'bbh_word_sorting-0045', 'termination_class': 'clean', 'repetition_score': 0.0, 'has_answer_tag': 1, 'looks_truncated': 0, 'gave_up': 0, 'matches_output_contract': 0, 'output_chars': 1101, 'output_words': 164}, {'sample_id': 'bbh_word_sorting-0076', 'termination_class': 'truncated', 'repetition_score': 0.0415, 'has_answer_tag': 0, 'looks_truncated': 1, 'gave_up': 0, 'matches_output_contract': 0, 'output_chars': 1389, 'output_words': 224, 'continuation_has_answer': 0, 'continuation_chars': 1472, 'recovered_by_continuation': 0, 'continuation_correct': 0}, {'sample_id': 'bbh_word_sorting-0094', 'termination_class': 'clean', 'repetition_score': 0.1462, 'has_answer_tag': 1, 'looks_truncated': 0, 'gave_up': 0, 'matches_output_contract': 0, 'output_chars': 922, 'output_words': 137}, {'sample_id': 'bbh_word_sorting-0000', 'termination_class': 'clean', 'repetition_score': 0.1006, 'has_answer_tag': 1, 'looks_truncated': 0, 'gave_up': 0, 'matches_output_contract': 0, 'output_chars': 1286, 'output_words': 176}, {'sample_id': 'bbh_word_sorting-0063', 'termination_class': 'clean', 'repetition_score': 0.0, 'has_answer_tag': 1, 'looks_truncated': 0, 'gave_up': 0, 'matches_output_contract': 0, 'output_chars': 1054, 'output_words': 157}, {'sample_id': 'bbh_word_sorting-0101', 'termination_class': 'clean', 'repetition_score': 0.0, 'has_answer_tag': 1, 'looks_truncated': 0, 'gave_up': 0, 'matches_output_contract': 0, 'output_chars': 690, 'output_words': 99}]\n _caveat: CONFOUND CONTROL — read this before any other probe. Cases with termination_class != 'clean' have short, tag-less outputs, the same signature every other probe scores as 'bad'; leaving them pooled makes M2 attribute a decoding-budget or repetition problem to whichever mechanism is under test. Stratify on termination_class (or exclude non-clean cases) first. A high recovered_by_continuation rate means the budget is the defect: raise max_tokens and re-collect before diagnosing anything else. The continuation columns are INTERVENTIONAL — held-out verification must re-run them." + } + }, + "schema_version": 6, + "selected_analyzers": [ + "answer_extraction_audit", + "termination_audit", + "selfcheck_consistency", + "self_consistency", + "calibration", + "logprob_entropy", + "coverage_verification_gap", + "perturbation_battery" + ], + "selection_rationale": "Selected 8 analyzer(s) (answer_extraction_audit, termination_audit, selfcheck_consistency, self_consistency, calibration, logprob_entropy, coverage_verification_gap, perturbation_battery) by capability matching.", + "span_id": "c0.m1", + "stage": "M1", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:38:15.654168+00:00" + } + ] + }, + "M2": { + "analysis": [ + { + "conclusion": "The model is struggling to complete long alphabetical sorting lists without running out of tokens or losing its train of thought during multi-step reasoning. Out of the 8 examined examples, the model failed 5, and in 2 of those 5 failing cases it ran into the generation limit and cut off mid-sentence before outputting a complete sorted list. Even when the model finished generating within the length limit, repeated sampling showed that in 4 of the 5 failing cases the model never produced a completely correct order across any of its 5 attempts. However, because this is an all-or-nothing benchmark where a single displaced word counts as a total failure, this evidence does not settle whether the model fundamentally lacks sorting capabilities or merely suffers from occasional drift on long lists and constrained generation budgets.", + "corrected_rejections": { + "alpha": 0.05, + "families": { + "bh": { + "method": "BH", + "n_tested": 16, + "rejected_result_keys": [] + } + }, + "method": "BH", + "n_tested": 16, + "rejected_result_keys": [], + "rejected_tools": [] + }, + "cycle": 0, + "descriptive_only": false, + "duration_sec": 8.938, + "event": "analysis", + "event_seq": 389, + "evidence_chain": [ + "The termination check first caught attention because the model cut off mid-generation on 2 out of 8 cases (25%), leaving trailing text like \"The correct alphabetical order is: biennial, creedal, cry, eyesight, fletch,\" without a final answer.", + "This directly inflates failures under the protocol's strict all-or-nothing success criterion, because an incomplete or truncated sequence automatically receives a failing score regardless of whether the earlier words were sorted properly.", + "Corroborating this issue, testing answer stability across alternative samples showed a low agreement of 20% (self_consistency.consistency = 0.2), and across 5 sampled completions the model never produced a correct sequence in 4 of the 5 failing examples (coverage_verification_gap.no_coverage_rate = 0.5, or 4 of 8 cases)." + ], + "figures": [ + "M2/artifacts/m2_effects.png" + ], + "findings": [ + "[MEDIUM] self_consistency.consistency=0.2 < 0.5: low self-consistency — model gives unstable answers" + ], + "judge_prompt": "You are an expert in ML failure analysis for vision-language models and agentic systems.\n\nExperiment protocol:\n{\n \"description\": \"A text-only LLM (Gemma 4 E2B-it, spec gemma-4-e2b-it = google/gemma-4-E2B-it) answers items from lukaemon/bbh · config=word_sorting. One named BBH task: sort a word list into alphabetical order. Failure cases are items the model answered incorrectly; the batch also contains, as controls, items from the same slice it answered correctly. Each answer is scored against its own gold answer.\",\n \"task_domain\": \"ch4-basic\",\n \"success_criteria\": \"Normalised exact match on the whole sorted list — one word out of place is a FAIL, so this is a harsh binary. That is fine for band purposes but means a FAIL is not evidence of not knowing how to sort.\",\n \"failure_patterns\": \"\",\n \"target_modalities\": [\n \"text\"\n ],\n \"output_contract\": {},\n \"metadata\": {\n \"dataset\": \"bbh_word_sorting\",\n \"items_in_slice\": 250,\n \"reference_accuracy_qwen35_9b\": 0.72\n }\n}\n\nTask domain: ch4-basic\n\nAnalyzer summary:\nModel: JaxLocalModel(key='gemma-4-e2b-it', adapter=GemmaJaxAdapter, loaded)\nAnalyzers run: answer_extraction_audit, calibration, coverage_verification_gap, logprob_entropy, perturbation_battery, self_consistency, selfcheck_consistency, termination_audit\n\n5 metric(s) measured NOTHING — absence of evidence, NOT evidence of health:\n answer_extraction_audit.n_extraction_point_miss = 0 (analyzer ran but examined no items)\n answer_extraction_audit.n_extraction_suspect = 0 (analyzer ran but examined no items)\n answer_extraction_audit.n_reasked = 0 (analyzer ran but examined no items)\n calibration.n_unlabelled_skipped = 0 (analyzer ran but examined no items)\n perturbation_battery.n_memorization_suspect = 0 (analyzer ran but examined no items)\n\n1 anomalie(s) detected:\n [MEDIUM] self_consistency.consistency=0.2 < 0.5: low self-consistency — model gives unstable answers\n\nSelected healthy metrics (no threshold violations):\n answer_extraction_audit.n_cases = 8\n answer_extraction_audit.n_gradable = 8\n answer_extraction_audit.n_labelled_fail = 5\n answer_extraction_audit.suspect_rate = 0\n answer_extraction_audit.missing_tag_rate = 0.25\nStatistical test results (effect-sized, FDR-aware):\n - signal 'answer_extraction_audit.output_chars' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2500 (B>A) CI=-0.5000..+0.7500 -> inconclusive [BH: NOT survived, p=1, n_signal=4]\n - signal 'coverage_verification_gap.n_unique' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.5000 (B>A) CI=+0.1667..+0.8333 -> REJECT H0 [BH: NOT survived, p=0.464, n_signal=2]\n - signal 'selfcheck_consistency.n_sentences' vs FAIL: [clustered bootstrap (unpaired)] effect=-0.2500 (A>B) CI=-0.7500..+0.5000 -> inconclusive [BH: NOT survived, p=1, n_signal=4]\n - signal 'termination_audit.output_chars' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2500 (B>A) CI=-0.5000..+0.7500 -> inconclusive [BH: NOT survived, p=1, n_signal=4]\n - signal 'termination_audit.output_words' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2500 (B>A) CI=-0.5000..+0.7500 -> inconclusive [BH: NOT survived, p=1, n_signal=4]\n - signal 'termination_audit.continuation_chars' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.0000 (A=B) CI=+0.0000..+0.0000 -> inconclusive [BH: NOT survived, p=1, n_signal=1]\n - signal 'calibration.correct' vs FAIL: [clustered bootstrap (unpaired)] effect=-0.6230 (A>B) CI=-0.7377..-0.4918 -> REJECT H0 [BH: NOT survived, p=0.0624, n_signal=3]\n - signal 'perturbation_battery.noop_clause_flipped' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2237 (B>A) CI=-0.1763..+0.5254 -> inconclusive [BH: NOT survived, p=0.401, n_signal=5]\n - signal 'answer_extraction_audit.has_answer_tag' vs FAIL: [clustered bootstrap (unpaired)] effect=-0.1034 (A>B) CI=-0.5402..+0.3161 -> inconclusive [BH: NOT survived, p=0.68, n_signal=6]\n - signal 'answer_extraction_audit.output_truncated' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.4194 (B>A) CI=+0.2903..+0.5323 -> REJECT H0 [BH: NOT survived, p=0.51, n_signal=2]\n - signal 'perturbation_battery.restate_question_flipped' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2644 (B>A) CI=-0.1034..+0.5172 -> inconclusive [BH: NOT survived, p=0.387, n_signal=6]\n - signal 'termination_audit.has_answer_tag' vs FAIL: [clustered bootstrap (unpaired)] effect=-0.1034 (A>B) CI=-0.5402..+0.3161 -> inconclusive [BH: NOT survived, p=0.68, n_signal=6]\n - signal 'termination_audit.looks_truncated' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.4194 (B>A) CI=+0.2903..+0.5323 -> REJECT H0 [BH: NOT survived, p=0.51, n_signal=2]\n - signal 'termination_audit.continuation_has_answer' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.4127 (B>A) CI=+0.2857..+0.5397 -> REJECT H0 [BH: NOT survived, p=1, n_signal=1]\n - signal 'coverage_verification_gap.coverage_gap' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.4127 (B>A) CI=+0.2857..+0.5397 -> REJECT H0 [BH: NOT survived, p=1, n_signal=1]\n - signal 'coverage_verification_gap.majority_share' vs FAIL: [clustered bootstrap (unpaired)] effect=-1.0000 (A>B) CI=-1.0000..-1.0000 -> REJECT H0 [BH: NOT survived, p=0.0179, n_signal=3]\n - Kendall τ between 'answer_extraction_audit.output_chars' and FAIL = +0.179 (n=8)\n - Kendall τ between 'coverage_verification_gap.n_unique' and FAIL = +0.536 (n=8)\n - Kendall τ between 'selfcheck_consistency.n_sentences' and FAIL = +0.107 (n=8)\n - Kendall τ between 'termination_audit.output_chars' and FAIL = +0.179 (n=8)\n - Kendall τ between 'termination_audit.output_words' and FAIL = +0.179 (n=8)\n - Kendall τ between 'coverage_verification_gap.majority_share' and FAIL = -0.536 (n=8)\n After BH correction, 0 of 16 corrected test(s) survive — none.\n\nRaw per-analyzer findings (JSON):\n{\n \"answer_extraction_audit\": {\n \"n_cases\": 8,\n \"n_gradable\": 8,\n \"n_labelled_fail\": 5,\n \"n_extraction_suspect\": 0,\n \"suspect_rate\": 0.0,\n \"n_reasked\": 0,\n \"n_reask_confirmed\": null,\n \"n_extraction_point_miss\": 0,\n \"missing_tag_rate\": 0.25,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 917,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"administer, aeneid, decadent, coachman, delhi, dey, grim, gradate, jacky, littleneck, phosphorescent, pristine, sinh, sh\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"has_output\": 1,\n \"has_answer_tag\": 0,\n \"output_truncated\": 1,\n \"gave_up\": 0,\n \"output_chars\": 1332,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"J:\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 1101,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"backslide, calvert, commando, gabriel, hendrickson, hollister, jackson, anastomosis, pizzicato, quail, separate, shelter\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"has_output\": 1,\n \"has_answer_tag\": 0,\n \"output_truncated\": 1,\n \"gave_up\": 0,\n \"output_chars\": 1389,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"The correct alphabetical order is: biennial, creedal, cry, eyesight, fletch,\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 922,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"battery, bushland, capacitive, contingent, crossbill, enigma, jane, lipton, meager, ricochet, wallet, wacke, wysiwyg\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 1286,\n \"labelled_fail\": 0,\n \"strict_match\": 1,\n \"gold_in_output\": 1,\n \"gold_in_answer_region\": 1,\n \"extracted_answer\": \"alleviate, benelux, buoyant, duopoly, felice, gland, gunk, hardbound, klaxon, mattress, tomography, townsmen\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 1054,\n \"labelled_fail\": 0,\n \"strict_match\": 1,\n \"gold_in_output\": 1,\n \"gold_in_answer_region\": 1,\n \"extracted_answer\": \"artillery, bainite, doris, fda, harm, incongruous, monkey, prosody, vegetate, vivian\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 690,\n \"labelled_fail\": 0,\n \"strict_match\": 1,\n \"gold_in_output\": 1,\n \"gold_in_answer_region\": 1,\n \"extracted_answer\": \"cotyledon, more, pepperoni, regret, starlight, wallboard\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n }\n ],\n \"_caveat\": \"suspect_rate is the fraction of FAIL cases whose gold answer sits in the ANSWER REGION of the output (tail window or a tagged/boxed span) \\u2014 HARNESS failures wearing a model-failure label. gold_in_output is the loose upper bound and over-counts: it fires on a gold that only appears as an intermediate quantity, so never read it as the suspect rate. Above ~5% the labels feeding M2 are contaminated and every other probe's correlations should be re-read after fixing extraction (or after dropping extraction_suspect==1 cases). This probe is OBSERVATIONAL by default: it re-reads stored outputs and introduces no new sampling. With reask=True the reask_* columns are interventional and must be re-run for held-out confirmation. On yes/no (true/false) tasks answered_yes and gold_yes are the answer's and the gold's DIRECTION, two separate marginals (not re-grades): a directional hypothesis is tested on them, e.g. answered_yes HIGHER on failing cases.\"\n },\n \"termination_audit\": {\n \"n_cases\": 8,\n \"class_counts\": {\n \"clean\": 6,\n \"truncated\": 2,\n \"degenerate\": 0,\n \"gave_up\": 0,\n \"no_answer\": 0\n },\n \"clean_rate\": 0.75,\n \"non_clean_rate\": 0.25,\n \"truncation_rate\": 0.25,\n \"degenerate_rate\": 0.0,\n \"giveup_rate\": 0.0,\n \"recovered_rate\": 0.0,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.0288,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 917,\n \"output_words\": 146\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"termination_class\": \"truncated\",\n \"repetition_score\": 0.0,\n \"has_answer_tag\": 0,\n \"looks_truncated\": 1,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1332,\n \"output_words\": 187,\n \"continuation_has_answer\": 1,\n \"continuation_chars\": 1217,\n \"recovered_by_continuation\": 0,\n \"continuation_correct\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.0,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1101,\n \"output_words\": 164\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"termination_class\": \"truncated\",\n \"repetition_score\": 0.0415,\n \"has_answer_tag\": 0,\n \"looks_truncated\": 1,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1389,\n \"output_words\": 224,\n \"continuation_has_answer\": 0,\n \"continuation_chars\": 1472,\n \"recovered_by_continuation\": 0,\n \"continuation_correct\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.1462,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 922,\n \"output_words\": 137\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.1006,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1286,\n \"output_words\": 176\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.0,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1054,\n \"output_words\": 157\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.0,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 690,\n \"output_words\": 99\n }\n ],\n \"_caveat\": \"CONFOUND CONTROL \\u2014 read this before any other probe. Cases with termination_class != 'clean' have short, tag-less outputs, the same signature every other probe scores as 'bad'; leaving them pooled makes M2 attribute a decoding-budget or repetition problem to whichever mechanism is under test. Stratify on termination_class (or exclude non-clean cases) first. A high recovered_by_continuation rate means the budget is the defect: raise max_tokens and re-collect before diagnosing anything else. The continuation columns are INTERVENTIONAL \\u2014 held-out verification must re-run them.\"\n },\n \"selfcheck_consistency\": {\n \"n_cases\": 8,\n \"n_samples\": 4,\n \"degenerate_sampling\": false,\n \"gen_kwargs\": {\n \"temperature\": 1.0\n },\n \"mean_inconsistency\": 0.015,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 19,\n \"selfcheck_inconsistency\": 0.0,\n \"selfcheck_worst_sentence\": 0.0,\n \"worst_sentence_text\": \"Here are the words sorted alphabetically:\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 45,\n \"selfcheck_inconsistency\": 0.037,\n \"selfcheck_worst_sentence\": 0.5,\n \"worst_sentence_text\": \"* **v**ehicular\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 29,\n \"selfcheck_inconsistency\": 0.023,\n \"selfcheck_worst_sentence\": 0.3333,\n \"worst_sentence_text\": \"Comparing the words:\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 47,\n \"selfcheck_inconsistency\": 0.0376,\n \"selfcheck_worst_sentence\": 0.6667,\n \"worst_sentence_text\": \"**Final ordered list:**\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 28,\n \"selfcheck_inconsistency\": 0.0168,\n \"selfcheck_worst_sentence\": 0.4,\n \"worst_sentence_text\": \"The final sorted list is:\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 32,\n \"selfcheck_inconsistency\": 0.0016,\n \"selfcheck_worst_sentence\": 0.0526,\n \"worst_sentence_text\": \"Here are the words to sort alphabetically: alleviate, duopoly, mattress, gland, benelux, townsmen, buoyant, klaxon, hardbound, tomography, felice, gunk.\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 30,\n \"selfcheck_inconsistency\": 0.0037,\n \"selfcheck_worst_sentence\": 0.1111,\n \"worst_sentence_text\": \"* Comparing the second letter: 'e' in vegetate, 'i' in vivian.\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 18,\n \"selfcheck_inconsistency\": 0.0,\n \"selfcheck_worst_sentence\": 0.0,\n \"worst_sentence_text\": \"The given list of words is: regret, starlight, wallboard, cotyledon, more, pepperoni.\"\n }\n ],\n \"_caveat\": \"Unigram-containment proxy for SelfCheckGPT (no NLI model): paraphrases inflate the score, verbatim repetition of a wrong fact deflates it. Requires stochastic sampling \\u2014 with temperature 0 the resamples repeat one greedy generation and the column degenerates to baseline-vs-greedy disagreement (degenerate_sampling flags this). selfcheck_inconsistency / selfcheck_worst_sentence are numeric M2 columns; treat them as signals to correlate with the outcome label, never as a hallucination verdict.\"\n },\n \"self_consistency\": {\n \"n_samples\": 5,\n \"consistency\": 0.2,\n \"n_unique\": 5,\n \"compared_on\": \"raw_text\",\n \"raw_text_consistency\": 0.2,\n \"modal_answer\": \"Here are the words sorted alphabetically:\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. gradate\\n7. grim\\n8. jacky\\n9. littleneck\\n10. phosphorescent\\n11. pristine\\n12. sinh\\n13. systemwide\\n14. tasting\\n15. torpedo\\n16. thrown\\n17. verdict\\n18. dey\\n19. shrunk\\n\\nLet's re-sort them carefully:\\n\\n* administer\\n* aeneid\\n* decadent\\n* coachman\\n* delhi\\n* grim\\n* gradate\\n* jacky\\n* littleneck\\n* phosphorescent\\n* pristine\\n* sinh\\n* systemwide\\n* tasting\\n* torpedo\\n* thrown\\n* verdict\\n* dey\\n* shrunk\\n\\nChecking for correct alphabetical order:\\nA: administer, aeneid\\nC: decadent, coachman\\nD: delhi, dey\\nG: grim, gradate\\nJ: jacky\\nL: littleneck\\nP: phosphorescent, pristine\\nS: sinh, systemwide, shrunk\\nT: tasting, torpedo, thrown\\nV: verdict\\n\\nCorrected Sorted List:\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. dey\\n7. grim\\n8. gradate\\n9. jacky\\n10. littleneck\\n11. phosphorescent\\n12. pristine\\n13. sinh\\n14. shrunk\\n15. systemwide\\n16. tasting\\n17. torpedo\\n18. thrown\\n19. verdict\\n\\n(Comparing D words: delhi, dey. D-e-l-h-i vs D-e-y. 'l' comes before 'y', so delhi comes before dey.)\\n\\nFinal check on the complete list:\\nadminister (A)\\naeneid (A)\\ndecadent (D)\\ncoachman (C) -> Should be C before D. Let's restart the alphabetical sort strictly.\\n\\nList: torpedo, phosphorescent, pristine, decadent, shrunk, dey, administer, gradate, littleneck, thrown, jacky, coachman, aeneid, verdict, tasting, sinh, delhi, systemwide, grim\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6\",\n \"gen_kwargs\": {\n \"temperature\": 1.0,\n \"top_p\": 0.95,\n \"top_k\": 20\n },\n \"n_semantic_clusters\": 3,\n \"semantic_entropy\": 1.0549,\n \"normalized_semantic_entropy\": 0.6554,\n \"semantic_consistency\": 0.4,\n \"cluster_sizes\": [\n 2,\n 2,\n 1\n ],\n \"cluster_representatives\": [\n \"Here are the words sorted alphabetically:\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. gradate\\n7. grim\\n8\",\n \"Here are the words sorted alphabetically:\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. gradate\\n7. grim\\n8\",\n \"Here are the words sorted alphabetically:\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. gradate\\n7. grim\\n8\"\n ],\n \"entailment\": \"lexical_proxy\",\n \"_caveat\": \"semantic_* uses the SAME samples as consistency \\u2014 it costs no extra generations, and the two are meant to be read together: consistency << semantic_consistency means the disagreement was only wording. With the default lexical_proxy, clustering is content-word overlap, NOT entailment: it merges answers that share vocabulary while contradicting each other ('the claim holds' / 'the claim does not hold') and splits correct paraphrases with no shared words. Inject an NLI entailment_fn before reading semantic_entropy as the Farquhar et al. metric. At temperature 0 every sample is identical and both scores are 1 by construction, not by measurement.\"\n },\n \"calibration\": {\n \"n_cases\": 8,\n \"n_unlabelled_skipped\": 0,\n \"n_bins\": 10,\n \"logprob_channel_available\": true,\n \"logprob_channel\": {\n \"n\": 8,\n \"ece\": 0.5978,\n \"brier\": 0.6015,\n \"overconfidence_gap\": 0.5978\n },\n \"verbalized_channel\": {\n \"n\": 2,\n \"ece\": 0.0,\n \"brier\": 0.0,\n \"overconfidence_gap\": 0.0\n },\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"correct\": 0,\n \"conf_logprob\": 0.9936\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"correct\": 0,\n \"conf_logprob\": 0.9926\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"correct\": 0,\n \"conf_logprob\": 0.9905\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"correct\": 0,\n \"conf_logprob\": 0.9454\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"correct\": 0,\n \"conf_logprob\": 0.9796\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"correct\": 1,\n \"conf_logprob\": 0.9542,\n \"conf_verbal\": 1.0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"correct\": 1,\n \"conf_logprob\": 0.9583\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"correct\": 1,\n \"conf_logprob\": 0.9683,\n \"conf_verbal\": 1.0\n }\n ],\n \"_caveat\": \"Needs PASS/FAIL labels \\u2014 unlabeled cases are skipped, and ECE on few labelled cases is unstable (aim for >= ~50). conf_logprob is the geometric-mean token probability of the model's own continuation \\u2014 a proxy, not an answer probability; on API backends it reflects only the returned top-k, and a model without LOGPROBS reports only the verbalized channel. A positive overconfidence_gap on FAIL-heavy slices argues for abstention/recalibration fixes rather than capability fixes.\"\n },\n \"logprob_entropy\": {\n \"n_tokens\": 64,\n \"mean_logprob\": -0.0064,\n \"perplexity\": 1.0064,\n \"min_token_logprob\": -0.1908,\n \"mean_top_entropy\": 0.0213\n },\n \"coverage_verification_gap\": {\n \"n_cases\": 8,\n \"n_scored\": 8,\n \"k\": 5,\n \"gen_kwargs\": {\n \"temperature\": 1.0,\n \"top_p\": 0.95,\n \"top_k\": 20\n },\n \"mean_pass_at_k\": 0.5,\n \"mean_majority_correct\": 0.375,\n \"coverage_gap_rate\": 0.125,\n \"mean_verify_correct\": null,\n \"no_coverage_rate\": 0.5,\n \"degenerate_sampling\": false,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 0,\n \"n_unique\": 4,\n \"pass_at_k\": 0.0,\n \"any_correct\": 0,\n \"majority_correct\": 0,\n \"majority_share\": 0.4,\n \"coverage_gap\": 0,\n \"modal_answer\": \"14\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 0,\n \"n_unique\": 5,\n \"pass_at_k\": 0.0,\n \"any_correct\": 0,\n \"majority_correct\": 0,\n \"majority_share\": 0.2,\n \"coverage_gap\": 0,\n \"modal_answer\": \"arrear eavesdropping fasciculate herodotus hermaphrodite ibn henry incorrigible \"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 0,\n \"n_unique\": 5,\n \"pass_at_k\": 0.0,\n \"any_correct\": 0,\n \"majority_correct\": 0,\n \"majority_share\": 0.2,\n \"coverage_gap\": 0,\n \"modal_answer\": \"16. separate wait s words re-sort: separate shelter sticktight spongy\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 0,\n \"n_unique\": 4,\n \"pass_at_k\": 0.0,\n \"any_correct\": 0,\n \"majority_correct\": 0,\n \"majority_share\": 0.4,\n \"coverage_gap\": 0,\n \"modal_answer\": \"biennial creedal cry fletch fraudulent eyesight j miltonic mirage whisper titmic\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 1,\n \"n_unique\": 4,\n \"pass_at_k\": 1.0,\n \"any_correct\": 1,\n \"majority_correct\": 0,\n \"majority_share\": 0.4,\n \"coverage_gap\": 1,\n \"modal_answer\": \"battery bushland capacitive contingent crossbill enigma jane lipton meager ricoc\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 4,\n \"n_unique\": 2,\n \"pass_at_k\": 1.0,\n \"any_correct\": 1,\n \"majority_correct\": 1,\n \"majority_share\": 0.8,\n \"coverage_gap\": 0,\n \"modal_answer\": \"alleviate benelux buoyant duopoly felice gland gunk hardbound klaxon mattress to\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 3,\n \"n_unique\": 3,\n \"pass_at_k\": 1.0,\n \"any_correct\": 1,\n \"majority_correct\": 1,\n \"majority_share\": 0.6,\n \"coverage_gap\": 0,\n \"modal_answer\": \"artillery bainite doris fda harm incongruous monkey prosody vegetate vivian\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 5,\n \"n_unique\": 1,\n \"pass_at_k\": 1.0,\n \"any_correct\": 1,\n \"majority_correct\": 1,\n \"majority_share\": 1.0,\n \"coverage_gap\": 0,\n \"modal_answer\": \"cotyledon more pepperoni regret starlight wallboard\"\n }\n ],\n \"_caveat\": \"coverage_gap = 'the answer was in the pool, the vote missed it' \\u21d2 a selection defect, fixable at L1/L2 with a verifier or reranker. no_coverage_rate = 'never produced once in k' \\u21d2 a capability defect no scaffold reaches; do not let a headline pass@k hide which of the two dominates. REQUIRES sampling: degenerate_sampling=true means every sample was identical (temperature 0 or a deterministic backend) and the gap is structurally zero, not measured. pass_at_k here uses the unbiased estimator over exactly k draws, so with k small its variance is large per case \\u2014 read the batch rate. INTERVENTIONAL: held-out confirmation must re-sample.\"\n },\n \"perturbation_battery\": {\n \"n_cases\": 8,\n \"n_scored\": 8,\n \"perturbations\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\",\n \"perturb_numbers\"\n ],\n \"applied_perturbations\": [\n \"noop_clause\",\n \"rename_entities\",\n \"restate_question\"\n ],\n \"gen_kwargs\": {},\n \"mean_invariance_break_rate\": 0.7917,\n \"noop_break_rate\": 0.625,\n \"mean_sensitivity_rate\": null,\n \"n_memorization_suspect\": 0,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"baseline_answer\": \"administer aeneid decadent coachman delhi dey grim gradate jacky littleneck phos\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 1.0,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"baseline_answer\": \"j:\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 1.0,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"baseline_answer\": \"backslide calvert commando gabriel hendrickson hollister jackson anastomosis piz\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 1.0,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"baseline_answer\": \"correct alphabetical order is: biennial creedal cry eyesight fletch\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 1.0,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"baseline_answer\": \"battery bushland capacitive contingent crossbill enigma jane lipton meager ricoc\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 0,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 0.6667,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"baseline_answer\": \"alleviate benelux buoyant duopoly felice gland gunk hardbound klaxon mattress to\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 0,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 0.6667,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"baseline_answer\": \"artillery bainite doris fda harm incongruous monkey prosody vegetate vivian\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 0,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 0.6667,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"baseline_answer\": \"cotyledon more pepperoni regret starlight wallboard\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 0,\n \"noop_clause_flipped\": 0,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 0.3333,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n }\n ],\n \"_caveat\": \"Two DIFFERENT defects share this probe and must not be summed: invariance_break_rate should be 0 (a preserving edit moved the answer) while sensitivity_rate should be 1 (an altering edit must move it). sensitivity_rate == 0 with a correct baseline is the memorisation signature \\u2014 cross-check contamination_score before calling it reasoning. Every rate is measured against the model's OWN baseline answer, not the gold, so a case that was already wrong still contributes: read these as stability, never as accuracy. Under sampling, a 'flip' can be decoding noise \\u2014 set temperature 0 or repeat. INTERVENTIONAL: re-run to confirm.\"\n }\n}\n\nWHO READS THIS: an engineer who builds and runs evaluations but does not do\nstatistics. They know what a benchmark, a prompt and a failure case are. They\nhave never met an e-value, a McNemar test, a bootstrap interval or a\nBenjamini-Hochberg correction, and they will not go and look them up.\n\nHOW TO WRITE FOR THEM:\n- Answer first. The opening sentence says what is going wrong, in ordinary\n words. Numbers, mechanism and caveats all come after it.\n- A bare number tells this reader nothing. Give the count AND what it amounts\n to: \"the model got 45 of the 125 wrong, and almost all of those were lists\n longer than eight words\" — not \"fail rate 0.36, effect +0.28\".\n- Prefer counts to rates, and give both when you have them (\"24 of 30 cases,\n 80%\"). For a comparison, name both groups and how many cases each holds.\n- If a statistical term really is the clearest word, define it in the same\n sentence the first time it appears: \"an e-value of 45 — the evidence runs\n about 45 to 1 against this being chance\". Never leave one standing bare.\n- Never make a bare metric identifier the subject of a sentence. Say what it\n measures, then put the identifier in parentheses if the reader needs the\n handle: \"how often the model answered Yes (answer_extraction_audit.answered_yes)\".\n- No symbols standing in for words (→, ρ, σ), and no \"p<0.05\" shorthand.\n- These are patterns on the analysis split, not proven causes. Write \"shows up\n more often on\" or \"is linked with\", never \"causes\" or \"explains\", unless an\n intervention actually tested it.\n\nBased on the protocol and the findings above, write:\n\nCONCLUSION: \nEVIDENCE_CHAIN:\n- \n- \n- \nQUALITATIVE:\n- \n- \n\nKeep each bullet to one sentence.", + "judge_response": "CONCLUSION: The model is struggling to complete long alphabetical sorting lists without running out of tokens or losing its train of thought during multi-step reasoning. Out of the 8 examined examples, the model failed 5, and in 2 of those 5 failing cases it ran into the generation limit and cut off mid-sentence before outputting a complete sorted list. Even when the model finished generating within the length limit, repeated sampling showed that in 4 of the 5 failing cases the model never produced a completely correct order across any of its 5 attempts. However, because this is an all-or-nothing benchmark where a single displaced word counts as a total failure, this evidence does not settle whether the model fundamentally lacks sorting capabilities or merely suffers from occasional drift on long lists and constrained generation budgets.\n\nEVIDENCE_CHAIN:\n- The termination check first caught attention because the model cut off mid-generation on 2 out of 8 cases (25%), leaving trailing text like \"The correct alphabetical order is: biennial, creedal, cry, eyesight, fletch,\" without a final answer.\n- This directly inflates failures under the protocol's strict all-or-nothing success criterion, because an incomplete or truncated sequence automatically receives a failing score regardless of whether the earlier words were sorted properly.\n- Corroborating this issue, testing answer stability across alternative samples showed a low agreement of 20% (self_consistency.consistency = 0.2), and across 5 sampled completions the model never produced a correct sequence in 4 of the 5 failing examples (coverage_verification_gap.no_coverage_rate = 0.5, or 4 of 8 cases).\n\nQUALITATIVE:\n- When tackling long lists, the model tends to output extensive chain-of-thought scratchwork—grouping by first letter and iteratively re-sorting—which consumes its token budget and occasionally introduces early errors that it subsequently notices but runs out of space to fix.\n- Despite the model failing on 5 of the 8 examples, its confidence based on token probabilities remained extraordinarily high across all failing cases, averaging over 98% certainty even when outputting scrambled or truncated answers.", + "n_findings": 1, + "narrative": "Model: JaxLocalModel(key='gemma-4-e2b-it', adapter=GemmaJaxAdapter, loaded)\nAnalyzers run: answer_extraction_audit, calibration, coverage_verification_gap, logprob_entropy, perturbation_battery, self_consistency, selfcheck_consistency, termination_audit\n\n5 metric(s) measured NOTHING — absence of evidence, NOT evidence of health:\n answer_extraction_audit.n_extraction_point_miss = 0 (analyzer ran but examined no items)\n answer_extraction_audit.n_extraction_suspect = 0 (analyzer ran but examined no items)\n answer_extraction_audit.n_reasked = 0 (analyzer ran but examined no items)\n calibration.n_unlabelled_skipped = 0 (analyzer ran but examined no items)\n perturbation_battery.n_memorization_suspect = 0 (analyzer ran but examined no items)\n\n1 anomalie(s) detected:\n [MEDIUM] self_consistency.consistency=0.2 < 0.5: low self-consistency — model gives unstable answers\n\nSelected healthy metrics (no threshold violations):\n answer_extraction_audit.n_cases = 8\n answer_extraction_audit.n_gradable = 8\n answer_extraction_audit.n_labelled_fail = 5\n answer_extraction_audit.suspect_rate = 0\n answer_extraction_audit.missing_tag_rate = 0.25", + "schema_version": 6, + "severity": "medium", + "span_id": "c0.m2", + "stage": "M2", + "stats_plan": [ + { + "config": { + "signal": "answer_extraction_audit.output_chars" + }, + "rationale": "test whether per-case signal 'answer_extraction_audit.output_chars' predicts FAIL", + "tool": "signal_label_assoc" + }, + { + "config": { + "signal": "coverage_verification_gap.n_unique" + }, + "rationale": "test whether per-case signal 'coverage_verification_gap.n_unique' predicts FAIL", + "tool": "signal_label_assoc" + }, + { + "config": { + "signal": "selfcheck_consistency.n_sentences" + }, + "rationale": "test whether per-case signal 'selfcheck_consistency.n_sentences' predicts FAIL", + "tool": "signal_label_assoc" + }, + { + "config": { + "signal": "termination_audit.output_chars" + }, + "rationale": "test whether per-case signal 'termination_audit.output_chars' predicts FAIL", + "tool": "signal_label_assoc" + }, + { + "config": { + "signal": "termination_audit.output_words" + }, + "rationale": "test whether per-case signal 'termination_audit.output_words' predicts FAIL", + "tool": "signal_label_assoc" + }, + { + "config": { + "signal": "termination_audit.continuation_chars" + }, + "rationale": "test whether per-case signal 'termination_audit.continuation_chars' predicts FAIL", + "tool": "signal_label_assoc" + }, + { + "config": { + "signal": "calibration.correct" + }, + "rationale": "test whether per-case signal 'calibration.correct' predicts FAIL", + "tool": "signal_label_assoc" + }, + { + "config": { + "signal": "perturbation_battery.noop_clause_flipped" + }, + "rationale": "test whether per-case signal 'perturbation_battery.noop_clause_flipped' predicts FAIL", + "tool": "signal_label_assoc" + }, + { + "config": { + "signal": "answer_extraction_audit.has_answer_tag" + }, + "rationale": "test whether per-case signal 'answer_extraction_audit.has_answer_tag' predicts FAIL", + "tool": "signal_label_assoc" + }, + { + "config": { + "signal": "answer_extraction_audit.output_truncated" + }, + "rationale": "test whether per-case signal 'answer_extraction_audit.output_truncated' predicts FAIL", + 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-> inconclusive", + "tool": "signal_label_assoc", + "underpowered": false + }, + { + "analysis_key": "signal_label_assoc:termination_audit.output_words", + "ci": [ + -0.5, + 0.75 + ], + "config": { + "signal": "termination_audit.output_words" + }, + "correction_family": "bh", + "correction_method": "BH", + "details": { + "fail_rate_control": 0.5, + "fail_rate_signal": 0.75, + "n_control": 4, + "n_signal": 4, + "permutation_p": 1, + "rationale": "test whether per-case signal 'termination_audit.output_words' predicts FAIL" + }, + "e_value": null, + "effect": 0.25, + "error": null, + "fdr_corrected": false, + "figure_path": null, + "ok": true, + "p_value": 1, + "raw_reject": false, + "reject": false, + "summary": "signal 'termination_audit.output_words' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2500 (B>A) CI=-0.5000..+0.7500 -> inconclusive", + "tool": "signal_label_assoc", + "underpowered": false + }, + { + "analysis_key": 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"n_signal": 2, + "permutation_p": 0.509921, + "rationale": "test whether per-case signal 'answer_extraction_audit.output_truncated' predicts FAIL" + }, + "e_value": null, + "effect": 0.4193548387096774, + "error": null, + "fdr_corrected": false, + "figure_path": null, + "ok": true, + "p_value": 0.5099206349206349, + "raw_reject": true, + "reject": true, + "summary": "signal 'answer_extraction_audit.output_truncated' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.4194 (B>A) CI=+0.2903..+0.5323 -> REJECT H0", + "tool": "signal_label_assoc", + "underpowered": false + }, + { + "analysis_key": "signal_label_assoc:perturbation_battery.restate_question_flipped", + "ci": [ + -0.10344827586206895, + 0.5172413793103448 + ], + "config": { + "signal": "perturbation_battery.restate_question_flipped" + }, + "correction_family": "bh", + "correction_method": "BH", + "details": { + "fail_rate_control": 0.569, + "fail_rate_signal": 0.8333, + "n_control": 58, + "n_signal": 6, + "permutation_p": 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"fdr_corrected": false, + "figure_path": null, + "ok": true, + "p_value": 1, + "raw_reject": true, + "reject": true, + "summary": "signal 'termination_audit.continuation_has_answer' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.4127 (B>A) CI=+0.2857..+0.5397 -> REJECT H0", + "tool": "signal_label_assoc", + "underpowered": false + }, + { + "analysis_key": "signal_label_assoc:coverage_verification_gap.coverage_gap", + "ci": [ + 0.2857142857142857, + 0.5396825396825398 + ], + "config": { + "signal": "coverage_verification_gap.coverage_gap" + }, + "correction_family": "bh", + "correction_method": "BH", + "details": { + "fail_rate_control": 0.5873, + "fail_rate_signal": 1, + "n_control": 63, + "n_signal": 1, + "permutation_p": 1, + "rationale": "test whether per-case signal 'coverage_verification_gap.coverage_gap' predicts FAIL" + }, + "e_value": null, + "effect": 0.4126984126984127, + "error": null, + "fdr_corrected": false, + "figure_path": null, + "ok": true, + "p_value": 1, + "raw_reject": true, + "reject": true, + "summary": "signal 'coverage_verification_gap.coverage_gap' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.4127 (B>A) CI=+0.2857..+0.5397 -> REJECT H0", + "tool": "signal_label_assoc", + "underpowered": false + }, + { + "analysis_key": "signal_label_assoc:coverage_verification_gap.majority_share", + "ci": [ + -1, + -1 + ], + "config": { + "signal": "coverage_verification_gap.majority_share" + }, + "correction_family": "bh", + "correction_method": "BH", + "details": { + "fail_rate_control": 1, + "fail_rate_signal": 0, + "n_control": 5, + "n_signal": 3, + "permutation_p": 0.017857, + "rationale": "test whether per-case signal 'coverage_verification_gap.majority_share' predicts FAIL" + }, + "e_value": null, + "effect": -1, + "error": null, + "fdr_corrected": false, + "figure_path": null, + "ok": true, + "p_value": 0.017857142857142856, + "raw_reject": true, + "reject": true, + "summary": "signal 'coverage_verification_gap.majority_share' vs FAIL: [clustered bootstrap (unpaired)] effect=-1.0000 (A>B) CI=-1.0000..-1.0000 -> REJECT H0", + "tool": "signal_label_assoc", + "underpowered": false + }, + { + "analysis_key": "rank_corr:answer_extraction_audit.output_chars", + "ci": null, + "config": { + "signal": "answer_extraction_audit.output_chars" + }, + "correction_family": null, + "correction_method": null, + "details": { + "n": 8, + "rationale": "monotonic association between continuous 'answer_extraction_audit.output_chars' and FAIL", + "signal": "answer_extraction_audit.output_chars", + "tau": 0.1786 + }, + "e_value": null, + "effect": 0.1786, + "error": null, + "fdr_corrected": false, + "figure_path": null, + "ok": true, + "p_value": null, + "raw_reject": null, + "reject": null, + "summary": "Kendall τ between 'answer_extraction_audit.output_chars' and FAIL = +0.179 (n=8)", + "tool": "rank_corr", + "underpowered": false + }, + { + "analysis_key": "rank_corr:coverage_verification_gap.n_unique", + "ci": null, + "config": { 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"selfcheck_consistency.n_sentences", + "tau": 0.1071 + }, + "e_value": null, + "effect": 0.1071, + "error": null, + "fdr_corrected": false, + "figure_path": null, + "ok": true, + "p_value": null, + "raw_reject": null, + "reject": null, + "summary": "Kendall τ between 'selfcheck_consistency.n_sentences' and FAIL = +0.107 (n=8)", + "tool": "rank_corr", + "underpowered": false + }, + { + "analysis_key": "rank_corr:termination_audit.output_chars", + "ci": null, + "config": { + "signal": "termination_audit.output_chars" + }, + "correction_family": null, + "correction_method": null, + "details": { + "n": 8, + "rationale": "monotonic association between continuous 'termination_audit.output_chars' and FAIL", + "signal": "termination_audit.output_chars", + "tau": 0.1786 + }, + "e_value": null, + "effect": 0.1786, + "error": null, + "fdr_corrected": false, + "figure_path": null, + "ok": true, + "p_value": null, + "raw_reject": null, + "reject": null, + "summary": "Kendall τ between 'termination_audit.output_chars' and FAIL = +0.179 (n=8)", + "tool": "rank_corr", + "underpowered": false + }, + { + "analysis_key": "rank_corr:termination_audit.output_words", + "ci": null, + "config": { + "signal": "termination_audit.output_words" + }, + "correction_family": null, + "correction_method": null, + "details": { + "n": 8, + "rationale": "monotonic association between continuous 'termination_audit.output_words' and FAIL", + "signal": "termination_audit.output_words", + "tau": 0.1786 + }, + "e_value": null, + "effect": 0.1786, + "error": null, + "fdr_corrected": false, + "figure_path": null, + "ok": true, + "p_value": null, + "raw_reject": null, + "reject": null, + "summary": "Kendall τ between 'termination_audit.output_words' and FAIL = +0.179 (n=8)", + "tool": "rank_corr", + "underpowered": false + }, + { + "analysis_key": "rank_corr:coverage_verification_gap.majority_share", + "ci": null, + "config": { + "signal": "coverage_verification_gap.majority_share" + }, + "correction_family": null, + "correction_method": null, + "details": { + "n": 8, + "rationale": "monotonic association between continuous 'coverage_verification_gap.majority_share' and FAIL", + "signal": "coverage_verification_gap.majority_share", + "tau": -0.5357 + }, + "e_value": null, + "effect": -0.5357, + "error": null, + "fdr_corrected": false, + "figure_path": null, + "ok": true, + "p_value": null, + "raw_reject": null, + "reject": null, + "summary": "Kendall τ between 'coverage_verification_gap.majority_share' and FAIL = -0.536 (n=8)", + "tool": "rank_corr", + "underpowered": false + } + ], + "stats_tool": "llm_guided", + "stats_tool_results": [ + { + "conclusion": "Collected 49 numeric metric(s) across analyzer outputs.", + "metrics": { + "max": 64, + "mean": 3.709643, + "min": -0.1908, + "n_scalar_metrics": 49 + }, + "name": "scalar_summary", + "rationale": "Summarize numeric analyzer findings before hypothesis generation.", + "tables": { + "scalar_metrics": [ + { + "analyzer": "answer_extraction_audit", + "metric": "missing_tag_rate", + "value": 0.25 + }, + { + "analyzer": "answer_extraction_audit", + "metric": "n_cases", + "value": 8 + }, + { + "analyzer": "answer_extraction_audit", + "metric": "n_extraction_point_miss", + "value": 0 + }, + { + "analyzer": "answer_extraction_audit", + "metric": "n_extraction_suspect", + "value": 0 + }, + { + "analyzer": "answer_extraction_audit", + "metric": "n_gradable", + "value": 8 + }, + { + "analyzer": "answer_extraction_audit", + "metric": "n_labelled_fail", + "value": 5 + }, + { + "analyzer": "answer_extraction_audit", + "metric": "n_reasked", + "value": 0 + }, + { + "analyzer": "answer_extraction_audit", + "metric": "suspect_rate", + "value": 0 + }, + { + "analyzer": "calibration", + "metric": "logprob_channel_available", + "value": 1 + }, + { + "analyzer": "calibration", + "metric": "n_bins", + "value": 10 + }, + { + "analyzer": "calibration", + "metric": "n_cases", + "value": 8 + }, + { + "analyzer": "calibration", + "metric": "n_unlabelled_skipped", + "value": 0 + }, + { + "analyzer": "coverage_verification_gap", + "metric": "coverage_gap_rate", + "value": 0.125 + }, + { + "analyzer": "coverage_verification_gap", + "metric": "degenerate_sampling", + "value": 0 + }, + { + "analyzer": "coverage_verification_gap", + "metric": "k", + "value": 5 + }, + { + "analyzer": "coverage_verification_gap", + "metric": "mean_majority_correct", + "value": 0.375 + }, + { + "analyzer": "coverage_verification_gap", + "metric": "mean_pass_at_k", + "value": 0.5 + }, + { + "analyzer": "coverage_verification_gap", + "metric": "n_cases", + "value": 8 + }, + { + "analyzer": "coverage_verification_gap", + "metric": "n_scored", + "value": 8 + }, + { + "analyzer": "coverage_verification_gap", + "metric": "no_coverage_rate", + "value": 0.5 + }, + { + "analyzer": "logprob_entropy", + "metric": "mean_logprob", + "value": -0.0064 + }, + { + "analyzer": "logprob_entropy", + "metric": "mean_top_entropy", + "value": 0.0213 + }, + { + "analyzer": "logprob_entropy", + "metric": "min_token_logprob", + "value": -0.1908 + }, + { + "analyzer": "logprob_entropy", + "metric": "n_tokens", + "value": 64 + }, + { + "analyzer": "logprob_entropy", + "metric": "perplexity", + "value": 1.0064 + }, + { + "analyzer": "perturbation_battery", + "metric": "mean_invariance_break_rate", + "value": 0.7917 + }, + { + "analyzer": "perturbation_battery", + "metric": "n_cases", + "value": 8 + }, + { + "analyzer": "perturbation_battery", + "metric": "n_memorization_suspect", + "value": 0 + }, + { + "analyzer": "perturbation_battery", + "metric": "n_scored", + "value": 8 + }, + { + "analyzer": "perturbation_battery", + "metric": "noop_break_rate", + "value": 0.625 + }, + { + "analyzer": "self_consistency", + "metric": "consistency", + "value": 0.2 + }, + { + "analyzer": "self_consistency", + "metric": "n_samples", + "value": 5 + }, + { + "analyzer": "self_consistency", + "metric": "n_semantic_clusters", + "value": 3 + }, + { + "analyzer": "self_consistency", + "metric": "n_unique", + "value": 5 + }, + { + "analyzer": "self_consistency", + "metric": "normalized_semantic_entropy", + "value": 0.6554 + }, + { + "analyzer": "self_consistency", + "metric": "raw_text_consistency", + "value": 0.2 + }, + { + "analyzer": "self_consistency", + "metric": "semantic_consistency", + "value": 0.4 + }, + { + "analyzer": "self_consistency", + "metric": "semantic_entropy", + "value": 1.0549 + }, + { + "analyzer": "selfcheck_consistency", + "metric": "degenerate_sampling", + "value": 0 + }, + { + "analyzer": "selfcheck_consistency", + "metric": "mean_inconsistency", + "value": 0.015 + }, + { + "analyzer": "selfcheck_consistency", + "metric": "n_cases", + "value": 8 + }, + { + "analyzer": "selfcheck_consistency", + "metric": "n_samples", + "value": 4 + }, + { + "analyzer": "termination_audit", + "metric": "clean_rate", + "value": 0.75 + }, + { + "analyzer": "termination_audit", + "metric": "degenerate_rate", + "value": 0 + }, + { + "analyzer": "termination_audit", + "metric": "giveup_rate", + "value": 0 + }, + { + "analyzer": "termination_audit", + "metric": "n_cases", + "value": 8 + }, + { + "analyzer": "termination_audit", + "metric": "non_clean_rate", + "value": 0.25 + }, + { + "analyzer": "termination_audit", + "metric": "recovered_rate", + "value": 0 + }, + { + "analyzer": "termination_audit", + "metric": "truncation_rate", + "value": 0.25 + } + ] + }, + "visualizations": [ + { + "kind": "bar", + "title": "Analyzer scalar metrics", + "x": [ + "answer_extraction_audit.missing_tag_rate", + "answer_extraction_audit.n_cases", + "answer_extraction_audit.n_extraction_point_miss", + "answer_extraction_audit.n_extraction_suspect", + "answer_extraction_audit.n_gradable", + "answer_extraction_audit.n_labelled_fail", + "answer_extraction_audit.n_reasked", + "answer_extraction_audit.suspect_rate", + "calibration.logprob_channel_available", + "calibration.n_bins", + "calibration.n_cases", + "calibration.n_unlabelled_skipped", + "coverage_verification_gap.coverage_gap_rate", + "coverage_verification_gap.degenerate_sampling", + "coverage_verification_gap.k", + "coverage_verification_gap.mean_majority_correct", + "coverage_verification_gap.mean_pass_at_k", + "coverage_verification_gap.n_cases", + "coverage_verification_gap.n_scored", + "coverage_verification_gap.no_coverage_rate", + "logprob_entropy.mean_logprob", + "logprob_entropy.mean_top_entropy", + "logprob_entropy.min_token_logprob", + "logprob_entropy.n_tokens", + "logprob_entropy.perplexity", + "perturbation_battery.mean_invariance_break_rate", + "perturbation_battery.n_cases", + "perturbation_battery.n_memorization_suspect", + "perturbation_battery.n_scored", + "perturbation_battery.noop_break_rate", + "self_consistency.consistency", + "self_consistency.n_samples", + "self_consistency.n_semantic_clusters", + "self_consistency.n_unique", + "self_consistency.normalized_semantic_entropy", + "self_consistency.raw_text_consistency", + "self_consistency.semantic_consistency", + "self_consistency.semantic_entropy", + "selfcheck_consistency.degenerate_sampling", + "selfcheck_consistency.mean_inconsistency", + "selfcheck_consistency.n_cases", + "selfcheck_consistency.n_samples", + "termination_audit.clean_rate", + "termination_audit.degenerate_rate", + "termination_audit.giveup_rate", + "termination_audit.n_cases", + "termination_audit.non_clean_rate", + "termination_audit.recovered_rate", + "termination_audit.truncation_rate" + ], + "y": [ + 0.25, + 8, + 0, + 0, + 8, + 5, + 0, + 0, + 1, + 10, + 8, + 0, + 0.125, + 0, + 5, + 0.375, + 0.5, + 8, + 8, + 0.5, + -0.0064, + 0.0213, + -0.1908, + 64, + 1.0064, + 0.7917, + 8, + 0, + 8, + 0.625, + 0.2, + 5, + 3, + 5, + 0.6554, + 0.2, + 0.4, + 1.0549, + 0, + 0.015, + 8, + 4, + 0.75, + 0, + 0, + 8, + 0.25, + 0, + 0.25 + ], + "y_label": "value" + } + ] + }, + { + "conclusion": "Computed per-case diagnostic signal coverage.", + "metrics": { + "fail_rate_signal": 0.625, + "n_cases_with_signal_entries": 8, + "n_no_signal": 0, + "n_signal": 8 + }, + "name": "per_case_signal_label_association", + "rationale": "Check whether analyzer per-case signals align with PASS/FAIL outcomes.", + "tables": { + "per_case_signal": [ + { + "case_id": "bbh_word_sorting-0000", + "has_signal": true, + "label": "pass" + }, + { + "case_id": "bbh_word_sorting-0006", + "has_signal": true, + "label": "fail" + }, + { + "case_id": "bbh_word_sorting-0025", + "has_signal": true, + "label": "fail" + }, + { + "case_id": "bbh_word_sorting-0045", + "has_signal": true, + "label": "fail" + }, + { + "case_id": "bbh_word_sorting-0063", + "has_signal": true, + "label": "pass" + }, + { + "case_id": "bbh_word_sorting-0076", + "has_signal": true, + "label": "fail" + }, + { + "case_id": "bbh_word_sorting-0094", + "has_signal": true, + "label": "fail" + }, + { + "case_id": "bbh_word_sorting-0101", + "has_signal": true, + "label": "pass" + } + ] + }, + "visualizations": [ + { + "kind": "bar", + "title": "Per-case diagnostic signal split", + "x": [ + "signal", + "no_signal" + ], + "y": [ + 8, + 0 + ], + "y_label": "case count" + }, + { + "kind": "bar", + "title": "Fail rate by diagnostic signal", + "x": [ + "signal", + "no_signal" + ], + "y": [ + 0.625, + 0 + ], + "y_label": "fail rate" + } + ] + } + ], + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:39:38.959771+00:00", + "visualizations": [ + { + "kind": "bar", + "title": "Analyzer scalar metrics", + "x": [ + "answer_extraction_audit.missing_tag_rate", + "answer_extraction_audit.n_cases", + "answer_extraction_audit.n_extraction_point_miss", + "answer_extraction_audit.n_extraction_suspect", + "answer_extraction_audit.n_gradable", + "answer_extraction_audit.n_labelled_fail", + "answer_extraction_audit.n_reasked", + "answer_extraction_audit.suspect_rate", + "calibration.logprob_channel_available", + "calibration.n_bins", + "calibration.n_cases", + "calibration.n_unlabelled_skipped", + "coverage_verification_gap.coverage_gap_rate", + "coverage_verification_gap.degenerate_sampling", + "coverage_verification_gap.k", + "coverage_verification_gap.mean_majority_correct", + "coverage_verification_gap.mean_pass_at_k", + "coverage_verification_gap.n_cases", + "coverage_verification_gap.n_scored", + "coverage_verification_gap.no_coverage_rate", + "logprob_entropy.mean_logprob", + "logprob_entropy.mean_top_entropy", + "logprob_entropy.min_token_logprob", + "logprob_entropy.n_tokens", + "logprob_entropy.perplexity", + "perturbation_battery.mean_invariance_break_rate", + "perturbation_battery.n_cases", + "perturbation_battery.n_memorization_suspect", + "perturbation_battery.n_scored", + "perturbation_battery.noop_break_rate", + "self_consistency.consistency", + "self_consistency.n_samples", + "self_consistency.n_semantic_clusters", + "self_consistency.n_unique", + "self_consistency.normalized_semantic_entropy", + "self_consistency.raw_text_consistency", + "self_consistency.semantic_consistency", + "self_consistency.semantic_entropy", + "selfcheck_consistency.degenerate_sampling", + "selfcheck_consistency.mean_inconsistency", + "selfcheck_consistency.n_cases", + "selfcheck_consistency.n_samples", + "termination_audit.clean_rate", + "termination_audit.degenerate_rate", + "termination_audit.giveup_rate", + "termination_audit.n_cases", + "termination_audit.non_clean_rate", + "termination_audit.recovered_rate", + "termination_audit.truncation_rate" + ], + "y": [ + 0.25, + 8, + 0, + 0, + 8, + 5, + 0, + 0, + 1, + 10, + 8, + 0, + 0.125, + 0, + 5, + 0.375, + 0.5, + 8, + 8, + 0.5, + -0.0064, + 0.0213, + -0.1908, + 64, + 1.0064, + 0.7917, + 8, + 0, + 8, + 0.625, + 0.2, + 5, + 3, + 5, + 0.6554, + 0.2, + 0.4, + 1.0549, + 0, + 0.015, + 8, + 4, + 0.75, + 0, + 0, + 8, + 0.25, + 0, + 0.25 + ], + "y_label": "value" + }, + { + "kind": "bar", + "title": "Per-case diagnostic signal split", + "x": [ + "signal", + "no_signal" + ], + "y": [ + 8, + 0 + ], + "y_label": "case count" + }, + { + "kind": "bar", + "title": "Fail rate by diagnostic signal", + "x": [ + "signal", + "no_signal" + ], + "y": [ + 0.625, + 0 + ], + "y_label": "fail rate" + } + ] + } + ], + "explore": [ + { + "adjudication": { + "alpha": 0.05, + "method": "e-BH", + "n_host_adjudicated": 0, + "n_in_family": 0, + "n_rejected": 0, + "split": "in_sample" + }, + "attempts": 1, + "caveats": [ + "M1 diagnostic analyzer columns are populated for 8 of the 64 cases; 56 cases contain label-only information.", + "All candidate thresholds were identified on this exploration sample and must be re-evaluated verbatim on held-out cases.", + "Findings represent descriptive associations within this single task slice (BBH word sorting) and may not generalize to other reasoning tasks." + ], + "code": "\"\"\"\nExploratory data analysis of Gemma 4 E2B-it on BBH word_sorting cases.\nDistinguishes FAIL cases from PASS cases using recorded M1 analyzer signals.\n\"\"\"\nimport json\nimport os\nimport math\nimport numpy as np\nimport pandas as pd\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\n\ndef main():\n # Load records\n with open('records.json', 'r') as f:\n records = json.load(f)\n \n df = pd.DataFrame(records)\n n_total = len(df)\n n_pass_total = int((df['label'] == 'pass').sum())\n n_fail_total = int((df['label'] == 'fail').sum())\n \n # Subsets\n df_analyzed = df.dropna(subset=['calibration_correct']).copy()\n n_analyzed = len(df_analyzed)\n n_fail_analyzed = int((df_analyzed['label'] == 'fail').sum())\n n_pass_analyzed = int((df_analyzed['label'] == 'pass').sum())\n \n os.makedirs('tables', exist_ok=True)\n os.makedirs('figures', exist_ok=True)\n \n # Palette\n C_FAIL = \"#D95F02\"\n C_PASS = \"#1B9E77\"\n \n # -------------------------------------------------------------\n # 1. Class Balance Table & CSV\n # -------------------------------------------------------------\n cb_df = pd.DataFrame([\n {\"outcome\": \"FAIL\", \"count\": n_fail_total, \"pct\": round(n_fail_total / n_total * 100, 1)},\n {\"outcome\": \"PASS\", \"count\": n_pass_total, \"pct\": round(n_pass_total / n_total * 100, 1)}\n ])\n cb_df.to_csv('tables/class_balance.csv', index=False)\n \n # -------------------------------------------------------------\n # 2. Key Numeric Signals & Discrimination\n # -------------------------------------------------------------\n signals = [\n 'calibration_correct',\n 'perturbation_battery_invariance_break_rate',\n 'coverage_verification_gap_majority_share',\n 'coverage_verification_gap_n_unique',\n 'selfcheck_consistency_selfcheck_worst_sentence',\n 'selfcheck_consistency_selfcheck_inconsistency',\n 'selfcheck_consistency_n_sentences',\n 'termination_audit_output_chars',\n 'termination_audit_output_words',\n 'termination_audit_repetition_score',\n 'calibration_conf_logprob',\n 'perturbation_battery_restate_question_flipped',\n 'perturbation_battery_noop_clause_flipped',\n 'termination_audit_looks_truncated'\n ]\n \n disc_rows = []\n for sig in signals:\n f_vals = df_analyzed[df_analyzed['label'] == 'fail'][sig].dropna()\n p_vals = df_analyzed[df_analyzed['label'] == 'pass'][sig].dropna()\n if len(f_vals) > 0 and len(p_vals) > 0:\n m_f = f_vals.mean()\n m_p = p_vals.mean()\n s_pooled = math.sqrt((f_vals.var() + p_vals.var()) / 2.0) if (f_vals.var() + p_vals.var()) > 0 else 1.0\n diff = m_f - m_p\n smd = abs(diff) / s_pooled if s_pooled > 0 else 0.0\n disc_rows.append({\n \"signal\": sig,\n \"mean_fail\": round(m_f, 4),\n \"mean_pass\": round(m_p, 4),\n \"diff\": round(diff, 4),\n \"smd\": round(smd, 3)\n })\n disc_df = pd.DataFrame(disc_rows).sort_values(by=\"smd\", ascending=False)\n disc_df.to_csv('tables/top_discriminators.csv', index=False)\n \n # -------------------------------------------------------------\n # 3. Binned / Discrete Curve Tables\n # -------------------------------------------------------------\n # 3a. Invariance break rate curve\n # Values are 0.3333, 0.6667, 1.0\n invar_groups = []\n for val, name in [(0.3333, \"Low (0.33)\"), (0.6667, \"Mid (0.67)\"), (1.0, \"High (1.00)\")]:\n sub = df_analyzed[np.isclose(df_analyzed['perturbation_battery_invariance_break_rate'], val, atol=0.05)]\n n_sub = len(sub)\n n_f = int((sub['label'] == 'fail').sum())\n rate = n_f / n_sub if n_sub > 0 else 0.0\n invar_groups.append({\n \"invariance_break_bin\": name,\n \"fail_rate\": round(rate, 3),\n \"n\": n_sub,\n \"n_fail\": n_f\n })\n pd.DataFrame(invar_groups).to_csv('tables/failrate_by_invariance_break.csv', index=False)\n \n # 3b. Consistency majority share curve\n # Bins: <=0.4 (low agreement), 0.6-0.8 (moderate), 1.0 (perfect)\n cov_groups = []\n sub_low = df_analyzed[df_analyzed['coverage_verification_gap_majority_share'] <= 0.4]\n sub_mid = df_analyzed[(df_analyzed['coverage_verification_gap_majority_share'] > 0.4) & (df_analyzed['coverage_verification_gap_majority_share'] < 1.0)]\n sub_hi = df_analyzed[df_analyzed['coverage_verification_gap_majority_share'] == 1.0]\n for sub, name in [(sub_low, \"<= 0.4 (Low agreement)\"), (sub_mid, \"0.6 - 0.8 (Moderate)\"), (sub_hi, \"1.0 (Unanimous)\")]:\n n_sub = len(sub)\n n_f = int((sub['label'] == 'fail').sum())\n rate = n_f / n_sub if n_sub > 0 else 0.0\n cov_groups.append({\n \"majority_share_bin\": name,\n \"fail_rate\": round(rate, 3),\n \"n\": n_sub,\n \"n_fail\": n_f\n })\n pd.DataFrame(cov_groups).to_csv('tables/failrate_by_majority_share.csv', index=False)\n \n # 3c. Output length curve (tertiles: short <1000, medium 1000-1200, long >1200)\n len_groups = []\n sub_s = df_analyzed[df_analyzed['termination_audit_output_chars'] < 1000]\n sub_m = df_analyzed[(df_analyzed['termination_audit_output_chars'] >= 1000) & (df_analyzed['termination_audit_output_chars'] <= 1200)]\n sub_l = df_analyzed[df_analyzed['termination_audit_output_chars'] > 1200]\n for sub, name in [(sub_s, \"< 1000 chars\"), (sub_m, \"1000-1200 chars\"), (sub_l, \"> 1200 chars\")]:\n n_sub = len(sub)\n n_f = int((sub['label'] == 'fail').sum())\n rate = n_f / n_sub if n_sub > 0 else 0.0\n len_groups.append({\n \"output_chars_bin\": name,\n \"fail_rate\": round(rate, 3),\n \"n\": n_sub,\n \"n_fail\": n_f\n })\n pd.DataFrame(len_groups).to_csv('tables/failrate_by_output_chars.csv', index=False)\n \n # 3d. Selfcheck worst sentence inconsistency curve (0.0 vs > 0.0)\n sc_groups = []\n sub_zero = df_analyzed[df_analyzed['selfcheck_consistency_selfcheck_worst_sentence'] == 0.0]\n sub_pos = df_analyzed[df_analyzed['selfcheck_consistency_selfcheck_worst_sentence'] > 0.0]\n for sub, name in [(sub_zero, \"0.0 (No contradiction)\"), (sub_pos, \"> 0.0 (Contradictions present)\")]:\n n_sub = len(sub)\n n_f = int((sub['label'] == 'fail').sum())\n rate = n_f / n_sub if n_sub > 0 else 0.0\n sc_groups.append({\n \"selfcheck_worst_bin\": name,\n \"fail_rate\": round(rate, 3),\n \"n\": n_sub,\n \"n_fail\": n_f\n })\n pd.DataFrame(sc_groups).to_csv('tables/failrate_by_selfcheck_inconsistency.csv', index=False)\n\n # 3e. Scatter table: majority share vs invariance break rate\n scatter_df = df_analyzed[['case_id', 'label', 'coverage_verification_gap_majority_share', 'perturbation_battery_invariance_break_rate']].copy()\n scatter_df.columns = ['case_id', 'label', 'majority_share', 'invariance_break_rate']\n scatter_df.to_csv('tables/scatter_agreement_vs_invariance.csv', index=False)\n \n # -------------------------------------------------------------\n # 4. Rich PNG Plots\n # -------------------------------------------------------------\n # Plot 1: Forest plot of top discriminators\n plt.figure(figsize=(8, 4.5), dpi=300)\n top_disc = disc_df.head(8).sort_values(by=\"smd\", ascending=True)\n y_pos = np.arange(len(top_disc))\n plt.hlines(y=y_pos, xmin=0, xmax=top_disc['smd'], color=\"#8da0cb\", alpha=0.7, linewidth=2)\n plt.scatter(top_disc['smd'], y_pos, color=\"#2b5c8f\", s=80, zorder=3)\n plt.yticks(y_pos, [s.replace('_', ' ') for s in top_disc['signal']], fontsize=9)\n plt.xlabel(\"Standardized Separation (|Mean FAIL - Mean PASS| / Pooled SD)\", fontsize=10)\n plt.title(\"Top Per-Case Signals Separating FAIL from PASS (n=8)\", fontsize=11, fontweight='bold', loc='left')\n plt.grid(axis='x', linestyle='--', alpha=0.5)\n plt.tight_layout()\n plt.savefig('figures/top_discriminators_forest.png')\n plt.close()\n \n # Plot 2: Strip/Jitter comparison of key signals\n fig, axes = plt.subplots(1, 3, figsize=(11, 4), dpi=300)\n plot_signals = [\n ('perturbation_battery_invariance_break_rate', 'Invariance Break Rate', axes[0]),\n ('coverage_verification_gap_majority_share', 'Sample Agreement Share', axes[1]),\n ('selfcheck_consistency_selfcheck_worst_sentence', 'Worst Sentence Inconsistency', axes[2])\n ]\n for col, title, ax in plot_signals:\n for lbl, color, offset in [('pass', C_PASS, -0.15), ('fail', C_FAIL, 0.15)]:\n vals = df_analyzed[df_analyzed['label'] == lbl][col]\n jitter = np.random.normal(0, 0.03, size=len(vals))\n ax.scatter(np.zeros(len(vals)) + offset + jitter, vals, color=color, label=lbl.upper(), s=60, alpha=0.85, edgecolors='black', linewidth=0.5)\n # Plot mean line\n ax.plot([offset - 0.08, offset + 0.08], [vals.mean(), vals.mean()], color=color, linewidth=2.5)\n ax.set_xticks([-0.15, 0.15])\n ax.set_xticklabels(['PASS (n=3)', 'FAIL (n=5)'], fontweight='bold')\n ax.set_title(title, fontsize=10, fontweight='bold')\n ax.grid(axis='y', linestyle='--', alpha=0.4)\n axes[0].set_ylabel(\"Signal Value\", fontsize=10)\n plt.suptitle(\"Distribution of Leading Diagnostic Signals across Outcomes\", fontsize=12, fontweight='bold', y=1.02)\n plt.tight_layout()\n plt.savefig('figures/signal_distributions_strip.png')\n plt.close()\n\n # Plot 3: 2D Scatter of Stability vs Consensus\n plt.figure(figsize=(6.5, 5), dpi=300)\n for lbl, color in [('pass', C_PASS), ('fail', C_FAIL)]:\n sub = df_analyzed[df_analyzed['label'] == lbl]\n plt.scatter(\n sub['coverage_verification_gap_majority_share'],\n sub['perturbation_battery_invariance_break_rate'],\n color=color, label=f\"{lbl.upper()} (n={len(sub)})\",\n s=90, alpha=0.85, edgecolors='black', linewidth=0.6\n )\n # Decision boundary line candidate: majority_share <= 0.4 or invariance >= 0.8\n plt.axvline(0.5, color='gray', linestyle=':', label='Threshold: Majority Share = 0.5')\n plt.xlabel(\"Sampling Agreement Share (Higher = More Consistent Answers)\", fontsize=10)\n plt.ylabel(\"Invariance Break Rate (Higher = More Fragile to Phrasing)\", fontsize=10)\n plt.title(\"Interaction: Sampling Consensus vs. Phrasing Fragility\", fontsize=11, fontweight='bold', loc='left')\n plt.legend(frameon=True, facecolor='white', loc='upper right')\n plt.grid(True, linestyle='--', alpha=0.4)\n plt.tight_layout()\n plt.savefig('figures/scatter_consensus_vs_invariance.png')\n plt.close()\n\n # Plot 4: Correlation Heatmap among numerical signals\n corr_cols = [\n 'perturbation_battery_invariance_break_rate',\n 'coverage_verification_gap_majority_share',\n 'coverage_verification_gap_n_unique',\n 'selfcheck_consistency_selfcheck_worst_sentence',\n 'termination_audit_output_chars',\n 'calibration_conf_logprob'\n ]\n corr_mat = df_analyzed[corr_cols].corr()\n plt.figure(figsize=(6.5, 5.5), dpi=300)\n plt.imshow(corr_mat, cmap='coolwarm', vmin=-1, vmax=1)\n plt.colorbar(fraction=0.046, pad=0.04)\n col_labels = [c.split('_')[-2] + '_' + c.split('_')[-1] for c in corr_cols]\n plt.xticks(np.arange(len(corr_cols)), col_labels, rotation=45, ha='right', fontsize=9)\n plt.yticks(np.arange(len(corr_cols)), col_labels, fontsize=9)\n for i in range(len(corr_cols)):\n for j in range(len(corr_cols)):\n text = f\"{corr_mat.iloc[i, j]:.2f}\"\n plt.text(j, i, text, ha='center', va='center', color='black' if abs(corr_mat.iloc[i, j]) < 0.6 else 'white', fontsize=8)\n plt.title(\"Correlation Matrix of Numeric Diagnostic Signals\", fontsize=11, fontweight='bold', loc='left')\n plt.tight_layout()\n plt.savefig('figures/corr_heatmap.png')\n plt.close()\n\n # -------------------------------------------------------------\n # 5. Output JSON assembly\n # -------------------------------------------------------------\n result = {\n \"plain_question\": \"Which recorded case measurements tell apart questions the language model sorted wrong from those it sorted right?\",\n \"observations\": [\n \"Dataset comprises 64 evaluated word sorting items from BBH, with 37 failures (57.8%) and 27 passes (42.2%).\",\n \"M1 diagnostic analyzer signals were collected on an exploratory diagnostic sub-sample of 8 items (5 FAIL, 3 PASS), while the remaining 56 rows contain outcome labels only.\",\n \"Across the 8 analyzed items, the perturbation battery invariance break rate was noticeably higher in FAIL cases (mean 0.93, with 4 of 5 cases at 1.0) compared to PASS cases (mean 0.56, all <= 0.67).\",\n \"Sampling majority agreement share separated cases cleanly: all 3 PASS cases had majority agreement of 60% or higher, whereas 4 of 5 FAIL cases had majority agreement of 40% or lower.\",\n \"Truncation and tag omissions occurred exclusively in FAIL cases (2 of 5 failures had truncated outputs and missing answer tags, whereas 0 of 3 passes did).\",\n \"Selfcheck worst sentence contradiction scores were zero in all 3 PASS cases, but positive in 4 of 5 FAIL cases.\",\n \"Model confidence (calibration logprob) remained high across both groups (PASS mean 0.960, FAIL mean 0.980), showing model overconfidence even on failed word sorting runs.\"\n ],\n \"visual_plan\": [\n {\n \"name\": \"class_balance\",\n \"display_name\": \"Overall FAIL and PASS Case Counts\",\n \"question\": \"What is the overall balance between incorrect and correct sorting cases in this batch?\",\n \"data_shape\": \"categorical\",\n \"plot_kind\": \"composition_strip\",\n \"fallback_kind\": \"bar\",\n \"required_columns\": [\"label\"],\n \"rationale\": \"A composition count shows the total cohort split (37 fail vs 27 pass) across the complete set of 64 cases.\",\n \"disposition\": \"primary\",\n \"not_promoted_reason\": \"\"\n },\n {\n \"name\": \"top_discriminators\",\n \"display_name\": \"Ranked Signal Separation\",\n \"question\": \"Which diagnostic signals show the largest differences between FAIL and PASS cases?\",\n \"data_shape\": \"many-numeric\",\n \"plot_kind\": \"forest\",\n \"fallback_kind\": \"forest\",\n \"required_columns\": [\"label\"],\n \"rationale\": \"Ranked standardized mean difference highlights which indicators provide the strongest contrast without assuming normal distributions.\",\n \"disposition\": \"primary\",\n \"not_promoted_reason\": \"\"\n },\n {\n \"name\": \"failrate_by_invariance_break\",\n \"display_name\": \"Failure Rate by Invariance Break Rate\",\n \"question\": \"How does prompt fragility to minor phrasing changes correspond to task failure?\",\n \"data_shape\": \"numeric-vs-binary\",\n \"plot_kind\": \"line\",\n \"fallback_kind\": \"line\",\n \"required_columns\": [\"perturbation_battery_invariance_break_rate\", \"label\"],\n \"rationale\": \"Ordered discrete levels of perturbation instability display failure concentration at high sensitivity levels.\",\n \"disposition\": \"primary\",\n \"not_promoted_reason\": \"\"\n },\n {\n \"name\": \"failrate_by_majority_share\",\n \"display_name\": \"Failure Rate by Sampling Consensus\",\n \"question\": \"Does agreement across repeated samples distinguish correct answers from errors?\",\n \"data_shape\": \"numeric-vs-binary\",\n \"plot_kind\": \"line\",\n \"fallback_kind\": \"line\",\n \"required_columns\": [\"coverage_verification_gap_majority_share\", \"label\"],\n \"rationale\": \"Binned repeated-sample consensus rates reveal how self-consistency drops when the model fails.\",\n \"disposition\": \"primary\",\n \"not_promoted_reason\": \"\"\n },\n {\n \"name\": \"failrate_by_selfcheck_inconsistency\",\n \"display_name\": \"Failure Rate by Reasoning Contradiction\",\n \"question\": \"Are internal chain-of-thought contradictions associated with incorrect final sorting?\",\n \"data_shape\": \"numeric-vs-binary\",\n \"plot_kind\": \"line\",\n \"fallback_kind\": \"line\",\n \"required_columns\": [\"selfcheck_consistency_selfcheck_worst_sentence\", \"label\"],\n \"rationale\": \"Separates cases with completely consistent reasoning traces from those with detected sentence-level inconsistencies.\",\n \"disposition\": \"primary\",\n \"not_promoted_reason\": \"\"\n },\n {\n \"name\": \"scatter_agreement_vs_invariance\",\n \"display_name\": \"Consensus vs Invariance Scatter\",\n \"question\": \"How do sampling consensus and prompt sensitivity jointly separate correct and incorrect items?\",\n \"data_shape\": \"numeric-vs-numeric\",\n \"plot_kind\": \"scatter\",\n \"fallback_kind\": \"scatter\",\n \"required_columns\": [\"coverage_verification_gap_majority_share\", \"perturbation_battery_invariance_break_rate\", \"label\"],\n \"rationale\": \"A 2D scatter visually inspects whether combining consistency and prompt stability creates an unambiguous boundary between outcomes.\",\n \"disposition\": \"primary\",\n \"not_promoted_reason\": \"\"\n },\n {\n \"name\": \"failrate_by_output_chars\",\n \"display_name\": \"Failure Rate by Output Length\",\n \"question\": \"Do unusually long or short responses correspond to higher error rates?\",\n \"data_shape\": \"numeric-vs-binary\",\n \"plot_kind\": \"line\",\n \"fallback_kind\": \"line\",\n \"required_columns\": [\"termination_audit_output_chars\", \"label\"],\n \"rationale\": \"Tertiles of response character length check whether verbosity or truncation flags correlate with failure.\",\n \"disposition\": \"supporting\",\n \"not_promoted_reason\": \"Output length exhibits non-monotonic differences driven by truncated outliers rather than a stable linear trend.\"\n }\n ],\n \"takeaways\": [\n {\n \"plain_title\": \"Items where rephrasing the question caused the model to change its answer failed almost every time.\",\n \"title\": \"A perturbation invariance break rate of 1.0 occurred in 4 of 5 FAIL cases (80%), compared with 0 of 3 PASS cases (0%).\",\n \"chart_names\": [\"failrate_by_invariance_break\", \"top_discriminators\"],\n \"table_names\": [\"failrate_by_invariance_break\", \"top_discriminators\"],\n \"analysis\": \"In cases where minor question rewording altered the model's sorting sequence (break rate equal to 1.0), all 4 cases resulted in failures. By contrast, all 3 passing cases maintained break rates of 0.67 or lower. This shows that brittleness to prompt wording strongly separates failures from successes in this batch, though the small analyzed sample of 8 cases means exact error rates require validation on larger batches.\",\n \"caveat\": \"Descriptive pattern evaluated on 8 diagnostic rows; prompt sensitivity may also reflect overall question difficulty rather than an isolated cause.\"\n },\n {\n \"plain_title\": \"When repeated attempts produced conflicting answers, the model was consistently wrong.\",\n \"title\": \"Low majority agreement (40% or below across 5 samples) captured 4 of 5 FAIL cases (80%) and 0 of 3 PASS cases (0%).\",\n \"chart_names\": [\"failrate_by_majority_share\", \"scatter_agreement_vs_invariance\"],\n \"table_names\": [\"failrate_by_majority_share\", \"scatter_agreement_vs_invariance\"],\n \"analysis\": \"The majority share among 5 sampled completions was 0.40 or lower for 4 out of 5 failed items, while all 3 successful items achieved majority agreement between 0.60 and 1.00. This matters because self-consistency sampling offers a clear practical signal of sorting failure. However, whether the lack of consensus is specific to word sorting remains unconfirmed until tested on other task types.\",\n \"caveat\": \"Based on 8 analyzed cases; threshold choice (majority share <= 0.4) was identified on this exploratory batch.\"\n },\n {\n \"plain_title\": \"Internal contradictions in the reasoning text appeared only in failure cases.\",\n \"title\": \"A non-zero sentence inconsistency score occurred in 4 of 5 FAIL cases (80%) versus 0 of 3 PASS cases (0%).\",\n \"chart_names\": [\"failrate_by_selfcheck_inconsistency\"],\n \"table_names\": [\"failrate_by_selfcheck_inconsistency\"],\n \"analysis\": \"Inspection of internal sentence consistency revealed that all 3 passing cases had zero detected contradictions (score 0.0), whereas 4 of 5 failing cases contained contradictory statements (scores ranging from 0.33 to 0.67). This indicates that the model frequently makes contradictory claims during intermediate reasoning when failing to sort words. It remains uncertain whether the contradiction directly leads to the wrong answer or is simply a byproduct of confusion.\",\n \"caveat\": \"Observed on 8 cases; sentence inconsistency detectors can suffer from false positives on long complex chains of thought.\"\n },\n {\n \"plain_title\": \"High model confidence does not guarantee correct sorting.\",\n \"title\": \"The model assigned high token probability (averaging 98.0%) even across the 5 failed sorting attempts.\",\n \"chart_names\": [\"top_discriminators\"],\n \"table_names\": [\"top_discriminators\"],\n \"analysis\": \"Across all 8 analyzed cases, the model's reported calibration logprob confidence remained above 0.94, with failures averaging 0.980 and passes averaging 0.960. This demonstrates that the model is severely overconfident when producing incorrect alphabetical sequences. Downstream systems cannot rely on raw token confidence alone to filter out incorrect word sorting outputs.\",\n \"caveat\": \"Token probability reflects next-token likelihood under the generation path rather than genuine correctness probability.\"\n },\n {\n \"plain_title\": \"The full dataset has a moderate error rate, but detailed diagnostics were captured on an eight-case slice.\",\n \"title\": \"Across all 64 evaluated cases, 37 failed (57.8%) and 27 passed (42.2%), while M1 signals were recorded on 8 cases.\",\n \"chart_names\": [\"class_balance\"],\n \"table_names\": [\"class_balance\"],\n \"analysis\": \"The overall dataset contains 64 cases, showing that word sorting is a challenging task for Gemma 4 E2B-it with a 57.8% failure rate (37 of 64). Detailed diagnostic signals were gathered on an 8-case subset (5 failures, 3 passes), while 56 cases retain label-only information. This establishes the overall task difficulty while highlighting that signal thresholds must be treated as candidates for wider confirmation.\",\n \"caveat\": \"The diagnostic subset of 8 cases may have different sampling properties than the remaining 56 unmeasured cases.\"\n }\n ],\n \"chart_readings\": {\n \"class_balance\": {\n \"chart\": \"Overall FAIL and PASS Case Counts\",\n \"reading\": \"Failures account for 37 of 64 cases (57.8%), while passes account for 27 of 64 cases (42.2%).\",\n \"do_not_infer\": \"This chart shows dataset composition; it does not measure signal performance.\"\n },\n \"top_discriminators\": {\n \"chart\": \"Ranked Signal Separation\",\n \"reading\": \"Perturbation invariance break rate, sample majority agreement share, and sentence inconsistency show the strongest standardized contrasts between the 5 fail and 3 pass cases.\",\n \"do_not_infer\": \"High separation on an exploratory batch of 8 cases does not guarantee equal predictive power on unmeasured cases.\"\n },\n \"failrate_by_invariance_break\": {\n \"chart\": \"Failure Rate by Invariance Break Rate\",\n \"reading\": \"Items with invariance break rates of 1.0 failed in 4 of 4 cases (100%), whereas items with lower break rates failed in 1 of 4 cases (25%).\",\n \"do_not_infer\": \"Prompt sensitivity does not prove rephrasing caused the failure; both may stem from underlying problem complexity.\"\n },\n \"failrate_by_majority_share\": {\n \"chart\": \"Failure Rate by Sampling Consensus\",\n \"reading\": \"Items where the top sample won 40% or less of repeated generations failed in 4 of 4 instances (100%), while items with 60% or higher agreement failed in 1 of 4 instances (25%).\",\n \"do_not_infer\": \"Low consensus reflects generation dispersion, not necessarily an inability to produce the right answer under different decoding parameters.\"\n },\n \"failrate_by_selfcheck_inconsistency\": {\n \"chart\": \"Failure Rate by Reasoning Contradiction\",\n \"reading\": \"All 4 cases with detected sentence inconsistencies resulted in failure (100%), compared to 1 failure out of 4 cases with zero inconsistency (25%).\",\n \"do_not_infer\": \"Internal inconsistency is a descriptive symptom of degraded reasoning, not a proven cause of the final answer error.\"\n },\n \"scatter_agreement_vs_invariance\": {\n \"chart\": \"Consensus vs Invariance Scatter\",\n \"reading\": \"Passing cases cluster in the high-consensus (>=0.6) and lower-fragility (<=0.67) quadrant, while 4 of 5 failures occupy the low-consensus or high-fragility region.\",\n \"do_not_infer\": \"A visual decision boundary on 8 points is exploratory and must be tested on held-out data.\"\n },\n \"failrate_by_output_chars\": {\n \"chart\": \"Failure Rate by Output Length\",\n \"reading\": \"Failure rates are elevated in both short responses (<1000 chars, 1 of 2 fail) and long responses (>1200 chars, 2 of 2 fail), with the longest responses including truncated failures.\",\n \"do_not_infer\": \"Response length does not linearly drive failure; length variation is partly influenced by truncation and verbosity.\"\n },\n \"top_discriminators_forest\": {\n \"chart\": \"Top Per-Case Signals Separating FAIL from PASS (Forest Plot)\",\n \"reading\": \"Invariance break rate, consensus majority share, and sentence-level inconsistency display the largest standardized differences between the 5 FAIL and 3 PASS cases.\",\n \"do_not_infer\": \"Standardized mean differences on 8 observations are exploratory estimates subject to wide sampling variance.\"\n },\n \"signal_distributions_strip\": {\n \"chart\": \"Distribution of Leading Diagnostic Signals across Outcomes (Strip Plot)\",\n \"reading\": \"Individual point markers highlight distinct separation in break rate, sample agreement, and worst sentence inconsistency between FAIL and PASS items.\",\n \"do_not_infer\": \"Small sample size (n=8) limits conclusions about distributional shape or tails.\"\n },\n \"scatter_consensus_vs_invariance\": {\n \"chart\": \"Interaction: Sampling Consensus vs. Phrasing Fragility\",\n \"reading\": \"Combining sampling consensus (majority share <= 0.4) with prompt fragility (invariance break rate >= 0.8) completely separates 4 of 5 failure cases from all passes.\",\n \"do_not_infer\": \"Thresholds chosen to separate this 8-case sample may exhibit optimism when applied to new cases.\"\n },\n \"corr_heatmap\": {\n \"chart\": \"Correlation Matrix of Numeric Diagnostic Signals\",\n \"reading\": \"Sampling consensus shares strong negative correlation with unique sample count and invariance break rate, indicating coupled dimensions of model uncertainty.\",\n \"do_not_infer\": \"Correlations among 8 cases indicate co-occurrence patterns, not causal dependencies.\"\n }\n },\n \"claims\": [\n {\n \"id\": \"C1\",\n \"text\": \"High prompt invariance break rate (>= 0.8) is descriptively associated with task failure.\",\n \"status\": \"descriptive\",\n \"evidence_ids\": [\"chart:failrate_by_invariance_break\", \"figure:signal_distributions_strip\"],\n \"interpretation\": \"A candidate signal indicating that sensitivity to prompt wording correlates with sorting mistakes.\",\n \"do_not_infer\": \"Does not prove causal sensitivity; observed on 8 cases.\"\n },\n {\n \"id\": \"C2\",\n \"text\": \"Low repeated-sample agreement (majority share <= 0.4) is descriptively associated with task failure.\",\n \"status\": \"descriptive\",\n \"evidence_ids\": [\"chart:failrate_by_majority_share\", \"figure:scatter_consensus_vs_invariance\"],\n \"interpretation\": \"A candidate signal indicating that lack of generation consensus correlates with incorrect word sorting.\",\n \"do_not_infer\": \"Does not establish that sampling consensus alone causes correctness; requires confirmatory testing.\"\n },\n {\n \"id\": \"C3\",\n \"text\": \"Internal sentence inconsistency (> 0.0) is descriptively concentrated in failure cases.\",\n \"status\": \"descriptive\",\n \"evidence_ids\": [\"chart:failrate_by_selfcheck_inconsistency\"],\n \"interpretation\": \"A candidate signal indicating that detected reasoning contradictions coincide with failed answers.\",\n \"do_not_infer\": \"Does not prove internal contradiction caused the final output sorting mistake.\"\n }\n ],\n \"dashboard_storyboard\": [\n {\n \"id\": \"problem_setting\",\n \"title\": \"Problem Setting\",\n \"summary\": \"This run studies Gemma 4 E2B-it on the Big-Bench Hard word sorting task, comparing cases where the model sorted words incorrectly (FAIL) against those where it succeeded (PASS).\",\n \"items\": [\n \"User question: which per-case diagnostic signals distinguish FAIL cases from PASS cases?\",\n \"Unit of analysis: one row is one evaluated BBH word sorting case.\",\n \"Outcome definition: FAIL means the model's sorted word list did not match the gold alphabetical order; PASS means it matched exactly.\",\n \"Dataset scale: 64 total cases evaluated (37 FAIL, 27 PASS); 8 cases contain full M1 diagnostic analyzer measurements.\"\n ],\n \"artifact_refs\": [\"data_profile\"]\n }\n ],\n \"candidate_signals\": [\n {\n \"name\": \"high_invariance_break_rate\",\n \"display_name\": \"High Phrasing Fragility\",\n \"rationale\": \"Identifies cases where rephrasing the question caused the model to alter its answer at least two-thirds of the time.\",\n \"suggested_test\": \"Fisher's exact test on held-out cases comparing failure rates above and below break rate 0.80.\",\n \"recipe\": {\n \"name\": \"high_invariance_break_rate\",\n \"kind\": \"expr\",\n \"expr\": \"(perturbation_battery_invariance_break_rate >= 0.8)\"\n }\n },\n {\n \"name\": \"low_sampling_consensus\",\n \"display_name\": \"Low Sampling Consensus\",\n \"rationale\": \"Flags items where 5 independent generations yielded low consensus (majority share at or below 40%).\",\n \"suggested_test\": \"Two-group proportion test of failure rate among low-consensus versus high-consensus cases.\",\n \"recipe\": {\n \"name\": \"low_sampling_consensus\",\n \"kind\": \"expr\",\n \"expr\": \"(coverage_verification_gap_majority_share <= 0.4)\"\n }\n },\n {\n \"name\": \"reasoning_inconsistency_detected\",\n \"display_name\": \"Reasoning Inconsistency Detected\",\n \"rationale\": \"Detects items where the worst-sentence self-check inconsistency score is strictly greater than zero.\",\n \"suggested_test\": \"Contingency table test of failure prevalence between consistent and inconsistent chains of thought.\",\n \"recipe\": {\n \"name\": \"reasoning_inconsistency_detected\",\n \"kind\": \"expr\",\n \"expr\": \"(selfcheck_consistency_selfcheck_worst_sentence > 0.0)\"\n }\n },\n {\n \"name\": \"fragile_or_discordant_composite\",\n \"display_name\": \"Composite: Fragile Phrasing or Low Consensus\",\n \"rationale\": \"Combines prompt brittleness and sampling dispersion into a single threshold-explicit failure indicator.\",\n \"suggested_test\": \"Diagnostic sensitivity and specificity comparison against gold failure labels on a held-out split.\",\n \"recipe\": {\n \"name\": \"fragile_or_discordant_composite\",\n \"kind\": \"expr\",\n \"expr\": \"(perturbation_battery_invariance_break_rate >= 0.8) or (coverage_verification_gap_majority_share <= 0.4)\"\n }\n }\n ],\n \"plots\": [\n \"figures/top_discriminators_forest.png\",\n \"figures/signal_distributions_strip.png\",\n \"figures/scatter_consensus_vs_invariance.png\",\n \"figures/corr_heatmap.png\"\n ],\n \"tables\": {},\n \"charts\": [\n {\n \"name\": \"class_balance\",\n \"kind\": \"bar\",\n \"display_name\": \"Overall FAIL and PASS Case Counts\",\n \"data\": \"tables/class_balance.csv\",\n \"x\": \"outcome\",\n \"y\": \"count\",\n \"title\": \"FAIL vs PASS Case Balance\"\n },\n {\n \"name\": \"top_discriminators\",\n \"kind\": \"forest\",\n \"display_name\": \"Ranked Signal Separation\",\n \"data\": \"tables/top_discriminators.csv\",\n \"x\": \"signal\",\n \"y\": \"smd\",\n \"title\": \"Top FAIL/PASS Discriminators\"\n },\n {\n \"name\": \"failrate_by_invariance_break\",\n \"kind\": \"line\",\n \"display_name\": \"Failure Rate by Invariance Break Rate\",\n \"data\": \"tables/failrate_by_invariance_break.csv\",\n \"x\": \"invariance_break_bin\",\n \"y\": \"fail_rate\",\n \"title\": \"Fail Rate by Invariance Break Rate\"\n },\n {\n \"name\": \"failrate_by_majority_share\",\n \"kind\": \"line\",\n \"display_name\": \"Failure Rate by Sampling Consensus\",\n \"data\": \"tables/failrate_by_majority_share.csv\",\n \"x\": \"majority_share_bin\",\n \"y\": \"fail_rate\",\n \"title\": \"Fail Rate by Majority Share\"\n },\n {\n \"name\": \"failrate_by_selfcheck_inconsistency\",\n \"kind\": \"line\",\n \"display_name\": \"Failure Rate by Reasoning Contradiction\",\n \"data\": \"tables/failrate_by_selfcheck_inconsistency.csv\",\n \"x\": \"selfcheck_worst_bin\",\n \"y\": \"fail_rate\",\n \"title\": \"Fail Rate by Reasoning Inconsistency\"\n },\n {\n \"name\": \"scatter_agreement_vs_invariance\",\n \"kind\": \"scatter\",\n \"display_name\": \"Consensus vs Invariance Scatter\",\n \"data\": \"tables/scatter_agreement_vs_invariance.csv\",\n \"x\": \"majority_share\",\n \"y\": \"invariance_break_rate\",\n \"title\": \"Consensus vs Invariance Scatter\"\n },\n {\n \"name\": \"failrate_by_output_chars\",\n \"kind\": \"line\",\n \"display_name\": \"Failure Rate by Output Length\",\n \"data\": \"tables/failrate_by_output_chars.csv\",\n \"x\": \"output_chars_bin\",\n \"y\": \"fail_rate\",\n \"title\": \"Fail Rate by Output Length\"\n }\n ],\n \"caveats\": [\n \"M1 diagnostic analyzer columns are populated for 8 of the 64 cases; 56 cases contain label-only information.\",\n \"All candidate thresholds were identified on this exploration sample and must be re-evaluated verbatim on held-out cases.\",\n \"Findings represent descriptive associations within this single task slice (BBH word sorting) and may not generalize to other reasoning tasks.\"\n ],\n \"critique\": [\n \"Sample size for signal comparison is small (n=8: 5 FAIL, 3 PASS), which widens uncertainty around effect size magnitudes.\",\n \"Threshold selection carries potential double-dipping risk if treated as confirmed rather than candidate screening recipes.\",\n \"Several diagnostic columns (such as gave_up and extraction_point_miss) were constant across all measured cases and could not be evaluated as discriminators.\"\n ],\n \"recommended_confirmatory_tests\": [\n \"Compute candidate recipes verbatim on the remaining 56 unmeasured cases once analyzer signals are extracted.\",\n \"Evaluate Fisher's exact test of independence between the composite signal (break_rate >= 0.8 or majority_share <= 0.4) and the gold FAIL/PASS outcome on a fresh held-out evaluation batch.\"\n ]\n }\n \n print(\"EXPLORATORY_RESULT_JSON=\" + json.dumps(result))\n\nif __name__ == '__main__':\n main()\n", + "cycle": 0, + "duration_sec": 73.944, + "event": "explore", + "event_seq": 387, + "figures": [ + "M2/artifacts/00_class_balance_1e8aff09018f.png", + "M2/artifacts/01_top_discriminators_dd5bd2b6a2c2.png", + "M2/artifacts/02_failrate_by_invariance_break_5e2ba9931cce.png", + "M2/artifacts/03_failrate_by_majority_share_6f6305f7313e.png", + "M2/artifacts/04_failrate_by_selfcheck_inconsistency_b69f924533fb.png", + "M2/artifacts/05_scatter_agreement_vs_invariance_c4ff454cc6d8.png", + "M2/artifacts/06_failrate_by_output_chars_4ec9c30acdbf.png", + "M2/artifacts/corr_heatmap_b4ee85533fb3.png", + "M2/artifacts/scatter_consensus_vs_invariance_9db056488a46.png", + "M2/artifacts/signal_distributions_strip_246e4ab2972b.png", + "M2/artifacts/top_discriminators_forest_50a42c9a0d47.png" + ], + "n_candidate_signals": 4, + "n_charts": 7, + "n_charts_rendered": 11, + "n_hypotheses": 0, + "n_observations": 7, + "n_tables": 0, + "observations": [ + "Dataset comprises 64 evaluated word sorting items from BBH, with 37 failures (57.8%) and 27 passes (42.2%).", + "M1 diagnostic analyzer signals were collected on an exploratory diagnostic sub-sample of 8 items (5 FAIL, 3 PASS), while the remaining 56 rows contain outcome labels only.", + "Across the 8 analyzed items, the perturbation battery invariance break rate was noticeably higher in FAIL cases (mean 0.93, with 4 of 5 cases at 1.0) compared to PASS cases (mean 0.56, all <= 0.67).", + "Sampling majority agreement share separated cases cleanly: all 3 PASS cases had majority agreement of 60% or higher, whereas 4 of 5 FAIL cases had majority agreement of 40% or lower.", + "Truncation and tag omissions occurred exclusively in FAIL cases (2 of 5 failures had truncated outputs and missing answer tags, whereas 0 of 3 passes did).", + "Selfcheck worst sentence contradiction scores were zero in all 3 PASS cases, but positive in 4 of 5 FAIL cases.", + "Model confidence (calibration logprob) remained high across both groups (PASS mean 0.960, FAIL mean 0.980), showing model overconfidence even on failed word sorting runs." + ], + "ok": true, + "raw_outputs": [ + "The exploratory analysis script has been written to [analysis.py](file:///tmp/evalrx-logs-48lc0s2h/explore/sandbox/analysis.py).\n\n### Summary of Analysis Implementation\n1. **Data Intake & Subsetting**:\n - Ingests `records.json` consisting of 64 total evaluated word sorting cases (37 FAIL, 27 PASS).\n - Handles the 8-case diagnostic slice where M1 analyzer signals were computed (5 FAIL, 3 PASS), while documenting missingness across the remaining 56 cases.\n\n2. **Visual Plan & Aggregated Tables**:\n - Generates pre-aggregated CSV tables under `tables/` conforming to host rendering rules (class composition, binned failure-rate lines, ranked forest table, and multi-variable scatter).\n - Generates publication-quality figure plots under `figures/`:\n - Forest plot of top discriminators sorted by standardized separation (`figures/top_discriminators_forest.png`)\n - Strip/distribution plot comparing key signals across FAIL and PASS (`figures/signal_distributions_strip.png`)\n - 2D interaction scatter comparing sampling consensus vs phrasing fragility (`figures/scatter_consensus_vs_invariance.png`)\n - Correlation heatmap among numerical diagnostic signals (`figures/corr_heatmap.png`)\n\n3. **Takeaways & Frozen Threshold Recipes**:\n - Provides plain-language headlines and companion technical sentences strictly avoiding causal claims and adhering to descriptive evidence.\n - Formulates frozen, threshold-explicit candidate recipes for held-out confirmatory evaluation:\n - `high_invariance_break_rate`: `(perturbation_battery_invariance_break_rate >= 0.8)`\n - `low_sampling_consensus`: `(coverage_verification_gap_majority_share <= 0.4)`\n - `reasoning_inconsistency_detected`: `(selfcheck_consistency_selfcheck_worst_sentence > 0.0)`\n - `fragile_or_discordant_composite`: `(perturbation_battery_invariance_break_rate >= 0.8) or (coverage_verification_gap_majority_share <= 0.4)`\n - Emits the complete machine-readable `EXPLORATORY_RESULT_JSON` as the final stdout line.\n" + ], + "schema_version": 6, + "span_id": "c0.explore", + "stage": "M2", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:39:30.017096+00:00", + "workspace_snapshot": { + "files": { + "agent_audit.json": "{\n \"schema_version\": 1,\n \"attempts\": [\n {\n \"schema_version\": 1,\n \"provider\": \"antigravity\",\n \"skills_requested\": [\n \"eval-chart-style\",\n \"evalrx-report-ui\",\n \"nature-figure\",\n \"outcome-driver-analysis\"\n ],\n \"skills_installed\": [\n \"eval-chart-style\",\n \"evalrx-report-ui\",\n \"nature-figure\",\n \"outcome-driver-analysis\"\n ],\n \"skills_invoked\": [],\n \"skill_observability\": \"not_observable\",\n \"evidence\": [],\n \"execution\": {\n \"status\": \"completed\",\n \"returncode\": 0,\n \"timed_out\": false,\n \"elapsed_sec\": 71.379,\n \"produced_files\": [\n \"analysis.py\"\n ],\n \"error\": \"\",\n \"stderr\": \"\",\n \"raw_stream\": \"agent_raw_stream.txt\"\n }\n }\n ]\n}", + "agent_raw_output.txt": "The exploratory analysis script has been written to [analysis.py](file:///tmp/evalrx-logs-48lc0s2h/explore/sandbox/analysis.py).\n\n### Summary of Analysis Implementation\n1. **Data Intake & Subsetting**:\n - Ingests `records.json` consisting of 64 total evaluated word sorting cases (37 FAIL, 27 PASS).\n - Handles the 8-case diagnostic slice where M1 analyzer signals were computed (5 FAIL, 3 PASS), while documenting missingness across the remaining 56 cases.\n\n2. **Visual Plan & Aggregated Tables**:\n - Generates pre-aggregated CSV tables under `tables/` conforming to host rendering rules (class composition, binned failure-rate lines, ranked forest table, and multi-variable scatter).\n - Generates publication-quality figure plots under `figures/`:\n - Forest plot of top discriminators sorted by standardized separation (`figures/top_discriminators_forest.png`)\n - Strip/distribution plot comparing key signals across FAIL and PASS (`figures/signal_distributions_strip.png`)\n - 2D interaction scatter comparing sampling consensus vs phrasing fragility (`figures/scatter_consensus_vs_invariance.png`)\n - Correlation heatmap among numerical diagnostic signals (`figures/corr_heatmap.png`)\n\n3. **Takeaways & Frozen Threshold Recipes**:\n - Provides plain-language headlines and companion technical sentences strictly avoiding causal claims and adhering to descriptive evidence.\n - Formulates frozen, threshold-explicit candidate recipes for held-out confirmatory evaluation:\n - `high_invariance_break_rate`: `(perturbation_battery_invariance_break_rate >= 0.8)`\n - `low_sampling_consensus`: `(coverage_verification_gap_majority_share <= 0.4)`\n - `reasoning_inconsistency_detected`: `(selfcheck_consistency_selfcheck_worst_sentence > 0.0)`\n - `fragile_or_discordant_composite`: `(perturbation_battery_invariance_break_rate >= 0.8) or (coverage_verification_gap_majority_share <= 0.4)`\n - Emits the complete machine-readable `EXPLORATORY_RESULT_JSON` as the final stdout line.\n", + "agent_raw_streams.txt": "The exploratory analysis script has been written to [analysis.py](file:///tmp/evalrx-logs-48lc0s2h/explore/sandbox/analysis.py).\n\n### Summary of Analysis Implementation\n1. **Data Intake & Subsetting**:\n - Ingests `records.json` consisting of 64 total evaluated word sorting cases (37 FAIL, 27 PASS).\n - Handles the 8-case diagnostic slice where M1 analyzer signals were computed (5 FAIL, 3 PASS), while documenting missingness across the remaining 56 cases.\n\n2. **Visual Plan & Aggregated Tables**:\n - Generates pre-aggregated CSV tables under `tables/` conforming to host rendering rules (class composition, binned failure-rate lines, ranked forest table, and multi-variable scatter).\n - Generates publication-quality figure plots under `figures/`:\n - Forest plot of top discriminators sorted by standardized separation (`figures/top_discriminators_forest.png`)\n - Strip/distribution plot comparing key signals across FAIL and PASS (`figures/signal_distributions_strip.png`)\n - 2D interaction scatter comparing sampling consensus vs phrasing fragility (`figures/scatter_consensus_vs_invariance.png`)\n - Correlation heatmap among numerical diagnostic signals (`figures/corr_heatmap.png`)\n\n3. **Takeaways & Frozen Threshold Recipes**:\n - Provides plain-language headlines and companion technical sentences strictly avoiding causal claims and adhering to descriptive evidence.\n - Formulates frozen, threshold-explicit candidate recipes for held-out confirmatory evaluation:\n - `high_invariance_break_rate`: `(perturbation_battery_invariance_break_rate >= 0.8)`\n - `low_sampling_consensus`: `(coverage_verification_gap_majority_share <= 0.4)`\n - `reasoning_inconsistency_detected`: `(selfcheck_consistency_selfcheck_worst_sentence > 0.0)`\n - `fragile_or_discordant_composite`: `(perturbation_battery_invariance_break_rate >= 0.8) or (coverage_verification_gap_majority_share <= 0.4)`\n - Emits the complete machine-readable `EXPLORATORY_RESULT_JSON` as the final stdout line.\n", + "analysis.py": "\"\"\"\nExploratory data analysis of Gemma 4 E2B-it on BBH word_sorting cases.\nDistinguishes FAIL cases from PASS cases using recorded M1 analyzer signals.\n\"\"\"\nimport json\nimport os\nimport math\nimport numpy as np\nimport pandas as pd\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\n\ndef main():\n # Load records\n with open('records.json', 'r') as f:\n records = json.load(f)\n \n df = pd.DataFrame(records)\n n_total = len(df)\n n_pass_total = int((df['label'] == 'pass').sum())\n n_fail_total = int((df['label'] == 'fail').sum())\n \n # Subsets\n df_analyzed = df.dropna(subset=['calibration_correct']).copy()\n n_analyzed = len(df_analyzed)\n n_fail_analyzed = int((df_analyzed['label'] == 'fail').sum())\n n_pass_analyzed = int((df_analyzed['label'] == 'pass').sum())\n \n os.makedirs('tables', exist_ok=True)\n os.makedirs('figures', exist_ok=True)\n \n # Palette\n C_FAIL = \"#D95F02\"\n C_PASS = \"#1B9E77\"\n \n # -------------------------------------------------------------\n # 1. Class Balance Table & CSV\n # -------------------------------------------------------------\n cb_df = pd.DataFrame([\n {\"outcome\": \"FAIL\", \"count\": n_fail_total, \"pct\": round(n_fail_total / n_total * 100, 1)},\n {\"outcome\": \"PASS\", \"count\": n_pass_total, \"pct\": round(n_pass_total / n_total * 100, 1)}\n ])\n cb_df.to_csv('tables/class_balance.csv', index=False)\n \n # -------------------------------------------------------------\n # 2. Key Numeric Signals & Discrimination\n # -------------------------------------------------------------\n signals = [\n 'calibration_correct',\n 'perturbation_battery_invariance_break_rate',\n 'coverage_verification_gap_majority_share',\n 'coverage_verification_gap_n_unique',\n 'selfcheck_consistency_selfcheck_worst_sentence',\n 'selfcheck_consistency_selfcheck_inconsistency',\n 'selfcheck_consistency_n_sentences',\n 'termination_audit_output_chars',\n 'termination_audit_output_words',\n 'termination_audit_repetition_score',\n 'calibration_conf_logprob',\n 'perturbation_battery_restate_question_flipped',\n 'perturbation_battery_noop_clause_flipped',\n 'termination_audit_looks_truncated'\n ]\n \n disc_rows = []\n for sig in signals:\n f_vals = df_analyzed[df_analyzed['label'] == 'fail'][sig].dropna()\n p_vals = df_analyzed[df_analyzed['label'] == 'pass'][sig].dropna()\n if len(f_vals) > 0 and len(p_vals) > 0:\n m_f = f_vals.mean()\n m_p = p_vals.mean()\n s_pooled = math.sqrt((f_vals.var() + p_vals.var()) / 2.0) if (f_vals.var() + p_vals.var()) > 0 else 1.0\n diff = m_f - m_p\n smd = abs(diff) / s_pooled if s_pooled > 0 else 0.0\n disc_rows.append({\n \"signal\": sig,\n \"mean_fail\": round(m_f, 4),\n \"mean_pass\": round(m_p, 4),\n \"diff\": round(diff, 4),\n \"smd\": round(smd, 3)\n })\n disc_df = pd.DataFrame(disc_rows).sort_values(by=\"smd\", ascending=False)\n disc_df.to_csv('tables/top_discriminators.csv', index=False)\n \n # -------------------------------------------------------------\n # 3. Binned / Discrete Curve Tables\n # -------------------------------------------------------------\n # 3a. Invariance break rate curve\n # Values are 0.3333, 0.6667, 1.0\n invar_groups = []\n for val, name in [(0.3333, \"Low (0.33)\"), (0.6667, \"Mid (0.67)\"), (1.0, \"High (1.00)\")]:\n sub = df_analyzed[np.isclose(df_analyzed['perturbation_battery_invariance_break_rate'], val, atol=0.05)]\n n_sub = len(sub)\n n_f = int((sub['label'] == 'fail').sum())\n rate = n_f / n_sub if n_sub > 0 else 0.0\n invar_groups.append({\n \"invariance_break_bin\": name,\n \"fail_rate\": round(rate, 3),\n \"n\": n_sub,\n \"n_fail\": n_f\n })\n pd.DataFrame(invar_groups).to_csv('tables/failrate_by_invariance_break.csv', index=False)\n \n # 3b. Consistency majority share curve\n # Bins: <=0.4 (low agreement), 0.6-0.8 (moderate), 1.0 (perfect)\n cov_groups = []\n sub_low = df_analyzed[df_analyzed['coverage_verification_gap_majority_share'] <= 0.4]\n sub_mid = df_analyzed[(df_analyzed['coverage_verification_gap_majority_share'] > 0.4) & (df_analyzed['coverage_verification_gap_majority_share'] < 1.0)]\n sub_hi = df_analyzed[df_analyzed['coverage_verification_gap_majority_share'] == 1.0]\n for sub, name in [(sub_low, \"<= 0.4 (Low agreement)\"), (sub_mid, \"0.6 - 0.8 (Moderate)\"), (sub_hi, \"1.0 (Unanimous)\")]:\n n_sub = len(sub)\n n_f = int((sub['label'] == 'fail').sum())\n rate = n_f / n_sub if n_sub > 0 else 0.0\n cov_groups.append({\n \"majority_share_bin\": name,\n \"fail_rate\": round(rate, 3),\n \"n\": n_sub,\n \"n_fail\": n_f\n })\n pd.DataFrame(cov_groups).to_csv('tables/failrate_by_majority_share.csv', index=False)\n \n # 3c. Output length curve (tertiles: short <1000, medium 1000-1200, long >1200)\n len_groups = []\n sub_s = df_analyzed[df_analyzed['termination_audit_output_chars'] < 1000]\n sub_m = df_analyzed[(df_analyzed['termination_audit_output_chars'] >= 1000) & (df_analyzed['termination_audit_output_chars'] <= 1200)]\n sub_l = df_analyzed[df_analyzed['termination_audit_output_chars'] > 1200]\n for sub, name in [(sub_s, \"< 1000 chars\"), (sub_m, \"1000-1200 chars\"), (sub_l, \"> 1200 chars\")]:\n n_sub = len(sub)\n n_f = int((sub['label'] == 'fail').sum())\n rate = n_f / n_sub if n_sub > 0 else 0.0\n len_groups.append({\n \"output_chars_bin\": name,\n \"fail_rate\": round(rate, 3),\n \"n\": n_sub,\n \"n_fail\": n_f\n })\n pd.DataFrame(len_groups).to_csv('tables/failrate_by_output_chars.csv', index=False)\n \n # 3d. Selfcheck worst sentence inconsistency curve (0.0 vs > 0.0)\n sc_groups = []\n sub_zero = df_analyzed[df_analyzed['selfcheck_consistency_selfcheck_worst_sentence'] == 0.0]\n sub_pos = df_analyzed[df_analyzed['selfcheck_consistency_selfcheck_worst_sentence'] > 0.0]\n for sub, name in [(sub_zero, \"0.0 (No contradiction)\"), (sub_pos, \"> 0.0 (Contradictions present)\")]:\n n_sub = len(sub)\n n_f = int((sub['label'] == 'fail').sum())\n rate = n_f / n_sub if n_sub > 0 else 0.0\n sc_groups.append({\n \"selfcheck_worst_bin\": name,\n \"fail_rate\": round(rate, 3),\n \"n\": n_sub,\n \"n_fail\": n_f\n })\n pd.DataFrame(sc_groups).to_csv('tables/failrate_by_selfcheck_inconsistency.csv', index=False)\n\n # 3e. Scatter table: majority share vs invariance break rate\n scatter_df = df_analyzed[['case_id', 'label', 'coverage_verification_gap_majority_share', 'perturbation_battery_invariance_break_rate']].copy()\n scatter_df.columns = ['case_id', 'label', 'majority_share', 'invariance_break_rate']\n scatter_df.to_csv('tables/scatter_agreement_vs_invariance.csv', index=False)\n \n # -------------------------------------------------------------\n # 4. Rich PNG Plots\n # -------------------------------------------------------------\n # Plot 1: Forest plot of top discriminators\n plt.figure(figsize=(8, 4.5), dpi=300)\n top_disc = disc_df.head(8).sort_values(by=\"smd\", ascending=True)\n y_pos = np.arange(len(top_disc))\n plt.hlines(y=y_pos, xmin=0, xmax=top_disc['smd'], color=\"#8da0cb\", alpha=0.7, linewidth=2)\n plt.scatter(top_disc['smd'], y_pos, color=\"#2b5c8f\", s=80, zorder=3)\n plt.yticks(y_pos, [s.replace('_', ' ') for s in top_disc['signal']], fontsize=9)\n plt.xlabel(\"Standardized Separation (|Mean FAIL - Mean PASS| / Pooled SD)\", fontsize=10)\n plt.title(\"Top Per-Case Signals Separating FAIL from PASS (n=8)\", fontsize=11, fontweight='bold', loc='left')\n plt.grid(axis='x', linestyle='--', alpha=0.5)\n plt.tight_layout()\n plt.savefig('figures/top_discriminators_forest.png')\n plt.close()\n \n # Plot 2: Strip/Jitter comparison of key signals\n fig, axes = plt.subplots(1, 3, figsize=(11, 4), dpi=300)\n plot_signals = [\n ('perturbation_battery_invariance_break_rate', 'Invariance Break Rate', axes[0]),\n ('coverage_verification_gap_majority_share', 'Sample Agreement Share', axes[1]),\n ('selfcheck_consistency_selfcheck_worst_sentence', 'Worst Sentence Inconsistency', axes[2])\n ]\n for col, title, ax in plot_signals:\n for lbl, color, offset in [('pass', C_PASS, -0.15), ('fail', C_FAIL, 0.15)]:\n vals = df_analyzed[df_analyzed['label'] == lbl][col]\n jitter = np.random.normal(0, 0.03, size=len(vals))\n ax.scatter(np.zeros(len(vals)) + offset + jitter, vals, color=color, label=lbl.upper(), s=60, alpha=0.85, edgecolors='black', linewidth=0.5)\n # Plot mean line\n ax.plot([offset - 0.08, offset + 0.08], [vals.mean(), vals.mean()], color=color, linewidth=2.5)\n ax.set_xticks([-0.15, 0.15])\n ax.set_xticklabels(['PASS (n=3)', 'FAIL (n=5)'], fontweight='bold')\n ax.set_title(title, fontsize=10, fontweight='bold')\n ax.grid(axis='y', linestyle='--', alpha=0.4)\n axes[0].set_ylabel(\"Signal Value\", fontsize=10)\n plt.suptitle(\"Distribution of Leading Diagnostic Signals across Outcomes\", fontsize=12, fontweight='bold', y=1.02)\n plt.tight_layout()\n plt.savefig('figures/signal_distributions_strip.png')\n plt.close()\n\n # Plot 3: 2D Scatter of Stability vs Consensus\n plt.figure(figsize=(6.5, 5), dpi=300)\n for lbl, color in [('pass', C_PASS), ('fail', C_FAIL)]:\n sub = df_analyzed[df_analyzed['label'] == lbl]\n plt.scatter(\n sub['coverage_verification_gap_majority_share'],\n sub['perturbation_battery_invariance_break_rate'],\n color=color, label=f\"{lbl.upper()} (n={len(sub)})\",\n s=90, alpha=0.85, edgecolors='black', linewidth=0.6\n )\n # Decision boundary line candidate: majority_share <= 0.4 or invariance >= 0.8\n plt.axvline(0.5, color='gray', linestyle=':', label='Threshold: Majority Share = 0.5')\n plt.xlabel(\"Sampling Agreement Share (Higher = More Consistent Answers)\", fontsize=10)\n plt.ylabel(\"Invariance Break Rate (Higher = More Fragile to Phrasing)\", fontsize=10)\n plt.title(\"Interaction: Sampling Consensus vs. Phrasing Fragility\", fontsize=11, fontweight='bold', loc='left')\n plt.legend(frameon=True, facecolor='white', loc='upper right')\n plt.grid(True, linestyle='--', alpha=0.4)\n plt.tight_layout()\n plt.savefig('figures/scatter_consensus_vs_invariance.png')\n plt.close()\n\n # Plot 4: Correlation Heatmap among numerical signals\n corr_cols = [\n 'perturbation_battery_invariance_break_rate',\n 'coverage_verification_gap_majority_share',\n 'coverage_verification_gap_n_unique',\n 'selfcheck_consistency_selfcheck_worst_sentence',\n 'termination_audit_output_chars',\n 'calibration_conf_logprob'\n ]\n corr_mat = df_analyzed[corr_cols].corr()\n plt.figure(figsize=(6.5, 5.5), dpi=300)\n plt.imshow(corr_mat, cmap='coolwarm', vmin=-1, vmax=1)\n plt.colorbar(fraction=0.046, pad=0.04)\n col_labels = [c.split('_')[-2] + '_' + c.split('_')[-1] for c in corr_cols]\n plt.xticks(np.arange(len(corr_cols)), col_labels, rotation=45, ha='right', fontsize=9)\n plt.yticks(np.arange(len(corr_cols)), col_labels, fontsize=9)\n for i in range(len(corr_cols)):\n for j in range(len(corr_cols)):\n text = f\"{corr_mat.iloc[i, j]:.2f}\"\n plt.text(j, i, text, ha='center', va='center', color='black' if abs(corr_mat.iloc[i, j]) < 0.6 else 'white', fontsize=8)\n plt.title(\"Correlation Matrix of Numeric Diagnostic Signals\", fontsize=11, fontweight='bold', loc='left')\n plt.tight_layout()\n plt.savefig('figures/corr_heatmap.png')\n plt.close()\n\n # -------------------------------------------------------------\n # 5. Output JSON assembly\n # -------------------------------------------------------------\n result = {\n \"plain_question\": \"Which recorded case measurements tell apart questions the language model sorted wrong from those it sorted right?\",\n \"observations\": [\n \"Dataset comprises 64 evaluated word sorting items from BBH, with 37 failures (57.8%) and 27 passes (42.2%).\",\n \"M1 diagnostic analyzer signals were collected on an exploratory diagnostic sub-sample of 8 items (5 FAIL, 3 PASS), while the remaining 56 rows contain outcome labels only.\",\n \"Across the 8 analyzed items, the perturbation battery invariance break rate was noticeably higher in FAIL cases (mean 0.93, with 4 of 5 cases at 1.0) compared to PASS cases (mean 0.56, all <= 0.67).\",\n \"Sampling majority agreement share separated cases cleanly: all 3 PASS cases had majority agreement of 60% or higher, whereas 4 of 5 FAIL cases had majority agreement of 40% or lower.\",\n \"Truncation and tag omissions occurred exclusively in FAIL cases (2 of 5 failures had truncated outputs and missing answer tags, whereas 0 of 3 passes did).\",\n \"Selfcheck worst sentence contradiction scores were zero in all 3 PASS cases, but positive in 4 of 5 FAIL cases.\",\n \"Model confidence (calibration logprob) remained high across both groups (PASS mean 0.960, FAIL mean 0.980), showing model overconfidence even on failed word sorting runs.\"\n ],\n \"visual_plan\": [\n {\n \"name\": \"class_balance\",\n \"display_name\": \"Overall FAIL and PASS Case Counts\",\n \"question\": \"What is the overall balance between incorrect and correct sorting cases in this batch?\",\n \"data_shape\": \"categorical\",\n \"plot_kind\": \"composition_strip\",\n \"fallback_kind\": \"bar\",\n \"required_columns\": [\"label\"],\n \"rationale\": \"A composition count shows the total cohort split (37 fail vs 27 pass) across the complete set of 64 cases.\",\n \"disposition\": \"primary\",\n \"not_promoted_reason\": \"\"\n },\n {\n \"name\": \"top_discriminators\",\n \"display_name\": \"Ranked Signal Separation\",\n \"question\": \"Which diagnostic signals show the largest differences between FAIL and PASS cases?\",\n \"data_shape\": \"many-numeric\",\n \"plot_kind\": \"forest\",\n \"fallback_kind\": \"forest\",\n \"required_columns\": [\"label\"],\n \"rationale\": \"Ranked standardized mean difference highlights which indicators provide the strongest contrast without assuming normal distributions.\",\n \"disposition\": \"primary\",\n \"not_promoted_reason\": \"\"\n },\n {\n \"name\": \"failrate_by_invariance_break\",\n \"display_name\": \"Failure Rate by Invariance Break Rate\",\n \"question\": \"How does prompt fragility to minor phrasing changes correspond to task failure?\",\n \"data_shape\": \"numeric-vs-binary\",\n \"plot_kind\": \"line\",\n \"fallback_kind\": \"line\",\n \"required_columns\": [\"perturbation_battery_invariance_break_rate\", \"label\"],\n \"rationale\": \"Ordered discrete levels of perturbation instability display failure concentration at high sensitivity levels.\",\n \"disposition\": \"primary\",\n \"not_promoted_reason\": \"\"\n },\n {\n \"name\": \"failrate_by_majority_share\",\n \"display_name\": \"Failure Rate by Sampling Consensus\",\n \"question\": \"Does agreement across repeated samples distinguish correct answers from errors?\",\n \"data_shape\": \"numeric-vs-binary\",\n \"plot_kind\": \"line\",\n \"fallback_kind\": \"line\",\n \"required_columns\": [\"coverage_verification_gap_majority_share\", \"label\"],\n \"rationale\": \"Binned repeated-sample consensus rates reveal how self-consistency drops when the model fails.\",\n \"disposition\": \"primary\",\n \"not_promoted_reason\": \"\"\n },\n {\n \"name\": \"failrate_by_selfcheck_inconsistency\",\n \"display_name\": \"Failure Rate by Reasoning Contradiction\",\n \"question\": \"Are internal chain-of-thought contradictions associated with incorrect final sorting?\",\n \"data_shape\": \"numeric-vs-binary\",\n \"plot_kind\": \"line\",\n \"fallback_kind\": \"line\",\n \"required_columns\": [\"selfcheck_consistency_selfcheck_worst_sentence\", \"label\"],\n \"rationale\": \"Separates cases with completely consistent reasoning traces from those with detected sentence-level inconsistencies.\",\n \"disposition\": \"primary\",\n \"not_promoted_reason\": \"\"\n },\n {\n \"name\": \"scatter_agreement_vs_invariance\",\n \"display_name\": \"Consensus vs Invariance Scatter\",\n \"question\": \"How do sampling consensus and prompt sensitivity jointly separate correct and incorrect items?\",\n \"data_shape\": \"numeric-vs-numeric\",\n \"plot_kind\": \"scatter\",\n \"fallback_kind\": \"scatter\",\n \"required_columns\": [\"coverage_verification_gap_majority_share\", \"perturbation_battery_invariance_break_rate\", \"label\"],\n \"rationale\": \"A 2D scatter visually inspects whether combining consistency and prompt stability creates an unambiguous boundary between outcomes.\",\n \"disposition\": \"primary\",\n \"not_promoted_reason\": \"\"\n },\n {\n \"name\": \"failrate_by_output_chars\",\n \"display_name\": \"Failure Rate by Output Length\",\n \"question\": \"Do unusually long or short responses correspond to higher error rates?\",\n \"data_shape\": \"numeric-vs-binary\",\n \"plot_kind\": \"line\",\n \"fallback_kind\": \"line\",\n \"required_columns\": [\"termination_audit_output_chars\", \"label\"],\n \"rationale\": \"Tertiles of response character length check whether verbosity or truncation flags correlate with failure.\",\n \"disposition\": \"supporting\",\n \"not_promoted_reason\": \"Output length exhibits non-monotonic differences driven by truncated outliers rather than a stable linear trend.\"\n }\n ],\n \"takeaways\": [\n {\n \"plain_title\": \"Items where rephrasing the question caused the model to change its answer failed almost every time.\",\n \"title\": \"A perturbation invariance break rate of 1.0 occurred in 4 of 5 FAIL cases (80%), compared with 0 of 3 PASS cases (0%).\",\n \"chart_names\": [\"failrate_by_invariance_break\", \"top_discriminators\"],\n \"table_names\": [\"failrate_by_invariance_break\", \"top_discriminators\"],\n \"analysis\": \"In cases where minor question rewording altered the model's sorting sequence (break rate equal to 1.0), all 4 cases resulted in failures. By contrast, all 3 passing cases maintained break rates of 0.67 or lower. This shows that brittleness to prompt wording strongly separates failures from successes in this batch, though the small analyzed sample of 8 cases means exact error rates require validation on larger batches.\",\n \"caveat\": \"Descriptive pattern evaluated on 8 diagnostic rows; prompt sensitivity may also reflect overall question difficulty rather than an isolated cause.\"\n },\n {\n \"plain_title\": \"When repeated attempts produced conflicting answers, the model was consistently wrong.\",\n \"title\": \"Low majority agreement (40% or below across 5 samples) captured 4 of 5 FAIL cases (80%) and 0 of 3 PASS cases (0%).\",\n \"chart_names\": [\"failrate_by_majority_share\", \"scatter_agreement_vs_invariance\"],\n \"table_names\": [\"failrate_by_majority_share\", \"scatter_agreement_vs_invariance\"],\n \"analysis\": \"The majority share among 5 sampled completions was 0.40 or lower for 4 out of 5 failed items, while all 3 successful items achieved majority agreement between 0.60 and 1.00. This matters because self-consistency sampling offers a clear practical signal of sorting failure. However, whether the lack of consensus is specific to word sorting remains unconfirmed until tested on other task types.\",\n \"caveat\": \"Based on 8 analyzed cases; threshold choice (majority share <= 0.4) was identified on this exploratory batch.\"\n },\n {\n \"plain_title\": \"Internal contradictions in the reasoning text appeared only in failure cases.\",\n \"title\": \"A non-zero sentence inconsistency score occurred in 4 of 5 FAIL cases (80%) versus 0 of 3 PASS cases (0%).\",\n \"chart_names\": [\"failrate_by_selfcheck_inconsistency\"],\n \"table_names\": [\"failrate_by_selfcheck_inconsistency\"],\n \"analysis\": \"Inspection of internal sentence consistency revealed that all 3 passing cases had zero detected contradictions (score 0.0), whereas 4 of 5 failing cases contained contradictory statements (scores ranging from 0.33 to 0.67). This indicates that the model frequently makes contradictory claims during intermediate reasoning when failing to sort words. It remains uncertain whether the contradiction directly leads to the wrong answer or is simply a byproduct of confusion.\",\n \"caveat\": \"Observed on 8 cases; sentence inconsistency detectors can suffer from false positives on long complex chains of thought.\"\n },\n {\n \"plain_title\": \"High model confidence does not guarantee correct sorting.\",\n \"title\": \"The model assigned high token probability (averaging 98.0%) even across the 5 failed sorting attempts.\",\n \"chart_names\": [\"top_discriminators\"],\n \"table_names\": [\"top_discriminators\"],\n \"analysis\": \"Across all 8 analyzed cases, the model's reported calibration logprob confidence remained above 0.94, with failures averaging 0.980 and passes averaging 0.960. This demonstrates that the model is severely overconfident when producing incorrect alphabetical sequences. Downstream systems cannot rely on raw token confidence alone to filter out incorrect word sorting outputs.\",\n \"caveat\": \"Token probability reflects next-token likelihood under the generation path rather than genuine correctness probability.\"\n },\n {\n \"plain_title\": \"The full dataset has a moderate error rate, but detailed diagnostics were captured on an eight-case slice.\",\n \"title\": \"Across all 64 evaluated cases, 37 failed (57.8%) and 27 passed (42.2%), while M1 signals were recorded on 8 cases.\",\n \"chart_names\": [\"class_balance\"],\n \"table_names\": [\"class_balance\"],\n \"analysis\": \"The overall dataset contains 64 cases, showing that word sorting is a challenging task for Gemma 4 E2B-it with a 57.8% failure rate (37 of 64). Detailed diagnostic signals were gathered on an 8-case subset (5 failures, 3 passes), while 56 cases retain label-only information. This establishes the overall task difficulty while highlighting that signal thresholds must be treated as candidates for wider confirmation.\",\n \"caveat\": \"The diagnostic subset of 8 cases may have different sampling properties than the remaining 56 unmeasured cases.\"\n }\n ],\n \"chart_readings\": {\n \"class_balance\": {\n \"chart\": \"Overall FAIL and PASS Case Counts\",\n \"reading\": \"Failures account for 37 of 64 cases (57.8%), while passes account for 27 of 64 cases (42.2%).\",\n \"do_not_infer\": \"This chart shows dataset composition; it does not measure signal performance.\"\n },\n \"top_discriminators\": {\n \"chart\": \"Ranked Signal Separation\",\n \"reading\": \"Perturbation invariance break rate, sample majority agreement share, and sentence inconsistency show the strongest standardized contrasts between the 5 fail and 3 pass cases.\",\n \"do_not_infer\": \"High separation on an exploratory batch of 8 cases does not guarantee equal predictive power on unmeasured cases.\"\n },\n \"failrate_by_invariance_break\": {\n \"chart\": \"Failure Rate by Invariance Break Rate\",\n \"reading\": \"Items with invariance break rates of 1.0 failed in 4 of 4 cases (100%), whereas items with lower break rates failed in 1 of 4 cases (25%).\",\n \"do_not_infer\": \"Prompt sensitivity does not prove rephrasing caused the failure; both may stem from underlying problem complexity.\"\n },\n \"failrate_by_majority_share\": {\n \"chart\": \"Failure Rate by Sampling Consensus\",\n \"reading\": \"Items where the top sample won 40% or less of repeated generations failed in 4 of 4 instances (100%), while items with 60% or higher agreement failed in 1 of 4 instances (25%).\",\n \"do_not_infer\": \"Low consensus reflects generation dispersion, not necessarily an inability to produce the right answer under different decoding parameters.\"\n },\n \"failrate_by_selfcheck_inconsistency\": {\n \"chart\": \"Failure Rate by Reasoning Contradiction\",\n \"reading\": \"All 4 cases with detected sentence inconsistencies resulted in failure (100%), compared to 1 failure out of 4 cases with zero inconsistency (25%).\",\n \"do_not_infer\": \"Internal inconsistency is a descriptive symptom of degraded reasoning, not a proven cause of the final answer error.\"\n },\n \"scatter_agreement_vs_invariance\": {\n \"chart\": \"Consensus vs Invariance Scatter\",\n \"reading\": \"Passing cases cluster in the high-consensus (>=0.6) and lower-fragility (<=0.67) quadrant, while 4 of 5 failures occupy the low-consensus or high-fragility region.\",\n \"do_not_infer\": \"A visual decision boundary on 8 points is exploratory and must be tested on held-out data.\"\n },\n \"failrate_by_output_chars\": {\n \"chart\": \"Failure Rate by Output Length\",\n \"reading\": \"Failure rates are elevated in both short responses (<1000 chars, 1 of 2 fail) and long responses (>1200 chars, 2 of 2 fail), with the longest responses including truncated failures.\",\n \"do_not_infer\": \"Response length does not linearly drive failure; length variation is partly influenced by truncation and verbosity.\"\n },\n \"top_discriminators_forest\": {\n \"chart\": \"Top Per-Case Signals Separating FAIL from PASS (Forest Plot)\",\n \"reading\": \"Invariance break rate, consensus majority share, and sentence-level inconsistency display the largest standardized differences between the 5 FAIL and 3 PASS cases.\",\n \"do_not_infer\": \"Standardized mean differences on 8 observations are exploratory estimates subject to wide sampling variance.\"\n },\n \"signal_distributions_strip\": {\n \"chart\": \"Distribution of Leading Diagnostic Signals across Outcomes (Strip Plot)\",\n \"reading\": \"Individual point markers highlight distinct separation in break rate, sample agreement, and worst sentence inconsistency between FAIL and PASS items.\",\n \"do_not_infer\": \"Small sample size (n=8) limits conclusions about distributional shape or tails.\"\n },\n \"scatter_consensus_vs_invariance\": {\n \"chart\": \"Interaction: Sampling Consensus vs. Phrasing Fragility\",\n \"reading\": \"Combining sampling consensus (majority share <= 0.4) with prompt fragility (invariance break rate >= 0.8) completely separates 4 of 5 failure cases from all passes.\",\n \"do_not_infer\": \"Thresholds chosen to separate this 8-case sample may exhibit optimism when applied to new cases.\"\n },\n \"corr_heatmap\": {\n \"chart\": \"Correlation Matrix of Numeric Diagnostic Signals\",\n \"reading\": \"Sampling consensus shares strong negative correlation with unique sample count and invariance break rate, indicating coupled dimensions of model uncertainty.\",\n \"do_not_infer\": \"Correlations among 8 cases indicate co-occurrence patterns, not causal dependencies.\"\n }\n },\n \"claims\": [\n {\n \"id\": \"C1\",\n \"text\": \"High prompt invariance break rate (>= 0.8) is descriptively associated with task failure.\",\n \"status\": \"descriptive\",\n \"evidence_ids\": [\"chart:failrate_by_invariance_break\", \"figure:signal_distributions_strip\"],\n \"interpretation\": \"A candidate signal indicating that sensitivity to prompt wording correlates with sorting mistakes.\",\n \"do_not_infer\": \"Does not prove causal sensitivity; observed on 8 cases.\"\n },\n {\n \"id\": \"C2\",\n \"text\": \"Low repeated-sample agreement (majority share <= 0.4) is descriptively associated with task failure.\",\n \"status\": \"descriptive\",\n \"evidence_ids\": [\"chart:failrate_by_majority_share\", \"figure:scatter_consensus_vs_invariance\"],\n \"interpretation\": \"A candidate signal indicating that lack of generation consensus correlates with incorrect word sorting.\",\n \"do_not_infer\": \"Does not establish that sampling consensus alone causes correctness; requires confirmatory testing.\"\n },\n {\n \"id\": \"C3\",\n \"text\": \"Internal sentence inconsistency (> 0.0) is descriptively concentrated in failure cases.\",\n \"status\": \"descriptive\",\n \"evidence_ids\": [\"chart:failrate_by_selfcheck_inconsistency\"],\n \"interpretation\": \"A candidate signal indicating that detected reasoning contradictions coincide with failed answers.\",\n \"do_not_infer\": \"Does not prove internal contradiction caused the final output sorting mistake.\"\n }\n ],\n \"dashboard_storyboard\": [\n {\n \"id\": \"problem_setting\",\n \"title\": \"Problem Setting\",\n \"summary\": \"This run studies Gemma 4 E2B-it on the Big-Bench Hard word sorting task, comparing cases where the model sorted words incorrectly (FAIL) against those where it succeeded (PASS).\",\n \"items\": [\n \"User question: which per-case diagnostic signals distinguish FAIL cases from PASS cases?\",\n \"Unit of analysis: one row is one evaluated BBH word sorting case.\",\n \"Outcome definition: FAIL means the model's sorted word list did not match the gold alphabetical order; PASS means it matched exactly.\",\n \"Dataset scale: 64 total cases evaluated (37 FAIL, 27 PASS); 8 cases contain full M1 diagnostic analyzer measurements.\"\n ],\n \"artifact_refs\": [\"data_profile\"]\n }\n ],\n \"candidate_signals\": [\n {\n \"name\": \"high_invariance_break_rate\",\n \"display_name\": \"High Phrasing Fragility\",\n \"rationale\": \"Identifies cases where rephrasing the question caused the model to alter its answer at least two-thirds of the time.\",\n \"suggested_test\": \"Fisher's exact test on held-out cases comparing failure rates above and below break rate 0.80.\",\n \"recipe\": {\n \"name\": \"high_invariance_break_rate\",\n \"kind\": \"expr\",\n \"expr\": \"(perturbation_battery_invariance_break_rate >= 0.8)\"\n }\n },\n {\n \"name\": \"low_sampling_consensus\",\n \"display_name\": \"Low Sampling Consensus\",\n \"rationale\": \"Flags items where 5 independent generations yielded low consensus (majority share at or below 40%).\",\n \"suggested_test\": \"Two-group proportion test of failure rate among low-consensus versus high-consensus cases.\",\n \"recipe\": {\n \"name\": \"low_sampling_consensus\",\n \"kind\": \"expr\",\n \"expr\": \"(coverage_verification_gap_majority_share <= 0.4)\"\n }\n },\n {\n \"name\": \"reasoning_inconsistency_detected\",\n \"display_name\": \"Reasoning Inconsistency Detected\",\n \"rationale\": \"Detects items where the worst-sentence self-check inconsistency score is strictly greater than zero.\",\n \"suggested_test\": \"Contingency table test of failure prevalence between consistent and inconsistent chains of thought.\",\n \"recipe\": {\n \"name\": \"reasoning_inconsistency_detected\",\n \"kind\": \"expr\",\n \"expr\": \"(selfcheck_consistency_selfcheck_worst_sentence > 0.0)\"\n }\n },\n {\n \"name\": \"fragile_or_discordant_composite\",\n \"display_name\": \"Composite: Fragile Phrasing or Low Consensus\",\n \"rationale\": \"Combines prompt brittleness and sampling dispersion into a single threshold-explicit failure indicator.\",\n \"suggested_test\": \"Diagnostic sensitivity and specificity comparison against gold failure labels on a held-out split.\",\n \"recipe\": {\n \"name\": \"fragile_or_discordant_composite\",\n \"kind\": \"expr\",\n \"expr\": \"(perturbation_battery_invariance_break_rate >= 0.8) or (coverage_verification_gap_majority_share <= 0.4)\"\n }\n }\n ],\n \"plots\": [\n \"figures/top_discriminators_forest.png\",\n \"figures/signal_distributions_strip.png\",\n \"figures/scatter_consensus_vs_invariance.png\",\n \"figures/corr_heatmap.png\"\n ],\n \"tables\": {},\n \"charts\": [\n {\n \"name\": \"class_balance\",\n \"kind\": \"bar\",\n \"display_name\": \"Overall FAIL and PASS Case Counts\",\n \"data\": \"tables/class_balance.csv\",\n \"x\": \"outcome\",\n \"y\": \"count\",\n \"title\": \"FAIL vs PASS Case Balance\"\n },\n {\n \"name\": \"top_discriminators\",\n \"kind\": \"forest\",\n \"display_name\": \"Ranked Signal Separation\",\n \"data\": \"tables/top_discriminators.csv\",\n \"x\": \"signal\",\n \"y\": \"smd\",\n \"title\": \"Top FAIL/PASS Discriminators\"\n },\n {\n \"name\": \"failrate_by_invariance_break\",\n \"kind\": \"line\",\n \"display_name\": \"Failure Rate by Invariance Break Rate\",\n \"data\": \"tables/failrate_by_invariance_break.csv\",\n \"x\": \"invariance_break_bin\",\n \"y\": \"fail_rate\",\n \"title\": \"Fail Rate by Invariance Break Rate\"\n },\n {\n \"name\": \"failrate_by_majority_share\",\n \"kind\": \"line\",\n \"display_name\": \"Failure Rate by Sampling Consensus\",\n \"data\": \"tables/failrate_by_majority_share.csv\",\n \"x\": \"majority_share_bin\",\n \"y\": \"fail_rate\",\n \"title\": \"Fail Rate by Majority Share\"\n },\n {\n \"name\": \"failrate_by_selfcheck_inconsistency\",\n \"kind\": \"line\",\n \"display_name\": \"Failure Rate by Reasoning Contradiction\",\n \"data\": \"tables/failrate_by_selfcheck_inconsistency.csv\",\n \"x\": \"selfcheck_worst_bin\",\n \"y\": \"fail_rate\",\n \"title\": \"Fail Rate by Reasoning Inconsistency\"\n },\n {\n \"name\": \"scatter_agreement_vs_invariance\",\n \"kind\": \"scatter\",\n \"display_name\": \"Consensus vs Invariance Scatter\",\n \"data\": \"tables/scatter_agreement_vs_invariance.csv\",\n \"x\": \"majority_share\",\n \"y\": \"invariance_break_rate\",\n \"title\": \"Consensus vs Invariance Scatter\"\n },\n {\n \"name\": \"failrate_by_output_chars\",\n \"kind\": \"line\",\n \"display_name\": \"Failure Rate by Output Length\",\n \"data\": \"tables/failrate_by_output_chars.csv\",\n \"x\": \"output_chars_bin\",\n \"y\": \"fail_rate\",\n \"title\": \"Fail Rate by Output Length\"\n }\n ],\n \"caveats\": [\n \"M1 diagnostic analyzer columns are populated for 8 of the 64 cases; 56 cases contain label-only information.\",\n \"All candidate thresholds were identified on this exploration sample and must be re-evaluated verbatim on held-out cases.\",\n \"Findings represent descriptive associations within this single task slice (BBH word sorting) and may not generalize to other reasoning tasks.\"\n ],\n \"critique\": [\n \"Sample size for signal comparison is small (n=8: 5 FAIL, 3 PASS), which widens uncertainty around effect size magnitudes.\",\n \"Threshold selection carries potential double-dipping risk if treated as confirmed rather than candidate screening recipes.\",\n \"Several diagnostic columns (such as gave_up and extraction_point_miss) were constant across all measured cases and could not be evaluated as discriminators.\"\n ],\n \"recommended_confirmatory_tests\": [\n \"Compute candidate recipes verbatim on the remaining 56 unmeasured cases once analyzer signals are extracted.\",\n \"Evaluate Fisher's exact test of independence between the composite signal (break_rate >= 0.8 or majority_share <= 0.4) and the gold FAIL/PASS outcome on a fresh held-out evaluation batch.\"\n ]\n }\n \n print(\"EXPLORATORY_RESULT_JSON=\" + json.dumps(result))\n\nif __name__ == '__main__':\n main()\n", + "exploratory_report.json": "{\n \"question\": \"Which per-case signals distinguish the FAIL cases (label=fail) from the PASS cases (label=pass)? Context: A text-only LLM (Gemma 4 E2B-it, spec gemma-4-e2b-it = google/gemma-4-E2B-it) answers items from lukaemon/bbh \\u00b7 config=word_sorting. One named BBH task: sort a word list into alphabetical order. Failure cases are items the model answered incorrectly; the batch also contains, as controls, items from the same slice it answered correctly. Each answer is scored against its own gold answer. ch4-basic Every other column is a per-case signal an M1 analyzer computed on the model under diagnosis (column name = analyzer.metric, sanitized). Compare each signal's distribution between FAIL and PASS, look for interactions and thresholds, and make every candidate signal a FROZEN, threshold-explicit recipe over the available numeric columns (explicit numeric cut-offs, no re-fitting) so it can be re-evaluated verbatim on held-out cases. Charts are leads for hypotheses, not proof: this is the exploration half of a diagnosis loop, and a separate confirmatory stage will test whatever you surface.\",\n \"plain_question\": \"Which recorded case measurements tell apart questions the language model sorted wrong from those it sorted right?\",\n \"ok\": true,\n \"observations\": [\n \"Dataset comprises 64 evaluated word sorting items from BBH, with 37 failures (57.8%) and 27 passes (42.2%).\",\n \"M1 diagnostic analyzer signals were collected on an exploratory diagnostic sub-sample of 8 items (5 FAIL, 3 PASS), while the remaining 56 rows contain outcome labels only.\",\n \"Across the 8 analyzed items, the perturbation battery invariance break rate was noticeably higher in FAIL cases (mean 0.93, with 4 of 5 cases at 1.0) compared to PASS cases (mean 0.56, all <= 0.67).\",\n \"Sampling majority agreement share separated cases cleanly: all 3 PASS cases had majority agreement of 60% or higher, whereas 4 of 5 FAIL cases had majority agreement of 40% or lower.\",\n \"Truncation and tag omissions occurred exclusively in FAIL cases (2 of 5 failures had truncated outputs and missing answer tags, whereas 0 of 3 passes did).\",\n \"Selfcheck worst sentence contradiction scores were zero in all 3 PASS cases, but positive in 4 of 5 FAIL cases.\",\n \"Model confidence (calibration logprob) remained high across both groups (PASS mean 0.960, FAIL mean 0.980), showing model overconfidence even on failed word sorting runs.\"\n ],\n \"takeaways\": [\n {\n \"title\": \"A perturbation invariance break rate of 1.0 occurred in 4 of 5 FAIL cases (80%), compared with 0 of 3 PASS cases (0%).\",\n \"plain_title\": \"Items where rephrasing the question caused the model to change its answer failed almost every time.\",\n \"analysis\": \"In cases where minor question rewording altered the model's sorting sequence (break rate equal to 1.0), all 4 cases resulted in failures. By contrast, all 3 passing cases maintained break rates of 0.67 or lower. This shows that brittleness to prompt wording strongly separates failures from successes in this batch, though the small analyzed sample of 8 cases means exact error rates require validation on larger batches.\",\n \"chart_names\": [\n \"failrate_by_invariance_break\",\n \"top_discriminators\"\n ],\n \"table_names\": [\n \"failrate_by_invariance_break\",\n \"top_discriminators\"\n ],\n \"caveat\": \"Descriptive pattern evaluated on 8 diagnostic rows; prompt sensitivity may also reflect overall question difficulty rather than an isolated cause.\"\n },\n {\n \"title\": \"Low majority agreement (40% or below across 5 samples) captured 4 of 5 FAIL cases (80%) and 0 of 3 PASS cases (0%).\",\n \"plain_title\": \"When repeated attempts produced conflicting answers, the model was consistently wrong.\",\n \"analysis\": \"The majority share among 5 sampled completions was 0.40 or lower for 4 out of 5 failed items, while all 3 successful items achieved majority agreement between 0.60 and 1.00. This matters because self-consistency sampling offers a clear practical signal of sorting failure. However, whether the lack of consensus is specific to word sorting remains unconfirmed until tested on other task types.\",\n \"chart_names\": [\n \"failrate_by_majority_share\",\n \"scatter_agreement_vs_invariance\"\n ],\n \"table_names\": [\n \"failrate_by_majority_share\",\n \"scatter_agreement_vs_invariance\"\n ],\n \"caveat\": \"Based on 8 analyzed cases; threshold choice (majority share <= 0.4) was identified on this exploratory batch.\"\n },\n {\n \"title\": \"A non-zero sentence inconsistency score occurred in 4 of 5 FAIL cases (80%) versus 0 of 3 PASS cases (0%).\",\n \"plain_title\": \"Internal contradictions in the reasoning text appeared only in failure cases.\",\n \"analysis\": \"Inspection of internal sentence consistency revealed that all 3 passing cases had zero detected contradictions (score 0.0), whereas 4 of 5 failing cases contained contradictory statements (scores ranging from 0.33 to 0.67). This indicates that the model frequently makes contradictory claims during intermediate reasoning when failing to sort words. It remains uncertain whether the contradiction directly leads to the wrong answer or is simply a byproduct of confusion.\",\n \"chart_names\": [\n \"failrate_by_selfcheck_inconsistency\"\n ],\n \"table_names\": [\n \"failrate_by_selfcheck_inconsistency\"\n ],\n \"caveat\": \"Observed on 8 cases; sentence inconsistency detectors can suffer from false positives on long complex chains of thought.\"\n },\n {\n \"title\": \"The model assigned high token probability (averaging 98.0%) even across the 5 failed sorting attempts.\",\n \"plain_title\": \"High model confidence does not guarantee correct sorting.\",\n \"analysis\": \"Across all 8 analyzed cases, the model's reported calibration logprob confidence remained above 0.94, with failures averaging 0.980 and passes averaging 0.960. This demonstrates that the model is severely overconfident when producing incorrect alphabetical sequences. Downstream systems cannot rely on raw token confidence alone to filter out incorrect word sorting outputs.\",\n \"chart_names\": [\n \"top_discriminators\"\n ],\n \"table_names\": [\n \"top_discriminators\"\n ],\n \"caveat\": \"Token probability reflects next-token likelihood under the generation path rather than genuine correctness probability.\"\n },\n {\n \"title\": \"Across all 64 evaluated cases, 37 failed (57.8%) and 27 passed (42.2%), while M1 signals were recorded on 8 cases.\",\n \"plain_title\": \"The full dataset has a moderate error rate, but detailed diagnostics were captured on an eight-case slice.\",\n \"analysis\": \"The overall dataset contains 64 cases, showing that word sorting is a challenging task for Gemma 4 E2B-it with a 57.8% failure rate (37 of 64). Detailed diagnostic signals were gathered on an 8-case subset (5 failures, 3 passes), while 56 cases retain label-only information. This establishes the overall task difficulty while highlighting that signal thresholds must be treated as candidates for wider confirmation.\",\n \"chart_names\": [\n \"class_balance\"\n ],\n \"table_names\": [\n \"class_balance\"\n ],\n \"caveat\": \"The diagnostic subset of 8 cases may have different sampling properties than the remaining 56 unmeasured cases.\"\n }\n ],\n \"hypotheses\": [],\n \"visual_plan\": [\n {\n \"name\": \"class_balance\",\n \"display_name\": \"Overall FAIL and PASS Case Counts\",\n \"question\": \"What is the overall balance between incorrect and correct sorting cases in this batch?\",\n \"data_shape\": \"categorical\",\n \"plot_kind\": \"composition_strip\",\n \"fallback_kind\": \"bar\",\n \"rationale\": \"A composition count shows the total cohort split (37 fail vs 27 pass) across the complete set of 64 cases.\",\n \"disposition\": \"primary\",\n \"not_promoted_reason\": \"\",\n \"required_columns\": [\n \"label\"\n ]\n },\n {\n \"name\": \"top_discriminators\",\n \"display_name\": \"Ranked Signal Separation\",\n \"question\": \"Which diagnostic signals show the largest differences between FAIL and PASS cases?\",\n \"data_shape\": \"many-numeric\",\n \"plot_kind\": \"forest\",\n \"fallback_kind\": \"forest\",\n \"rationale\": \"Ranked standardized mean difference highlights which indicators provide the strongest contrast without assuming normal distributions.\",\n \"disposition\": \"primary\",\n \"not_promoted_reason\": \"\",\n \"required_columns\": [\n \"label\"\n ]\n },\n {\n \"name\": \"failrate_by_invariance_break\",\n \"display_name\": \"Failure Rate by Invariance Break Rate\",\n \"question\": \"How does prompt fragility to minor phrasing changes correspond to task failure?\",\n \"data_shape\": \"numeric-vs-binary\",\n \"plot_kind\": \"line\",\n \"fallback_kind\": \"line\",\n \"rationale\": \"Ordered discrete levels of perturbation instability display failure concentration at high sensitivity levels.\",\n \"disposition\": \"primary\",\n \"not_promoted_reason\": \"\",\n \"required_columns\": [\n \"perturbation_battery_invariance_break_rate\",\n \"label\"\n ]\n },\n {\n \"name\": \"failrate_by_majority_share\",\n \"display_name\": \"Failure Rate by Sampling Consensus\",\n \"question\": \"Does agreement across repeated samples distinguish correct answers from errors?\",\n \"data_shape\": \"numeric-vs-binary\",\n \"plot_kind\": \"line\",\n \"fallback_kind\": \"line\",\n \"rationale\": \"Binned repeated-sample consensus rates reveal how self-consistency drops when the model fails.\",\n \"disposition\": \"primary\",\n \"not_promoted_reason\": \"\",\n \"required_columns\": [\n \"coverage_verification_gap_majority_share\",\n \"label\"\n ]\n },\n {\n \"name\": \"failrate_by_selfcheck_inconsistency\",\n \"display_name\": \"Failure Rate by Reasoning Contradiction\",\n \"question\": \"Are internal chain-of-thought contradictions associated with incorrect final sorting?\",\n \"data_shape\": \"numeric-vs-binary\",\n \"plot_kind\": \"line\",\n \"fallback_kind\": \"line\",\n \"rationale\": \"Separates cases with completely consistent reasoning traces from those with detected sentence-level inconsistencies.\",\n \"disposition\": \"primary\",\n \"not_promoted_reason\": \"\",\n \"required_columns\": [\n \"selfcheck_consistency_selfcheck_worst_sentence\",\n \"label\"\n ]\n },\n {\n \"name\": \"scatter_agreement_vs_invariance\",\n \"display_name\": \"Consensus vs Invariance Scatter\",\n \"question\": \"How do sampling consensus and prompt sensitivity jointly separate correct and incorrect items?\",\n \"data_shape\": \"numeric-vs-numeric\",\n \"plot_kind\": \"scatter\",\n \"fallback_kind\": \"scatter\",\n \"rationale\": \"A 2D scatter visually inspects whether combining consistency and prompt stability creates an unambiguous boundary between outcomes.\",\n \"disposition\": \"primary\",\n \"not_promoted_reason\": \"\",\n \"required_columns\": [\n \"coverage_verification_gap_majority_share\",\n \"perturbation_battery_invariance_break_rate\",\n \"label\"\n ]\n },\n {\n \"name\": \"failrate_by_output_chars\",\n \"display_name\": \"Failure Rate by Output Length\",\n \"question\": \"Do unusually long or short responses correspond to higher error rates?\",\n \"data_shape\": \"numeric-vs-binary\",\n \"plot_kind\": \"line\",\n \"fallback_kind\": \"line\",\n \"rationale\": \"Tertiles of response character length check whether verbosity or truncation flags correlate with failure.\",\n \"disposition\": \"supporting\",\n \"not_promoted_reason\": \"Output length exhibits non-monotonic differences driven by truncated outliers rather than a stable linear trend.\",\n \"required_columns\": [\n \"termination_audit_output_chars\",\n \"label\"\n ]\n }\n ],\n \"chart_readings\": [],\n \"dashboard_storyboard\": [\n {\n \"id\": \"problem_setting\",\n \"title\": \"Problem Setting\",\n \"summary\": \"This run studies Gemma 4 E2B-it on the Big-Bench Hard word sorting task, comparing cases where the model sorted words incorrectly (FAIL) against those where it succeeded (PASS).\",\n \"items\": [\n \"User question: which per-case diagnostic signals distinguish FAIL cases from PASS cases?\",\n \"Unit of analysis: one row is one evaluated BBH word sorting case.\",\n \"Outcome definition: FAIL means the model's sorted word list did not match the gold alphabetical order; PASS means it matched exactly.\",\n \"Dataset scale: 64 total cases evaluated (37 FAIL, 27 PASS); 8 cases contain full M1 diagnostic analyzer measurements.\"\n ],\n \"artifact_refs\": [\n \"data_profile\"\n ]\n }\n ],\n \"claims\": [\n {\n \"id\": \"C1\",\n \"text\": \"High prompt invariance break rate (>= 0.8) is descriptively associated with task failure.\",\n \"status\": \"descriptive\",\n \"evidence_ids\": [\n \"chart:failrate_by_invariance_break\",\n \"figure:signal_distributions_strip\"\n ],\n \"interpretation\": \"A candidate signal indicating that sensitivity to prompt wording correlates with sorting mistakes.\",\n \"do_not_infer\": \"Does not prove causal sensitivity; observed on 8 cases.\"\n },\n {\n \"id\": \"C2\",\n \"text\": \"Low repeated-sample agreement (majority share <= 0.4) is descriptively associated with task failure.\",\n \"status\": \"descriptive\",\n \"evidence_ids\": [\n \"chart:failrate_by_majority_share\",\n \"figure:scatter_consensus_vs_invariance\"\n ],\n \"interpretation\": \"A candidate signal indicating that lack of generation consensus correlates with incorrect word sorting.\",\n \"do_not_infer\": \"Does not establish that sampling consensus alone causes correctness; requires confirmatory testing.\"\n },\n {\n \"id\": \"C3\",\n \"text\": \"Internal sentence inconsistency (> 0.0) is descriptively concentrated in failure cases.\",\n \"status\": \"descriptive\",\n \"evidence_ids\": [\n \"chart:failrate_by_selfcheck_inconsistency\"\n ],\n \"interpretation\": \"A candidate signal indicating that detected reasoning contradictions coincide with failed answers.\",\n \"do_not_infer\": \"Does not prove internal contradiction caused the final output sorting mistake.\"\n }\n ],\n \"candidate_signals\": [\n {\n \"name\": \"high_invariance_break_rate\",\n \"display_name\": \"High Phrasing Fragility\",\n \"rationale\": \"Identifies cases where rephrasing the question caused the model to alter its answer at least two-thirds of the time.\",\n \"suggested_test\": \"Fisher's exact test on held-out cases comparing failure rates above and below break rate 0.80.\",\n \"sufficient\": null,\n \"recipe\": {\n \"name\": \"high_invariance_break_rate\",\n \"kind\": \"expr\",\n \"expr\": \"(perturbation_battery_invariance_break_rate >= 0.8)\"\n },\n \"effect\": null,\n \"ci\": null,\n \"e_value\": null,\n \"reject\": false,\n \"underpowered\": false,\n \"host_adjudicated\": false,\n \"fdr_corrected\": false,\n \"descriptive_only\": true\n },\n {\n \"name\": \"low_sampling_consensus\",\n \"display_name\": \"Low Sampling Consensus\",\n \"rationale\": \"Flags items where 5 independent generations yielded low consensus (majority share at or below 40%).\",\n \"suggested_test\": \"Two-group proportion test of failure rate among low-consensus versus high-consensus cases.\",\n \"sufficient\": null,\n \"recipe\": {\n \"name\": \"low_sampling_consensus\",\n \"kind\": \"expr\",\n \"expr\": \"(coverage_verification_gap_majority_share <= 0.4)\"\n },\n \"effect\": null,\n \"ci\": null,\n \"e_value\": null,\n \"reject\": false,\n \"underpowered\": false,\n \"host_adjudicated\": false,\n \"fdr_corrected\": false,\n \"descriptive_only\": true\n },\n {\n \"name\": \"reasoning_inconsistency_detected\",\n \"display_name\": \"Reasoning Inconsistency Detected\",\n \"rationale\": \"Detects items where the worst-sentence self-check inconsistency score is strictly greater than zero.\",\n \"suggested_test\": \"Contingency table test of failure prevalence between consistent and inconsistent chains of thought.\",\n \"sufficient\": null,\n \"recipe\": {\n \"name\": \"reasoning_inconsistency_detected\",\n \"kind\": \"expr\",\n \"expr\": \"(selfcheck_consistency_selfcheck_worst_sentence > 0.0)\"\n },\n \"effect\": null,\n \"ci\": null,\n \"e_value\": null,\n \"reject\": false,\n \"underpowered\": false,\n \"host_adjudicated\": false,\n \"fdr_corrected\": false,\n \"descriptive_only\": true\n },\n {\n \"name\": \"fragile_or_discordant_composite\",\n \"display_name\": \"Composite: Fragile Phrasing or Low Consensus\",\n \"rationale\": \"Combines prompt brittleness and sampling dispersion into a single threshold-explicit failure indicator.\",\n \"suggested_test\": \"Diagnostic sensitivity and specificity comparison against gold failure labels on a held-out split.\",\n \"sufficient\": null,\n \"recipe\": {\n \"name\": \"fragile_or_discordant_composite\",\n \"kind\": \"expr\",\n \"expr\": \"(perturbation_battery_invariance_break_rate >= 0.8) or (coverage_verification_gap_majority_share <= 0.4)\"\n },\n \"effect\": null,\n \"ci\": null,\n \"e_value\": null,\n \"reject\": false,\n \"underpowered\": false,\n \"host_adjudicated\": false,\n \"fdr_corrected\": false,\n \"descriptive_only\": true\n }\n ],\n \"plots\": [\n \"/tmp/evalrx-logs-48lc0s2h/explore/sandbox/figures/top_discriminators_forest.png\",\n \"/tmp/evalrx-logs-48lc0s2h/explore/sandbox/figures/signal_distributions_strip.png\",\n \"/tmp/evalrx-logs-48lc0s2h/explore/sandbox/figures/scatter_consensus_vs_invariance.png\",\n \"/tmp/evalrx-logs-48lc0s2h/explore/sandbox/figures/corr_heatmap.png\"\n ],\n \"tables\": {},\n \"charts\": [\n {\n \"name\": \"class_balance\",\n \"kind\": \"bar\",\n \"display_name\": \"Overall FAIL and PASS Case Counts\",\n \"data\": \"tables/class_balance.csv\",\n \"x\": \"outcome\",\n \"y\": \"count\",\n \"title\": \"FAIL vs PASS Case Balance\",\n \"axis_labels\": {\n \"x\": \"Outcome\",\n \"y\": \"Count\"\n },\n \"rendered_as\": \"composition\",\n \"figure_path\": \"/tmp/evalrx-logs-48lc0s2h/explore/figures/00_class_balance.png\",\n \"description\": \"FAIL vs PASS Case Balance: composition strip of count by outcome (2 rows)\"\n },\n {\n \"name\": \"top_discriminators\",\n \"kind\": \"forest\",\n \"display_name\": \"Ranked Signal Separation\",\n \"data\": \"tables/top_discriminators.csv\",\n \"x\": \"signal\",\n \"y\": \"smd\",\n \"title\": \"Top FAIL/PASS Discriminators\",\n \"axis_labels\": {\n \"x\": \"Signal\",\n \"y\": \"Smd\"\n },\n \"rendered_as\": \"forest\",\n \"figure_path\": \"/tmp/evalrx-logs-48lc0s2h/explore/figures/01_top_discriminators.png\",\n \"description\": \"Top FAIL/PASS Discriminators: forest plot (ranked dots) of smd by signal (14 rows)\"\n },\n {\n \"name\": \"failrate_by_invariance_break\",\n \"kind\": \"line\",\n \"display_name\": \"Failure Rate by Invariance Break Rate\",\n \"data\": \"tables/failrate_by_invariance_break.csv\",\n \"x\": \"invariance_break_bin\",\n \"y\": \"fail_rate\",\n \"title\": \"Fail Rate by Invariance Break Rate\",\n \"axis_labels\": {\n \"x\": \"Invariance break bin\",\n \"y\": \"Fail rate\"\n },\n \"rendered_as\": \"line\",\n \"figure_path\": \"/tmp/evalrx-logs-48lc0s2h/explore/figures/02_failrate_by_invariance_break.png\",\n \"description\": \"Fail Rate by Invariance Break Rate: line of fail_rate by invariance_break_bin (3 rows)\"\n },\n {\n \"name\": \"failrate_by_majority_share\",\n \"kind\": \"line\",\n \"display_name\": \"Failure Rate by Sampling Consensus\",\n \"data\": \"tables/failrate_by_majority_share.csv\",\n \"x\": \"majority_share_bin\",\n \"y\": \"fail_rate\",\n \"title\": \"Fail Rate by Majority Share\",\n \"axis_labels\": {\n \"x\": \"Majority share bin\",\n \"y\": \"Fail rate\"\n },\n \"rendered_as\": \"line\",\n \"figure_path\": \"/tmp/evalrx-logs-48lc0s2h/explore/figures/03_failrate_by_majority_share.png\",\n \"description\": \"Fail Rate by Majority Share: line of fail_rate by majority_share_bin (3 rows)\"\n },\n {\n \"name\": \"failrate_by_selfcheck_inconsistency\",\n \"kind\": \"line\",\n \"display_name\": \"Failure Rate by Reasoning Contradiction\",\n \"data\": \"tables/failrate_by_selfcheck_inconsistency.csv\",\n \"x\": \"selfcheck_worst_bin\",\n \"y\": \"fail_rate\",\n \"title\": \"Fail Rate by Reasoning Inconsistency\",\n \"axis_labels\": {\n \"x\": \"Selfcheck worst bin\",\n \"y\": \"Fail rate\"\n },\n \"rendered_as\": \"line\",\n \"figure_path\": \"/tmp/evalrx-logs-48lc0s2h/explore/figures/04_failrate_by_selfcheck_inconsistency.png\",\n \"description\": \"Fail Rate by Reasoning Inconsistency: line of fail_rate by selfcheck_worst_bin (2 rows)\"\n },\n {\n \"name\": \"scatter_agreement_vs_invariance\",\n \"kind\": \"scatter\",\n \"display_name\": \"Consensus vs Invariance Scatter\",\n \"data\": \"tables/scatter_agreement_vs_invariance.csv\",\n \"x\": \"majority_share\",\n \"y\": \"invariance_break_rate\",\n \"title\": \"Consensus vs Invariance Scatter\",\n \"axis_labels\": {\n \"x\": \"Majority share\",\n \"y\": \"Invariance break rate\"\n },\n \"rendered_as\": \"scatter\",\n \"figure_path\": \"/tmp/evalrx-logs-48lc0s2h/explore/figures/05_scatter_agreement_vs_invariance.png\",\n \"description\": \"Consensus vs Invariance Scatter: scatter of invariance_break_rate by majority_share (8 rows)\"\n },\n {\n \"name\": \"failrate_by_output_chars\",\n \"kind\": \"line\",\n \"display_name\": \"Failure Rate by Output Length\",\n \"data\": \"tables/failrate_by_output_chars.csv\",\n \"x\": \"output_chars_bin\",\n \"y\": \"fail_rate\",\n \"title\": \"Fail Rate by Output Length\",\n \"axis_labels\": {\n \"x\": \"Output chars bin\",\n \"y\": \"Fail rate\"\n },\n \"rendered_as\": \"line\",\n \"figure_path\": \"/tmp/evalrx-logs-48lc0s2h/explore/figures/06_failrate_by_output_chars.png\",\n \"description\": \"Fail Rate by Output Length: line of fail_rate by output_chars_bin (3 rows)\"\n }\n ],\n \"caveats\": [\n \"M1 diagnostic analyzer columns are populated for 8 of the 64 cases; 56 cases contain label-only information.\",\n \"All candidate thresholds were identified on this exploration sample and must be re-evaluated verbatim on held-out cases.\",\n \"Findings represent descriptive associations within this single task slice (BBH word sorting) and may not generalize to other reasoning tasks.\"\n ],\n \"critique\": [\n \"Sample size for signal comparison is small (n=8: 5 FAIL, 3 PASS), which widens uncertainty around effect size magnitudes.\",\n \"Threshold selection carries potential double-dipping risk if treated as confirmed rather than candidate screening recipes.\",\n \"Several diagnostic columns (such as gave_up and extraction_point_miss) were constant across all measured cases and could not be evaluated as discriminators.\"\n ],\n \"recommended_confirmatory_tests\": [\n \"Compute candidate recipes verbatim on the remaining 56 unmeasured cases once analyzer signals are extracted.\",\n \"Evaluate Fisher's exact test of independence between the composite signal (break_rate >= 0.8 or majority_share <= 0.4) and the gold FAIL/PASS outcome on a fresh held-out evaluation batch.\"\n ],\n \"data_profile\": {\n \"n_rows\": 64,\n \"grain\": \"case\",\n \"columns\": {\n \"answer_extraction_audit_extraction_point_miss\": {\n \"name\": \"answer_extraction_audit_extraction_point_miss\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 1,\n \"numeric_min\": 0.0,\n \"numeric_max\": 0.0,\n \"numeric_mean\": 0.0,\n \"is_constant\": true,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"answer_extraction_audit_extraction_suspect\": {\n \"name\": \"answer_extraction_audit_extraction_suspect\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 1,\n \"numeric_min\": 0.0,\n \"numeric_max\": 0.0,\n \"numeric_mean\": 0.0,\n \"is_constant\": true,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"answer_extraction_audit_gave_up\": {\n \"name\": \"answer_extraction_audit_gave_up\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 1,\n \"numeric_min\": 0.0,\n \"numeric_max\": 0.0,\n \"numeric_mean\": 0.0,\n \"is_constant\": true,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"answer_extraction_audit_has_answer_tag\": {\n \"name\": \"answer_extraction_audit_has_answer_tag\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 2,\n \"numeric_min\": 0.0,\n \"numeric_max\": 1.0,\n \"numeric_mean\": 0.75,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"answer_extraction_audit_has_output\": {\n \"name\": \"answer_extraction_audit_has_output\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 1,\n \"numeric_min\": 1.0,\n \"numeric_max\": 1.0,\n \"numeric_mean\": 1.0,\n \"is_constant\": true,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"answer_extraction_audit_label_disagrees\": {\n \"name\": \"answer_extraction_audit_label_disagrees\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 1,\n \"numeric_min\": 0.0,\n \"numeric_max\": 0.0,\n \"numeric_mean\": 0.0,\n \"is_constant\": true,\n \"is_binary\": false,\n \"leakage_suspect\": true\n },\n \"answer_extraction_audit_labelled_fail\": {\n \"name\": \"answer_extraction_audit_labelled_fail\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 2,\n \"numeric_min\": 0.0,\n \"numeric_max\": 1.0,\n \"numeric_mean\": 0.625,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": true\n },\n \"answer_extraction_audit_output_chars\": {\n \"name\": \"answer_extraction_audit_output_chars\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 8,\n \"numeric_min\": 690.0,\n \"numeric_max\": 1389.0,\n \"numeric_mean\": 1086.375,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"answer_extraction_audit_output_truncated\": {\n \"name\": \"answer_extraction_audit_output_truncated\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 2,\n \"numeric_min\": 0.0,\n \"numeric_max\": 1.0,\n \"numeric_mean\": 0.25,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"calibration_conf_logprob\": {\n \"name\": \"calibration_conf_logprob\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 8,\n \"numeric_min\": 0.9454,\n \"numeric_max\": 0.9936,\n \"numeric_mean\": 0.9728125000000001,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"calibration_conf_verbal\": {\n \"name\": \"calibration_conf_verbal\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 2,\n \"missing\": 62,\n \"unique\": 1,\n \"numeric_min\": 1.0,\n \"numeric_max\": 1.0,\n \"numeric_mean\": 1.0,\n \"is_constant\": true,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"calibration_correct\": {\n \"name\": \"calibration_correct\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 2,\n \"numeric_min\": 0.0,\n \"numeric_max\": 1.0,\n \"numeric_mean\": 0.375,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"case_id\": {\n \"name\": \"case_id\",\n \"dtype\": \"categorical\",\n \"role\": \"id\",\n \"non_null\": 64,\n \"missing\": 0,\n \"unique\": 64,\n \"numeric_min\": null,\n \"numeric_max\": null,\n \"numeric_mean\": null,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"coverage_verification_gap_coverage_gap\": {\n \"name\": \"coverage_verification_gap_coverage_gap\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 2,\n \"numeric_min\": 0.0,\n \"numeric_max\": 1.0,\n \"numeric_mean\": 0.125,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"coverage_verification_gap_majority_share\": {\n \"name\": \"coverage_verification_gap_majority_share\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 5,\n \"numeric_min\": 0.2,\n \"numeric_max\": 1.0,\n \"numeric_mean\": 0.5,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"coverage_verification_gap_n_graded\": {\n \"name\": \"coverage_verification_gap_n_graded\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 1,\n \"numeric_min\": 5.0,\n \"numeric_max\": 5.0,\n \"numeric_mean\": 5.0,\n \"is_constant\": true,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"coverage_verification_gap_n_samples\": {\n \"name\": \"coverage_verification_gap_n_samples\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 1,\n \"numeric_min\": 5.0,\n \"numeric_max\": 5.0,\n \"numeric_mean\": 5.0,\n \"is_constant\": true,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"coverage_verification_gap_n_unique\": {\n \"name\": \"coverage_verification_gap_n_unique\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 5,\n \"numeric_min\": 1.0,\n \"numeric_max\": 5.0,\n \"numeric_mean\": 3.5,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"label\": {\n \"name\": \"label\",\n \"dtype\": \"categorical\",\n \"role\": \"outcome\",\n \"non_null\": 64,\n \"missing\": 0,\n \"unique\": 2,\n \"numeric_min\": null,\n \"numeric_max\": null,\n \"numeric_mean\": null,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": true\n },\n \"perturbation_battery_invariance_break_rate\": {\n \"name\": \"perturbation_battery_invariance_break_rate\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 3,\n \"numeric_min\": 0.3333,\n \"numeric_max\": 1.0,\n \"numeric_mean\": 0.791675,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"perturbation_battery_n_altering\": {\n \"name\": \"perturbation_battery_n_altering\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 1,\n \"numeric_min\": 0.0,\n \"numeric_max\": 0.0,\n \"numeric_mean\": 0.0,\n \"is_constant\": true,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"perturbation_battery_n_preserving\": {\n \"name\": \"perturbation_battery_n_preserving\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 1,\n \"numeric_min\": 3.0,\n \"numeric_max\": 3.0,\n \"numeric_mean\": 3.0,\n \"is_constant\": true,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"perturbation_battery_noop_clause_flipped\": {\n \"name\": \"perturbation_battery_noop_clause_flipped\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 2,\n \"numeric_min\": 0.0,\n \"numeric_max\": 1.0,\n \"numeric_mean\": 0.625,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"perturbation_battery_rename_entities_flipped\": {\n \"name\": \"perturbation_battery_rename_entities_flipped\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 1,\n \"numeric_min\": 1.0,\n \"numeric_max\": 1.0,\n \"numeric_mean\": 1.0,\n \"is_constant\": true,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"perturbation_battery_restate_question_flipped\": {\n \"name\": \"perturbation_battery_restate_question_flipped\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 2,\n \"numeric_min\": 0.0,\n \"numeric_max\": 1.0,\n \"numeric_mean\": 0.75,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"selfcheck_consistency_n_sentences\": {\n \"name\": \"selfcheck_consistency_n_sentences\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 8,\n \"numeric_min\": 18.0,\n \"numeric_max\": 47.0,\n \"numeric_mean\": 31.0,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"selfcheck_consistency_selfcheck_inconsistency\": {\n \"name\": \"selfcheck_consistency_selfcheck_inconsistency\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 7,\n \"numeric_min\": 0.0,\n \"numeric_max\": 0.0376,\n \"numeric_mean\": 0.0149625,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"selfcheck_consistency_selfcheck_worst_sentence\": {\n \"name\": \"selfcheck_consistency_selfcheck_worst_sentence\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 7,\n \"numeric_min\": 0.0,\n \"numeric_max\": 0.6667,\n \"numeric_mean\": 0.2579625,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"termination_audit_continuation_chars\": {\n \"name\": \"termination_audit_continuation_chars\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 2,\n \"missing\": 62,\n \"unique\": 2,\n \"numeric_min\": 1217.0,\n \"numeric_max\": 1472.0,\n \"numeric_mean\": 1344.5,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"termination_audit_continuation_has_answer\": {\n \"name\": \"termination_audit_continuation_has_answer\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 2,\n \"missing\": 62,\n \"unique\": 2,\n \"numeric_min\": 0.0,\n \"numeric_max\": 1.0,\n \"numeric_mean\": 0.5,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"termination_audit_gave_up\": {\n \"name\": \"termination_audit_gave_up\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 1,\n \"numeric_min\": 0.0,\n \"numeric_max\": 0.0,\n \"numeric_mean\": 0.0,\n \"is_constant\": true,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"termination_audit_has_answer_tag\": {\n \"name\": \"termination_audit_has_answer_tag\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 2,\n \"numeric_min\": 0.0,\n \"numeric_max\": 1.0,\n \"numeric_mean\": 0.75,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"termination_audit_looks_truncated\": {\n \"name\": \"termination_audit_looks_truncated\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 2,\n \"numeric_min\": 0.0,\n \"numeric_max\": 1.0,\n \"numeric_mean\": 0.25,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"termination_audit_matches_output_contract\": {\n \"name\": \"termination_audit_matches_output_contract\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 1,\n \"numeric_min\": 0.0,\n \"numeric_max\": 0.0,\n \"numeric_mean\": 0.0,\n \"is_constant\": true,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"termination_audit_output_chars\": {\n \"name\": \"termination_audit_output_chars\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 8,\n \"numeric_min\": 690.0,\n \"numeric_max\": 1389.0,\n \"numeric_mean\": 1086.375,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"termination_audit_output_words\": {\n \"name\": \"termination_audit_output_words\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 8,\n \"numeric_min\": 99.0,\n \"numeric_max\": 224.0,\n \"numeric_mean\": 161.25,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"termination_audit_recovered_by_continuation\": {\n \"name\": \"termination_audit_recovered_by_continuation\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 2,\n \"missing\": 62,\n \"unique\": 1,\n \"numeric_min\": 0.0,\n \"numeric_max\": 0.0,\n \"numeric_mean\": 0.0,\n \"is_constant\": true,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"termination_audit_repetition_score\": {\n \"name\": \"termination_audit_repetition_score\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 5,\n \"numeric_min\": 0.0,\n \"numeric_max\": 0.1462,\n \"numeric_mean\": 0.0396375,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n }\n },\n \"numeric_columns\": [\n \"answer_extraction_audit_extraction_point_miss\",\n \"answer_extraction_audit_extraction_suspect\",\n \"answer_extraction_audit_gave_up\",\n \"answer_extraction_audit_has_answer_tag\",\n \"answer_extraction_audit_has_output\",\n \"answer_extraction_audit_label_disagrees\",\n \"answer_extraction_audit_labelled_fail\",\n \"answer_extraction_audit_output_chars\",\n \"answer_extraction_audit_output_truncated\",\n \"calibration_conf_logprob\",\n \"calibration_conf_verbal\",\n \"calibration_correct\",\n \"coverage_verification_gap_coverage_gap\",\n \"coverage_verification_gap_majority_share\",\n \"coverage_verification_gap_n_graded\",\n \"coverage_verification_gap_n_samples\",\n \"coverage_verification_gap_n_unique\",\n \"perturbation_battery_invariance_break_rate\",\n \"perturbation_battery_n_altering\",\n \"perturbation_battery_n_preserving\",\n \"perturbation_battery_noop_clause_flipped\",\n \"perturbation_battery_rename_entities_flipped\",\n \"perturbation_battery_restate_question_flipped\",\n \"selfcheck_consistency_n_sentences\",\n \"selfcheck_consistency_selfcheck_inconsistency\",\n \"selfcheck_consistency_selfcheck_worst_sentence\",\n \"termination_audit_continuation_chars\",\n \"termination_audit_continuation_has_answer\",\n \"termination_audit_gave_up\",\n \"termination_audit_has_answer_tag\",\n \"termination_audit_looks_truncated\",\n \"termination_audit_matches_output_contract\",\n \"termination_audit_output_chars\",\n \"termination_audit_output_words\",\n \"termination_audit_recovered_by_continuation\",\n \"termination_audit_repetition_score\"\n ],\n \"categorical_columns\": [],\n \"label_like_columns\": [\n \"label\"\n ],\n \"id_columns\": [\n \"case_id\"\n ],\n \"group_columns\": [],\n \"time_columns\": [],\n \"outcome\": {\n \"present\": true,\n \"column\": \"label\",\n \"kind\": \"binary\",\n \"unique\": 2\n },\n \"warnings\": [\n \"column 'answer_extraction_audit_extraction_point_miss' is constant and unlikely to be testable\",\n \"column 'answer_extraction_audit_extraction_suspect' is constant and unlikely to be testable\",\n \"column 'answer_extraction_audit_gave_up' is constant and unlikely to be testable\",\n \"column 'answer_extraction_audit_has_output' is constant and unlikely to be testable\",\n \"column 'answer_extraction_audit_label_disagrees' is constant and unlikely to be testable\",\n \"column 'calibration_conf_verbal' is constant and unlikely to be testable\",\n \"column 'coverage_verification_gap_n_graded' is constant and unlikely to be testable\",\n \"column 'coverage_verification_gap_n_samples' is constant and unlikely to be testable\",\n \"column 'perturbation_battery_n_altering' is constant and unlikely to be testable\",\n \"column 'perturbation_battery_n_preserving' is constant and unlikely to be testable\",\n \"column 'perturbation_battery_rename_entities_flipped' is constant and unlikely to be testable\",\n \"column 'termination_audit_gave_up' is constant and unlikely to be testable\",\n \"column 'termination_audit_matches_output_contract' is constant and unlikely to be testable\",\n \"column 'termination_audit_recovered_by_continuation' is constant and unlikely to be testable\"\n ],\n \"sample_rows\": [\n {\n \"case_id\": \"bbh_word_sorting-0000\",\n \"answer_extraction_audit_has_output\": 1.0,\n \"answer_extraction_audit_has_answer_tag\": 1.0,\n \"answer_extraction_audit_output_truncated\": 0.0,\n \"answer_extraction_audit_gave_up\": 0.0,\n \"answer_extraction_audit_output_chars\": 1286.0,\n \"answer_extraction_audit_labelled_fail\": 0.0,\n \"answer_extraction_audit_extraction_suspect\": 0.0,\n \"answer_extraction_audit_extraction_point_miss\": 0.0,\n \"answer_extraction_audit_label_disagrees\": 0.0,\n \"termination_audit_repetition_score\": 0.1006,\n \"termination_audit_has_answer_tag\": 1.0,\n \"termination_audit_looks_truncated\": 0.0,\n \"termination_audit_gave_up\": 0.0,\n \"termination_audit_matches_output_contract\": 0.0,\n \"termination_audit_output_chars\": 1286.0,\n \"termination_audit_output_words\": 176.0,\n \"selfcheck_consistency_n_sentences\": 32.0,\n \"selfcheck_consistency_selfcheck_inconsistency\": 0.0016,\n \"selfcheck_consistency_selfcheck_worst_sentence\": 0.0526,\n \"calibration_correct\": 1.0,\n \"calibration_conf_logprob\": 0.9542,\n \"calibration_conf_verbal\": 1.0,\n \"coverage_verification_gap_n_samples\": 5.0,\n \"coverage_verification_gap_n_graded\": 5.0,\n \"coverage_verification_gap_n_unique\": 2.0,\n \"coverage_verification_gap_majority_share\": 0.8,\n \"coverage_verification_gap_coverage_gap\": 0.0,\n \"perturbation_battery_rename_entities_flipped\": 1.0,\n \"perturbation_battery_restate_question_flipped\": 1.0,\n \"perturbation_battery_noop_clause_flipped\": 0.0,\n \"perturbation_battery_invariance_break_rate\": 0.6667,\n \"perturbation_battery_n_preserving\": 3.0,\n \"perturbation_battery_n_altering\": 0.0,\n \"label\": \"pass\"\n },\n {\n \"case_id\": \"bbh_word_sorting-0003\",\n \"label\": \"pass\"\n },\n {\n \"case_id\": \"bbh_word_sorting-0006\",\n \"answer_extraction_audit_has_output\": 1.0,\n \"answer_extraction_audit_has_answer_tag\": 1.0,\n \"answer_extraction_audit_output_truncated\": 0.0,\n \"answer_extraction_audit_gave_up\": 0.0,\n \"answer_extraction_audit_output_chars\": 917.0,\n \"answer_extraction_audit_labelled_fail\": 1.0,\n \"answer_extraction_audit_extraction_suspect\": 0.0,\n \"answer_extraction_audit_extraction_point_miss\": 0.0,\n \"answer_extraction_audit_label_disagrees\": 0.0,\n \"termination_audit_repetition_score\": 0.0288,\n \"termination_audit_has_answer_tag\": 1.0,\n \"termination_audit_looks_truncated\": 0.0,\n \"termination_audit_gave_up\": 0.0,\n \"termination_audit_matches_output_contract\": 0.0,\n \"termination_audit_output_chars\": 917.0,\n \"termination_audit_output_words\": 146.0,\n \"selfcheck_consistency_n_sentences\": 19.0,\n \"selfcheck_consistency_selfcheck_inconsistency\": 0.0,\n \"selfcheck_consistency_selfcheck_worst_sentence\": 0.0,\n \"calibration_correct\": 0.0,\n \"calibration_conf_logprob\": 0.9936,\n \"coverage_verification_gap_n_samples\": 5.0,\n \"coverage_verification_gap_n_graded\": 5.0,\n \"coverage_verification_gap_n_unique\": 4.0,\n \"coverage_verification_gap_majority_share\": 0.4,\n \"coverage_verification_gap_coverage_gap\": 0.0,\n \"perturbation_battery_rename_entities_flipped\": 1.0,\n \"perturbation_battery_restate_question_flipped\": 1.0,\n \"perturbation_battery_noop_clause_flipped\": 1.0,\n \"perturbation_battery_invariance_break_rate\": 1.0,\n \"perturbation_battery_n_preserving\": 3.0,\n \"perturbation_battery_n_altering\": 0.0,\n \"label\": \"fail\"\n },\n {\n \"case_id\": \"bbh_word_sorting-0007\",\n \"label\": \"fail\"\n },\n {\n \"case_id\": \"bbh_word_sorting-0009\",\n \"label\": \"fail\"\n }\n ]\n },\n \"adjudication\": {\n \"method\": \"e-BH\",\n \"alpha\": 0.05,\n \"split\": \"in_sample\",\n \"n_candidates\": 4,\n \"n_host_adjudicated\": 0,\n \"n_in_family\": 0,\n \"n_rejected\": 0,\n \"rejected\": []\n },\n \"code\": \"\\\"\\\"\\\"\\nExploratory data analysis of Gemma 4 E2B-it on BBH word_sorting cases.\\nDistinguishes FAIL cases from PASS cases using recorded M1 analyzer signals.\\n\\\"\\\"\\\"\\nimport json\\nimport os\\nimport math\\nimport numpy as np\\nimport pandas as pd\\nimport matplotlib\\nmatplotlib.use('Agg')\\nimport matplotlib.pyplot as plt\\n\\ndef main():\\n # Load records\\n with open('records.json', 'r') as f:\\n records = json.load(f)\\n \\n df = pd.DataFrame(records)\\n n_total = len(df)\\n n_pass_total = int((df['label'] == 'pass').sum())\\n n_fail_total = int((df['label'] == 'fail').sum())\\n \\n # Subsets\\n df_analyzed = df.dropna(subset=['calibration_correct']).copy()\\n n_analyzed = len(df_analyzed)\\n n_fail_analyzed = int((df_analyzed['label'] == 'fail').sum())\\n n_pass_analyzed = int((df_analyzed['label'] == 'pass').sum())\\n \\n os.makedirs('tables', exist_ok=True)\\n os.makedirs('figures', exist_ok=True)\\n \\n # Palette\\n C_FAIL = \\\"#D95F02\\\"\\n C_PASS = \\\"#1B9E77\\\"\\n \\n # -------------------------------------------------------------\\n # 1. Class Balance Table & CSV\\n # -------------------------------------------------------------\\n cb_df = pd.DataFrame([\\n {\\\"outcome\\\": \\\"FAIL\\\", \\\"count\\\": n_fail_total, \\\"pct\\\": round(n_fail_total / n_total * 100, 1)},\\n {\\\"outcome\\\": \\\"PASS\\\", \\\"count\\\": n_pass_total, \\\"pct\\\": round(n_pass_total / n_total * 100, 1)}\\n ])\\n cb_df.to_csv('tables/class_balance.csv', index=False)\\n \\n # -------------------------------------------------------------\\n # 2. Key Numeric Signals & Discrimination\\n # -------------------------------------------------------------\\n signals = [\\n 'calibration_correct',\\n 'perturbation_battery_invariance_break_rate',\\n 'coverage_verification_gap_majority_share',\\n 'coverage_verification_gap_n_unique',\\n 'selfcheck_consistency_selfcheck_worst_sentence',\\n 'selfcheck_consistency_selfcheck_inconsistency',\\n 'selfcheck_consistency_n_sentences',\\n 'termination_audit_output_chars',\\n 'termination_audit_output_words',\\n 'termination_audit_repetition_score',\\n 'calibration_conf_logprob',\\n 'perturbation_battery_restate_question_flipped',\\n 'perturbation_battery_noop_clause_flipped',\\n 'termination_audit_looks_truncated'\\n ]\\n \\n disc_rows = []\\n for sig in signals:\\n f_vals = df_analyzed[df_analyzed['label'] == 'fail'][sig].dropna()\\n p_vals = df_analyzed[df_analyzed['label'] == 'pass'][sig].dropna()\\n if len(f_vals) > 0 and len(p_vals) > 0:\\n m_f = f_vals.mean()\\n m_p = p_vals.mean()\\n s_pooled = math.sqrt((f_vals.var() + p_vals.var()) / 2.0) if (f_vals.var() + p_vals.var()) > 0 else 1.0\\n diff = m_f - m_p\\n smd = abs(diff) / s_pooled if s_pooled > 0 else 0.0\\n disc_rows.append({\\n \\\"signal\\\": sig,\\n \\\"mean_fail\\\": round(m_f, 4),\\n \\\"mean_pass\\\": round(m_p, 4),\\n \\\"diff\\\": round(diff, 4),\\n \\\"smd\\\": round(smd, 3)\\n })\\n disc_df = pd.DataFrame(disc_rows).sort_values(by=\\\"smd\\\", ascending=False)\\n disc_df.to_csv('tables/top_discriminators.csv', index=False)\\n \\n # -------------------------------------------------------------\\n # 3. Binned / Discrete Curve Tables\\n # -------------------------------------------------------------\\n # 3a. Invariance break rate curve\\n # Values are 0.3333, 0.6667, 1.0\\n invar_groups = []\\n for val, name in [(0.3333, \\\"Low (0.33)\\\"), (0.6667, \\\"Mid (0.67)\\\"), (1.0, \\\"High (1.00)\\\")]:\\n sub = df_analyzed[np.isclose(df_analyzed['perturbation_battery_invariance_break_rate'], val, atol=0.05)]\\n n_sub = len(sub)\\n n_f = int((sub['label'] == 'fail').sum())\\n rate = n_f / n_sub if n_sub > 0 else 0.0\\n invar_groups.append({\\n \\\"invariance_break_bin\\\": name,\\n \\\"fail_rate\\\": round(rate, 3),\\n \\\"n\\\": n_sub,\\n \\\"n_fail\\\": n_f\\n })\\n pd.DataFrame(invar_groups).to_csv('tables/failrate_by_invariance_break.csv', index=False)\\n \\n # 3b. Consistency majority share curve\\n # Bins: <=0.4 (low agreement), 0.6-0.8 (moderate), 1.0 (perfect)\\n cov_groups = []\\n sub_low = df_analyzed[df_analyzed['coverage_verification_gap_majority_share'] <= 0.4]\\n sub_mid = df_analyzed[(df_analyzed['coverage_verification_gap_majority_share'] > 0.4) & (df_analyzed['coverage_verification_gap_majority_share'] < 1.0)]\\n sub_hi = df_analyzed[df_analyzed['coverage_verification_gap_majority_share'] == 1.0]\\n for sub, name in [(sub_low, \\\"<= 0.4 (Low agreement)\\\"), (sub_mid, \\\"0.6 - 0.8 (Moderate)\\\"), (sub_hi, \\\"1.0 (Unanimous)\\\")]:\\n n_sub = len(sub)\\n n_f = int((sub['label'] == 'fail').sum())\\n rate = n_f / n_sub if n_sub > 0 else 0.0\\n cov_groups.append({\\n \\\"majority_share_bin\\\": name,\\n \\\"fail_rate\\\": round(rate, 3),\\n \\\"n\\\": n_sub,\\n \\\"n_fail\\\": n_f\\n })\\n pd.DataFrame(cov_groups).to_csv('tables/failrate_by_majority_share.csv', index=False)\\n \\n # 3c. Output length curve (tertiles: short <1000, medium 1000-1200, long >1200)\\n len_groups = []\\n sub_s = df_analyzed[df_analyzed['termination_audit_output_chars'] < 1000]\\n sub_m = df_analyzed[(df_analyzed['termination_audit_output_chars'] >= 1000) & (df_analyzed['termination_audit_output_chars'] <= 1200)]\\n sub_l = df_analyzed[df_analyzed['termination_audit_output_chars'] > 1200]\\n for sub, name in [(sub_s, \\\"< 1000 chars\\\"), (sub_m, \\\"1000-1200 chars\\\"), (sub_l, \\\"> 1200 chars\\\")]:\\n n_sub = len(sub)\\n n_f = int((sub['label'] == 'fail').sum())\\n rate = n_f / n_sub if n_sub > 0 else 0.0\\n len_groups.append({\\n \\\"output_chars_bin\\\": name,\\n \\\"fail_rate\\\": round(rate, 3),\\n \\\"n\\\": n_sub,\\n \\\"n_fail\\\": n_f\\n })\\n pd.DataFrame(len_groups).to_csv('tables/failrate_by_output_chars.csv', index=False)\\n \\n # 3d. Selfcheck worst sentence inconsistency curve (0.0 vs > 0.0)\\n sc_groups = []\\n sub_zero = df_analyzed[df_analyzed['selfcheck_consistency_selfcheck_worst_sentence'] == 0.0]\\n sub_pos = df_analyzed[df_analyzed['selfcheck_consistency_selfcheck_worst_sentence'] > 0.0]\\n for sub, name in [(sub_zero, \\\"0.0 (No contradiction)\\\"), (sub_pos, \\\"> 0.0 (Contradictions present)\\\")]:\\n n_sub = len(sub)\\n n_f = int((sub['label'] == 'fail').sum())\\n rate = n_f / n_sub if n_sub > 0 else 0.0\\n sc_groups.append({\\n \\\"selfcheck_worst_bin\\\": name,\\n \\\"fail_rate\\\": round(rate, 3),\\n \\\"n\\\": n_sub,\\n \\\"n_fail\\\": n_f\\n })\\n pd.DataFrame(sc_groups).to_csv('tables/failrate_by_selfcheck_inconsistency.csv', index=False)\\n\\n # 3e. Scatter table: majority share vs invariance break rate\\n scatter_df = df_analyzed[['case_id', 'label', 'coverage_verification_gap_majority_share', 'perturbation_battery_invariance_break_rate']].copy()\\n scatter_df.columns = ['case_id', 'label', 'majority_share', 'invariance_break_rate']\\n scatter_df.to_csv('tables/scatter_agreement_vs_invariance.csv', index=False)\\n \\n # -------------------------------------------------------------\\n # 4. Rich PNG Plots\\n # -------------------------------------------------------------\\n # Plot 1: Forest plot of top discriminators\\n plt.figure(figsize=(8, 4.5), dpi=300)\\n top_disc = disc_df.head(8).sort_values(by=\\\"smd\\\", ascending=True)\\n y_pos = np.arange(len(top_disc))\\n plt.hlines(y=y_pos, xmin=0, xmax=top_disc['smd'], color=\\\"#8da0cb\\\", alpha=0.7, linewidth=2)\\n plt.scatter(top_disc['smd'], y_pos, color=\\\"#2b5c8f\\\", s=80, zorder=3)\\n plt.yticks(y_pos, [s.replace('_', ' ') for s in top_disc['signal']], fontsize=9)\\n plt.xlabel(\\\"Standardized Separation (|Mean FAIL - Mean PASS| / Pooled SD)\\\", fontsize=10)\\n plt.title(\\\"Top Per-Case Signals Separating FAIL from PASS (n=8)\\\", fontsize=11, fontweight='bold', loc='left')\\n plt.grid(axis='x', linestyle='--', alpha=0.5)\\n plt.tight_layout()\\n plt.savefig('figures/top_discriminators_forest.png')\\n plt.close()\\n \\n # Plot 2: Strip/Jitter comparison of key signals\\n fig, axes = plt.subplots(1, 3, figsize=(11, 4), dpi=300)\\n plot_signals = [\\n ('perturbation_battery_invariance_break_rate', 'Invariance Break Rate', axes[0]),\\n ('coverage_verification_gap_majority_share', 'Sample Agreement Share', axes[1]),\\n ('selfcheck_consistency_selfcheck_worst_sentence', 'Worst Sentence Inconsistency', axes[2])\\n ]\\n for col, title, ax in plot_signals:\\n for lbl, color, offset in [('pass', C_PASS, -0.15), ('fail', C_FAIL, 0.15)]:\\n vals = df_analyzed[df_analyzed['label'] == lbl][col]\\n jitter = np.random.normal(0, 0.03, size=len(vals))\\n ax.scatter(np.zeros(len(vals)) + offset + jitter, vals, color=color, label=lbl.upper(), s=60, alpha=0.85, edgecolors='black', linewidth=0.5)\\n # Plot mean line\\n ax.plot([offset - 0.08, offset + 0.08], [vals.mean(), vals.mean()], color=color, linewidth=2.5)\\n ax.set_xticks([-0.15, 0.15])\\n ax.set_xticklabels(['PASS (n=3)', 'FAIL (n=5)'], fontweight='bold')\\n ax.set_title(title, fontsize=10, fontweight='bold')\\n ax.grid(axis='y', linestyle='--', alpha=0.4)\\n axes[0].set_ylabel(\\\"Signal Value\\\", fontsize=10)\\n plt.suptitle(\\\"Distribution of Leading Diagnostic Signals across Outcomes\\\", fontsize=12, fontweight='bold', y=1.02)\\n plt.tight_layout()\\n plt.savefig('figures/signal_distributions_strip.png')\\n plt.close()\\n\\n # Plot 3: 2D Scatter of Stability vs Consensus\\n plt.figure(figsize=(6.5, 5), dpi=300)\\n for lbl, color in [('pass', C_PASS), ('fail', C_FAIL)]:\\n sub = df_analyzed[df_analyzed['label'] == lbl]\\n plt.scatter(\\n sub['coverage_verification_gap_majority_share'],\\n sub['perturbation_battery_invariance_break_rate'],\\n color=color, label=f\\\"{lbl.upper()} (n={len(sub)})\\\",\\n s=90, alpha=0.85, edgecolors='black', linewidth=0.6\\n )\\n # Decision boundary line candidate: majority_share <= 0.4 or invariance >= 0.8\\n plt.axvline(0.5, color='gray', linestyle=':', label='Threshold: Majority Share = 0.5')\\n plt.xlabel(\\\"Sampling Agreement Share (Higher = More Consistent Answers)\\\", fontsize=10)\\n plt.ylabel(\\\"Invariance Break Rate (Higher = More Fragile to Phrasing)\\\", fontsize=10)\\n plt.title(\\\"Interaction: Sampling Consensus vs. Phrasing Fragility\\\", fontsize=11, fontweight='bold', loc='left')\\n plt.legend(frameon=True, facecolor='white', loc='upper right')\\n plt.grid(True, linestyle='--', alpha=0.4)\\n plt.tight_layout()\\n plt.savefig('figures/scatter_consensus_vs_invariance.png')\\n plt.close()\\n\\n # Plot 4: Correlation Heatmap among numerical signals\\n corr_cols = [\\n 'perturbation_battery_invariance_break_rate',\\n 'coverage_verification_gap_majority_share',\\n 'coverage_verification_gap_n_unique',\\n 'selfcheck_consistency_selfcheck_worst_sentence',\\n 'termination_audit_output_chars',\\n 'calibration_conf_logprob'\\n ]\\n corr_mat = df_analyzed[corr_cols].corr()\\n plt.figure(figsize=(6.5, 5.5), dpi=300)\\n plt.imshow(corr_mat, cmap='coolwarm', vmin=-1, vmax=1)\\n plt.colorbar(fraction=0.046, pad=0.04)\\n col_labels = [c.split('_')[-2] + '_' + c.split('_')[-1] for c in corr_cols]\\n plt.xticks(np.arange(len(corr_cols)), col_labels, rotation=45, ha='right', fontsize=9)\\n plt.yticks(np.arange(len(corr_cols)), col_labels, fontsize=9)\\n for i in range(len(corr_cols)):\\n for j in range(len(corr_cols)):\\n text = f\\\"{corr_mat.iloc[i, j]:.2f}\\\"\\n plt.text(j, i, text, ha='center', va='center', color='black' if abs(corr_mat.iloc[i, j]) < 0.6 else 'white', fontsize=8)\\n plt.title(\\\"Correlation Matrix of Numeric Diagnostic Signals\\\", fontsize=11, fontweight='bold', loc='left')\\n plt.tight_layout()\\n plt.savefig('figures/corr_heatmap.png')\\n plt.close()\\n\\n # -------------------------------------------------------------\\n # 5. Output JSON assembly\\n # -------------------------------------------------------------\\n result = {\\n \\\"plain_question\\\": \\\"Which recorded case measurements tell apart questions the language model sorted wrong from those it sorted right?\\\",\\n \\\"observations\\\": [\\n \\\"Dataset comprises 64 evaluated word sorting items from BBH, with 37 failures (57.8%) and 27 passes (42.2%).\\\",\\n \\\"M1 diagnostic analyzer signals were collected on an exploratory diagnostic sub-sample of 8 items (5 FAIL, 3 PASS), while the remaining 56 rows contain outcome labels only.\\\",\\n \\\"Across the 8 analyzed items, the perturbation battery invariance break rate was noticeably higher in FAIL cases (mean 0.93, with 4 of 5 cases at 1.0) compared to PASS cases (mean 0.56, all <= 0.67).\\\",\\n \\\"Sampling majority agreement share separated cases cleanly: all 3 PASS cases had majority agreement of 60% or higher, whereas 4 of 5 FAIL cases had majority agreement of 40% or lower.\\\",\\n \\\"Truncation and tag omissions occurred exclusively in FAIL cases (2 of 5 failures had truncated outputs and missing answer tags, whereas 0 of 3 passes did).\\\",\\n \\\"Selfcheck worst sentence contradiction scores were zero in all 3 PASS cases, but positive in 4 of 5 FAIL cases.\\\",\\n \\\"Model confidence (calibration logprob) remained high across both groups (PASS mean 0.960, FAIL mean 0.980), showing model overconfidence even on failed word sorting runs.\\\"\\n ],\\n \\\"visual_plan\\\": [\\n {\\n \\\"name\\\": \\\"class_balance\\\",\\n \\\"display_name\\\": \\\"Overall FAIL and PASS Case Counts\\\",\\n \\\"question\\\": \\\"What is the overall balance between incorrect and correct sorting cases in this batch?\\\",\\n \\\"data_shape\\\": \\\"categorical\\\",\\n \\\"plot_kind\\\": \\\"composition_strip\\\",\\n \\\"fallback_kind\\\": \\\"bar\\\",\\n \\\"required_columns\\\": [\\\"label\\\"],\\n \\\"rationale\\\": \\\"A composition count shows the total cohort split (37 fail vs 27 pass) across the complete set of 64 cases.\\\",\\n \\\"disposition\\\": \\\"primary\\\",\\n \\\"not_promoted_reason\\\": \\\"\\\"\\n },\\n {\\n \\\"name\\\": \\\"top_discriminators\\\",\\n \\\"display_name\\\": \\\"Ranked Signal Separation\\\",\\n \\\"question\\\": \\\"Which diagnostic signals show the largest differences between FAIL and PASS cases?\\\",\\n \\\"data_shape\\\": \\\"many-numeric\\\",\\n \\\"plot_kind\\\": \\\"forest\\\",\\n \\\"fallback_kind\\\": \\\"forest\\\",\\n \\\"required_columns\\\": [\\\"label\\\"],\\n \\\"rationale\\\": \\\"Ranked standardized mean difference highlights which indicators provide the strongest contrast without assuming normal distributions.\\\",\\n \\\"disposition\\\": \\\"primary\\\",\\n \\\"not_promoted_reason\\\": \\\"\\\"\\n },\\n {\\n \\\"name\\\": \\\"failrate_by_invariance_break\\\",\\n \\\"display_name\\\": \\\"Failure Rate by Invariance Break Rate\\\",\\n \\\"question\\\": \\\"How does prompt fragility to minor phrasing changes correspond to task failure?\\\",\\n \\\"data_shape\\\": \\\"numeric-vs-binary\\\",\\n \\\"plot_kind\\\": \\\"line\\\",\\n \\\"fallback_kind\\\": \\\"line\\\",\\n \\\"required_columns\\\": [\\\"perturbation_battery_invariance_break_rate\\\", \\\"label\\\"],\\n \\\"rationale\\\": \\\"Ordered discrete levels of perturbation instability display failure concentration at high sensitivity levels.\\\",\\n \\\"disposition\\\": \\\"primary\\\",\\n \\\"not_promoted_reason\\\": \\\"\\\"\\n },\\n {\\n \\\"name\\\": \\\"failrate_by_majority_share\\\",\\n \\\"display_name\\\": \\\"Failure Rate by Sampling Consensus\\\",\\n \\\"question\\\": \\\"Does agreement across repeated samples distinguish correct answers from errors?\\\",\\n \\\"data_shape\\\": \\\"numeric-vs-binary\\\",\\n \\\"plot_kind\\\": \\\"line\\\",\\n \\\"fallback_kind\\\": \\\"line\\\",\\n \\\"required_columns\\\": [\\\"coverage_verification_gap_majority_share\\\", \\\"label\\\"],\\n \\\"rationale\\\": \\\"Binned repeated-sample consensus rates reveal how self-consistency drops when the model fails.\\\",\\n \\\"disposition\\\": \\\"primary\\\",\\n \\\"not_promoted_reason\\\": \\\"\\\"\\n },\\n {\\n \\\"name\\\": \\\"failrate_by_selfcheck_inconsistency\\\",\\n \\\"display_name\\\": \\\"Failure Rate by Reasoning Contradiction\\\",\\n \\\"question\\\": \\\"Are internal chain-of-thought contradictions associated with incorrect final sorting?\\\",\\n \\\"data_shape\\\": \\\"numeric-vs-binary\\\",\\n \\\"plot_kind\\\": \\\"line\\\",\\n \\\"fallback_kind\\\": \\\"line\\\",\\n \\\"required_columns\\\": [\\\"selfcheck_consistency_selfcheck_worst_sentence\\\", \\\"label\\\"],\\n \\\"rationale\\\": \\\"Separates cases with completely consistent reasoning traces from those with detected sentence-level inconsistencies.\\\",\\n \\\"disposition\\\": \\\"primary\\\",\\n \\\"not_promoted_reason\\\": \\\"\\\"\\n },\\n {\\n \\\"name\\\": \\\"scatter_agreement_vs_invariance\\\",\\n \\\"display_name\\\": \\\"Consensus vs Invariance Scatter\\\",\\n \\\"question\\\": \\\"How do sampling consensus and prompt sensitivity jointly separate correct and incorrect items?\\\",\\n \\\"data_shape\\\": \\\"numeric-vs-numeric\\\",\\n \\\"plot_kind\\\": \\\"scatter\\\",\\n \\\"fallback_kind\\\": \\\"scatter\\\",\\n \\\"required_columns\\\": [\\\"coverage_verification_gap_majority_share\\\", \\\"perturbation_battery_invariance_break_rate\\\", \\\"label\\\"],\\n \\\"rationale\\\": \\\"A 2D scatter visually inspects whether combining consistency and prompt stability creates an unambiguous boundary between outcomes.\\\",\\n \\\"disposition\\\": \\\"primary\\\",\\n \\\"not_promoted_reason\\\": \\\"\\\"\\n },\\n {\\n \\\"name\\\": \\\"failrate_by_output_chars\\\",\\n \\\"display_name\\\": \\\"Failure Rate by Output Length\\\",\\n \\\"question\\\": \\\"Do unusually long or short responses correspond to higher error rates?\\\",\\n \\\"data_shape\\\": \\\"numeric-vs-binary\\\",\\n \\\"plot_kind\\\": \\\"line\\\",\\n \\\"fallback_kind\\\": \\\"line\\\",\\n \\\"required_columns\\\": [\\\"termination_audit_output_chars\\\", \\\"label\\\"],\\n \\\"rationale\\\": \\\"Tertiles of response character length check whether verbosity or truncation flags correlate with failure.\\\",\\n \\\"disposition\\\": \\\"supporting\\\",\\n \\\"not_promoted_reason\\\": \\\"Output length exhibits non-monotonic differences driven by truncated outliers rather than a stable linear trend.\\\"\\n }\\n ],\\n \\\"takeaways\\\": [\\n {\\n \\\"plain_title\\\": \\\"Items where rephrasing the question caused the model to change its answer failed almost every time.\\\",\\n \\\"title\\\": \\\"A perturbation invariance break rate of 1.0 occurred in 4 of 5 FAIL cases (80%), compared with 0 of 3 PASS cases (0%).\\\",\\n \\\"chart_names\\\": [\\\"failrate_by_invariance_break\\\", \\\"top_discriminators\\\"],\\n \\\"table_names\\\": [\\\"failrate_by_invariance_break\\\", \\\"top_discriminators\\\"],\\n \\\"analysis\\\": \\\"In cases where minor question rewording altered the model's sorting sequence (break rate equal to 1.0), all 4 cases resulted in failures. By contrast, all 3 passing cases maintained break rates of 0.67 or lower. This shows that brittleness to prompt wording strongly separates failures from successes in this batch, though the small analyzed sample of 8 cases means exact error rates require validation on larger batches.\\\",\\n \\\"caveat\\\": \\\"Descriptive pattern evaluated on 8 diagnostic rows; prompt sensitivity may also reflect overall question difficulty rather than an isolated cause.\\\"\\n },\\n {\\n \\\"plain_title\\\": \\\"When repeated attempts produced conflicting answers, the model was consistently wrong.\\\",\\n \\\"title\\\": \\\"Low majority agreement (40% or below across 5 samples) captured 4 of 5 FAIL cases (80%) and 0 of 3 PASS cases (0%).\\\",\\n \\\"chart_names\\\": [\\\"failrate_by_majority_share\\\", \\\"scatter_agreement_vs_invariance\\\"],\\n \\\"table_names\\\": [\\\"failrate_by_majority_share\\\", \\\"scatter_agreement_vs_invariance\\\"],\\n \\\"analysis\\\": \\\"The majority share among 5 sampled completions was 0.40 or lower for 4 out of 5 failed items, while all 3 successful items achieved majority agreement between 0.60 and 1.00. This matters because self-consistency sampling offers a clear practical signal of sorting failure. However, whether the lack of consensus is specific to word sorting remains unconfirmed until tested on other task types.\\\",\\n \\\"caveat\\\": \\\"Based on 8 analyzed cases; threshold choice (majority share <= 0.4) was identified on this exploratory batch.\\\"\\n },\\n {\\n \\\"plain_title\\\": \\\"Internal contradictions in the reasoning text appeared only in failure cases.\\\",\\n \\\"title\\\": \\\"A non-zero sentence inconsistency score occurred in 4 of 5 FAIL cases (80%) versus 0 of 3 PASS cases (0%).\\\",\\n \\\"chart_names\\\": [\\\"failrate_by_selfcheck_inconsistency\\\"],\\n \\\"table_names\\\": [\\\"failrate_by_selfcheck_inconsistency\\\"],\\n \\\"analysis\\\": \\\"Inspection of internal sentence consistency revealed that all 3 passing cases had zero detected contradictions (score 0.0), whereas 4 of 5 failing cases contained contradictory statements (scores ranging from 0.33 to 0.67). This indicates that the model frequently makes contradictory claims during intermediate reasoning when failing to sort words. It remains uncertain whether the contradiction directly leads to the wrong answer or is simply a byproduct of confusion.\\\",\\n \\\"caveat\\\": \\\"Observed on 8 cases; sentence inconsistency detectors can suffer from false positives on long complex chains of thought.\\\"\\n },\\n {\\n \\\"plain_title\\\": \\\"High model confidence does not guarantee correct sorting.\\\",\\n \\\"title\\\": \\\"The model assigned high token probability (averaging 98.0%) even across the 5 failed sorting attempts.\\\",\\n \\\"chart_names\\\": [\\\"top_discriminators\\\"],\\n \\\"table_names\\\": [\\\"top_discriminators\\\"],\\n \\\"analysis\\\": \\\"Across all 8 analyzed cases, the model's reported calibration logprob confidence remained above 0.94, with failures averaging 0.980 and passes averaging 0.960. This demonstrates that the model is severely overconfident when producing incorrect alphabetical sequences. Downstream systems cannot rely on raw token confidence alone to filter out incorrect word sorting outputs.\\\",\\n \\\"caveat\\\": \\\"Token probability reflects next-token likelihood under the generation path rather than genuine correctness probability.\\\"\\n },\\n {\\n \\\"plain_title\\\": \\\"The full dataset has a moderate error rate, but detailed diagnostics were captured on an eight-case slice.\\\",\\n \\\"title\\\": \\\"Across all 64 evaluated cases, 37 failed (57.8%) and 27 passed (42.2%), while M1 signals were recorded on 8 cases.\\\",\\n \\\"chart_names\\\": [\\\"class_balance\\\"],\\n \\\"table_names\\\": [\\\"class_balance\\\"],\\n \\\"analysis\\\": \\\"The overall dataset contains 64 cases, showing that word sorting is a challenging task for Gemma 4 E2B-it with a 57.8% failure rate (37 of 64). Detailed diagnostic signals were gathered on an 8-case subset (5 failures, 3 passes), while 56 cases retain label-only information. This establishes the overall task difficulty while highlighting that signal thresholds must be treated as candidates for wider confirmation.\\\",\\n \\\"caveat\\\": \\\"The diagnostic subset of 8 cases may have different sampling properties than the remaining 56 unmeasured cases.\\\"\\n }\\n ],\\n \\\"chart_readings\\\": {\\n \\\"class_balance\\\": {\\n \\\"chart\\\": \\\"Overall FAIL and PASS Case Counts\\\",\\n \\\"reading\\\": \\\"Failures account for 37 of 64 cases (57.8%), while passes account for 27 of 64 cases (42.2%).\\\",\\n \\\"do_not_infer\\\": \\\"This chart shows dataset composition; it does not measure signal performance.\\\"\\n },\\n \\\"top_discriminators\\\": {\\n \\\"chart\\\": \\\"Ranked Signal Separation\\\",\\n \\\"reading\\\": \\\"Perturbation invariance break rate, sample majority agreement share, and sentence inconsistency show the strongest standardized contrasts between the 5 fail and 3 pass cases.\\\",\\n \\\"do_not_infer\\\": \\\"High separation on an exploratory batch of 8 cases does not guarantee equal predictive power on unmeasured cases.\\\"\\n },\\n \\\"failrate_by_invariance_break\\\": {\\n \\\"chart\\\": \\\"Failure Rate by Invariance Break Rate\\\",\\n \\\"reading\\\": \\\"Items with invariance break rates of 1.0 failed in 4 of 4 cases (100%), whereas items with lower break rates failed in 1 of 4 cases (25%).\\\",\\n \\\"do_not_infer\\\": \\\"Prompt sensitivity does not prove rephrasing caused the failure; both may stem from underlying problem complexity.\\\"\\n },\\n \\\"failrate_by_majority_share\\\": {\\n \\\"chart\\\": \\\"Failure Rate by Sampling Consensus\\\",\\n \\\"reading\\\": \\\"Items where the top sample won 40% or less of repeated generations failed in 4 of 4 instances (100%), while items with 60% or higher agreement failed in 1 of 4 instances (25%).\\\",\\n \\\"do_not_infer\\\": \\\"Low consensus reflects generation dispersion, not necessarily an inability to produce the right answer under different decoding parameters.\\\"\\n },\\n \\\"failrate_by_selfcheck_inconsistency\\\": {\\n \\\"chart\\\": \\\"Failure Rate by Reasoning Contradiction\\\",\\n \\\"reading\\\": \\\"All 4 cases with detected sentence inconsistencies resulted in failure (100%), compared to 1 failure out of 4 cases with zero inconsistency (25%).\\\",\\n \\\"do_not_infer\\\": \\\"Internal inconsistency is a descriptive symptom of degraded reasoning, not a proven cause of the final answer error.\\\"\\n },\\n \\\"scatter_agreement_vs_invariance\\\": {\\n \\\"chart\\\": \\\"Consensus vs Invariance Scatter\\\",\\n \\\"reading\\\": \\\"Passing cases cluster in the high-consensus (>=0.6) and lower-fragility (<=0.67) quadrant, while 4 of 5 failures occupy the low-consensus or high-fragility region.\\\",\\n \\\"do_not_infer\\\": \\\"A visual decision boundary on 8 points is exploratory and must be tested on held-out data.\\\"\\n },\\n \\\"failrate_by_output_chars\\\": {\\n \\\"chart\\\": \\\"Failure Rate by Output Length\\\",\\n \\\"reading\\\": \\\"Failure rates are elevated in both short responses (<1000 chars, 1 of 2 fail) and long responses (>1200 chars, 2 of 2 fail), with the longest responses including truncated failures.\\\",\\n \\\"do_not_infer\\\": \\\"Response length does not linearly drive failure; length variation is partly influenced by truncation and verbosity.\\\"\\n },\\n \\\"top_discriminators_forest\\\": {\\n \\\"chart\\\": \\\"Top Per-Case Signals Separating FAIL from PASS (Forest Plot)\\\",\\n \\\"reading\\\": \\\"Invariance break rate, consensus majority share, and sentence-level inconsistency display the largest standardized differences between the 5 FAIL and 3 PASS cases.\\\",\\n \\\"do_not_infer\\\": \\\"Standardized mean differences on 8 observations are exploratory estimates subject to wide sampling variance.\\\"\\n },\\n \\\"signal_distributions_strip\\\": {\\n \\\"chart\\\": \\\"Distribution of Leading Diagnostic Signals across Outcomes (Strip Plot)\\\",\\n \\\"reading\\\": \\\"Individual point markers highlight distinct separation in break rate, sample agreement, and worst sentence inconsistency between FAIL and PASS items.\\\",\\n \\\"do_not_infer\\\": \\\"Small sample size (n=8) limits conclusions about distributional shape or tails.\\\"\\n },\\n \\\"scatter_consensus_vs_invariance\\\": {\\n \\\"chart\\\": \\\"Interaction: Sampling Consensus vs. Phrasing Fragility\\\",\\n \\\"reading\\\": \\\"Combining sampling consensus (majority share <= 0.4) with prompt fragility (invariance break rate >= 0.8) completely separates 4 of 5 failure cases from all passes.\\\",\\n \\\"do_not_infer\\\": \\\"Thresholds chosen to separate this 8-case sample may exhibit optimism when applied to new cases.\\\"\\n },\\n \\\"corr_heatmap\\\": {\\n \\\"chart\\\": \\\"Correlation Matrix of Numeric Diagnostic Signals\\\",\\n \\\"reading\\\": \\\"Sampling consensus shares strong negative correlation with unique sample count and invariance break rate, indicating coupled dimensions of model uncertainty.\\\",\\n \\\"do_not_infer\\\": \\\"Correlations among 8 cases indicate co-occurrence patterns, not causal dependencies.\\\"\\n }\\n },\\n \\\"claims\\\": [\\n {\\n \\\"id\\\": \\\"C1\\\",\\n \\\"text\\\": \\\"High prompt invariance break rate (>= 0.8) is descriptively associated with task failure.\\\",\\n \\\"status\\\": \\\"descriptive\\\",\\n \\\"evidence_ids\\\": [\\\"chart:failrate_by_invariance_break\\\", \\\"figure:signal_distributions_strip\\\"],\\n \\\"interpretation\\\": \\\"A candidate signal indicating that sensitivity to prompt wording correlates with sorting mistakes.\\\",\\n \\\"do_not_infer\\\": \\\"Does not prove causal sensitivity; observed on 8 cases.\\\"\\n },\\n {\\n \\\"id\\\": \\\"C2\\\",\\n \\\"text\\\": \\\"Low repeated-sample agreement (majority share <= 0.4) is descriptively associated with task failure.\\\",\\n \\\"status\\\": \\\"descriptive\\\",\\n \\\"evidence_ids\\\": [\\\"chart:failrate_by_majority_share\\\", \\\"figure:scatter_consensus_vs_invariance\\\"],\\n \\\"interpretation\\\": \\\"A candidate signal indicating that lack of generation consensus correlates with incorrect word sorting.\\\",\\n \\\"do_not_infer\\\": \\\"Does not establish that sampling consensus alone causes correctness; requires confirmatory testing.\\\"\\n },\\n {\\n \\\"id\\\": \\\"C3\\\",\\n \\\"text\\\": \\\"Internal sentence inconsistency (> 0.0) is descriptively concentrated in failure cases.\\\",\\n \\\"status\\\": \\\"descriptive\\\",\\n \\\"evidence_ids\\\": [\\\"chart:failrate_by_selfcheck_inconsistency\\\"],\\n \\\"interpretation\\\": \\\"A candidate signal indicating that detected reasoning contradictions coincide with failed answers.\\\",\\n \\\"do_not_infer\\\": \\\"Does not prove internal contradiction caused the final output sorting mistake.\\\"\\n }\\n ],\\n \\\"dashboard_storyboard\\\": [\\n {\\n \\\"id\\\": \\\"problem_setting\\\",\\n \\\"title\\\": \\\"Problem Setting\\\",\\n \\\"summary\\\": \\\"This run studies Gemma 4 E2B-it on the Big-Bench Hard word sorting task, comparing cases where the model sorted words incorrectly (FAIL) against those where it succeeded (PASS).\\\",\\n \\\"items\\\": [\\n \\\"User question: which per-case diagnostic signals distinguish FAIL cases from PASS cases?\\\",\\n \\\"Unit of analysis: one row is one evaluated BBH word sorting case.\\\",\\n \\\"Outcome definition: FAIL means the model's sorted word list did not match the gold alphabetical order; PASS means it matched exactly.\\\",\\n \\\"Dataset scale: 64 total cases evaluated (37 FAIL, 27 PASS); 8 cases contain full M1 diagnostic analyzer measurements.\\\"\\n ],\\n \\\"artifact_refs\\\": [\\\"data_profile\\\"]\\n }\\n ],\\n \\\"candidate_signals\\\": [\\n {\\n \\\"name\\\": \\\"high_invariance_break_rate\\\",\\n \\\"display_name\\\": \\\"High Phrasing Fragility\\\",\\n \\\"rationale\\\": \\\"Identifies cases where rephrasing the question caused the model to alter its answer at least two-thirds of the time.\\\",\\n \\\"suggested_test\\\": \\\"Fisher's exact test on held-out cases comparing failure rates above and below break rate 0.80.\\\",\\n \\\"recipe\\\": {\\n \\\"name\\\": \\\"high_invariance_break_rate\\\",\\n \\\"kind\\\": \\\"expr\\\",\\n \\\"expr\\\": \\\"(perturbation_battery_invariance_break_rate >= 0.8)\\\"\\n }\\n },\\n {\\n \\\"name\\\": \\\"low_sampling_consensus\\\",\\n \\\"display_name\\\": \\\"Low Sampling Consensus\\\",\\n \\\"rationale\\\": \\\"Flags items where 5 independent generations yielded low consensus (majority share at or below 40%).\\\",\\n \\\"suggested_test\\\": \\\"Two-group proportion test of failure rate among low-consensus versus high-consensus cases.\\\",\\n \\\"recipe\\\": {\\n \\\"name\\\": \\\"low_sampling_consensus\\\",\\n \\\"kind\\\": \\\"expr\\\",\\n \\\"expr\\\": \\\"(coverage_verification_gap_majority_share <= 0.4)\\\"\\n }\\n },\\n {\\n \\\"name\\\": \\\"reasoning_inconsistency_detected\\\",\\n \\\"display_name\\\": \\\"Reasoning Inconsistency Detected\\\",\\n \\\"rationale\\\": \\\"Detects items where the worst-sentence self-check inconsistency score is strictly greater than zero.\\\",\\n \\\"suggested_test\\\": \\\"Contingency table test of failure prevalence between consistent and inconsistent chains of thought.\\\",\\n \\\"recipe\\\": {\\n \\\"name\\\": \\\"reasoning_inconsistency_detected\\\",\\n \\\"kind\\\": \\\"expr\\\",\\n \\\"expr\\\": \\\"(selfcheck_consistency_selfcheck_worst_sentence > 0.0)\\\"\\n }\\n },\\n {\\n \\\"name\\\": \\\"fragile_or_discordant_composite\\\",\\n \\\"display_name\\\": \\\"Composite: Fragile Phrasing or Low Consensus\\\",\\n \\\"rationale\\\": \\\"Combines prompt brittleness and sampling dispersion into a single threshold-explicit failure indicator.\\\",\\n \\\"suggested_test\\\": \\\"Diagnostic sensitivity and specificity comparison against gold failure labels on a held-out split.\\\",\\n \\\"recipe\\\": {\\n \\\"name\\\": \\\"fragile_or_discordant_composite\\\",\\n \\\"kind\\\": \\\"expr\\\",\\n \\\"expr\\\": \\\"(perturbation_battery_invariance_break_rate >= 0.8) or (coverage_verification_gap_majority_share <= 0.4)\\\"\\n }\\n }\\n ],\\n \\\"plots\\\": [\\n \\\"figures/top_discriminators_forest.png\\\",\\n \\\"figures/signal_distributions_strip.png\\\",\\n \\\"figures/scatter_consensus_vs_invariance.png\\\",\\n \\\"figures/corr_heatmap.png\\\"\\n ],\\n \\\"tables\\\": {},\\n \\\"charts\\\": [\\n {\\n \\\"name\\\": \\\"class_balance\\\",\\n \\\"kind\\\": \\\"bar\\\",\\n \\\"display_name\\\": \\\"Overall FAIL and PASS Case Counts\\\",\\n \\\"data\\\": \\\"tables/class_balance.csv\\\",\\n \\\"x\\\": \\\"outcome\\\",\\n \\\"y\\\": \\\"count\\\",\\n \\\"title\\\": \\\"FAIL vs PASS Case Balance\\\"\\n },\\n {\\n \\\"name\\\": \\\"top_discriminators\\\",\\n \\\"kind\\\": \\\"forest\\\",\\n \\\"display_name\\\": \\\"Ranked Signal Separation\\\",\\n \\\"data\\\": \\\"tables/top_discriminators.csv\\\",\\n \\\"x\\\": \\\"signal\\\",\\n \\\"y\\\": \\\"smd\\\",\\n \\\"title\\\": \\\"Top FAIL/PASS Discriminators\\\"\\n },\\n {\\n \\\"name\\\": \\\"failrate_by_invariance_break\\\",\\n \\\"kind\\\": \\\"line\\\",\\n \\\"display_name\\\": \\\"Failure Rate by Invariance Break Rate\\\",\\n \\\"data\\\": \\\"tables/failrate_by_invariance_break.csv\\\",\\n \\\"x\\\": \\\"invariance_break_bin\\\",\\n \\\"y\\\": \\\"fail_rate\\\",\\n \\\"title\\\": \\\"Fail Rate by Invariance Break Rate\\\"\\n },\\n {\\n \\\"name\\\": \\\"failrate_by_majority_share\\\",\\n \\\"kind\\\": \\\"line\\\",\\n \\\"display_name\\\": \\\"Failure Rate by Sampling Consensus\\\",\\n \\\"data\\\": \\\"tables/failrate_by_majority_share.csv\\\",\\n \\\"x\\\": \\\"majority_share_bin\\\",\\n \\\"y\\\": \\\"fail_rate\\\",\\n \\\"title\\\": \\\"Fail Rate by Majority Share\\\"\\n },\\n {\\n \\\"name\\\": \\\"failrate_by_selfcheck_inconsistency\\\",\\n \\\"kind\\\": \\\"line\\\",\\n \\\"display_name\\\": \\\"Failure Rate by Reasoning Contradiction\\\",\\n \\\"data\\\": \\\"tables/failrate_by_selfcheck_inconsistency.csv\\\",\\n \\\"x\\\": \\\"selfcheck_worst_bin\\\",\\n \\\"y\\\": \\\"fail_rate\\\",\\n \\\"title\\\": \\\"Fail Rate by Reasoning Inconsistency\\\"\\n },\\n {\\n \\\"name\\\": \\\"scatter_agreement_vs_invariance\\\",\\n \\\"kind\\\": \\\"scatter\\\",\\n \\\"display_name\\\": \\\"Consensus vs Invariance Scatter\\\",\\n \\\"data\\\": \\\"tables/scatter_agreement_vs_invariance.csv\\\",\\n \\\"x\\\": \\\"majority_share\\\",\\n \\\"y\\\": \\\"invariance_break_rate\\\",\\n \\\"title\\\": \\\"Consensus vs Invariance Scatter\\\"\\n },\\n {\\n \\\"name\\\": \\\"failrate_by_output_chars\\\",\\n \\\"kind\\\": \\\"line\\\",\\n \\\"display_name\\\": \\\"Failure Rate by Output Length\\\",\\n \\\"data\\\": \\\"tables/failrate_by_output_chars.csv\\\",\\n \\\"x\\\": \\\"output_chars_bin\\\",\\n \\\"y\\\": \\\"fail_rate\\\",\\n \\\"title\\\": \\\"Fail Rate by Output Length\\\"\\n }\\n ],\\n \\\"caveats\\\": [\\n \\\"M1 diagnostic analyzer columns are populated for 8 of the 64 cases; 56 cases contain label-only information.\\\",\\n \\\"All candidate thresholds were identified on this exploration sample and must be re-evaluated verbatim on held-out cases.\\\",\\n \\\"Findings represent descriptive associations within this single task slice (BBH word sorting) and may not generalize to other reasoning tasks.\\\"\\n ],\\n \\\"critique\\\": [\\n \\\"Sample size for signal comparison is small (n=8: 5 FAIL, 3 PASS), which widens uncertainty around effect size magnitudes.\\\",\\n \\\"Threshold selection carries potential double-dipping risk if treated as confirmed rather than candidate screening recipes.\\\",\\n \\\"Several diagnostic columns (such as gave_up and extraction_point_miss) were constant across all measured cases and could not be evaluated as discriminators.\\\"\\n ],\\n \\\"recommended_confirmatory_tests\\\": [\\n \\\"Compute candidate recipes verbatim on the remaining 56 unmeasured cases once analyzer signals are extracted.\\\",\\n \\\"Evaluate Fisher's exact test of independence between the composite signal (break_rate >= 0.8 or majority_share <= 0.4) and the gold FAIL/PASS outcome on a fresh held-out evaluation batch.\\\"\\n ]\\n }\\n \\n print(\\\"EXPLORATORY_RESULT_JSON=\\\" + json.dumps(result))\\n\\nif __name__ == '__main__':\\n main()\\n\",\n \"stdout\": \"EXPLORATORY_RESULT_JSON={\\\"plain_question\\\": \\\"Which recorded case measurements tell apart questions the language model sorted wrong from those it sorted right?\\\", \\\"observations\\\": [\\\"Dataset comprises 64 evaluated word sorting items from BBH, with 37 failures (57.8%) and 27 passes (42.2%).\\\", \\\"M1 diagnostic analyzer signals were collected on an exploratory diagnostic sub-sample of 8 items (5 FAIL, 3 PASS), while the remaining 56 rows contain outcome labels only.\\\", \\\"Across the 8 analyzed items, the perturbation battery invariance break rate was noticeably higher in FAIL cases (mean 0.93, with 4 of 5 cases at 1.0) compared to PASS cases (mean 0.56, all <= 0.67).\\\", \\\"Sampling majority agreement share separated cases cleanly: all 3 PASS cases had majority agreement of 60% or higher, whereas 4 of 5 FAIL cases had majority agreement of 40% or lower.\\\", \\\"Truncation and tag omissions occurred exclusively in FAIL cases (2 of 5 failures had truncated outputs and missing answer tags, whereas 0 of 3 passes did).\\\", \\\"Selfcheck worst sentence contradiction scores were zero in all 3 PASS cases, but positive in 4 of 5 FAIL cases.\\\", \\\"Model confidence (calibration logprob) remained high across both groups (PASS mean 0.960, FAIL mean 0.980), showing model overconfidence even on failed word sorting runs.\\\"], \\\"visual_plan\\\": [{\\\"name\\\": \\\"class_balance\\\", \\\"display_name\\\": \\\"Overall FAIL and PASS Case Counts\\\", \\\"question\\\": \\\"What is the overall balance between incorrect and correct sorting cases in this batch?\\\", \\\"data_shape\\\": \\\"categorical\\\", \\\"plot_kind\\\": \\\"composition_strip\\\", \\\"fallback_kind\\\": \\\"bar\\\", \\\"required_columns\\\": [\\\"label\\\"], \\\"rationale\\\": \\\"A composition count shows the total cohort split (37 fail vs 27 pass) across the complete set of 64 cases.\\\", \\\"disposition\\\": \\\"primary\\\", \\\"not_promoted_reason\\\": \\\"\\\"}, {\\\"name\\\": \\\"top_discriminators\\\", \\\"display_name\\\": \\\"Ranked Signal Separation\\\", \\\"question\\\": \\\"Which diagnostic signals show the largest differences between FAIL and PASS cases?\\\", \\\"data_shape\\\": \\\"many-numeric\\\", \\\"plot_kind\\\": \\\"forest\\\", \\\"fallback_kind\\\": \\\"forest\\\", \\\"required_columns\\\": [\\\"label\\\"], \\\"rationale\\\": \\\"Ranked standardized mean difference highlights which indicators provide the strongest contrast without assuming normal distributions.\\\", \\\"disposition\\\": \\\"primary\\\", \\\"not_promoted_reason\\\": \\\"\\\"}, {\\\"name\\\": \\\"failrate_by_invariance_break\\\", \\\"display_name\\\": \\\"Failure Rate by Invariance Break Rate\\\", \\\"question\\\": \\\"How does prompt fragility to minor phrasing changes correspond to task failure?\\\", \\\"data_shape\\\": \\\"numeric-vs-binary\\\", \\\"plot_kind\\\": \\\"line\\\", \\\"fallback_kind\\\": \\\"line\\\", \\\"required_columns\\\": [\\\"perturbation_battery_invariance_break_rate\\\", \\\"label\\\"], \\\"rationale\\\": \\\"Ordered discrete levels of perturbation instability display failure concentration at high sensitivity levels.\\\", \\\"disposition\\\": \\\"primary\\\", \\\"not_promoted_reason\\\": \\\"\\\"}, {\\\"name\\\": \\\"failrate_by_majority_share\\\", \\\"display_name\\\": \\\"Failure Rate by Sampling Consensus\\\", \\\"question\\\": \\\"Does agreement across repeated samples distinguish correct answers from errors?\\\", \\\"data_shape\\\": \\\"numeric-vs-binary\\\", \\\"plot_kind\\\": \\\"line\\\", \\\"fallback_kind\\\": \\\"line\\\", \\\"required_columns\\\": [\\\"coverage_verification_gap_majority_share\\\", \\\"label\\\"], \\\"rationale\\\": \\\"Binned repeated-sample consensus rates reveal how self-consistency drops when the model fails.\\\", \\\"disposition\\\": \\\"primary\\\", \\\"not_promoted_reason\\\": \\\"\\\"}, {\\\"name\\\": \\\"failrate_by_selfcheck_inconsistency\\\", \\\"display_name\\\": \\\"Failure Rate by Reasoning Contradiction\\\", \\\"question\\\": \\\"Are internal chain-of-thought contradictions associated with incorrect final sorting?\\\", \\\"data_shape\\\": \\\"numeric-vs-binary\\\", \\\"plot_kind\\\": \\\"line\\\", \\\"fallback_kind\\\": \\\"line\\\", \\\"required_columns\\\": [\\\"selfcheck_consistency_selfcheck_worst_sentence\\\", \\\"label\\\"], \\\"rationale\\\": \\\"Separates cases with completely consistent reasoning traces from those with detected sentence-level inconsistencies.\\\", \\\"disposition\\\": \\\"primary\\\", \\\"not_promoted_reason\\\": \\\"\\\"}, {\\\"name\\\": \\\"scatter_agreement_vs_invariance\\\", \\\"display_name\\\": \\\"Consensus vs Invariance Scatter\\\", \\\"question\\\": \\\"How do sampling consensus and prompt sensitivity jointly separate correct and incorrect items?\\\", \\\"data_shape\\\": \\\"numeric-vs-numeric\\\", \\\"plot_kind\\\": \\\"scatter\\\", \\\"fallback_kind\\\": \\\"scatter\\\", \\\"required_columns\\\": [\\\"coverage_verification_gap_majority_share\\\", \\\"perturbation_battery_invariance_break_rate\\\", \\\"label\\\"], \\\"rationale\\\": \\\"A 2D scatter visually inspects whether combining consistency and prompt stability creates an unambiguous boundary between outcomes.\\\", \\\"disposition\\\": \\\"primary\\\", \\\"not_promoted_reason\\\": \\\"\\\"}, {\\\"name\\\": \\\"failrate_by_output_chars\\\", \\\"display_name\\\": \\\"Failure Rate by Output Length\\\", \\\"question\\\": \\\"Do unusually long or short responses correspond to higher error rates?\\\", \\\"data_shape\\\": \\\"numeric-vs-binary\\\", \\\"plot_kind\\\": \\\"line\\\", \\\"fallback_kind\\\": \\\"line\\\", \\\"required_columns\\\": [\\\"termination_audit_output_chars\\\", \\\"label\\\"], \\\"rationale\\\": \\\"Tertiles of response character length check whether verbosity or truncation flags correlate with failure.\\\", \\\"disposition\\\": \\\"supporting\\\", \\\"not_promoted_reason\\\": \\\"Output length exhibits non-monotonic differences driven by truncated outliers rather than a stable linear trend.\\\"}], \\\"takeaways\\\": [{\\\"plain_title\\\": \\\"Items where rephrasing the question caused the model to change its answer failed almost every time.\\\", \\\"title\\\": \\\"A perturbation invariance break rate of 1.0 occurred in 4 of 5 FAIL cases (80%), compared with 0 of 3 PASS cases (0%).\\\", \\\"chart_names\\\": [\\\"failrate_by_invariance_break\\\", \\\"top_discriminators\\\"], \\\"table_names\\\": [\\\"failrate_by_invariance_break\\\", \\\"top_discriminators\\\"], \\\"analysis\\\": \\\"In cases where minor question rewording altered the model's sorting sequence (break rate equal to 1.0), all 4 cases resulted in failures. By contrast, all 3 passing cases maintained break rates of 0.67 or lower. This shows that brittleness to prompt wording strongly separates failures from successes in this batch, though the small analyzed sample of 8 cases means exact error rates require validation on larger batches.\\\", \\\"caveat\\\": \\\"Descriptive pattern evaluated on 8 diagnostic rows; prompt sensitivity may also reflect overall question difficulty rather than an isolated cause.\\\"}, {\\\"plain_title\\\": \\\"When repeated attempts produced conflicting answers, the model was consistently wrong.\\\", \\\"title\\\": \\\"Low majority agreement (40% or below across 5 samples) captured 4 of 5 FAIL cases (80%) and 0 of 3 PASS cases (0%).\\\", \\\"chart_names\\\": [\\\"failrate_by_majority_share\\\", \\\"scatter_agreement_vs_invariance\\\"], \\\"table_names\\\": [\\\"failrate_by_majority_share\\\", \\\"scatter_agreement_vs_invariance\\\"], \\\"analysis\\\": \\\"The majority share among 5 sampled completions was 0.40 or lower for 4 out of 5 failed items, while all 3 successful items achieved majority agreement between 0.60 and 1.00. This matters because self-consistency sampling offers a clear practical signal of sorting failure. However, whether the lack of consensus is specific to word sorting remains unconfirmed until tested on other task types.\\\", \\\"caveat\\\": \\\"Based on 8 analyzed cases; threshold choice (majority share <= 0.4) was identified on this exploratory batch.\\\"}, {\\\"plain_title\\\": \\\"Internal contradictions in the reasoning text appeared only in failure cases.\\\", \\\"title\\\": \\\"A non-zero sentence inconsistency score occurred in 4 of 5 FAIL cases (80%) versus 0 of 3 PASS cases (0%).\\\", \\\"chart_names\\\": [\\\"failrate_by_selfcheck_inconsistency\\\"], \\\"table_names\\\": [\\\"failrate_by_selfcheck_inconsistency\\\"], \\\"analysis\\\": \\\"Inspection of internal sentence consistency revealed that all 3 passing cases had zero detected contradictions (score 0.0), whereas 4 of 5 failing cases contained contradictory statements (scores ranging from 0.33 to 0.67). This indicates that the model frequently makes contradictory claims during intermediate reasoning when failing to sort words. It remains uncertain whether the contradiction directly leads to the wrong answer or is simply a byproduct of confusion.\\\", \\\"caveat\\\": \\\"Observed on 8 cases; sentence inconsistency detectors can suffer from false positives on long complex chains of thought.\\\"}, {\\\"plain_title\\\": \\\"High model confidence does not guarantee correct sorting.\\\", \\\"title\\\": \\\"The model assigned high token probability (averaging 98.0%) even across the 5 failed sorting attempts.\\\", \\\"chart_names\\\": [\\\"top_discriminators\\\"], \\\"table_names\\\": [\\\"top_discriminators\\\"], \\\"analysis\\\": \\\"Across all 8 analyzed cases, the model's reported calibration logprob confidence remained above 0.94, with failures averaging 0.980 and passes averaging 0.960. This demonstrates that the model is severely overconfident when producing incorrect alphabetical sequences. Downstream systems cannot rely on raw token confidence alone to filter out incorrect word sorting outputs.\\\", \\\"caveat\\\": \\\"Token probability reflects next-token likelihood under the generation path rather than genuine correctness probability.\\\"}, {\\\"plain_title\\\": \\\"The full dataset has a moderate error rate, but detailed diagnostics were captured on an eight-case slice.\\\", \\\"title\\\": \\\"Across all 64 evaluated cases, 37 failed (57.8%) and 27 passed (42.2%), while M1 signals were recorded on 8 cases.\\\", \\\"chart_names\\\": [\\\"class_balance\\\"], \\\"table_names\\\": [\\\"class_balance\\\"], \\\"analysis\\\": \\\"The overall dataset contains 64 cases, showing that word sorting is a challenging task for Gemma 4 E2B-it with a 57.8% failure rate (37 of 64). Detailed diagnostic signals were gathered on an 8-case subset (5 failures, 3 passes), while 56 cases retain label-only information. This establishes the overall task difficulty while highlighting that signal thresholds must be treated as candidates for wider confirmation.\\\", \\\"caveat\\\": \\\"The diagnostic subset of 8 cases may have different sampling properties than the remaining 56 unmeasured cases.\\\"}], \\\"chart_readings\\\": {\\\"class_balance\\\": {\\\"chart\\\": \\\"Overall FAIL and PASS Case Counts\\\", \\\"reading\\\": \\\"Failures account for 37 of 64 cases (57.8%), while passes account for 27 of 64 cases (42.2%).\\\", \\\"do_not_infer\\\": \\\"This chart shows dataset composition; it does not measure signal performance.\\\"}, \\\"top_discriminators\\\": {\\\"chart\\\": \\\"Ranked Signal Separation\\\", \\\"reading\\\": \\\"Perturbation invariance break rate, sample majority agreement share, and sentence inconsistency show the strongest standardized contrasts between the 5 fail and 3 pass cases.\\\", \\\"do_not_infer\\\": \\\"High separation on an exploratory batch of 8 cases does not guarantee equal predictive power on unmeasured cases.\\\"}, \\\"failrate_by_invariance_break\\\": {\\\"chart\\\": \\\"Failure Rate by Invariance Break Rate\\\", \\\"reading\\\": \\\"Items with invariance break rates of 1.0 failed in 4 of 4 cases (100%), whereas items with lower break rates failed in 1 of 4 cases (25%).\\\", \\\"do_not_infer\\\": \\\"Prompt sensitivity does not prove rephrasing caused the failure; both may stem from underlying problem complexity.\\\"}, \\\"failrate_by_majority_share\\\": {\\\"chart\\\": \\\"Failure Rate by Sampling Consensus\\\", \\\"reading\\\": \\\"Items where the top sample won 40% or less of repeated generations failed in 4 of 4 instances (100%), while items with 60% or higher agreement failed in 1 of 4 instances (25%).\\\", \\\"do_not_infer\\\": \\\"Low consensus reflects generation dispersion, not necessarily an inability to produce the right answer under different decoding parameters.\\\"}, \\\"failrate_by_selfcheck_inconsistency\\\": {\\\"chart\\\": \\\"Failure Rate by Reasoning Contradiction\\\", \\\"reading\\\": \\\"All 4 cases with detected sentence inconsistencies resulted in failure (100%), compared to 1 failure out of 4 cases with zero inconsistency (25%).\\\", \\\"do_not_infer\\\": \\\"Internal inconsistency is a descriptive symptom of degraded reasoning, not a proven cause of the final answer error.\\\"}, \\\"scatter_agreement_vs_invariance\\\": {\\\"chart\\\": \\\"Consensus vs Invariance Scatter\\\", \\\"reading\\\": \\\"Passing cases cluster in the high-consensus (>=0.6) and lower-fragility (<=0.67) quadrant, while 4 of 5 failures occupy the low-consensus or high-fragility region.\\\", \\\"do_not_infer\\\": \\\"A visual decision boundary on 8 points is exploratory and must be tested on held-out data.\\\"}, \\\"failrate_by_output_chars\\\": {\\\"chart\\\": \\\"Failure Rate by Output Length\\\", \\\"reading\\\": \\\"Failure rates are elevated in both short responses (<1000 chars, 1 of 2 fail) and long responses (>1200 chars, 2 of 2 fail), with the longest responses including truncated failures.\\\", \\\"do_not_infer\\\": \\\"Response length does not linearly drive failure; length variation is partly influenced by truncation and verbosity.\\\"}, \\\"top_discriminators_forest\\\": {\\\"chart\\\": \\\"Top Per-Case Signals Separating FAIL from PASS (Forest Plot)\\\", \\\"reading\\\": \\\"Invariance break rate, consensus majority share, and sentence-level inconsistency display the largest standardized differences between the 5 FAIL and 3 PASS cases.\\\", \\\"do_not_infer\\\": \\\"Standardized mean differences on 8 observations are exploratory estimates subject to wide sampling variance.\\\"}, \\\"signal_distributions_strip\\\": {\\\"chart\\\": \\\"Distribution of Leading Diagnostic Signals across Outcomes (Strip Plot)\\\", \\\"reading\\\": \\\"Individual point markers highlight distinct separation in break rate, sample agreement, and worst sentence inconsistency between FAIL and PASS items.\\\", \\\"do_not_infer\\\": \\\"Small sample size (n=8) limits conclusions about distributional shape or tails.\\\"}, \\\"scatter_consensus_vs_invariance\\\": {\\\"chart\\\": \\\"Interaction: Sampling Consensus vs. Phrasing Fragility\\\", \\\"reading\\\": \\\"Combining sampling consensus (majority share <= 0.4) with prompt fragility (invariance break rate >= 0.8) completely separates 4 of 5 failure cases from all passes.\\\", \\\"do_not_infer\\\": \\\"Thresholds chosen to separate this 8-case sample may exhibit optimism when applied to new cases.\\\"}, \\\"corr_heatmap\\\": {\\\"chart\\\": \\\"Correlation Matrix of Numeric Diagnostic Signals\\\", \\\"reading\\\": \\\"Sampling consensus shares strong negative correlation with unique sample count and invariance break rate, indicating coupled dimensions of model uncertainty.\\\", \\\"do_not_infer\\\": \\\"Correlations among 8 cases indicate co-occurrence patterns, not causal dependencies.\\\"}}, \\\"claims\\\": [{\\\"id\\\": \\\"C1\\\", \\\"text\\\": \\\"High prompt invariance break rate (>= 0.8) is descriptively associated with task failure.\\\", \\\"status\\\": \\\"descriptive\\\", \\\"evidence_ids\\\": [\\\"chart:failrate_by_invariance_break\\\", \\\"figure:signal_distributions_strip\\\"], \\\"interpretation\\\": \\\"A candidate signal indicating that sensitivity to prompt wording correlates with sorting mistakes.\\\", \\\"do_not_infer\\\": \\\"Does not prove causal sensitivity; observed on 8 cases.\\\"}, {\\\"id\\\": \\\"C2\\\", \\\"text\\\": \\\"Low repeated-sample agreement (majority share <= 0.4) is descriptively associated with task failure.\\\", \\\"status\\\": \\\"descriptive\\\", \\\"evidence_ids\\\": [\\\"chart:failrate_by_majority_share\\\", \\\"figure:scatter_consensus_vs_invariance\\\"], \\\"interpretation\\\": \\\"A candidate signal indicating that lack of generation consensus correlates with incorrect word sorting.\\\", \\\"do_not_infer\\\": \\\"Does not establish that sampling consensus alone causes correctness; requires confirmatory testing.\\\"}, {\\\"id\\\": \\\"C3\\\", \\\"text\\\": \\\"Internal sentence inconsistency (> 0.0) is descriptively concentrated in failure cases.\\\", \\\"status\\\": \\\"descriptive\\\", \\\"evidence_ids\\\": [\\\"chart:failrate_by_selfcheck_inconsistency\\\"], \\\"interpretation\\\": \\\"A candidate signal indicating that detected reasoning contradictions coincide with failed answers.\\\", \\\"do_not_infer\\\": \\\"Does not prove internal contradiction caused the final output sorting mistake.\\\"}], \\\"dashboard_storyboard\\\": [{\\\"id\\\": \\\"problem_setting\\\", \\\"title\\\": \\\"Problem Setting\\\", \\\"summary\\\": \\\"This run studies Gemma 4 E2B-it on the Big-Bench Hard word sorting task, comparing cases where the model sorted words incorrectly (FAIL) against those where it succeeded (PASS).\\\", \\\"items\\\": [\\\"User question: which per-case diagnostic signals distinguish FAIL cases from PASS cases?\\\", \\\"Unit of analysis: one row is one evaluated BBH word sorting case.\\\", \\\"Outcome definition: FAIL means the model's sorted word list did not match the gold alphabetical order; PASS means it matched exactly.\\\", \\\"Dataset scale: 64 total cases evaluated (37 FAIL, 27 PASS); 8 cases contain full M1 diagnostic analyzer measurements.\\\"], \\\"artifact_refs\\\": [\\\"data_profile\\\"]}], \\\"candidate_signals\\\": [{\\\"name\\\": \\\"high_invariance_break_rate\\\", \\\"display_name\\\": \\\"High Phrasing Fragility\\\", \\\"rationale\\\": \\\"Identifies cases where rephrasing the question caused the model to alter its answer at least two-thirds of the time.\\\", \\\"suggested_test\\\": \\\"Fisher's exact test on held-out cases comparing failure rates above and below break rate 0.80.\\\", \\\"recipe\\\": {\\\"name\\\": \\\"high_invariance_break_rate\\\", \\\"kind\\\": \\\"expr\\\", \\\"expr\\\": \\\"(perturbation_battery_invariance_break_rate >= 0.8)\\\"}}, {\\\"name\\\": \\\"low_sampling_consensus\\\", \\\"display_name\\\": \\\"Low Sampling Consensus\\\", \\\"rationale\\\": \\\"Flags items where 5 independent generations yielded low consensus (majority share at or below 40%).\\\", \\\"suggested_test\\\": \\\"Two-group proportion test of failure rate among low-consensus versus high-consensus cases.\\\", \\\"recipe\\\": {\\\"name\\\": \\\"low_sampling_consensus\\\", \\\"kind\\\": \\\"expr\\\", \\\"expr\\\": \\\"(coverage_verification_gap_majority_share <= 0.4)\\\"}}, {\\\"name\\\": \\\"reasoning_inconsistency_detected\\\", \\\"display_name\\\": \\\"Reasoning Inconsistency Detected\\\", \\\"rationale\\\": \\\"Detects items where the worst-sentence self-check inconsistency score is strictly greater than zero.\\\", \\\"suggested_test\\\": \\\"Contingency table test of failure prevalence between consistent and inconsistent chains of thought.\\\", \\\"recipe\\\": {\\\"name\\\": \\\"reasoning_inconsistency_detected\\\", \\\"kind\\\": \\\"expr\\\", \\\"expr\\\": \\\"(selfcheck_consistency_selfcheck_worst_sentence > 0.0)\\\"}}, {\\\"name\\\": \\\"fragile_or_discordant_composite\\\", \\\"display_name\\\": \\\"Composite: Fragile Phrasing or Low Consensus\\\", \\\"rationale\\\": \\\"Combines prompt brittleness and sampling dispersion into a single threshold-explicit failure indicator.\\\", \\\"suggested_test\\\": \\\"Diagnostic sensitivity and specificity comparison against gold failure labels on a held-out split.\\\", \\\"recipe\\\": {\\\"name\\\": \\\"fragile_or_discordant_composite\\\", \\\"kind\\\": \\\"expr\\\", \\\"expr\\\": \\\"(perturbation_battery_invariance_break_rate >= 0.8) or (coverage_verification_gap_majority_share <= 0.4)\\\"}}], \\\"plots\\\": [\\\"figures/top_discriminators_forest.png\\\", \\\"figures/signal_distributions_strip.png\\\", \\\"figures/scatter_consensus_vs_invariance.png\\\", \\\"figures/corr_heatmap.png\\\"], \\\"tables\\\": {}, \\\"charts\\\": [{\\\"name\\\": \\\"class_balance\\\", \\\"kind\\\": \\\"bar\\\", \\\"display_name\\\": \\\"Overall FAIL and PASS Case Counts\\\", \\\"data\\\": \\\"tables/class_balance.csv\\\", \\\"x\\\": \\\"outcome\\\", \\\"y\\\": \\\"count\\\", \\\"title\\\": \\\"FAIL vs PASS Case Balance\\\"}, {\\\"name\\\": \\\"top_discriminators\\\", \\\"kind\\\": \\\"forest\\\", \\\"display_name\\\": \\\"Ranked Signal Separation\\\", \\\"data\\\": \\\"tables/top_discriminators.csv\\\", \\\"x\\\": \\\"signal\\\", \\\"y\\\": \\\"smd\\\", \\\"title\\\": \\\"Top FAIL/PASS Discriminators\\\"}, {\\\"name\\\": \\\"failrate_by_invariance_break\\\", \\\"kind\\\": \\\"line\\\", \\\"display_name\\\": \\\"Failure Rate by Invariance Break Rate\\\", \\\"data\\\": \\\"tables/failrate_by_invariance_break.csv\\\", \\\"x\\\": \\\"invariance_break_bin\\\", \\\"y\\\": \\\"fail_rate\\\", \\\"title\\\": \\\"Fail Rate by Invariance Break Rate\\\"}, {\\\"name\\\": \\\"failrate_by_majority_share\\\", \\\"kind\\\": \\\"line\\\", \\\"display_name\\\": \\\"Failure Rate by Sampling Consensus\\\", \\\"data\\\": \\\"tables/failrate_by_majority_share.csv\\\", \\\"x\\\": \\\"majority_share_bin\\\", \\\"y\\\": \\\"fail_rate\\\", \\\"title\\\": \\\"Fail Rate by Majority Share\\\"}, {\\\"name\\\": \\\"failrate_by_selfcheck_inconsistency\\\", \\\"kind\\\": \\\"line\\\", \\\"display_name\\\": \\\"Failure Rate by Reasoning Contradiction\\\", \\\"data\\\": \\\"tables/failrate_by_selfcheck_inconsistency.csv\\\", \\\"x\\\": \\\"selfcheck_worst_bin\\\", \\\"y\\\": \\\"fail_rate\\\", \\\"title\\\": \\\"Fail Rate by Reasoning Inconsistency\\\"}, {\\\"name\\\": \\\"scatter_agreement_vs_invariance\\\", \\\"kind\\\": \\\"scatter\\\", \\\"display_name\\\": \\\"Consensus vs Invariance Scatter\\\", \\\"data\\\": \\\"tables/scatter_agreement_vs_invariance.csv\\\", \\\"x\\\": \\\"majority_share\\\", \\\"y\\\": \\\"invariance_break_rate\\\", \\\"title\\\": \\\"Consensus vs Invariance Scatter\\\"}, {\\\"name\\\": \\\"failrate_by_output_chars\\\", \\\"kind\\\": \\\"line\\\", \\\"display_name\\\": \\\"Failure Rate by Output Length\\\", \\\"data\\\": \\\"tables/failrate_by_output_chars.csv\\\", \\\"x\\\": \\\"output_chars_bin\\\", \\\"y\\\": \\\"fail_rate\\\", \\\"title\\\": \\\"Fail Rate by Output Length\\\"}], \\\"caveats\\\": [\\\"M1 diagnostic analyzer columns are populated for 8 of the 64 cases; 56 cases contain label-only information.\\\", \\\"All candidate thresholds were identified on this exploration sample and must be re-evaluated verbatim on held-out cases.\\\", \\\"Findings represent descriptive associations within this single task slice (BBH word sorting) and may not generalize to other reasoning tasks.\\\"], \\\"critique\\\": [\\\"Sample size for signal comparison is small (n=8: 5 FAIL, 3 PASS), which widens uncertainty around effect size magnitudes.\\\", \\\"Threshold selection carries potential double-dipping risk if treated as confirmed rather than candidate screening recipes.\\\", \\\"Several diagnostic columns (such as gave_up and extraction_point_miss) were constant across all measured cases and could not be evaluated as discriminators.\\\"], \\\"recommended_confirmatory_tests\\\": [\\\"Compute candidate recipes verbatim on the remaining 56 unmeasured cases once analyzer signals are extracted.\\\", \\\"Evaluate Fisher's exact test of independence between the composite signal (break_rate >= 0.8 or majority_share <= 0.4) and the gold FAIL/PASS outcome on a fresh held-out evaluation batch.\\\"]}\\n\",\n \"stderr\": \"\",\n \"error\": \"\",\n \"attempts\": 1,\n \"workdir\": \"/tmp/evalrx-logs-48lc0s2h/explore/sandbox\",\n \"agent_audits\": [\n {\n \"schema_version\": 1,\n \"provider\": \"antigravity\",\n \"skills_requested\": [\n \"eval-chart-style\",\n \"evalrx-report-ui\",\n \"nature-figure\",\n \"outcome-driver-analysis\"\n ],\n \"skills_installed\": [\n \"eval-chart-style\",\n \"evalrx-report-ui\",\n \"nature-figure\",\n \"outcome-driver-analysis\"\n ],\n \"skills_invoked\": [],\n \"skill_observability\": \"not_observable\",\n \"evidence\": [],\n \"execution\": {\n \"status\": \"completed\",\n \"returncode\": 0,\n \"timed_out\": false,\n \"elapsed_sec\": 71.379,\n \"produced_files\": [\n \"analysis.py\"\n ],\n \"error\": \"\",\n \"stderr\": \"\",\n \"raw_stream\": \"agent_raw_stream.txt\"\n }\n }\n ],\n \"model_calls\": [\n {\n \"role\": \"explore_cli_coder\",\n \"operation\": \"write_code\",\n \"inputs\": \"You are an exploratory data-analysis agent (Lambda-style): given ANY tabular\\ndataset \\u2014 with a binary outcome, a multi-class outcome, a continuous outcome,\\nor no outcome at all \\u2014 you write Python that discovers and charts the\\nstructure that actually matters, adapting the story to what the data is.\\n\\nQuestion:\\nWhich per-case signals distinguish the FAIL cases (label=fail) from the PASS cases (label=pass)? Context: A text-only LLM (Gemma 4 E2B-it, spec gemma-4-e2b-it = google/gemma-4-E2B-it) answers items from lukaemon/bbh \\u00b7 config=word_sorting. One named BBH task: sort a word list into alphabetical order. Failure cases are items the model answered incorrectly; the batch also contains, as controls, items from the same slice it answered correctly. Each answer is scored against its own gold answer. ch4-basic Every other column is a per-case signal an M1 analyzer computed on the model under diagnosis (column name = analyzer.metric, sanitized). Compare each signal's distribution between FAIL and PASS, look for interactions and thresholds, and make every candidate signal a FROZEN, threshold-explicit recipe over the available numeric columns (explicit numeric cut-offs, no re-fitting) so it can be re-evaluated verbatim on held-out cases. Charts are leads for hypotheses, not proof: this is the exploration half of a diagnosis loop, and a separate confirmatory stage will test whatever you surface.\\n\\nA JSON file named \\\"records.json\\\" is in the current working directory.\\nIt contains a list of row dictionaries. Data profile (the host already\\ninferred column roles/dtypes and classified the outcome below \\u2014 trust this\\nover re-guessing from raw values):\\n{\\n \\\"n_rows\\\": 64,\\n \\\"grain\\\": \\\"case\\\",\\n \\\"columns\\\": {\\n \\\"answer_extraction_audit_extraction_point_miss\\\": {\\n \\\"name\\\": \\\"answer_extraction_audit_extraction_point_miss\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 8,\\n \\\"missing\\\": 56,\\n \\\"unique\\\": 1,\\n \\\"numeric_min\\\": 0.0,\\n \\\"numeric_max\\\": 0.0,\\n \\\"numeric_mean\\\": 0.0,\\n \\\"is_constant\\\": true,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n },\\n \\\"answer_extraction_audit_extraction_suspect\\\": {\\n \\\"name\\\": \\\"answer_extraction_audit_extraction_suspect\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 8,\\n \\\"missing\\\": 56,\\n \\\"unique\\\": 1,\\n \\\"numeric_min\\\": 0.0,\\n \\\"numeric_max\\\": 0.0,\\n \\\"numeric_mean\\\": 0.0,\\n \\\"is_constant\\\": true,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n },\\n \\\"answer_extraction_audit_gave_up\\\": {\\n \\\"name\\\": \\\"answer_extraction_audit_gave_up\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 8,\\n \\\"missing\\\": 56,\\n \\\"unique\\\": 1,\\n \\\"numeric_min\\\": 0.0,\\n \\\"numeric_max\\\": 0.0,\\n \\\"numeric_mean\\\": 0.0,\\n \\\"is_constant\\\": true,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n },\\n \\\"answer_extraction_audit_has_answer_tag\\\": {\\n \\\"name\\\": \\\"answer_extraction_audit_has_answer_tag\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 8,\\n \\\"missing\\\": 56,\\n \\\"unique\\\": 2,\\n \\\"numeric_min\\\": 0.0,\\n \\\"numeric_max\\\": 1.0,\\n \\\"numeric_mean\\\": 0.75,\\n \\\"is_constant\\\": false,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n },\\n \\\"answer_extraction_audit_has_output\\\": {\\n \\\"name\\\": \\\"answer_extraction_audit_has_output\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 8,\\n \\\"missing\\\": 56,\\n \\\"unique\\\": 1,\\n \\\"numeric_min\\\": 1.0,\\n \\\"numeric_max\\\": 1.0,\\n \\\"numeric_mean\\\": 1.0,\\n \\\"is_constant\\\": true,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n },\\n \\\"answer_extraction_audit_label_disagrees\\\": {\\n \\\"name\\\": \\\"answer_extraction_audit_label_disagrees\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 8,\\n \\\"missing\\\": 56,\\n \\\"unique\\\": 1,\\n \\\"numeric_min\\\": 0.0,\\n \\\"numeric_max\\\": 0.0,\\n \\\"numeric_mean\\\": 0.0,\\n \\\"is_constant\\\": true,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": true\\n },\\n \\\"answer_extraction_audit_labelled_fail\\\": {\\n \\\"name\\\": \\\"answer_extraction_audit_labelled_fail\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 8,\\n \\\"missing\\\": 56,\\n \\\"unique\\\": 2,\\n \\\"numeric_min\\\": 0.0,\\n \\\"numeric_max\\\": 1.0,\\n \\\"numeric_mean\\\": 0.625,\\n \\\"is_constant\\\": false,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": true\\n },\\n \\\"answer_extraction_audit_output_chars\\\": {\\n \\\"name\\\": \\\"answer_extraction_audit_output_chars\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 8,\\n \\\"missing\\\": 56,\\n \\\"unique\\\": 8,\\n \\\"numeric_min\\\": 690.0,\\n \\\"numeric_max\\\": 1389.0,\\n \\\"numeric_mean\\\": 1086.375,\\n \\\"is_constant\\\": false,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n },\\n \\\"answer_extraction_audit_output_truncated\\\": {\\n \\\"name\\\": \\\"answer_extraction_audit_output_truncated\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 8,\\n \\\"missing\\\": 56,\\n \\\"unique\\\": 2,\\n \\\"numeric_min\\\": 0.0,\\n \\\"numeric_max\\\": 1.0,\\n \\\"numeric_mean\\\": 0.25,\\n \\\"is_constant\\\": false,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n },\\n \\\"calibration_conf_logprob\\\": {\\n \\\"name\\\": \\\"calibration_conf_logprob\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 8,\\n \\\"missing\\\": 56,\\n \\\"unique\\\": 8,\\n \\\"numeric_min\\\": 0.9454,\\n \\\"numeric_max\\\": 0.9936,\\n \\\"numeric_mean\\\": 0.9728125000000001,\\n \\\"is_constant\\\": false,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n },\\n \\\"calibration_conf_verbal\\\": {\\n \\\"name\\\": \\\"calibration_conf_verbal\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 2,\\n \\\"missing\\\": 62,\\n \\\"unique\\\": 1,\\n \\\"numeric_min\\\": 1.0,\\n \\\"numeric_max\\\": 1.0,\\n \\\"numeric_mean\\\": 1.0,\\n \\\"is_constant\\\": true,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n },\\n \\\"calibration_correct\\\": {\\n \\\"name\\\": \\\"calibration_correct\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 8,\\n \\\"missing\\\": 56,\\n \\\"unique\\\": 2,\\n \\\"numeric_min\\\": 0.0,\\n \\\"numeric_max\\\": 1.0,\\n \\\"numeric_mean\\\": 0.375,\\n \\\"is_constant\\\": false,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n },\\n \\\"case_id\\\": {\\n \\\"name\\\": \\\"case_id\\\",\\n \\\"dtype\\\": \\\"categorical\\\",\\n \\\"role\\\": \\\"id\\\",\\n \\\"non_null\\\": 64,\\n \\\"missing\\\": 0,\\n \\\"unique\\\": 64,\\n \\\"numeric_min\\\": null,\\n \\\"numeric_max\\\": null,\\n \\\"numeric_mean\\\": null,\\n \\\"is_constant\\\": false,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n },\\n \\\"coverage_verification_gap_coverage_gap\\\": {\\n \\\"name\\\": \\\"coverage_verification_gap_coverage_gap\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 8,\\n \\\"missing\\\": 56,\\n \\\"unique\\\": 2,\\n \\\"numeric_min\\\": 0.0,\\n \\\"numeric_max\\\": 1.0,\\n \\\"numeric_mean\\\": 0.125,\\n \\\"is_constant\\\": false,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n },\\n \\\"coverage_verification_gap_majority_share\\\": {\\n \\\"name\\\": \\\"coverage_verification_gap_majority_share\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 8,\\n \\\"missing\\\": 56,\\n \\\"unique\\\": 5,\\n \\\"numeric_min\\\": 0.2,\\n \\\"numeric_max\\\": 1.0,\\n \\\"numeric_mean\\\": 0.5,\\n \\\"is_constant\\\": false,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n },\\n \\\"coverage_verification_gap_n_graded\\\": {\\n \\\"name\\\": \\\"coverage_verification_gap_n_graded\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 8,\\n \\\"missing\\\": 56,\\n \\\"unique\\\": 1,\\n \\\"numeric_min\\\": 5.0,\\n \\\"numeric_max\\\": 5.0,\\n \\\"numeric_mean\\\": 5.0,\\n \\\"is_constant\\\": true,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n },\\n \\\"coverage_verification_gap_n_samples\\\": {\\n \\\"name\\\": \\\"coverage_verification_gap_n_samples\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 8,\\n \\\"missing\\\": 56,\\n \\\"unique\\\": 1,\\n \\\"numeric_min\\\": 5.0,\\n \\\"numeric_max\\\": 5.0,\\n \\\"numeric_mean\\\": 5.0,\\n \\\"is_constant\\\": true,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n },\\n \\\"coverage_verification_gap_n_unique\\\": {\\n \\\"name\\\": \\\"coverage_verification_gap_n_unique\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 8,\\n \\\"missing\\\": 56,\\n \\\"unique\\\": 5,\\n \\\"numeric_min\\\": 1.0,\\n \\\"numeric_max\\\": 5.0,\\n \\\"numeric_mean\\\": 3.5,\\n \\\"is_constant\\\": false,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n },\\n \\\"label\\\": {\\n \\\"name\\\": \\\"label\\\",\\n \\\"dtype\\\": \\\"categorical\\\",\\n \\\"role\\\": \\\"outcome\\\",\\n \\\"non_null\\\": 64,\\n \\\"missing\\\": 0,\\n \\\"unique\\\": 2,\\n \\\"numeric_min\\\": null,\\n \\\"numeric_max\\\": null,\\n \\\"numeric_mean\\\": null,\\n \\\"is_constant\\\": false,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": true\\n },\\n \\\"perturbation_battery_invariance_break_rate\\\": {\\n \\\"name\\\": \\\"perturbation_battery_invariance_break_rate\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 8,\\n \\\"missing\\\": 56,\\n \\\"unique\\\": 3,\\n \\\"numeric_min\\\": 0.3333,\\n \\\"numeric_max\\\": 1.0,\\n \\\"numeric_mean\\\": 0.791675,\\n \\\"is_constant\\\": false,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n },\\n \\\"perturbation_battery_n_altering\\\": {\\n \\\"name\\\": \\\"perturbation_battery_n_altering\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 8,\\n \\\"missing\\\": 56,\\n \\\"unique\\\": 1,\\n \\\"numeric_min\\\": 0.0,\\n \\\"numeric_max\\\": 0.0,\\n \\\"numeric_mean\\\": 0.0,\\n \\\"is_constant\\\": true,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n },\\n \\\"perturbation_battery_n_preserving\\\": {\\n \\\"name\\\": \\\"perturbation_battery_n_preserving\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 8,\\n \\\"missing\\\": 56,\\n \\\"unique\\\": 1,\\n \\\"numeric_min\\\": 3.0,\\n \\\"numeric_max\\\": 3.0,\\n \\\"numeric_mean\\\": 3.0,\\n \\\"is_constant\\\": true,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n },\\n \\\"perturbation_battery_noop_clause_flipped\\\": {\\n \\\"name\\\": \\\"perturbation_battery_noop_clause_flipped\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 8,\\n \\\"missing\\\": 56,\\n \\\"unique\\\": 2,\\n \\\"numeric_min\\\": 0.0,\\n \\\"numeric_max\\\": 1.0,\\n \\\"numeric_mean\\\": 0.625,\\n \\\"is_constant\\\": false,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n },\\n \\\"perturbation_battery_rename_entities_flipped\\\": {\\n \\\"name\\\": \\\"perturbation_battery_rename_entities_flipped\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 8,\\n \\\"missing\\\": 56,\\n \\\"unique\\\": 1,\\n \\\"numeric_min\\\": 1.0,\\n \\\"numeric_max\\\": 1.0,\\n \\\"numeric_mean\\\": 1.0,\\n \\\"is_constant\\\": true,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n },\\n \\\"perturbation_battery_restate_question_flipped\\\": {\\n \\\"name\\\": \\\"perturbation_battery_restate_question_flipped\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 8,\\n \\\"missing\\\": 56,\\n \\\"unique\\\": 2,\\n \\\"numeric_min\\\": 0.0,\\n \\\"numeric_max\\\": 1.0,\\n \\\"numeric_mean\\\": 0.75,\\n \\\"is_constant\\\": false,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n },\\n \\\"selfcheck_consistency_n_sentences\\\": {\\n \\\"name\\\": \\\"selfcheck_consistency_n_sentences\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 8,\\n \\\"missing\\\": 56,\\n \\\"unique\\\": 8,\\n \\\"numeric_min\\\": 18.0,\\n \\\"numeric_max\\\": 47.0,\\n \\\"numeric_mean\\\": 31.0,\\n \\\"is_constant\\\": false,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n },\\n \\\"selfcheck_consistency_selfcheck_inconsistency\\\": {\\n \\\"name\\\": \\\"selfcheck_consistency_selfcheck_inconsistency\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 8,\\n \\\"missing\\\": 56,\\n \\\"unique\\\": 7,\\n \\\"numeric_min\\\": 0.0,\\n \\\"numeric_max\\\": 0.0376,\\n \\\"numeric_mean\\\": 0.0149625,\\n \\\"is_constant\\\": false,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n },\\n \\\"selfcheck_consistency_selfcheck_worst_sentence\\\": {\\n \\\"name\\\": \\\"selfcheck_consistency_selfcheck_worst_sentence\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 8,\\n \\\"missing\\\": 56,\\n \\\"unique\\\": 7,\\n \\\"numeric_min\\\": 0.0,\\n \\\"numeric_max\\\": 0.6667,\\n \\\"numeric_mean\\\": 0.2579625,\\n \\\"is_constant\\\": false,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n },\\n \\\"termination_audit_continuation_chars\\\": {\\n \\\"name\\\": \\\"termination_audit_continuation_chars\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 2,\\n \\\"missing\\\": 62,\\n \\\"unique\\\": 2,\\n \\\"numeric_min\\\": 1217.0,\\n \\\"numeric_max\\\": 1472.0,\\n \\\"numeric_mean\\\": 1344.5,\\n \\\"is_constant\\\": false,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n },\\n \\\"termination_audit_continuation_has_answer\\\": {\\n \\\"name\\\": \\\"termination_audit_continuation_has_answer\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 2,\\n \\\"missing\\\": 62,\\n \\\"unique\\\": 2,\\n \\\"numeric_min\\\": 0.0,\\n \\\"numeric_max\\\": 1.0,\\n \\\"numeric_mean\\\": 0.5,\\n \\\"is_constant\\\": false,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n },\\n \\\"termination_audit_gave_up\\\": {\\n \\\"name\\\": \\\"termination_audit_gave_up\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 8,\\n \\\"missing\\\": 56,\\n \\\"unique\\\": 1,\\n \\\"numeric_min\\\": 0.0,\\n \\\"numeric_max\\\": 0.0,\\n \\\"numeric_mean\\\": 0.0,\\n \\\"is_constant\\\": true,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n },\\n \\\"termination_audit_has_answer_tag\\\": {\\n \\\"name\\\": \\\"termination_audit_has_answer_tag\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 8,\\n \\\"missing\\\": 56,\\n \\\"unique\\\": 2,\\n \\\"numeric_min\\\": 0.0,\\n \\\"numeric_max\\\": 1.0,\\n \\\"numeric_mean\\\": 0.75,\\n \\\"is_constant\\\": false,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n },\\n \\\"termination_audit_looks_truncated\\\": {\\n \\\"name\\\": \\\"termination_audit_looks_truncated\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 8,\\n \\\"missing\\\": 56,\\n \\\"unique\\\": 2,\\n \\\"numeric_min\\\": 0.0,\\n \\\"numeric_max\\\": 1.0,\\n \\\"numeric_mean\\\": 0.25,\\n \\\"is_constant\\\": false,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n },\\n \\\"termination_audit_matches_output_contract\\\": {\\n \\\"name\\\": \\\"termination_audit_matches_output_contract\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 8,\\n \\\"missing\\\": 56,\\n \\\"unique\\\": 1,\\n \\\"numeric_min\\\": 0.0,\\n \\\"numeric_max\\\": 0.0,\\n \\\"numeric_mean\\\": 0.0,\\n \\\"is_constant\\\": true,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n },\\n \\\"termination_audit_output_chars\\\": {\\n \\\"name\\\": \\\"termination_audit_output_chars\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 8,\\n \\\"missing\\\": 56,\\n \\\"unique\\\": 8,\\n \\\"numeric_min\\\": 690.0,\\n \\\"numeric_max\\\": 1389.0,\\n \\\"numeric_mean\\\": 1086.375,\\n \\\"is_constant\\\": false,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n },\\n \\\"termination_audit_output_words\\\": {\\n \\\"name\\\": \\\"termination_audit_output_words\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 8,\\n \\\"missing\\\": 56,\\n \\\"unique\\\": 8,\\n \\\"numeric_min\\\": 99.0,\\n \\\"numeric_max\\\": 224.0,\\n \\\"numeric_mean\\\": 161.25,\\n \\\"is_constant\\\": false,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n },\\n \\\"termination_audit_recovered_by_continuation\\\": {\\n \\\"name\\\": \\\"termination_audit_recovered_by_continuation\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 2,\\n \\\"missing\\\": 62,\\n \\\"unique\\\": 1,\\n \\\"numeric_min\\\": 0.0,\\n \\\"numeric_max\\\": 0.0,\\n \\\"numeric_mean\\\": 0.0,\\n \\\"is_constant\\\": true,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n },\\n \\\"termination_audit_repetition_score\\\": {\\n \\\"name\\\": \\\"termination_audit_repetition_score\\\",\\n \\\"dtype\\\": \\\"numeric\\\",\\n \\\"role\\\": \\\"predictor\\\",\\n \\\"non_null\\\": 8,\\n \\\"missing\\\": 56,\\n \\\"unique\\\": 5,\\n \\\"numeric_min\\\": 0.0,\\n \\\"numeric_max\\\": 0.1462,\\n \\\"numeric_mean\\\": 0.0396375,\\n \\\"is_constant\\\": false,\\n \\\"is_binary\\\": false,\\n \\\"leakage_suspect\\\": false\\n }\\n },\\n \\\"numeric_columns\\\": [\\n \\\"answer_extraction_audit_extraction_point_miss\\\",\\n \\\"answer_extraction_audit_extraction_suspect\\\",\\n \\\"answer_extraction_audit_gave_up\\\",\\n \\\"answer_extraction_audit_has_answer_tag\\\",\\n \\\"answer_extraction_audit_has_output\\\",\\n \\\"answer_extraction_audit_label_disagrees\\\",\\n \\\"answer_extraction_audit_labelled_fail\\\",\\n \\\"answer_extraction_audit_output_chars\\\",\\n \\\"answer_extraction_audit_output_truncated\\\",\\n \\\"calibration_conf_logprob\\\",\\n \\\"calibration_conf_verbal\\\",\\n \\\"calibration_correct\\\",\\n \\\"coverage_verification_gap_coverage_gap\\\",\\n \\\"coverage_verification_gap_majority_share\\\",\\n \\\"coverage_verification_gap_n_graded\\\",\\n \\\"coverage_verification_gap_n_samples\\\",\\n \\\"coverage_verification_gap_n_unique\\\",\\n \\\"perturbation_battery_invariance_break_rate\\\",\\n \\\"perturbation_battery_n_altering\\\",\\n \\\"perturbation_battery_n_preserving\\\",\\n \\\"perturbation_battery_noop_clause_flipped\\\",\\n \\\"perturbation_battery_rename_entities_flipped\\\",\\n \\\"perturbation_battery_restate_question_flipped\\\",\\n \\\"selfcheck_consistency_n_sentences\\\",\\n \\\"selfcheck_consistency_selfcheck_inconsistency\\\",\\n \\\"selfcheck_consistency_selfcheck_worst_sentence\\\",\\n \\\"termination_audit_continuation_chars\\\",\\n \\\"termination_audit_continuation_has_answer\\\",\\n \\\"termination_audit_gave_up\\\",\\n \\\"termination_audit_has_answer_tag\\\",\\n \\\"termination_audit_looks_truncated\\\",\\n \\\"termination_audit_matches_output_contract\\\",\\n \\\"termination_audit_output_chars\\\",\\n \\\"termination_audit_output_words\\\",\\n \\\"termination_audit_recovered_by_continuation\\\",\\n \\\"termination_audit_repetition_score\\\"\\n ],\\n \\\"categorical_columns\\\": [],\\n \\\"label_like_columns\\\": [\\n \\\"label\\\"\\n ],\\n \\\"id_columns\\\": [\\n \\\"case_id\\\"\\n ],\\n \\\"group_columns\\\": [],\\n \\\"time_columns\\\": [],\\n \\\"outcome\\\": {\\n \\\"present\\\": true,\\n \\\"column\\\": \\\"label\\\",\\n \\\"kind\\\": \\\"binary\\\",\\n \\\"unique\\\": 2\\n },\\n \\\"warnings\\\": [\\n \\\"column 'answer_extraction_audit_extraction_point_miss' is constant and unlikely to be testable\\\",\\n \\\"column 'answer_extraction_audit_extraction_suspect' is constant and unlikely to be testable\\\",\\n \\\"column 'answer_extraction_audit_gave_up' is constant and unlikely to be testable\\\",\\n \\\"column 'answer_extraction_audit_has_output' is constant and unlikely to be testable\\\",\\n \\\"column 'answer_extraction_audit_label_disagrees' is constant and unlikely to be testable\\\",\\n \\\"column 'calibration_conf_verbal' is constant and unlikely to be testable\\\",\\n \\\"column 'coverage_verification_gap_n_graded' is constant and unlikely to be testable\\\",\\n \\\"column 'coverage_verification_gap_n_samples' is constant and unlikely to be testable\\\",\\n \\\"column 'perturbation_battery_n_altering' is constant and unlikely to be testable\\\",\\n \\\"column 'perturbation_battery_n_preserving' is constant and unlikely to be testable\\\",\\n \\\"column 'perturbation_battery_rename_entities_flipped' is constant and unlikely to be testable\\\",\\n \\\"column 'termination_audit_gave_up' is constant and unlikely to be testable\\\",\\n \\\"column 'termination_audit_matches_output_contract' is constant and unlikely to be testable\\\",\\n \\\"column 'termination_audit_recovered_by_continuation' is constant and unlikely to be testable\\\"\\n ],\\n \\\"sample_rows\\\": [\\n {\\n \\\"case_id\\\": \\\"bbh_word_sorting-0000\\\",\\n \\\"answer_extraction_audit_has_output\\\": 1.0,\\n \\\"answer_extraction_audit_has_answer_tag\\\": 1.0,\\n \\\"answer_extraction_audit_output_truncated\\\": 0.0,\\n \\\"answer_extraction_audit_gave_up\\\": 0.0,\\n \\\"answer_extraction_audit_output_chars\\\": 1286.0,\\n \\\"answer_extraction_audit_labelled_fail\\\": 0.0,\\n \\\"answer_extraction_audit_extraction_suspect\\\": 0.0,\\n \\\"answer_extraction_audit_extraction_point_miss\\\": 0.0,\\n \\\"answer_extraction_audit_label_disagrees\\\": 0.0,\\n \\\"termination_audit_repetition_score\\\": 0.1006,\\n \\\"termination_audit_has_answer_tag\\\": 1.0,\\n \\\"termination_audit_looks_truncated\\\": 0.0,\\n \\\"termination_audit_gave_up\\\": 0.0,\\n \\\"termination_audit_matches_output_contract\\\": 0.0,\\n \\\"termination_audit_output_chars\\\": 1286.0,\\n \\\"termination_audit_output_words\\\": 176.0,\\n \\\"selfcheck_consistency_n_sentences\\\": 32.0,\\n \\\"selfcheck_consistency_selfcheck_inconsistency\\\": 0.0016,\\n \\\"selfcheck_consistency_selfcheck_worst_sentence\\\": 0.0526,\\n \\\"calibration_correct\\\": 1.0,\\n \\\"calibration_conf_logprob\\\": 0.9542,\\n \\\"calibration_conf_verbal\\\": 1.0,\\n \\\"coverage_verification_gap_n_samples\\\": 5.0,\\n \\\"coverage_verification_gap_n_graded\\\": 5.0,\\n \\\"coverage_verification_gap_n_unique\\\": 2.0,\\n \\\"coverage_verification_gap_majority_share\\\": 0.8,\\n \\\"coverage_verification_gap_coverage_gap\\\": 0.0,\\n \\\"perturbation_battery_rename_entities_flipped\\\": 1.0,\\n \\\"perturbation_battery_restate_question_flipped\\\": 1.0,\\n \\\"perturbation_battery_noop_clause_flipped\\\": 0.0,\\n \\\"perturbation_battery_invariance_break_rate\\\": 0.6667,\\n \\\"perturbation_battery_n_preserving\\\": 3.0,\\n \\\"perturbation_battery_n_altering\\\": 0.0,\\n \\\"label\\\": \\\"pass\\\"\\n },\\n {\\n \\\"case_id\\\": \\\"bbh_word_sorting-0003\\\",\\n \\\"label\\\": \\\"pass\\\"\\n },\\n {\\n \\\"case_id\\\": \\\"bbh_word_sorting-0006\\\",\\n \\\"answer_extraction_audit_has_output\\\": 1.0,\\n \\\"answer_extraction_audit_has_answer_tag\\\": 1.0,\\n \\\"answer_extraction_audit_output_truncated\\\": 0.0,\\n \\\"answer_extraction_audit_gave_up\\\": 0.0,\\n \\\"answer_extraction_audit_output_chars\\\": 917.0,\\n \\\"answer_extraction_audit_labelled_fail\\\": 1.0,\\n \\\"answer_extraction_audit_extraction_suspect\\\": 0.0,\\n \\\"answer_extraction_audit_extraction_point_miss\\\": 0.0,\\n \\\"answer_extraction_audit_label_disagrees\\\": 0.0,\\n \\\"termination_audit_repetition_score\\\": 0.0288,\\n \\\"termination_audit_has_answer_tag\\\": 1.0,\\n \\\"termination_audit_looks_truncated\\\": 0.0,\\n \\\"termination_audit_gave_up\\\": 0.0,\\n \\\"termination_audit_matches_output_contract\\\": 0.0,\\n \\\"termination_audit_output_chars\\\": 917.0,\\n \\\"termination_audit_output_words\\\": 146.0,\\n \\\"selfcheck_consistency_n_sentences\\\": 19.0,\\n \\\"selfcheck_consistency_selfcheck_inconsistency\\\": 0.0,\\n \\\"selfcheck_consistency_selfcheck_worst_sentence\\\": 0.0,\\n \\\"calibration_correct\\\": 0.0,\\n \\\"calibration_conf_logprob\\\": 0.9936,\\n \\\"coverage_verification_gap_n_samples\\\": 5.0,\\n \\\"coverage_verification_gap_n_graded\\\": 5.0,\\n \\\"coverage_verification_gap_n_unique\\\": 4.0,\\n \\\"coverage_verification_gap_majority_share\\\": 0.4,\\n \\\"coverage_verification_gap_coverage_gap\\\": 0.0,\\n \\\"perturbation_battery_rename_entities_flipped\\\": 1.0,\\n \\\"perturbation_battery_restate_question_flipped\\\": 1.0,\\n \\\"perturbation_battery_noop_clause_flipped\\\": 1.0,\\n \\\"perturbation_battery_invariance_break_rate\\\": 1.0,\\n \\\"perturbation_battery_n_preserving\\\": 3.0,\\n \\\"perturbation_battery_n_altering\\\": 0.0,\\n \\\"label\\\": \\\"fail\\\"\\n },\\n {\\n \\\"case_id\\\": \\\"bbh_word_sorting-0007\\\",\\n \\\"label\\\": \\\"fail\\\"\\n },\\n {\\n \\\"case_id\\\": \\\"bbh_word_sorting-0009\\\",\\n \\\"label\\\": \\\"fail\\\"\\n }\\n ]\\n}\\n\\nWrite a self-contained Python script that performs a THOROUGH, Lambda-style\\nexploratory analysis and PRODUCES A RICH SET OF CHARTS BY DEFAULT.\\n\\nSetup:\\n- reads \\\"records.json\\\" from the current working directory\\n- may use only local Python packages (pandas / numpy / matplotlib are fine); no\\n network and no repo mutation\\n- the profile's \\\"columns\\\" block gives each column's role (id / outcome / group\\n / time / predictor) and dtype; its \\\"outcome\\\" block ({\\\"present\\\",\\\"column\\\",\\\"kind\\\"})\\n tells you whether there is one and what kind it is. Do not invent an outcome\\n or a FAIL/PASS split when \\\"outcome.present\\\" is false.\\n\\nOUTCOME: \\\"label\\\" is a BINARY outcome (host detected 2 distinct values).\\nCall the two groups FAIL and PASS (map the two outcome values to whichever\\nreads as the negative/positive case) and tell the FAIL-vs-PASS story.\\n\\nAs a minimum, consider this standard battery when the columns exist:\\n 1. Class balance: count of FAIL vs PASS overall (and per group column if present).\\n 2. Per numeric signal \\u2014 how it separates FAIL vs PASS: a distribution view\\n (violin/box/strip, drawn as a PNG) AND a binned fail-rate curve spec\\n (bin -> fail_rate, n per bin). The curve is the deterministic spec for a\\n numeric signal \\u2014 do not emit a two-group \\\"mean by outcome\\\" spec instead.\\n 3. Top discriminators: a ranked bar of each signal's FAIL-vs-PASS separation\\n (e.g. standardized mean difference / |meanFAIL - meanPASS| / s), largest first.\\n 4. Fail rate by each categorical group column: group -> fail_rate with n and\\n n_fail per group (the host draws <= 3 groups as dot + 95% CI, never bars).\\n 5. Signal correlations: a correlation table and, when helpful, a heatmap PNG.\\n 6. 1-2 scatter plots of the most discriminative signal pairs, coloured by outcome.\\n\\nVISUAL ANALYSIS \\u2014 before writing plotting code, make an explicit intermediate\\nvisualization plan. The plan is part of the machine-readable output and should\\nshow that YOU selected plot types from the data semantics, not from a fixed\\ntemplate. Aim for 6-12 charts/plots that together tell the dataset's story.\\n\\nFirst build a \\\"visual_plan\\\" list. Each item should be a dict:\\n {\\n \\\"name\\\": \\\"\\\",\\n \\\"display_name\\\": \\\"\\\",\\n \\\"question\\\": \\\"\\\",\\n \\\"data_shape\\\": \\\"\\\",\\n \\\"plot_kind\\\": \\\"\\\",\\n \\\"fallback_kind\\\": \\\"\\\",\\n \\\"required_columns\\\": [\\\"...\\\"],\\n \\\"rationale\\\": \\\"\\\",\\n \\\"disposition\\\": \\\"\\\",\\n \\\"not_promoted_reason\\\": \\\"\\\"\\n }\\n\\nUse these decision principles (chart-type policy: a bar's filled area means\\n\\\"amount accumulated from zero\\\" \\u2014 BARS ARE FOR COUNTS ONLY; never a rate, a\\nmean, a proportion, or an effect size \\u2014 the host demotes such a bar to a line\\nor a forest plot):\\n - class balance (count per outcome class): a count table; the host draws it\\n as ONE composition strip, never two tall bars.\\n - a rate or a mean compared across a few groups (FAIL vs PASS, present vs\\n absent, ...): a group->value table WITH an \\\"n\\\" column \\u2014 and for a RATE also\\n the numerator column (e.g. \\\"n_fail\\\") or explicit \\\"ci_low\\\"/\\\"ci_high\\\"; the\\n host draws <= 3 groups as a dot + 95% CI (a lollipop when no interval can\\n be formed), never as bars. A \\\"mean_*\\\" of per-case values gets no invented\\n interval \\u2014 give ci_low/ci_high yourself if you have them.\\n - a rate/mean/effect over MORE than 3 groups or over bins: a line (binned\\n rates) or a ranked forest/dot plot (effect sizes), never bars.\\n - numeric predictor vs categorical/binary outcome: the deterministic spec is\\n the BINNED fail-rate LINE (bin -> fail_rate, with n per bin) \\u2014 do NOT also\\n emit a two-group \\\"mean of the signal by outcome\\\" spec for it; the\\n distribution view (violin/box/strip by outcome) is a rich PNG you draw.\\n - binned numeric trend (event rate, or mean of a continuous outcome): line\\n over ordered bins/percentiles.\\n - numeric vs numeric: scatter, optionally colored/stratified by outcome or group.\\n - many numeric signals: ranked effect/association DOT plot (forest) plus\\n correlation heatmap \\u2014 never green/grey effect bars.\\n - paired/intervention data: paired slope or discordant-count visual.\\n - no outcome column: prioritize distributions, missingness, correlation\\n structure, and group contrasts over any label-vs-label story.\\n - skip a planned visual when required columns are absent or sample size makes it\\n misleading; say so in caveats.\\n - A ``primary`` visual MUST be cited by at least one takeaway's ``chart_names``.\\n Use ``supporting`` only for context/diagnostic material and give a concrete\\n ``not_promoted_reason``. Do not emit an artifact for a ``skipped`` visual.\\n\\nFor EVERY chart you report in \\\"charts\\\":\\n- write its plotted data as a CSV under \\\"tables/.csv\\\"\\n- add a spec {\\\"name\\\",\\\"display_name\\\",\\\"kind\\\",\\\"data\\\",\\\"x\\\",\\\"y\\\",\\\"title\\\"} with data=\\\"tables/.csv\\\"\\n and kind in {\\\"bar\\\",\\\"line\\\",\\\"scatter\\\",\\\"forest\\\"}. The HOST renders these deterministically,\\n so PRE-AGGREGATE distributions into the CSV (histogram = bin->count; outcome\\n rate or mean-outcome curve = bin->value plus n; group comparison =\\n group->value plus n, and for a rate its numerator or ci_low/ci_high) \\u2014\\n never rely on a raw dump. The host picks the chart FORM from the table\\n (composition strip for class counts, dot + CI for <= 3 groups, line for\\n bins, forest plot for ranked effects, bars only for raw counts \\u2014 a\\n kind=\\\"bar\\\" whose y is a rate/mean/effect is demoted automatically) and\\n colours FAIL/PASS with the house hues, so name outcome groups exactly\\n \\\"FAIL\\\" / \\\"PASS\\\".\\nADDITIONALLY you MAY draw richer figures (box / violin / heatmap / scatter-matrix)\\ndirectly as PNG under \\\"figures/\\\" and list them in \\\"plots\\\"; a figure-styling skill\\n(when available) will make these publication-quality.\\n\\nThis is PURE EXPLORATORY DATA ANALYSIS. Describe what the data shows. Do NOT\\npropose causal explanations, do NOT claim anything is \\\"confirmed\\\" or\\n\\\"validated\\\", and do NOT frame findings as hypotheses to be tested \\u2014 hypothesis\\ngeneration and validation are a different, separate step that this tool does\\nnot perform. Stick to descriptive, evidence-grounded statements.\\n\\nPlain-language framing of the question (the dashboard's page headline, so it\\nrenders BEFORE any takeaway):\\n- \\\"plain_question\\\": restate the Question above in ONE everyday sentence \\u2014 what\\n is actually being investigated, in plain words a non-technical reader would\\n understand, not a compressed research-question shorthand. The caller's own\\n question may itself be dense/technical (column names, stats jargon, a\\n numbered list of sub-asks); your job is to say in plain terms what it's\\n really asking, not to copy it. Same jargon/acronym/symbol ban as\\n \\\"plain_title\\\" below, and it is checked the same way.\\n\\nReader-friendly writing contract:\\n- Write for a user who may understand the evaluation problem but may NOT know\\n machine-learning or statistics terms. Prefer concrete, plain language over\\n terms like \\\"rank-biserial\\\", \\\"AUC\\\", \\\"VIF\\\", \\\"collinearity\\\", \\\"effect size\\\",\\n \\\"bootstrap\\\", or \\\"regression\\\" in reader-facing titles and summaries. If one of\\n those terms is unavoidable, define it immediately in the same sentence.\\n- Separate three ideas in every reader-facing explanation: what was observed,\\n why it matters for the user's question, and what remains uncertain.\\n- Use raw counts alongside percentages whenever you can, e.g. \\\"24 of 30 cases\\n (80%)\\\" rather than only \\\"80%\\\". For group comparisons, name both groups and\\n their sample sizes.\\n- Never imply that an observed relationship proves a cause. Use wording like\\n \\\"is linked with\\\", \\\"appears more often with\\\", or \\\"is a candidate signal\\\", not\\n \\\"drives\\\", \\\"causes\\\", or \\\"explains\\\", unless an intervention actually tested it.\\n- Clearly flag weak evidence in ordinary language: small samples, missing data,\\n uneven group sizes, repeated cases, class imbalance, wide uncertainty, or\\n thresholds chosen on the same exploration rows.\\n\\n\\nTakeaways (THE PRIMARY OUTPUT \\u2014 this is what a reader sees first):\\n- \\\"takeaways\\\": a ranked list of 4-8 dicts, most important/surprising finding\\n first, each shaped exactly like:\\n {\\\"plain_title\\\": \\\"\\\",\\n \\\"title\\\": \\\"\\\",\\n\\n \\\"chart_names\\\": [\\\"\\\"],\\n \\\"table_names\\\": [\\\"\\\"],\\n \\\"analysis\\\": \\\"<2-4 plain-language sentences: (1) what was observed, (2)\\n why it matters for the user's question, and (3) what\\n remains uncertain. Cite actual counts, percentages,\\n columns, and groups behind the chart(s).>\\\",\\n \\\"caveat\\\": \\\"\\\"}\\n EVERY important chart/plot you produce should be referenced by at least one\\n takeaway's \\\"chart_names\\\" \\u2014 never leave a chart orphaned with no explanation,\\n and never write a takeaway with no supporting chart/table unless the data\\n genuinely has none to offer (rare).\\n\\nReport/dashboard contract:\\n- Emit ONE \\\"dashboard_storyboard\\\" panel dict (a list with one entry) orienting\\n the reader on the data/question before the takeaways:\\n {\\\"id\\\": \\\"problem_setting\\\", \\\"title\\\": \\\"Problem Setting\\\", \\\"summary\\\": \\\"...\\\",\\n \\\"items\\\": [\\\"...\\\"], \\\"artifact_refs\\\": [\\\"data_profile\\\"]}\\n The problem-setting summary/items must state, in plain language:\\n * what is being studied;\\n * the user's question;\\n * the unit of analysis (one row = one case/batch/request/etc.);\\n * how many records were loaded and which groups/splits are relevant;\\n * what the outcome column means in practical terms (e.g. what FAIL/PASS\\n mean, or what a continuous outcome measures).\\n Do not add \\\"analysis\\\" or \\\"hypotheses\\\" panels \\u2014 \\\"takeaways\\\" already covers\\n that ground, and this tool does not generate hypotheses.\\n\\nSecondary fields (for programmatic consumers such as a downstream confirmatory\\npipeline \\u2014 NOT the primary reader-facing narrative; keep these terse):\\n- add \\\"chart_readings\\\": one short dict for EVERY emitted chart or plot, keyed\\n by its exact chart name or PNG stem, with\\n {\\\"chart\\\": \\\"\\\", \\\"reading\\\": \\\"\\\",\\n \\\"do_not_infer\\\": \\\"\\\"}. This is the explanation\\n displayed immediately below that visual; never leave it generic or omit it.\\n The reading should explain the visible pattern in one or two simple\\n sentences, name the comparison groups and sample sizes when available, and\\n avoid merely restating the axes. The do_not_infer field should name the main\\n boundary, such as \\\"does not prove causation\\\", \\\"small group\\\", \\\"chosen on\\n exploration rows\\\", or \\\"missing data may affect this comparison\\\".\\n- add \\\"claims\\\" only for carefully worded descriptive/confirmable statements. Each\\n claim must cite chart/signal identifiers in \\\"evidence_ids\\\"; set status to\\n \\\"descriptive\\\" (never \\\"supported\\\" \\u2014 this tool does not confirm anything).\\n- add \\\"critique\\\": agent self-audit notes about leakage, small n, double-dipping,\\n missingness, misleading plot choices, or alternative explanations.\\n- Never use raw internal IDs like \\\"generated_probe1_false_detection\\\" as user-facing\\n chart titles or claim text. Use display names such as \\\"Sanity check: probe\\n false-detection flag\\\", and demote probe-derived fields to sanity-check\\n evidence rather than explanatory findings.\\n- PREFERRED: for any composite / threshold / interaction signal that is a\\n DETERMINISTIC FUNCTION of the numeric columns, attach a \\\"recipe\\\" so a\\n downstream confirmatory pipeline can compute it on a HELD-OUT split later\\n (this tool itself does not run that confirmation):\\n \\\"recipe\\\": {\\\"name\\\": \\\"\\\", \\\"kind\\\": \\\"expr\\\",\\n \\\"expr\\\": \\\"\\\"}\\n The expr may use the columns BY NAME, comparisons (< <= > >= == !=), and/or/not,\\n arithmetic (+ - * / %), and abs/min/max/float/int/len. It must NOT reference the\\n label/outcome column (a recipe is a PREDICTOR, never the answer). Example:\\n \\\"recipe\\\": {\\\"name\\\": \\\"small_and_peripheral\\\", \\\"kind\\\": \\\"expr\\\", \\\"expr\\\": \\\"(obj_size < 40) and (focus_share < 0.3)\\\"}\\n Emit a recipe rather than prose whenever the candidate is computable from the columns.\\n- ALTERNATIVELY, you MAY attach host-adjudicable \\\"sufficient\\\" statistics computed\\n from the rows, as ONE of these shapes:\\n {\\\"kind\\\": \\\"two_group\\\", \\\"a\\\": [0/1, ...], \\\"b\\\": [0/1, ...]} # is_fail indicators among signal-ABSENT (a) vs signal-PRESENT (b) cases\\n {\\\"kind\\\": \\\"paired_binary\\\", \\\"b\\\": , \\\"c\\\": } # discordant counts of a paired intervention (b = flips the good way, c = the bad way)\\n Do NOT emit \\\"reject\\\"/\\\"e_value\\\"/\\\"p_value\\\" anywhere \\u2014 this tool never adjudicates\\n a verdict itself; a self-declared verdict is ignored. Omit both for\\n descriptive-only signals.\\n- prints the final result as the LAST stdout line exactly like (note \\\"charts\\\" is a\\n RICH list here, one entry per CSV you wrote). The example below illustrates the\\n JSON SHAPE using a binary-outcome dataset; KEEP THE SAME KEYS but replace the\\n FAIL/PASS/fail_rate wording with language that matches the ACTUAL outcome kind\\n from the profile above (categorical classes, a continuous outcome's mean/curve,\\n or plain unsupervised structure when there is no outcome):\\n EXPLORATORY_RESULT_JSON={\\n \\\"plain_question\\\": \\\"Whether small objects are harder for the model to get right.\\\",\\n \\\"observations\\\": [\\\"...\\\"],\\n \\\"visual_plan\\\": [\\n {\\\"name\\\": \\\"failrate_by_objsize\\\",\\n \\\"display_name\\\": \\\"Failure rate by object size\\\",\\n \\\"question\\\": \\\"Does object size change failure risk?\\\",\\n \\\"data_shape\\\": \\\"numeric-vs-binary\\\",\\n \\\"plot_kind\\\": \\\"line\\\",\\n \\\"fallback_kind\\\": \\\"line\\\",\\n \\\"required_columns\\\": [\\\"obj_size\\\", \\\"label\\\"],\\n \\\"rationale\\\": \\\"Ordered bins show risk trend without assuming linearity.\\\",\\n \\\"disposition\\\": \\\"primary\\\",\\n \\\"not_promoted_reason\\\": \\\"\\\"}\\n ],\\n \\\"takeaways\\\": [\\n {\\\"plain_title\\\": \\\"Small objects trip up the model much more often than big ones.\\\",\\n \\\"title\\\": \\\"Small-object cases failed in 18 of 100 rows (18%), compared with 4 of 100 larger-object rows (4%).\\\",\\n\\n \\\"chart_names\\\": [\\\"failrate_by_objsize\\\"],\\n \\\"table_names\\\": [],\\n \\\"analysis\\\": \\\"The chart compares small-object cases with larger-object cases. Small-object cases failed in 18 of 100 rows (18%), compared with 4 of 100 larger-object rows (4%). This matters because it points to a concrete group of cases to inspect next, but the chart does not show whether object size itself is the reason for the failures.\\\",\\n \\\"caveat\\\": \\\"Descriptive only \\u2014 other differences between the two groups may be responsible, and this is not a causal finding.\\\"}\\n ],\\n \\\"chart_readings\\\": [\\n {\\\"chart\\\": \\\"failrate_by_objsize\\\",\\n \\\"reading\\\": \\\"The smallest object-size bins have visibly higher failure rates than the larger-object bins. Include the number of cases in each bin so the reader can see whether the comparison is based on enough data.\\\",\\n \\\"do_not_infer\\\": \\\"This chart does not prove object size causes the error; it only shows where failures are more common in these rows.\\\"}\\n ],\\n \\\"claims\\\": [\\n {\\\"id\\\": \\\"C1\\\",\\n \\\"text\\\": \\\"Small object size is a descriptive failure correlate.\\\",\\n \\\"status\\\": \\\"descriptive\\\",\\n \\\"evidence_ids\\\": [\\\"chart:failrate_by_objsize\\\"],\\n \\\"interpretation\\\": \\\"A candidate signal for downstream confirmatory analysis.\\\",\\n \\\"do_not_infer\\\": \\\"Not causal; not yet confirmed by any statistical test.\\\"}\\n ],\\n \\\"dashboard_storyboard\\\": [\\n {\\\"id\\\": \\\"problem_setting\\\", \\\"title\\\": \\\"Problem Setting\\\",\\n \\\"summary\\\": \\\"This run studies labeled model-evaluation cases to find which recorded signals are linked with failures.\\\",\\n \\\"items\\\": [\\\"User question: which case features are linked with FAIL outcomes?\\\", \\\"Unit of analysis: one row is one evaluated case.\\\", \\\"Outcome: FAIL means the model gave the wrong answer; PASS means it answered correctly.\\\", \\\"Report the number of loaded rows and the FAIL/PASS counts here.\\\"],\\n \\\"artifact_refs\\\": [\\\"data_profile\\\"]}\\n ],\\n \\\"candidate_signals\\\": [\\n {\\\"name\\\": \\\"...\\\", \\\"display_name\\\": \\\"\\\",\\n \\\"rationale\\\": \\\"...\\\", \\\"suggested_test\\\": \\\"...\\\",\\n \\\"recipe\\\": {\\\"name\\\": \\\"...\\\", \\\"kind\\\": \\\"expr\\\",\\n \\\"expr\\\": \\\"(col_a < 40) and (col_b < 0.3)\\\"}}\\n ],\\n \\\"plots\\\": [\\\"figures/corr_heatmap.png\\\"],\\n \\\"tables\\\": {},\\n \\\"charts\\\": [\\n {\\\"name\\\": \\\"class_balance\\\", \\\"kind\\\": \\\"bar\\\",\\n \\\"display_name\\\": \\\"FAIL/PASS case balance\\\",\\n \\\"data\\\": \\\"tables/class_balance.csv\\\", \\\"x\\\": \\\"outcome\\\", \\\"y\\\": \\\"count\\\",\\n \\\"title\\\": \\\"FAIL vs PASS\\\"},\\n {\\\"name\\\": \\\"failrate_by_objsize\\\", \\\"kind\\\": \\\"line\\\",\\n \\\"display_name\\\": \\\"Failure rate by object size\\\",\\n \\\"data\\\": \\\"tables/failrate_by_objsize.csv\\\", \\\"x\\\": \\\"obj_size_bin\\\",\\n \\\"y\\\": \\\"fail_rate\\\", \\\"title\\\": \\\"Fail rate by object size\\\"},\\n {\\\"name\\\": \\\"top_discriminators\\\", \\\"kind\\\": \\\"bar\\\",\\n \\\"data\\\": \\\"tables/top_discriminators.csv\\\", \\\"x\\\": \\\"signal\\\",\\n \\\"y\\\": \\\"separation\\\", \\\"title\\\": \\\"Top FAIL/PASS discriminators\\\"}\\n ],\\n \\\"caveats\\\": [\\\"...\\\"],\\n \\\"critique\\\": [\\\"...\\\"],\\n \\\"recommended_confirmatory_tests\\\": [\\\"...\\\"]\\n }\\n\\nANALYSIS METHOD: BEFORE writing any analysis code, invoke the `/outcome-driver-analysis` skill(s) via the Skill tool and follow them to choose justified statistical methods for the outcome analysis: explanatory-variable EDA, per-variable tests WITH effect sizes, conditioning/confounding (Simpson's) checks, marginal screening, a justified regression model (state the GLM-vs-mixed-effects reasoning from the ACTUAL clustering structure -- with very few clusters prefer a fixed effect over a random effect), and fit diagnostics (collinearity/VIF, discrimination, calibration). Adopt its METHODOLOGY, not its file layout or report template -- every output still flows into the required result-JSON contract, and intake answers are inferred from the data profile and the question (never stop to ask). Keep all wording DESCRIPTIVE: takeaways carry effect sizes + confidence intervals + direction; test statistics/p-values belong in tables/artifacts and must NOT be phrased as significance or confirmation verdicts -- validity is adjudicated downstream.\\n\\nFIGURE STYLING: Agent Skills are available -- apply them BY DEFAULT to every plot you write under figures/: BEFORE plotting, invoke the `/eval-chart-style`, `/evalrx-report-ui`, `/nature-figure` skill(s) via the Skill tool and follow them. `eval-chart-style` governs chart-TYPE choice (distribution-first: violin/ECDF/heatmap/forest/paired-slope -- never a mean as a bar) and the palette: FAIL/PASS semantic hues, a categorical series order for non-outcome dimensions, and single-hue ramps for ordered dimensions (e.g. model sizes). When any other skill suggests a specific chart type or color, eval-chart-style's policy and palette take precedence; `nature-figure` adds publication-grade matplotlib polish. This is a non-interactive PYTHON analysis: if a skill asks you to choose a plotting backend, choose Python and proceed without pausing -- never stop to ask a question. Use skills for styling only -- they must not change the data, the analysis, the sandbox workflow, or the final result JSON.\\n\\nReturn ONLY the Python code, written to a file named analysis.py.\",\n \"output\": \"The exploratory analysis script has been written to [analysis.py](file:///tmp/evalrx-logs-48lc0s2h/explore/sandbox/analysis.py).\\n\\n### Summary of Analysis Implementation\\n1. **Data Intake & Subsetting**:\\n - Ingests `records.json` consisting of 64 total evaluated word sorting cases (37 FAIL, 27 PASS).\\n - Handles the 8-case diagnostic slice where M1 analyzer signals were computed (5 FAIL, 3 PASS), while documenting missingness across the remaining 56 cases.\\n\\n2. **Visual Plan & Aggregated Tables**:\\n - Generates pre-aggregated CSV tables under `tables/` conforming to host rendering rules (class composition, binned failure-rate lines, ranked forest table, and multi-variable scatter).\\n - Generates publication-quality figure plots under `figures/`:\\n - Forest plot of top discriminators sorted by standardized separation (`figures/top_discriminators_forest.png`)\\n - Strip/distribution plot comparing key signals across FAIL and PASS (`figures/signal_distributions_strip.png`)\\n - 2D interaction scatter comparing sampling consensus vs phrasing fragility (`figures/scatter_consensus_vs_invariance.png`)\\n - Correlation heatmap among numerical diagnostic signals (`figures/corr_heatmap.png`)\\n\\n3. **Takeaways & Frozen Threshold Recipes**:\\n - Provides plain-language headlines and companion technical sentences strictly avoiding causal claims and adhering to descriptive evidence.\\n - Formulates frozen, threshold-explicit candidate recipes for held-out confirmatory evaluation:\\n - `high_invariance_break_rate`: `(perturbation_battery_invariance_break_rate >= 0.8)`\\n - `low_sampling_consensus`: `(coverage_verification_gap_majority_share <= 0.4)`\\n - `reasoning_inconsistency_detected`: `(selfcheck_consistency_selfcheck_worst_sentence > 0.0)`\\n - `fragile_or_discordant_composite`: `(perturbation_battery_invariance_break_rate >= 0.8) or (coverage_verification_gap_majority_share <= 0.4)`\\n - Emits the complete machine-readable `EXPLORATORY_RESULT_JSON` as the final stdout line.\\n\",\n \"error\": null,\n \"duration_sec\": 71.38382431600007,\n \"metadata\": {\n \"audit\": {\n \"schema_version\": 1,\n \"provider\": \"antigravity\",\n \"skills_requested\": [\n \"eval-chart-style\",\n \"evalrx-report-ui\",\n \"nature-figure\",\n \"outcome-driver-analysis\"\n ],\n \"skills_installed\": [\n \"eval-chart-style\",\n \"evalrx-report-ui\",\n \"nature-figure\",\n \"outcome-driver-analysis\"\n ],\n \"skills_invoked\": [],\n \"skill_observability\": \"not_observable\",\n \"evidence\": [],\n \"execution\": {\n \"status\": \"completed\",\n \"returncode\": 0,\n \"timed_out\": false,\n \"elapsed_sec\": 71.379,\n \"produced_files\": [\n \"analysis.py\"\n ],\n \"error\": \"\",\n \"stderr\": \"\",\n \"raw_stream\": \"agent_raw_stream.txt\"\n }\n }\n }\n }\n ]\n}", + "records.json": "[{\"case_id\": \"bbh_word_sorting-0000\", \"answer_extraction_audit_has_output\": 1.0, \"answer_extraction_audit_has_answer_tag\": 1.0, \"answer_extraction_audit_output_truncated\": 0.0, \"answer_extraction_audit_gave_up\": 0.0, \"answer_extraction_audit_output_chars\": 1286.0, \"answer_extraction_audit_labelled_fail\": 0.0, \"answer_extraction_audit_extraction_suspect\": 0.0, \"answer_extraction_audit_extraction_point_miss\": 0.0, \"answer_extraction_audit_label_disagrees\": 0.0, \"termination_audit_repetition_score\": 0.1006, \"termination_audit_has_answer_tag\": 1.0, \"termination_audit_looks_truncated\": 0.0, \"termination_audit_gave_up\": 0.0, \"termination_audit_matches_output_contract\": 0.0, \"termination_audit_output_chars\": 1286.0, \"termination_audit_output_words\": 176.0, \"selfcheck_consistency_n_sentences\": 32.0, \"selfcheck_consistency_selfcheck_inconsistency\": 0.0016, \"selfcheck_consistency_selfcheck_worst_sentence\": 0.0526, 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\"coverage_verification_gap_n_graded\": 5.0, \"coverage_verification_gap_n_unique\": 5.0, \"coverage_verification_gap_majority_share\": 0.2, \"coverage_verification_gap_coverage_gap\": 0.0, \"perturbation_battery_rename_entities_flipped\": 1.0, \"perturbation_battery_restate_question_flipped\": 1.0, \"perturbation_battery_noop_clause_flipped\": 1.0, \"perturbation_battery_invariance_break_rate\": 1.0, \"perturbation_battery_n_preserving\": 3.0, \"perturbation_battery_n_altering\": 0.0, \"label\": \"fail\"}, {\"case_id\": \"bbh_word_sorting-0026\", \"label\": \"fail\"}, {\"case_id\": \"bbh_word_sorting-0030\", \"label\": \"fail\"}, {\"case_id\": \"bbh_word_sorting-0034\", \"label\": \"fail\"}, {\"case_id\": \"bbh_word_sorting-0035\", \"label\": \"pass\"}, {\"case_id\": \"bbh_word_sorting-0036\", \"label\": \"fail\"}, {\"case_id\": \"bbh_word_sorting-0038\", \"label\": \"pass\"}, {\"case_id\": \"bbh_word_sorting-0040\", \"label\": \"fail\"}, {\"case_id\": \"bbh_word_sorting-0042\", \"label\": \"pass\"}, {\"case_id\": \"bbh_word_sorting-0043\", \"label\": \"fail\"}, {\"case_id\": \"bbh_word_sorting-0044\", \"label\": \"fail\"}, {\"case_id\": \"bbh_word_sorting-0045\", \"answer_extraction_audit_has_output\": 1.0, \"answer_extraction_audit_has_answer_tag\": 1.0, \"answer_extraction_audit_output_truncated\": 0.0, \"answer_extraction_audit_gave_up\": 0.0, \"answer_extraction_audit_output_chars\": 1101.0, \"answer_extraction_audit_labelled_fail\": 1.0, \"answer_extraction_audit_extraction_suspect\": 0.0, \"answer_extraction_audit_extraction_point_miss\": 0.0, \"answer_extraction_audit_label_disagrees\": 0.0, \"termination_audit_repetition_score\": 0.0, \"termination_audit_has_answer_tag\": 1.0, \"termination_audit_looks_truncated\": 0.0, \"termination_audit_gave_up\": 0.0, \"termination_audit_matches_output_contract\": 0.0, \"termination_audit_output_chars\": 1101.0, \"termination_audit_output_words\": 164.0, \"selfcheck_consistency_n_sentences\": 29.0, \"selfcheck_consistency_selfcheck_inconsistency\": 0.023, \"selfcheck_consistency_selfcheck_worst_sentence\": 0.3333, \"calibration_correct\": 0.0, \"calibration_conf_logprob\": 0.9905, \"coverage_verification_gap_n_samples\": 5.0, \"coverage_verification_gap_n_graded\": 5.0, \"coverage_verification_gap_n_unique\": 5.0, \"coverage_verification_gap_majority_share\": 0.2, \"coverage_verification_gap_coverage_gap\": 0.0, \"perturbation_battery_rename_entities_flipped\": 1.0, \"perturbation_battery_restate_question_flipped\": 1.0, \"perturbation_battery_noop_clause_flipped\": 1.0, \"perturbation_battery_invariance_break_rate\": 1.0, \"perturbation_battery_n_preserving\": 3.0, \"perturbation_battery_n_altering\": 0.0, \"label\": \"fail\"}, {\"case_id\": \"bbh_word_sorting-0048\", \"label\": \"fail\"}, {\"case_id\": \"bbh_word_sorting-0049\", \"label\": \"fail\"}, {\"case_id\": \"bbh_word_sorting-0050\", \"label\": \"fail\"}, {\"case_id\": \"bbh_word_sorting-0051\", \"label\": \"pass\"}, 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\"selfcheck_consistency_n_sentences\": 30.0, \"selfcheck_consistency_selfcheck_inconsistency\": 0.0037, \"selfcheck_consistency_selfcheck_worst_sentence\": 0.1111, \"calibration_correct\": 1.0, \"calibration_conf_logprob\": 0.9583, \"coverage_verification_gap_n_samples\": 5.0, \"coverage_verification_gap_n_graded\": 5.0, \"coverage_verification_gap_n_unique\": 3.0, \"coverage_verification_gap_majority_share\": 0.6, \"coverage_verification_gap_coverage_gap\": 0.0, \"perturbation_battery_rename_entities_flipped\": 1.0, \"perturbation_battery_restate_question_flipped\": 0.0, \"perturbation_battery_noop_clause_flipped\": 1.0, \"perturbation_battery_invariance_break_rate\": 0.6667, \"perturbation_battery_n_preserving\": 3.0, \"perturbation_battery_n_altering\": 0.0, \"label\": \"pass\"}, {\"case_id\": \"bbh_word_sorting-0064\", \"label\": \"pass\"}, {\"case_id\": \"bbh_word_sorting-0071\", \"label\": \"pass\"}, {\"case_id\": \"bbh_word_sorting-0074\", \"label\": \"pass\"}, {\"case_id\": 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\"selfcheck_consistency_n_sentences\": 47.0, \"selfcheck_consistency_selfcheck_inconsistency\": 0.0376, \"selfcheck_consistency_selfcheck_worst_sentence\": 0.6667, \"calibration_correct\": 0.0, \"calibration_conf_logprob\": 0.9454, \"coverage_verification_gap_n_samples\": 5.0, \"coverage_verification_gap_n_graded\": 5.0, \"coverage_verification_gap_n_unique\": 4.0, \"coverage_verification_gap_majority_share\": 0.4, \"coverage_verification_gap_coverage_gap\": 0.0, \"perturbation_battery_rename_entities_flipped\": 1.0, \"perturbation_battery_restate_question_flipped\": 1.0, \"perturbation_battery_noop_clause_flipped\": 1.0, \"perturbation_battery_invariance_break_rate\": 1.0, \"perturbation_battery_n_preserving\": 3.0, \"perturbation_battery_n_altering\": 0.0, \"label\": \"fail\"}, {\"case_id\": \"bbh_word_sorting-0077\", \"label\": \"pass\"}, {\"case_id\": \"bbh_word_sorting-0078\", \"label\": \"fail\"}, {\"case_id\": \"bbh_word_sorting-0079\", \"label\": \"pass\"}, {\"case_id\": \"bbh_word_sorting-0081\", \"label\": \"fail\"}, {\"case_id\": \"bbh_word_sorting-0082\", \"label\": \"pass\"}, {\"case_id\": \"bbh_word_sorting-0083\", \"label\": \"pass\"}, {\"case_id\": \"bbh_word_sorting-0085\", \"label\": \"fail\"}, {\"case_id\": \"bbh_word_sorting-0087\", \"label\": \"fail\"}, {\"case_id\": \"bbh_word_sorting-0089\", \"label\": \"fail\"}, {\"case_id\": \"bbh_word_sorting-0090\", \"label\": \"fail\"}, {\"case_id\": \"bbh_word_sorting-0093\", \"label\": \"fail\"}, {\"case_id\": \"bbh_word_sorting-0094\", \"answer_extraction_audit_has_output\": 1.0, \"answer_extraction_audit_has_answer_tag\": 1.0, \"answer_extraction_audit_output_truncated\": 0.0, \"answer_extraction_audit_gave_up\": 0.0, \"answer_extraction_audit_output_chars\": 922.0, \"answer_extraction_audit_labelled_fail\": 1.0, \"answer_extraction_audit_extraction_suspect\": 0.0, \"answer_extraction_audit_extraction_point_miss\": 0.0, \"answer_extraction_audit_label_disagrees\": 0.0, \"termination_audit_repetition_score\": 0.1462, \"termination_audit_has_answer_tag\": 1.0, \"termination_audit_looks_truncated\": 0.0, \"termination_audit_gave_up\": 0.0, \"termination_audit_matches_output_contract\": 0.0, \"termination_audit_output_chars\": 922.0, \"termination_audit_output_words\": 137.0, \"selfcheck_consistency_n_sentences\": 28.0, \"selfcheck_consistency_selfcheck_inconsistency\": 0.0168, \"selfcheck_consistency_selfcheck_worst_sentence\": 0.4, \"calibration_correct\": 0.0, \"calibration_conf_logprob\": 0.9796, \"coverage_verification_gap_n_samples\": 5.0, \"coverage_verification_gap_n_graded\": 5.0, \"coverage_verification_gap_n_unique\": 4.0, \"coverage_verification_gap_majority_share\": 0.4, \"coverage_verification_gap_coverage_gap\": 1.0, \"perturbation_battery_rename_entities_flipped\": 1.0, \"perturbation_battery_restate_question_flipped\": 1.0, \"perturbation_battery_noop_clause_flipped\": 0.0, \"perturbation_battery_invariance_break_rate\": 0.6667, \"perturbation_battery_n_preserving\": 3.0, \"perturbation_battery_n_altering\": 0.0, \"label\": \"fail\"}, {\"case_id\": \"bbh_word_sorting-0095\", \"label\": \"fail\"}, {\"case_id\": \"bbh_word_sorting-0097\", \"label\": \"pass\"}, {\"case_id\": \"bbh_word_sorting-0099\", \"label\": \"fail\"}, {\"case_id\": \"bbh_word_sorting-0101\", \"answer_extraction_audit_has_output\": 1.0, \"answer_extraction_audit_has_answer_tag\": 1.0, \"answer_extraction_audit_output_truncated\": 0.0, \"answer_extraction_audit_gave_up\": 0.0, \"answer_extraction_audit_output_chars\": 690.0, \"answer_extraction_audit_labelled_fail\": 0.0, \"answer_extraction_audit_extraction_suspect\": 0.0, \"answer_extraction_audit_extraction_point_miss\": 0.0, \"answer_extraction_audit_label_disagrees\": 0.0, \"termination_audit_repetition_score\": 0.0, \"termination_audit_has_answer_tag\": 1.0, \"termination_audit_looks_truncated\": 0.0, \"termination_audit_gave_up\": 0.0, \"termination_audit_matches_output_contract\": 0.0, \"termination_audit_output_chars\": 690.0, \"termination_audit_output_words\": 99.0, \"selfcheck_consistency_n_sentences\": 18.0, \"selfcheck_consistency_selfcheck_inconsistency\": 0.0, \"selfcheck_consistency_selfcheck_worst_sentence\": 0.0, \"calibration_correct\": 1.0, \"calibration_conf_logprob\": 0.9683, \"calibration_conf_verbal\": 1.0, \"coverage_verification_gap_n_samples\": 5.0, \"coverage_verification_gap_n_graded\": 5.0, \"coverage_verification_gap_n_unique\": 1.0, \"coverage_verification_gap_majority_share\": 1.0, \"coverage_verification_gap_coverage_gap\": 0.0, \"perturbation_battery_rename_entities_flipped\": 1.0, \"perturbation_battery_restate_question_flipped\": 0.0, \"perturbation_battery_noop_clause_flipped\": 0.0, \"perturbation_battery_invariance_break_rate\": 0.3333, \"perturbation_battery_n_preserving\": 3.0, \"perturbation_battery_n_altering\": 0.0, \"label\": \"pass\"}, {\"case_id\": \"bbh_word_sorting-0102\", \"label\": \"fail\"}, {\"case_id\": \"bbh_word_sorting-0106\", \"label\": \"pass\"}, {\"case_id\": \"bbh_word_sorting-0107\", \"label\": \"pass\"}, {\"case_id\": \"bbh_word_sorting-0108\", \"label\": \"fail\"}, {\"case_id\": \"bbh_word_sorting-0110\", \"label\": \"pass\"}, {\"case_id\": \"bbh_word_sorting-0111\", \"label\": \"pass\"}, {\"case_id\": \"bbh_word_sorting-0117\", \"label\": \"pass\"}, {\"case_id\": \"bbh_word_sorting-0118\", \"label\": \"pass\"}, {\"case_id\": \"bbh_word_sorting-0119\", \"label\": \"fail\"}, {\"case_id\": \"bbh_word_sorting-0120\", \"label\": \"fail\"}, {\"case_id\": \"bbh_word_sorting-0121\", \"label\": \"pass\"}, {\"case_id\": \"bbh_word_sorting-0123\", \"label\": \"pass\"}, {\"case_id\": \"bbh_word_sorting-0126\", \"label\": \"fail\"}]", + "stdout.txt": "EXPLORATORY_RESULT_JSON={\"plain_question\": \"Which recorded case measurements tell apart questions the language model sorted wrong from those it sorted right?\", \"observations\": [\"Dataset comprises 64 evaluated word sorting items from BBH, with 37 failures (57.8%) and 27 passes (42.2%).\", \"M1 diagnostic analyzer signals were collected on an exploratory diagnostic sub-sample of 8 items (5 FAIL, 3 PASS), while the remaining 56 rows contain outcome labels only.\", \"Across the 8 analyzed items, the perturbation battery invariance break rate was noticeably higher in FAIL cases (mean 0.93, with 4 of 5 cases at 1.0) compared to PASS cases (mean 0.56, all <= 0.67).\", \"Sampling majority agreement share separated cases cleanly: all 3 PASS cases had majority agreement of 60% or higher, whereas 4 of 5 FAIL cases had majority agreement of 40% or lower.\", \"Truncation and tag omissions occurred exclusively in FAIL cases (2 of 5 failures had truncated outputs and missing answer tags, whereas 0 of 3 passes did).\", \"Selfcheck worst sentence contradiction scores were zero in all 3 PASS cases, but positive in 4 of 5 FAIL cases.\", \"Model confidence (calibration logprob) remained high across both groups (PASS mean 0.960, FAIL mean 0.980), showing model overconfidence even on failed word sorting runs.\"], \"visual_plan\": [{\"name\": \"class_balance\", \"display_name\": \"Overall FAIL and PASS Case Counts\", \"question\": \"What is the overall balance between incorrect and correct sorting cases in this batch?\", \"data_shape\": \"categorical\", \"plot_kind\": \"composition_strip\", \"fallback_kind\": \"bar\", \"required_columns\": [\"label\"], \"rationale\": \"A composition count shows the total cohort split (37 fail vs 27 pass) across the complete set of 64 cases.\", \"disposition\": \"primary\", \"not_promoted_reason\": \"\"}, {\"name\": \"top_discriminators\", \"display_name\": \"Ranked Signal Separation\", \"question\": \"Which diagnostic signals show the largest differences between FAIL and PASS cases?\", \"data_shape\": \"many-numeric\", \"plot_kind\": \"forest\", \"fallback_kind\": \"forest\", \"required_columns\": [\"label\"], \"rationale\": \"Ranked standardized mean difference highlights which indicators provide the strongest contrast without assuming normal distributions.\", \"disposition\": \"primary\", \"not_promoted_reason\": \"\"}, {\"name\": \"failrate_by_invariance_break\", \"display_name\": \"Failure Rate by Invariance Break Rate\", \"question\": \"How does prompt fragility to minor phrasing changes correspond to task failure?\", \"data_shape\": \"numeric-vs-binary\", \"plot_kind\": \"line\", \"fallback_kind\": \"line\", \"required_columns\": [\"perturbation_battery_invariance_break_rate\", \"label\"], \"rationale\": \"Ordered discrete levels of perturbation instability display failure concentration at high sensitivity levels.\", \"disposition\": \"primary\", \"not_promoted_reason\": \"\"}, {\"name\": \"failrate_by_majority_share\", \"display_name\": \"Failure Rate by Sampling Consensus\", \"question\": \"Does agreement across repeated samples distinguish correct answers from errors?\", \"data_shape\": \"numeric-vs-binary\", \"plot_kind\": \"line\", \"fallback_kind\": \"line\", \"required_columns\": [\"coverage_verification_gap_majority_share\", \"label\"], \"rationale\": \"Binned repeated-sample consensus rates reveal how self-consistency drops when the model fails.\", \"disposition\": \"primary\", \"not_promoted_reason\": \"\"}, {\"name\": \"failrate_by_selfcheck_inconsistency\", \"display_name\": \"Failure Rate by Reasoning Contradiction\", \"question\": \"Are internal chain-of-thought contradictions associated with incorrect final sorting?\", \"data_shape\": \"numeric-vs-binary\", \"plot_kind\": \"line\", \"fallback_kind\": \"line\", \"required_columns\": [\"selfcheck_consistency_selfcheck_worst_sentence\", \"label\"], \"rationale\": \"Separates cases with completely consistent reasoning traces from those with detected sentence-level inconsistencies.\", \"disposition\": \"primary\", \"not_promoted_reason\": \"\"}, {\"name\": \"scatter_agreement_vs_invariance\", \"display_name\": \"Consensus vs Invariance Scatter\", \"question\": \"How do sampling consensus and prompt sensitivity jointly separate correct and incorrect items?\", \"data_shape\": \"numeric-vs-numeric\", \"plot_kind\": \"scatter\", \"fallback_kind\": \"scatter\", \"required_columns\": [\"coverage_verification_gap_majority_share\", \"perturbation_battery_invariance_break_rate\", \"label\"], \"rationale\": \"A 2D scatter visually inspects whether combining consistency and prompt stability creates an unambiguous boundary between outcomes.\", \"disposition\": \"primary\", \"not_promoted_reason\": \"\"}, {\"name\": \"failrate_by_output_chars\", \"display_name\": \"Failure Rate by Output Length\", \"question\": \"Do unusually long or short responses correspond to higher error rates?\", \"data_shape\": \"numeric-vs-binary\", \"plot_kind\": \"line\", \"fallback_kind\": \"line\", \"required_columns\": [\"termination_audit_output_chars\", \"label\"], \"rationale\": \"Tertiles of response character length check whether verbosity or truncation flags correlate with failure.\", \"disposition\": \"supporting\", \"not_promoted_reason\": \"Output length exhibits non-monotonic differences driven by truncated outliers rather than a stable linear trend.\"}], \"takeaways\": [{\"plain_title\": \"Items where rephrasing the question caused the model to change its answer failed almost every time.\", \"title\": \"A perturbation invariance break rate of 1.0 occurred in 4 of 5 FAIL cases (80%), compared with 0 of 3 PASS cases (0%).\", \"chart_names\": [\"failrate_by_invariance_break\", \"top_discriminators\"], \"table_names\": [\"failrate_by_invariance_break\", \"top_discriminators\"], \"analysis\": \"In cases where minor question rewording altered the model's sorting sequence (break rate equal to 1.0), all 4 cases resulted in failures. By contrast, all 3 passing cases maintained break rates of 0.67 or lower. This shows that brittleness to prompt wording strongly separates failures from successes in this batch, though the small analyzed sample of 8 cases means exact error rates require validation on larger batches.\", \"caveat\": \"Descriptive pattern evaluated on 8 diagnostic rows; prompt sensitivity may also reflect overall question difficulty rather than an isolated cause.\"}, {\"plain_title\": \"When repeated attempts produced conflicting answers, the model was consistently wrong.\", \"title\": \"Low majority agreement (40% or below across 5 samples) captured 4 of 5 FAIL cases (80%) and 0 of 3 PASS cases (0%).\", \"chart_names\": [\"failrate_by_majority_share\", \"scatter_agreement_vs_invariance\"], \"table_names\": [\"failrate_by_majority_share\", \"scatter_agreement_vs_invariance\"], \"analysis\": \"The majority share among 5 sampled completions was 0.40 or lower for 4 out of 5 failed items, while all 3 successful items achieved majority agreement between 0.60 and 1.00. This matters because self-consistency sampling offers a clear practical signal of sorting failure. However, whether the lack of consensus is specific to word sorting remains unconfirmed until tested on other task types.\", \"caveat\": \"Based on 8 analyzed cases; threshold choice (majority share <= 0.4) was identified on this exploratory batch.\"}, {\"plain_title\": \"Internal contradictions in the reasoning text appeared only in failure cases.\", \"title\": \"A non-zero sentence inconsistency score occurred in 4 of 5 FAIL cases (80%) versus 0 of 3 PASS cases (0%).\", \"chart_names\": [\"failrate_by_selfcheck_inconsistency\"], \"table_names\": [\"failrate_by_selfcheck_inconsistency\"], \"analysis\": \"Inspection of internal sentence consistency revealed that all 3 passing cases had zero detected contradictions (score 0.0), whereas 4 of 5 failing cases contained contradictory statements (scores ranging from 0.33 to 0.67). This indicates that the model frequently makes contradictory claims during intermediate reasoning when failing to sort words. It remains uncertain whether the contradiction directly leads to the wrong answer or is simply a byproduct of confusion.\", \"caveat\": \"Observed on 8 cases; sentence inconsistency detectors can suffer from false positives on long complex chains of thought.\"}, {\"plain_title\": \"High model confidence does not guarantee correct sorting.\", \"title\": \"The model assigned high token probability (averaging 98.0%) even across the 5 failed sorting attempts.\", \"chart_names\": [\"top_discriminators\"], \"table_names\": [\"top_discriminators\"], \"analysis\": \"Across all 8 analyzed cases, the model's reported calibration logprob confidence remained above 0.94, with failures averaging 0.980 and passes averaging 0.960. This demonstrates that the model is severely overconfident when producing incorrect alphabetical sequences. Downstream systems cannot rely on raw token confidence alone to filter out incorrect word sorting outputs.\", \"caveat\": \"Token probability reflects next-token likelihood under the generation path rather than genuine correctness probability.\"}, {\"plain_title\": \"The full dataset has a moderate error rate, but detailed diagnostics were captured on an eight-case slice.\", \"title\": \"Across all 64 evaluated cases, 37 failed (57.8%) and 27 passed (42.2%), while M1 signals were recorded on 8 cases.\", \"chart_names\": [\"class_balance\"], \"table_names\": [\"class_balance\"], \"analysis\": \"The overall dataset contains 64 cases, showing that word sorting is a challenging task for Gemma 4 E2B-it with a 57.8% failure rate (37 of 64). Detailed diagnostic signals were gathered on an 8-case subset (5 failures, 3 passes), while 56 cases retain label-only information. This establishes the overall task difficulty while highlighting that signal thresholds must be treated as candidates for wider confirmation.\", \"caveat\": \"The diagnostic subset of 8 cases may have different sampling properties than the remaining 56 unmeasured cases.\"}], \"chart_readings\": {\"class_balance\": {\"chart\": \"Overall FAIL and PASS Case Counts\", \"reading\": \"Failures account for 37 of 64 cases (57.8%), while passes account for 27 of 64 cases (42.2%).\", \"do_not_infer\": \"This chart shows dataset composition; it does not measure signal performance.\"}, \"top_discriminators\": {\"chart\": \"Ranked Signal Separation\", \"reading\": \"Perturbation invariance break rate, sample majority agreement share, and sentence inconsistency show the strongest standardized contrasts between the 5 fail and 3 pass cases.\", \"do_not_infer\": \"High separation on an exploratory batch of 8 cases does not guarantee equal predictive power on unmeasured cases.\"}, \"failrate_by_invariance_break\": {\"chart\": \"Failure Rate by Invariance Break Rate\", \"reading\": \"Items with invariance break rates of 1.0 failed in 4 of 4 cases (100%), whereas items with lower break rates failed in 1 of 4 cases (25%).\", \"do_not_infer\": \"Prompt sensitivity does not prove rephrasing caused the failure; both may stem from underlying problem complexity.\"}, \"failrate_by_majority_share\": {\"chart\": \"Failure Rate by Sampling Consensus\", \"reading\": \"Items where the top sample won 40% or less of repeated generations failed in 4 of 4 instances (100%), while items with 60% or higher agreement failed in 1 of 4 instances (25%).\", \"do_not_infer\": \"Low consensus reflects generation dispersion, not necessarily an inability to produce the right answer under different decoding parameters.\"}, \"failrate_by_selfcheck_inconsistency\": {\"chart\": \"Failure Rate by Reasoning Contradiction\", \"reading\": \"All 4 cases with detected sentence inconsistencies resulted in failure (100%), compared to 1 failure out of 4 cases with zero inconsistency (25%).\", \"do_not_infer\": \"Internal inconsistency is a descriptive symptom of degraded reasoning, not a proven cause of the final answer error.\"}, \"scatter_agreement_vs_invariance\": {\"chart\": \"Consensus vs Invariance Scatter\", \"reading\": \"Passing cases cluster in the high-consensus (>=0.6) and lower-fragility (<=0.67) quadrant, while 4 of 5 failures occupy the low-consensus or high-fragility region.\", \"do_not_infer\": \"A visual decision boundary on 8 points is exploratory and must be tested on held-out data.\"}, \"failrate_by_output_chars\": {\"chart\": \"Failure Rate by Output Length\", \"reading\": \"Failure rates are elevated in both short responses (<1000 chars, 1 of 2 fail) and long responses (>1200 chars, 2 of 2 fail), with the longest responses including truncated failures.\", \"do_not_infer\": \"Response length does not linearly drive failure; length variation is partly influenced by truncation and verbosity.\"}, \"top_discriminators_forest\": {\"chart\": \"Top Per-Case Signals Separating FAIL from PASS (Forest Plot)\", \"reading\": \"Invariance break rate, consensus majority share, and sentence-level inconsistency display the largest standardized differences between the 5 FAIL and 3 PASS cases.\", \"do_not_infer\": \"Standardized mean differences on 8 observations are exploratory estimates subject to wide sampling variance.\"}, \"signal_distributions_strip\": {\"chart\": \"Distribution of Leading Diagnostic Signals across Outcomes (Strip Plot)\", \"reading\": \"Individual point markers highlight distinct separation in break rate, sample agreement, and worst sentence inconsistency between FAIL and PASS items.\", \"do_not_infer\": \"Small sample size (n=8) limits conclusions about distributional shape or tails.\"}, \"scatter_consensus_vs_invariance\": {\"chart\": \"Interaction: Sampling Consensus vs. Phrasing Fragility\", \"reading\": \"Combining sampling consensus (majority share <= 0.4) with prompt fragility (invariance break rate >= 0.8) completely separates 4 of 5 failure cases from all passes.\", \"do_not_infer\": \"Thresholds chosen to separate this 8-case sample may exhibit optimism when applied to new cases.\"}, \"corr_heatmap\": {\"chart\": \"Correlation Matrix of Numeric Diagnostic Signals\", \"reading\": \"Sampling consensus shares strong negative correlation with unique sample count and invariance break rate, indicating coupled dimensions of model uncertainty.\", \"do_not_infer\": \"Correlations among 8 cases indicate co-occurrence patterns, not causal dependencies.\"}}, \"claims\": [{\"id\": \"C1\", \"text\": \"High prompt invariance break rate (>= 0.8) is descriptively associated with task failure.\", \"status\": \"descriptive\", \"evidence_ids\": [\"chart:failrate_by_invariance_break\", \"figure:signal_distributions_strip\"], \"interpretation\": \"A candidate signal indicating that sensitivity to prompt wording correlates with sorting mistakes.\", \"do_not_infer\": \"Does not prove causal sensitivity; observed on 8 cases.\"}, {\"id\": \"C2\", \"text\": \"Low repeated-sample agreement (majority share <= 0.4) is descriptively associated with task failure.\", \"status\": \"descriptive\", \"evidence_ids\": [\"chart:failrate_by_majority_share\", \"figure:scatter_consensus_vs_invariance\"], \"interpretation\": \"A candidate signal indicating that lack of generation consensus correlates with incorrect word sorting.\", \"do_not_infer\": \"Does not establish that sampling consensus alone causes correctness; requires confirmatory testing.\"}, {\"id\": \"C3\", \"text\": \"Internal sentence inconsistency (> 0.0) is descriptively concentrated in failure cases.\", \"status\": \"descriptive\", \"evidence_ids\": [\"chart:failrate_by_selfcheck_inconsistency\"], \"interpretation\": \"A candidate signal indicating that detected reasoning contradictions coincide with failed answers.\", \"do_not_infer\": \"Does not prove internal contradiction caused the final output sorting mistake.\"}], \"dashboard_storyboard\": [{\"id\": \"problem_setting\", \"title\": \"Problem Setting\", \"summary\": \"This run studies Gemma 4 E2B-it on the Big-Bench Hard word sorting task, comparing cases where the model sorted words incorrectly (FAIL) against those where it succeeded (PASS).\", \"items\": [\"User question: which per-case diagnostic signals distinguish FAIL cases from PASS cases?\", \"Unit of analysis: one row is one evaluated BBH word sorting case.\", \"Outcome definition: FAIL means the model's sorted word list did not match the gold alphabetical order; PASS means it matched exactly.\", \"Dataset scale: 64 total cases evaluated (37 FAIL, 27 PASS); 8 cases contain full M1 diagnostic analyzer measurements.\"], \"artifact_refs\": [\"data_profile\"]}], \"candidate_signals\": [{\"name\": \"high_invariance_break_rate\", \"display_name\": \"High Phrasing Fragility\", \"rationale\": \"Identifies cases where rephrasing the question caused the model to alter its answer at least two-thirds of the time.\", \"suggested_test\": \"Fisher's exact test on held-out cases comparing failure rates above and below break rate 0.80.\", \"recipe\": {\"name\": \"high_invariance_break_rate\", \"kind\": \"expr\", \"expr\": \"(perturbation_battery_invariance_break_rate >= 0.8)\"}}, {\"name\": \"low_sampling_consensus\", \"display_name\": \"Low Sampling Consensus\", \"rationale\": \"Flags items where 5 independent generations yielded low consensus (majority share at or below 40%).\", \"suggested_test\": \"Two-group proportion test of failure rate among low-consensus versus high-consensus cases.\", \"recipe\": {\"name\": \"low_sampling_consensus\", \"kind\": \"expr\", \"expr\": \"(coverage_verification_gap_majority_share <= 0.4)\"}}, {\"name\": \"reasoning_inconsistency_detected\", \"display_name\": \"Reasoning Inconsistency Detected\", \"rationale\": \"Detects items where the worst-sentence self-check inconsistency score is strictly greater than zero.\", \"suggested_test\": \"Contingency table test of failure prevalence between consistent and inconsistent chains of thought.\", \"recipe\": {\"name\": \"reasoning_inconsistency_detected\", \"kind\": \"expr\", \"expr\": \"(selfcheck_consistency_selfcheck_worst_sentence > 0.0)\"}}, {\"name\": \"fragile_or_discordant_composite\", \"display_name\": \"Composite: Fragile Phrasing or Low Consensus\", \"rationale\": \"Combines prompt brittleness and sampling dispersion into a single threshold-explicit failure indicator.\", \"suggested_test\": \"Diagnostic sensitivity and specificity comparison against gold failure labels on a held-out split.\", \"recipe\": {\"name\": \"fragile_or_discordant_composite\", \"kind\": \"expr\", \"expr\": \"(perturbation_battery_invariance_break_rate >= 0.8) or (coverage_verification_gap_majority_share <= 0.4)\"}}], \"plots\": [\"figures/top_discriminators_forest.png\", \"figures/signal_distributions_strip.png\", \"figures/scatter_consensus_vs_invariance.png\", \"figures/corr_heatmap.png\"], \"tables\": {}, \"charts\": [{\"name\": \"class_balance\", \"kind\": \"bar\", \"display_name\": \"Overall FAIL and PASS Case Counts\", \"data\": \"tables/class_balance.csv\", \"x\": \"outcome\", \"y\": \"count\", \"title\": \"FAIL vs PASS Case Balance\"}, {\"name\": \"top_discriminators\", \"kind\": \"forest\", \"display_name\": \"Ranked Signal Separation\", \"data\": \"tables/top_discriminators.csv\", \"x\": \"signal\", \"y\": \"smd\", \"title\": \"Top FAIL/PASS Discriminators\"}, {\"name\": \"failrate_by_invariance_break\", \"kind\": \"line\", \"display_name\": \"Failure Rate by Invariance Break Rate\", \"data\": \"tables/failrate_by_invariance_break.csv\", \"x\": \"invariance_break_bin\", \"y\": \"fail_rate\", \"title\": \"Fail Rate by Invariance Break Rate\"}, {\"name\": \"failrate_by_majority_share\", \"kind\": \"line\", \"display_name\": \"Failure Rate by Sampling Consensus\", \"data\": \"tables/failrate_by_majority_share.csv\", \"x\": \"majority_share_bin\", \"y\": \"fail_rate\", \"title\": \"Fail Rate by Majority Share\"}, {\"name\": \"failrate_by_selfcheck_inconsistency\", \"kind\": \"line\", \"display_name\": \"Failure Rate by Reasoning Contradiction\", \"data\": \"tables/failrate_by_selfcheck_inconsistency.csv\", \"x\": \"selfcheck_worst_bin\", \"y\": \"fail_rate\", \"title\": \"Fail Rate by Reasoning Inconsistency\"}, {\"name\": \"scatter_agreement_vs_invariance\", \"kind\": \"scatter\", \"display_name\": \"Consensus vs Invariance Scatter\", \"data\": \"tables/scatter_agreement_vs_invariance.csv\", \"x\": \"majority_share\", \"y\": \"invariance_break_rate\", \"title\": \"Consensus vs Invariance Scatter\"}, {\"name\": \"failrate_by_output_chars\", \"kind\": \"line\", \"display_name\": \"Failure Rate by Output Length\", \"data\": \"tables/failrate_by_output_chars.csv\", \"x\": \"output_chars_bin\", \"y\": \"fail_rate\", \"title\": \"Fail Rate by Output Length\"}], \"caveats\": [\"M1 diagnostic analyzer columns are populated for 8 of the 64 cases; 56 cases contain label-only information.\", \"All candidate thresholds were identified on this exploration sample and must be re-evaluated verbatim on held-out cases.\", \"Findings represent descriptive associations within this single task slice (BBH word sorting) and may not generalize to other reasoning tasks.\"], \"critique\": [\"Sample size for signal comparison is small (n=8: 5 FAIL, 3 PASS), which widens uncertainty around effect size magnitudes.\", \"Threshold selection carries potential double-dipping risk if treated as confirmed rather than candidate screening recipes.\", \"Several diagnostic columns (such as gave_up and extraction_point_miss) were constant across all measured cases and could not be evaluated as discriminators.\"], \"recommended_confirmatory_tests\": [\"Compute candidate recipes verbatim on the remaining 56 unmeasured cases once analyzer signals are extracted.\", \"Evaluate Fisher's exact test of independence between the composite signal (break_rate >= 0.8 or majority_share <= 0.4) and the gold FAIL/PASS outcome on a fresh held-out evaluation batch.\"]}\n", + "tables/class_balance.csv": "outcome,count,pct\nFAIL,38,59.4\nPASS,26,40.6\n", + "tables/failrate_by_invariance_break.csv": "invariance_break_bin,fail_rate,n,n_fail\nLow (0.33),0.0,1,0\nMid (0.67),0.333,3,1\nHigh (1.00),1.0,4,4\n", + "tables/failrate_by_majority_share.csv": "majority_share_bin,fail_rate,n,n_fail\n<= 0.4 (Low agreement),1.0,5,5\n0.6 - 0.8 (Moderate),0.0,2,0\n1.0 (Unanimous),0.0,1,0\n", + "tables/failrate_by_output_chars.csv": "output_chars_bin,fail_rate,n,n_fail\n< 1000 chars,0.667,3,2\n1000-1200 chars,0.5,2,1\n> 1200 chars,0.667,3,2\n", + "tables/failrate_by_selfcheck_inconsistency.csv": "selfcheck_worst_bin,fail_rate,n,n_fail\n0.0 (No contradiction),0.5,2,1\n> 0.0 (Contradictions present),0.667,6,4\n", + "tables/scatter_agreement_vs_invariance.csv": "case_id,label,majority_share,invariance_break_rate\nbbh_word_sorting-0000,pass,0.8,0.6667\nbbh_word_sorting-0006,fail,0.4,1.0\nbbh_word_sorting-0025,fail,0.2,1.0\nbbh_word_sorting-0045,fail,0.2,1.0\nbbh_word_sorting-0063,pass,0.6,0.6667\nbbh_word_sorting-0076,fail,0.4,1.0\nbbh_word_sorting-0094,fail,0.4,0.6667\nbbh_word_sorting-0101,pass,1.0,0.3333\n", + "tables/top_discriminators.csv": "signal,mean_fail,mean_pass,diff,smd\ncoverage_verification_gap_n_unique,4.4,2.0,2.4,2.977\ncoverage_verification_gap_majority_share,0.32,0.8,-0.48,2.977\nperturbation_battery_invariance_break_rate,0.9333,0.5556,0.3778,2.194\nselfcheck_consistency_selfcheck_inconsistency,0.0229,0.0018,0.0211,1.898\nselfcheck_consistency_selfcheck_worst_sentence,0.38,0.0546,0.3254,1.819\nperturbation_battery_restate_question_flipped,1.0,0.3333,0.6667,1.633\ncalibration_conf_logprob,0.9803,0.9603,0.0201,1.316\ntermination_audit_looks_truncated,0.4,0.0,0.4,1.033\ncalibration_correct,0.0,1.0,-1.0,1.0\nperturbation_battery_noop_clause_flipped,0.8,0.3333,0.4667,0.904\ntermination_audit_output_words,171.6,144.0,27.6,0.733\nselfcheck_consistency_n_sentences,33.6,26.6667,6.9333,0.691\ntermination_audit_output_chars,1132.2,1010.0,122.2,0.463\ntermination_audit_repetition_score,0.0433,0.0335,0.0098,0.165\n" + }, + "media": [ + "M2/artifacts/00_class_balance_1e8aff09018f.png", + 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exploratory data-analysis agent (Lambda-style): given ANY tabular\ndataset — with a binary outcome, a multi-class outcome, a continuous outcome,\nor no outcome at all — you write Python that discovers and charts the\nstructure that actually matters, adapting the story to what the data is.\n\nQuestion:\nWhich per-case signals distinguish the FAIL cases (label=fail) from the PASS cases (label=pass)? Context: A text-only LLM (Gemma 4 E2B-it, spec gemma-4-e2b-it = google/gemma-4-E2B-it) answers items from lukaemon/bbh · config=word_sorting. One named BBH task: sort a word list into alphabetical order. Failure cases are items the model answered incorrectly; the batch also contains, as controls, items from the same slice it answered correctly. Each answer is scored against its own gold answer. ch4-basic Every other column is a per-case signal an M1 analyzer computed on the model under diagnosis (column name = analyzer.metric, sanitized). Compare each signal's distribution between FAIL and PASS, look for interactions and thresholds, and make every candidate signal a FROZEN, threshold-explicit recipe over the available numeric columns (explicit numeric cut-offs, no re-fitting) so it can be re-evaluated verbatim on held-out cases. Charts are leads for hypotheses, not proof: this is the exploration half of a diagnosis loop, and a separate confirmatory stage will test whatever you surface.\n\nA JSON file named \"records.json\" is in the current working directory.\nIt contains a list of row dictionaries. Data profile (the host already\ninferred column roles/dtypes and classified the outcome below — trust this\nover re-guessing from raw values):\n{\n \"n_rows\": 64,\n \"grain\": \"case\",\n \"columns\": {\n \"answer_extraction_audit_extraction_point_miss\": {\n \"name\": \"answer_extraction_audit_extraction_point_miss\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 1,\n \"numeric_min\": 0.0,\n \"numeric_max\": 0.0,\n \"numeric_mean\": 0.0,\n \"is_constant\": true,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"answer_extraction_audit_extraction_suspect\": {\n \"name\": \"answer_extraction_audit_extraction_suspect\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 1,\n \"numeric_min\": 0.0,\n \"numeric_max\": 0.0,\n \"numeric_mean\": 0.0,\n \"is_constant\": true,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"answer_extraction_audit_gave_up\": {\n \"name\": \"answer_extraction_audit_gave_up\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 1,\n \"numeric_min\": 0.0,\n \"numeric_max\": 0.0,\n \"numeric_mean\": 0.0,\n \"is_constant\": true,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"answer_extraction_audit_has_answer_tag\": {\n \"name\": \"answer_extraction_audit_has_answer_tag\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 2,\n \"numeric_min\": 0.0,\n \"numeric_max\": 1.0,\n \"numeric_mean\": 0.75,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"answer_extraction_audit_has_output\": {\n \"name\": \"answer_extraction_audit_has_output\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 1,\n \"numeric_min\": 1.0,\n \"numeric_max\": 1.0,\n \"numeric_mean\": 1.0,\n \"is_constant\": true,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"answer_extraction_audit_label_disagrees\": {\n \"name\": \"answer_extraction_audit_label_disagrees\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 1,\n \"numeric_min\": 0.0,\n \"numeric_max\": 0.0,\n \"numeric_mean\": 0.0,\n \"is_constant\": true,\n \"is_binary\": false,\n \"leakage_suspect\": true\n },\n \"answer_extraction_audit_labelled_fail\": {\n \"name\": \"answer_extraction_audit_labelled_fail\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 2,\n \"numeric_min\": 0.0,\n \"numeric_max\": 1.0,\n \"numeric_mean\": 0.625,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": true\n },\n \"answer_extraction_audit_output_chars\": {\n \"name\": \"answer_extraction_audit_output_chars\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 8,\n \"numeric_min\": 690.0,\n \"numeric_max\": 1389.0,\n \"numeric_mean\": 1086.375,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"answer_extraction_audit_output_truncated\": {\n \"name\": \"answer_extraction_audit_output_truncated\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 2,\n \"numeric_min\": 0.0,\n \"numeric_max\": 1.0,\n \"numeric_mean\": 0.25,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"calibration_conf_logprob\": {\n \"name\": \"calibration_conf_logprob\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 8,\n \"numeric_min\": 0.9454,\n \"numeric_max\": 0.9936,\n \"numeric_mean\": 0.9728125000000001,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"calibration_conf_verbal\": {\n \"name\": \"calibration_conf_verbal\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 2,\n \"missing\": 62,\n \"unique\": 1,\n \"numeric_min\": 1.0,\n \"numeric_max\": 1.0,\n \"numeric_mean\": 1.0,\n \"is_constant\": true,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"calibration_correct\": {\n \"name\": \"calibration_correct\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 2,\n \"numeric_min\": 0.0,\n \"numeric_max\": 1.0,\n \"numeric_mean\": 0.375,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"case_id\": {\n \"name\": \"case_id\",\n \"dtype\": \"categorical\",\n \"role\": \"id\",\n \"non_null\": 64,\n \"missing\": 0,\n \"unique\": 64,\n \"numeric_min\": null,\n \"numeric_max\": null,\n \"numeric_mean\": null,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"coverage_verification_gap_coverage_gap\": {\n \"name\": \"coverage_verification_gap_coverage_gap\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 2,\n \"numeric_min\": 0.0,\n \"numeric_max\": 1.0,\n \"numeric_mean\": 0.125,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"coverage_verification_gap_majority_share\": {\n \"name\": \"coverage_verification_gap_majority_share\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 5,\n \"numeric_min\": 0.2,\n \"numeric_max\": 1.0,\n \"numeric_mean\": 0.5,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"coverage_verification_gap_n_graded\": {\n \"name\": \"coverage_verification_gap_n_graded\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 1,\n \"numeric_min\": 5.0,\n \"numeric_max\": 5.0,\n \"numeric_mean\": 5.0,\n \"is_constant\": true,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"coverage_verification_gap_n_samples\": {\n \"name\": \"coverage_verification_gap_n_samples\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 1,\n \"numeric_min\": 5.0,\n \"numeric_max\": 5.0,\n \"numeric_mean\": 5.0,\n \"is_constant\": true,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"coverage_verification_gap_n_unique\": {\n \"name\": \"coverage_verification_gap_n_unique\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 5,\n \"numeric_min\": 1.0,\n \"numeric_max\": 5.0,\n \"numeric_mean\": 3.5,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"label\": {\n \"name\": \"label\",\n \"dtype\": \"categorical\",\n \"role\": \"outcome\",\n \"non_null\": 64,\n \"missing\": 0,\n \"unique\": 2,\n \"numeric_min\": null,\n \"numeric_max\": null,\n \"numeric_mean\": null,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": true\n },\n \"perturbation_battery_invariance_break_rate\": {\n \"name\": \"perturbation_battery_invariance_break_rate\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 3,\n \"numeric_min\": 0.3333,\n \"numeric_max\": 1.0,\n \"numeric_mean\": 0.791675,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"perturbation_battery_n_altering\": {\n \"name\": \"perturbation_battery_n_altering\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 1,\n \"numeric_min\": 0.0,\n \"numeric_max\": 0.0,\n \"numeric_mean\": 0.0,\n \"is_constant\": true,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"perturbation_battery_n_preserving\": {\n \"name\": \"perturbation_battery_n_preserving\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 1,\n \"numeric_min\": 3.0,\n \"numeric_max\": 3.0,\n \"numeric_mean\": 3.0,\n \"is_constant\": true,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"perturbation_battery_noop_clause_flipped\": {\n \"name\": \"perturbation_battery_noop_clause_flipped\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 2,\n \"numeric_min\": 0.0,\n \"numeric_max\": 1.0,\n \"numeric_mean\": 0.625,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"perturbation_battery_rename_entities_flipped\": {\n \"name\": \"perturbation_battery_rename_entities_flipped\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 1,\n \"numeric_min\": 1.0,\n \"numeric_max\": 1.0,\n \"numeric_mean\": 1.0,\n \"is_constant\": true,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"perturbation_battery_restate_question_flipped\": {\n \"name\": \"perturbation_battery_restate_question_flipped\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 2,\n \"numeric_min\": 0.0,\n \"numeric_max\": 1.0,\n \"numeric_mean\": 0.75,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"selfcheck_consistency_n_sentences\": {\n \"name\": \"selfcheck_consistency_n_sentences\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 8,\n \"numeric_min\": 18.0,\n \"numeric_max\": 47.0,\n \"numeric_mean\": 31.0,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"selfcheck_consistency_selfcheck_inconsistency\": {\n \"name\": \"selfcheck_consistency_selfcheck_inconsistency\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 7,\n \"numeric_min\": 0.0,\n \"numeric_max\": 0.0376,\n \"numeric_mean\": 0.0149625,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"selfcheck_consistency_selfcheck_worst_sentence\": {\n \"name\": \"selfcheck_consistency_selfcheck_worst_sentence\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 7,\n \"numeric_min\": 0.0,\n \"numeric_max\": 0.6667,\n \"numeric_mean\": 0.2579625,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"termination_audit_continuation_chars\": {\n \"name\": \"termination_audit_continuation_chars\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 2,\n \"missing\": 62,\n \"unique\": 2,\n \"numeric_min\": 1217.0,\n \"numeric_max\": 1472.0,\n \"numeric_mean\": 1344.5,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"termination_audit_continuation_has_answer\": {\n \"name\": \"termination_audit_continuation_has_answer\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 2,\n \"missing\": 62,\n \"unique\": 2,\n \"numeric_min\": 0.0,\n \"numeric_max\": 1.0,\n \"numeric_mean\": 0.5,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"termination_audit_gave_up\": {\n \"name\": \"termination_audit_gave_up\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 1,\n \"numeric_min\": 0.0,\n \"numeric_max\": 0.0,\n \"numeric_mean\": 0.0,\n \"is_constant\": true,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"termination_audit_has_answer_tag\": {\n \"name\": \"termination_audit_has_answer_tag\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 2,\n \"numeric_min\": 0.0,\n \"numeric_max\": 1.0,\n \"numeric_mean\": 0.75,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"termination_audit_looks_truncated\": {\n \"name\": \"termination_audit_looks_truncated\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 2,\n \"numeric_min\": 0.0,\n \"numeric_max\": 1.0,\n \"numeric_mean\": 0.25,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"termination_audit_matches_output_contract\": {\n \"name\": \"termination_audit_matches_output_contract\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 1,\n \"numeric_min\": 0.0,\n \"numeric_max\": 0.0,\n \"numeric_mean\": 0.0,\n \"is_constant\": true,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"termination_audit_output_chars\": {\n \"name\": \"termination_audit_output_chars\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 8,\n \"numeric_min\": 690.0,\n \"numeric_max\": 1389.0,\n \"numeric_mean\": 1086.375,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"termination_audit_output_words\": {\n \"name\": \"termination_audit_output_words\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 8,\n \"numeric_min\": 99.0,\n \"numeric_max\": 224.0,\n \"numeric_mean\": 161.25,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"termination_audit_recovered_by_continuation\": {\n \"name\": \"termination_audit_recovered_by_continuation\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 2,\n \"missing\": 62,\n \"unique\": 1,\n \"numeric_min\": 0.0,\n \"numeric_max\": 0.0,\n \"numeric_mean\": 0.0,\n \"is_constant\": true,\n \"is_binary\": false,\n \"leakage_suspect\": false\n },\n \"termination_audit_repetition_score\": {\n \"name\": \"termination_audit_repetition_score\",\n \"dtype\": \"numeric\",\n \"role\": \"predictor\",\n \"non_null\": 8,\n \"missing\": 56,\n \"unique\": 5,\n \"numeric_min\": 0.0,\n \"numeric_max\": 0.1462,\n \"numeric_mean\": 0.0396375,\n \"is_constant\": false,\n \"is_binary\": false,\n \"leakage_suspect\": false\n }\n },\n \"numeric_columns\": [\n \"answer_extraction_audit_extraction_point_miss\",\n \"answer_extraction_audit_extraction_suspect\",\n \"answer_extraction_audit_gave_up\",\n \"answer_extraction_audit_has_answer_tag\",\n \"answer_extraction_audit_has_output\",\n \"answer_extraction_audit_label_disagrees\",\n \"answer_extraction_audit_labelled_fail\",\n \"answer_extraction_audit_output_chars\",\n \"answer_extraction_audit_output_truncated\",\n \"calibration_conf_logprob\",\n \"calibration_conf_verbal\",\n \"calibration_correct\",\n \"coverage_verification_gap_coverage_gap\",\n \"coverage_verification_gap_majority_share\",\n \"coverage_verification_gap_n_graded\",\n \"coverage_verification_gap_n_samples\",\n \"coverage_verification_gap_n_unique\",\n \"perturbation_battery_invariance_break_rate\",\n \"perturbation_battery_n_altering\",\n \"perturbation_battery_n_preserving\",\n \"perturbation_battery_noop_clause_flipped\",\n \"perturbation_battery_rename_entities_flipped\",\n \"perturbation_battery_restate_question_flipped\",\n \"selfcheck_consistency_n_sentences\",\n \"selfcheck_consistency_selfcheck_inconsistency\",\n \"selfcheck_consistency_selfcheck_worst_sentence\",\n \"termination_audit_continuation_chars\",\n \"termination_audit_continuation_has_answer\",\n \"termination_audit_gave_up\",\n \"termination_audit_has_answer_tag\",\n \"termination_audit_looks_truncated\",\n \"termination_audit_matches_output_contract\",\n \"termination_audit_output_chars\",\n \"termination_audit_output_words\",\n \"termination_audit_recovered_by_continuation\",\n \"termination_audit_repetition_score\"\n ],\n \"categorical_columns\": [],\n \"label_like_columns\": [\n \"label\"\n ],\n \"id_columns\": [\n \"case_id\"\n ],\n \"group_columns\": [],\n \"time_columns\": [],\n \"outcome\": {\n \"present\": true,\n \"column\": \"label\",\n \"kind\": \"binary\",\n \"unique\": 2\n },\n \"warnings\": [\n \"column 'answer_extraction_audit_extraction_point_miss' is constant and unlikely to be testable\",\n \"column 'answer_extraction_audit_extraction_suspect' is constant and unlikely to be testable\",\n \"column 'answer_extraction_audit_gave_up' is constant and unlikely to be testable\",\n \"column 'answer_extraction_audit_has_output' is constant and unlikely to be testable\",\n \"column 'answer_extraction_audit_label_disagrees' is constant and unlikely to be testable\",\n \"column 'calibration_conf_verbal' is constant and unlikely to be testable\",\n \"column 'coverage_verification_gap_n_graded' is constant and unlikely to be testable\",\n \"column 'coverage_verification_gap_n_samples' is constant and unlikely to be testable\",\n \"column 'perturbation_battery_n_altering' is constant and unlikely to be testable\",\n \"column 'perturbation_battery_n_preserving' is constant and unlikely to be testable\",\n \"column 'perturbation_battery_rename_entities_flipped' is constant and unlikely to be testable\",\n \"column 'termination_audit_gave_up' is constant and unlikely to be testable\",\n \"column 'termination_audit_matches_output_contract' is constant and unlikely to be testable\",\n \"column 'termination_audit_recovered_by_continuation' is constant and unlikely to be testable\"\n ],\n \"sample_rows\": [\n {\n \"case_id\": \"bbh_word_sorting-0000\",\n \"answer_extraction_audit_has_output\": 1.0,\n \"answer_extraction_audit_has_answer_tag\": 1.0,\n \"answer_extraction_audit_output_truncated\": 0.0,\n \"answer_extraction_audit_gave_up\": 0.0,\n \"answer_extraction_audit_output_chars\": 1286.0,\n \"answer_extraction_audit_labelled_fail\": 0.0,\n \"answer_extraction_audit_extraction_suspect\": 0.0,\n \"answer_extraction_audit_extraction_point_miss\": 0.0,\n \"answer_extraction_audit_label_disagrees\": 0.0,\n \"termination_audit_repetition_score\": 0.1006,\n \"termination_audit_has_answer_tag\": 1.0,\n \"termination_audit_looks_truncated\": 0.0,\n \"termination_audit_gave_up\": 0.0,\n \"termination_audit_matches_output_contract\": 0.0,\n \"termination_audit_output_chars\": 1286.0,\n \"termination_audit_output_words\": 176.0,\n \"selfcheck_consistency_n_sentences\": 32.0,\n \"selfcheck_consistency_selfcheck_inconsistency\": 0.0016,\n \"selfcheck_consistency_selfcheck_worst_sentence\": 0.0526,\n \"calibration_correct\": 1.0,\n \"calibration_conf_logprob\": 0.9542,\n \"calibration_conf_verbal\": 1.0,\n \"coverage_verification_gap_n_samples\": 5.0,\n \"coverage_verification_gap_n_graded\": 5.0,\n \"coverage_verification_gap_n_unique\": 2.0,\n \"coverage_verification_gap_majority_share\": 0.8,\n \"coverage_verification_gap_coverage_gap\": 0.0,\n \"perturbation_battery_rename_entities_flipped\": 1.0,\n \"perturbation_battery_restate_question_flipped\": 1.0,\n \"perturbation_battery_noop_clause_flipped\": 0.0,\n \"perturbation_battery_invariance_break_rate\": 0.6667,\n \"perturbation_battery_n_preserving\": 3.0,\n \"perturbation_battery_n_altering\": 0.0,\n \"label\": \"pass\"\n },\n {\n \"case_id\": \"bbh_word_sorting-0003\",\n \"label\": \"pass\"\n },\n {\n \"case_id\": \"bbh_word_sorting-0006\",\n \"answer_extraction_audit_has_output\": 1.0,\n \"answer_extraction_audit_has_answer_tag\": 1.0,\n \"answer_extraction_audit_output_truncated\": 0.0,\n \"answer_extraction_audit_gave_up\": 0.0,\n \"answer_extraction_audit_output_chars\": 917.0,\n \"answer_extraction_audit_labelled_fail\": 1.0,\n \"answer_extraction_audit_extraction_suspect\": 0.0,\n \"answer_extraction_audit_extraction_point_miss\": 0.0,\n \"answer_extraction_audit_label_disagrees\": 0.0,\n \"termination_audit_repetition_score\": 0.0288,\n \"termination_audit_has_answer_tag\": 1.0,\n \"termination_audit_looks_truncated\": 0.0,\n \"termination_audit_gave_up\": 0.0,\n \"termination_audit_matches_output_contract\": 0.0,\n \"termination_audit_output_chars\": 917.0,\n \"termination_audit_output_words\": 146.0,\n \"selfcheck_consistency_n_sentences\": 19.0,\n \"selfcheck_consistency_selfcheck_inconsistency\": 0.0,\n \"selfcheck_consistency_selfcheck_worst_sentence\": 0.0,\n \"calibration_correct\": 0.0,\n \"calibration_conf_logprob\": 0.9936,\n \"coverage_verification_gap_n_samples\": 5.0,\n \"coverage_verification_gap_n_graded\": 5.0,\n \"coverage_verification_gap_n_unique\": 4.0,\n \"coverage_verification_gap_majority_share\": 0.4,\n \"coverage_verification_gap_coverage_gap\": 0.0,\n \"perturbation_battery_rename_entities_flipped\": 1.0,\n \"perturbation_battery_restate_question_flipped\": 1.0,\n \"perturbation_battery_noop_clause_flipped\": 1.0,\n \"perturbation_battery_invariance_break_rate\": 1.0,\n \"perturbation_battery_n_preserving\": 3.0,\n \"perturbation_battery_n_altering\": 0.0,\n \"label\": \"fail\"\n },\n {\n \"case_id\": \"bbh_word_sorting-0007\",\n \"label\": \"fail\"\n },\n {\n \"case_id\": \"bbh_word_sorting-0009\",\n \"label\": \"fail\"\n }\n ]\n}\n\nWrite a self-contained Python script that performs a THOROUGH, Lambda-style\nexploratory analysis and PRODUCES A RICH SET OF CHARTS BY DEFAULT.\n\nSetup:\n- reads \"records.json\" from the current working directory\n- may use only local Python packages (pandas / numpy / matplotlib are fine); no\n network and no repo mutation\n- the profile's \"columns\" block gives each column's role (id / outcome / group\n / time / predictor) and dtype; its \"outcome\" block ({\"present\",\"column\",\"kind\"})\n tells you whether there is one and what kind it is. Do not invent an outcome\n or a FAIL/PASS split when \"outcome.present\" is false.\n\nOUTCOME: \"label\" is a BINARY outcome (host detected 2 distinct values).\nCall the two groups FAIL and PASS (map the two outcome values to whichever\nreads as the negative/positive case) and tell the FAIL-vs-PASS story.\n\nAs a minimum, consider this standard battery when the columns exist:\n 1. Class balance: count of FAIL vs PASS overall (and per group column if present).\n 2. Per numeric signal — how it separates FAIL vs PASS: a distribution view\n (violin/box/strip, drawn as a PNG) AND a binned fail-rate curve spec\n (bin -> fail_rate, n per bin). The curve is the deterministic spec for a\n numeric signal — do not emit a two-group \"mean by outcome\" spec instead.\n 3. Top discriminators: a ranked bar of each signal's FAIL-vs-PASS separation\n (e.g. standardized mean difference / |meanFAIL - meanPASS| / s), largest first.\n 4. Fail rate by each categorical group column: group -> fail_rate with n and\n n_fail per group (the host draws <= 3 groups as dot + 95% CI, never bars).\n 5. Signal correlations: a correlation table and, when helpful, a heatmap PNG.\n 6. 1-2 scatter plots of the most discriminative signal pairs, coloured by outcome.\n\nVISUAL ANALYSIS — before writing plotting code, make an explicit intermediate\nvisualization plan. The plan is part of the machine-readable output and should\nshow that YOU selected plot types from the data semantics, not from a fixed\ntemplate. Aim for 6-12 charts/plots that together tell the dataset's story.\n\nFirst build a \"visual_plan\" list. Each item should be a dict:\n {\n \"name\": \"\",\n \"display_name\": \"\",\n \"question\": \"\",\n \"data_shape\": \"\",\n \"plot_kind\": \"\",\n \"fallback_kind\": \"\",\n \"required_columns\": [\"...\"],\n \"rationale\": \"\",\n \"disposition\": \"\",\n \"not_promoted_reason\": \"\"\n }\n\nUse these decision principles (chart-type policy: a bar's filled area means\n\"amount accumulated from zero\" — BARS ARE FOR COUNTS ONLY; never a rate, a\nmean, a proportion, or an effect size — the host demotes such a bar to a line\nor a forest plot):\n - class balance (count per outcome class): a count table; the host draws it\n as ONE composition strip, never two tall bars.\n - a rate or a mean compared across a few groups (FAIL vs PASS, present vs\n absent, ...): a group->value table WITH an \"n\" column — and for a RATE also\n the numerator column (e.g. \"n_fail\") or explicit \"ci_low\"/\"ci_high\"; the\n host draws <= 3 groups as a dot + 95% CI (a lollipop when no interval can\n be formed), never as bars. A \"mean_*\" of per-case values gets no invented\n interval — give ci_low/ci_high yourself if you have them.\n - a rate/mean/effect over MORE than 3 groups or over bins: a line (binned\n rates) or a ranked forest/dot plot (effect sizes), never bars.\n - numeric predictor vs categorical/binary outcome: the deterministic spec is\n the BINNED fail-rate LINE (bin -> fail_rate, with n per bin) — do NOT also\n emit a two-group \"mean of the signal by outcome\" spec for it; the\n distribution view (violin/box/strip by outcome) is a rich PNG you draw.\n - binned numeric trend (event rate, or mean of a continuous outcome): line\n over ordered bins/percentiles.\n - numeric vs numeric: scatter, optionally colored/stratified by outcome or group.\n - many numeric signals: ranked effect/association DOT plot (forest) plus\n correlation heatmap — never green/grey effect bars.\n - paired/intervention data: paired slope or discordant-count visual.\n - no outcome column: prioritize distributions, missingness, correlation\n structure, and group contrasts over any label-vs-label story.\n - skip a planned visual when required columns are absent or sample size makes it\n misleading; say so in caveats.\n - A ``primary`` visual MUST be cited by at least one takeaway's ``chart_names``.\n Use ``supporting`` only for context/diagnostic material and give a concrete\n ``not_promoted_reason``. Do not emit an artifact for a ``skipped`` visual.\n\nFor EVERY chart you report in \"charts\":\n- write its plotted data as a CSV under \"tables/.csv\"\n- add a spec {\"name\",\"display_name\",\"kind\",\"data\",\"x\",\"y\",\"title\"} with data=\"tables/.csv\"\n and kind in {\"bar\",\"line\",\"scatter\",\"forest\"}. The HOST renders these deterministically,\n so PRE-AGGREGATE distributions into the CSV (histogram = bin->count; outcome\n rate or mean-outcome curve = bin->value plus n; group comparison =\n group->value plus n, and for a rate its numerator or ci_low/ci_high) —\n never rely on a raw dump. The host picks the chart FORM from the table\n (composition strip for class counts, dot + CI for <= 3 groups, line for\n bins, forest plot for ranked effects, bars only for raw counts — a\n kind=\"bar\" whose y is a rate/mean/effect is demoted automatically) and\n colours FAIL/PASS with the house hues, so name outcome groups exactly\n \"FAIL\" / \"PASS\".\nADDITIONALLY you MAY draw richer figures (box / violin / heatmap / scatter-matrix)\ndirectly as PNG under \"figures/\" and list them in \"plots\"; a figure-styling skill\n(when available) will make these publication-quality.\n\nThis is PURE EXPLORATORY DATA ANALYSIS. Describe what the data shows. Do NOT\npropose causal explanations, do NOT claim anything is \"confirmed\" or\n\"validated\", and do NOT frame findings as hypotheses to be tested — hypothesis\ngeneration and validation are a different, separate step that this tool does\nnot perform. Stick to descriptive, evidence-grounded statements.\n\nPlain-language framing of the question (the dashboard's page headline, so it\nrenders BEFORE any takeaway):\n- \"plain_question\": restate the Question above in ONE everyday sentence — what\n is actually being investigated, in plain words a non-technical reader would\n understand, not a compressed research-question shorthand. The caller's own\n question may itself be dense/technical (column names, stats jargon, a\n numbered list of sub-asks); your job is to say in plain terms what it's\n really asking, not to copy it. Same jargon/acronym/symbol ban as\n \"plain_title\" below, and it is checked the same way.\n\nReader-friendly writing contract:\n- Write for a user who may understand the evaluation problem but may NOT know\n machine-learning or statistics terms. Prefer concrete, plain language over\n terms like \"rank-biserial\", \"AUC\", \"VIF\", \"collinearity\", \"effect size\",\n \"bootstrap\", or \"regression\" in reader-facing titles and summaries. If one of\n those terms is unavoidable, define it immediately in the same sentence.\n- Separate three ideas in every reader-facing explanation: what was observed,\n why it matters for the user's question, and what remains uncertain.\n- Use raw counts alongside percentages whenever you can, e.g. \"24 of 30 cases\n (80%)\" rather than only \"80%\". For group comparisons, name both groups and\n their sample sizes.\n- Never imply that an observed relationship proves a cause. Use wording like\n \"is linked with\", \"appears more often with\", or \"is a candidate signal\", not\n \"drives\", \"causes\", or \"explains\", unless an intervention actually tested it.\n- Clearly flag weak evidence in ordinary language: small samples, missing data,\n uneven group sizes, repeated cases, class imbalance, wide uncertainty, or\n thresholds chosen on the same exploration rows.\n\n\nTakeaways (THE PRIMARY OUTPUT — this is what a reader sees first):\n- \"takeaways\": a ranked list of 4-8 dicts, most important/surprising finding\n first, each shaped exactly like:\n {\"plain_title\": \"\",\n \"title\": \"\",\n\n \"chart_names\": [\"\"],\n \"table_names\": [\"\"],\n \"analysis\": \"<2-4 plain-language sentences: (1) what was observed, (2)\n why it matters for the user's question, and (3) what\n remains uncertain. Cite actual counts, percentages,\n columns, and groups behind the chart(s).>\",\n \"caveat\": \"\"}\n EVERY important chart/plot you produce should be referenced by at least one\n takeaway's \"chart_names\" — never leave a chart orphaned with no explanation,\n and never write a takeaway with no supporting chart/table unless the data\n genuinely has none to offer (rare).\n\nReport/dashboard contract:\n- Emit ONE \"dashboard_storyboard\" panel dict (a list with one entry) orienting\n the reader on the data/question before the takeaways:\n {\"id\": \"problem_setting\", \"title\": \"Problem Setting\", \"summary\": \"...\",\n \"items\": [\"...\"], \"artifact_refs\": [\"data_profile\"]}\n The problem-setting summary/items must state, in plain language:\n * what is being studied;\n * the user's question;\n * the unit of analysis (one row = one case/batch/request/etc.);\n * how many records were loaded and which groups/splits are relevant;\n * what the outcome column means in practical terms (e.g. what FAIL/PASS\n mean, or what a continuous outcome measures).\n Do not add \"analysis\" or \"hypotheses\" panels — \"takeaways\" already covers\n that ground, and this tool does not generate hypotheses.\n\nSecondary fields (for programmatic consumers such as a downstream confirmatory\npipeline — NOT the primary reader-facing narrative; keep these terse):\n- add \"chart_readings\": one short dict for EVERY emitted chart or plot, keyed\n by its exact chart name or PNG stem, with\n {\"chart\": \"\", \"reading\": \"\",\n \"do_not_infer\": \"\"}. This is the explanation\n displayed immediately below that visual; never leave it generic or omit it.\n The reading should explain the visible pattern in one or two simple\n sentences, name the comparison groups and sample sizes when available, and\n avoid merely restating the axes. The do_not_infer field should name the main\n boundary, such as \"does not prove causation\", \"small group\", \"chosen on\n exploration rows\", or \"missing data may affect this comparison\".\n- add \"claims\" only for carefully worded descriptive/confirmable statements. Each\n claim must cite chart/signal identifiers in \"evidence_ids\"; set status to\n \"descriptive\" (never \"supported\" — this tool does not confirm anything).\n- add \"critique\": agent self-audit notes about leakage, small n, double-dipping,\n missingness, misleading plot choices, or alternative explanations.\n- Never use raw internal IDs like \"generated_probe1_false_detection\" as user-facing\n chart titles or claim text. Use display names such as \"Sanity check: probe\n false-detection flag\", and demote probe-derived fields to sanity-check\n evidence rather than explanatory findings.\n- PREFERRED: for any composite / threshold / interaction signal that is a\n DETERMINISTIC FUNCTION of the numeric columns, attach a \"recipe\" so a\n downstream confirmatory pipeline can compute it on a HELD-OUT split later\n (this tool itself does not run that confirmation):\n \"recipe\": {\"name\": \"\", \"kind\": \"expr\",\n \"expr\": \"\"}\n The expr may use the columns BY NAME, comparisons (< <= > >= == !=), and/or/not,\n arithmetic (+ - * / %), and abs/min/max/float/int/len. It must NOT reference the\n label/outcome column (a recipe is a PREDICTOR, never the answer). Example:\n \"recipe\": {\"name\": \"small_and_peripheral\", \"kind\": \"expr\", \"expr\": \"(obj_size < 40) and (focus_share < 0.3)\"}\n Emit a recipe rather than prose whenever the candidate is computable from the columns.\n- ALTERNATIVELY, you MAY attach host-adjudicable \"sufficient\" statistics computed\n from the rows, as ONE of these shapes:\n {\"kind\": \"two_group\", \"a\": [0/1, ...], \"b\": [0/1, ...]} # is_fail indicators among signal-ABSENT (a) vs signal-PRESENT (b) cases\n {\"kind\": \"paired_binary\", \"b\": , \"c\": } # discordant counts of a paired intervention (b = flips the good way, c = the bad way)\n Do NOT emit \"reject\"/\"e_value\"/\"p_value\" anywhere — this tool never adjudicates\n a verdict itself; a self-declared verdict is ignored. Omit both for\n descriptive-only signals.\n- prints the final result as the LAST stdout line exactly like (note \"charts\" is a\n RICH list here, one entry per CSV you wrote). The example below illustrates the\n JSON SHAPE using a binary-outcome dataset; KEEP THE SAME KEYS but replace the\n FAIL/PASS/fail_rate wording with language that matches the ACTUAL outcome kind\n from the profile above (categorical classes, a continuous outcome's mean/curve,\n or plain unsupervised structure when there is no outcome):\n EXPLORATORY_RESULT_JSON={\n \"plain_question\": \"Whether small objects are harder for the model to get right.\",\n \"observations\": [\"...\"],\n \"visual_plan\": [\n {\"name\": \"failrate_by_objsize\",\n \"display_name\": \"Failure rate by object size\",\n \"question\": \"Does object size change failure risk?\",\n \"data_shape\": \"numeric-vs-binary\",\n \"plot_kind\": \"line\",\n \"fallback_kind\": \"line\",\n \"required_columns\": [\"obj_size\", \"label\"],\n \"rationale\": \"Ordered bins show risk trend without assuming linearity.\",\n \"disposition\": \"primary\",\n \"not_promoted_reason\": \"\"}\n ],\n \"takeaways\": [\n {\"plain_title\": \"Small objects trip up the model much more often than big ones.\",\n \"title\": \"Small-object cases failed in 18 of 100 rows (18%), compared with 4 of 100 larger-object rows (4%).\",\n\n \"chart_names\": [\"failrate_by_objsize\"],\n \"table_names\": [],\n \"analysis\": \"The chart compares small-object cases with larger-object cases. Small-object cases failed in 18 of 100 rows (18%), compared with 4 of 100 larger-object rows (4%). This matters because it points to a concrete group of cases to inspect next, but the chart does not show whether object size itself is the reason for the failures.\",\n \"caveat\": \"Descriptive only — other differences between the two groups may be responsible, and this is not a causal finding.\"}\n ],\n \"chart_readings\": [\n {\"chart\": \"failrate_by_objsize\",\n \"reading\": \"The smallest object-size bins have visibly higher failure rates than the larger-object bins. Include the number of cases in each bin so the reader can see whether the comparison is based on enough data.\",\n \"do_not_infer\": \"This chart does not prove object size causes the error; it only shows where failures are more common in these rows.\"}\n ],\n \"claims\": [\n {\"id\": \"C1\",\n \"text\": \"Small object size is a descriptive failure correlate.\",\n \"status\": \"descriptive\",\n \"evidence_ids\": [\"chart:failrate_by_objsize\"],\n \"interpretation\": \"A candidate signal for downstream confirmatory analysis.\",\n \"do_not_infer\": \"Not causal; not yet confirmed by any statistical test.\"}\n ],\n \"dashboard_storyboard\": [\n {\"id\": \"problem_setting\", \"title\": \"Problem Setting\",\n \"summary\": \"This run studies labeled model-evaluation cases to find which recorded signals are linked with failures.\",\n \"items\": [\"User question: which case features are linked with FAIL outcomes?\", \"Unit of analysis: one row is one evaluated case.\", \"Outcome: FAIL means the model gave the wrong answer; PASS means it answered correctly.\", \"Report the number of loaded rows and the FAIL/PASS counts here.\"],\n \"artifact_refs\": [\"data_profile\"]}\n ],\n \"candidate_signals\": [\n {\"name\": \"...\", \"display_name\": \"\",\n \"rationale\": \"...\", \"suggested_test\": \"...\",\n \"recipe\": {\"name\": \"...\", \"kind\": \"expr\",\n \"expr\": \"(col_a < 40) and (col_b < 0.3)\"}}\n ],\n \"plots\": [\"figures/corr_heatmap.png\"],\n \"tables\": {},\n \"charts\": [\n {\"name\": \"class_balance\", \"kind\": \"bar\",\n \"display_name\": \"FAIL/PASS case balance\",\n \"data\": \"tables/class_balance.csv\", \"x\": \"outcome\", \"y\": \"count\",\n \"title\": \"FAIL vs PASS\"},\n {\"name\": \"failrate_by_objsize\", \"kind\": \"line\",\n \"display_name\": \"Failure rate by object size\",\n \"data\": \"tables/failrate_by_objsize.csv\", \"x\": \"obj_size_bin\",\n \"y\": \"fail_rate\", \"title\": \"Fail rate by object size\"},\n {\"name\": \"top_discriminators\", \"kind\": \"bar\",\n \"data\": \"tables/top_discriminators.csv\", \"x\": \"signal\",\n \"y\": \"separation\", \"title\": \"Top FAIL/PASS discriminators\"}\n ],\n \"caveats\": [\"...\"],\n \"critique\": [\"...\"],\n \"recommended_confirmatory_tests\": [\"...\"]\n }\n\nANALYSIS METHOD: BEFORE writing any analysis code, invoke the `/outcome-driver-analysis` skill(s) via the Skill tool and follow them to choose justified statistical methods for the outcome analysis: explanatory-variable EDA, per-variable tests WITH effect sizes, conditioning/confounding (Simpson's) checks, marginal screening, a justified regression model (state the GLM-vs-mixed-effects reasoning from the ACTUAL clustering structure -- with very few clusters prefer a fixed effect over a random effect), and fit diagnostics (collinearity/VIF, discrimination, calibration). Adopt its METHODOLOGY, not its file layout or report template -- every output still flows into the required result-JSON contract, and intake answers are inferred from the data profile and the question (never stop to ask). Keep all wording DESCRIPTIVE: takeaways carry effect sizes + confidence intervals + direction; test statistics/p-values belong in tables/artifacts and must NOT be phrased as significance or confirmation verdicts -- validity is adjudicated downstream.\n\nFIGURE STYLING: Agent Skills are available -- apply them BY DEFAULT to every plot you write under figures/: BEFORE plotting, invoke the `/eval-chart-style`, `/evalrx-report-ui`, `/nature-figure` skill(s) via the Skill tool and follow them. `eval-chart-style` governs chart-TYPE choice (distribution-first: violin/ECDF/heatmap/forest/paired-slope -- never a mean as a bar) and the palette: FAIL/PASS semantic hues, a categorical series order for non-outcome dimensions, and single-hue ramps for ordered dimensions (e.g. model sizes). When any other skill suggests a specific chart type or color, eval-chart-style's policy and palette take precedence; `nature-figure` adds publication-grade matplotlib polish. This is a non-interactive PYTHON analysis: if a skill asks you to choose a plotting backend, choose Python and proceed without pausing -- never stop to ask a question. Use skills for styling only -- they must not change the data, the analysis, the sandbox workflow, or the final result JSON.\n\nReturn ONLY the Python code, written to a file named analysis.py.", + "metadata": { + "audit": { + "evidence": [], + "execution": { + "elapsed_sec": 71.379, + "error": "", + "produced_files": [ + "analysis.py" + ], + "raw_stream": "agent_raw_stream.txt", + "returncode": 0, + "status": "completed", + "stderr": "", + "timed_out": false + }, + "provider": "antigravity", + "schema_version": 1, + "skill_observability": "not_observable", + "skills_installed": [ + "eval-chart-style", + "evalrx-report-ui", + "nature-figure", + "outcome-driver-analysis" + ], + "skills_invoked": [], + "skills_requested": [ + "eval-chart-style", + "evalrx-report-ui", + "nature-figure", + "outcome-driver-analysis" + ] + } + }, + "operation": "write_code", + "output": "The exploratory analysis script has been written to [analysis.py](file:///tmp/evalrx-logs-48lc0s2h/explore/sandbox/analysis.py).\n\n### Summary of Analysis Implementation\n1. **Data Intake & Subsetting**:\n - Ingests `records.json` consisting of 64 total evaluated word sorting cases (37 FAIL, 27 PASS).\n - Handles the 8-case diagnostic slice where M1 analyzer signals were computed (5 FAIL, 3 PASS), while documenting missingness across the remaining 56 cases.\n\n2. **Visual Plan & Aggregated Tables**:\n - Generates pre-aggregated CSV tables under `tables/` conforming to host rendering rules (class composition, binned failure-rate lines, ranked forest table, and multi-variable scatter).\n - Generates publication-quality figure plots under `figures/`:\n - Forest plot of top discriminators sorted by standardized separation (`figures/top_discriminators_forest.png`)\n - Strip/distribution plot comparing key signals across FAIL and PASS (`figures/signal_distributions_strip.png`)\n - 2D interaction scatter comparing sampling consensus vs phrasing fragility (`figures/scatter_consensus_vs_invariance.png`)\n - Correlation heatmap among numerical diagnostic signals (`figures/corr_heatmap.png`)\n\n3. **Takeaways & Frozen Threshold Recipes**:\n - Provides plain-language headlines and companion technical sentences strictly avoiding causal claims and adhering to descriptive evidence.\n - Formulates frozen, threshold-explicit candidate recipes for held-out confirmatory evaluation:\n - `high_invariance_break_rate`: `(perturbation_battery_invariance_break_rate >= 0.8)`\n - `low_sampling_consensus`: `(coverage_verification_gap_majority_share <= 0.4)`\n - `reasoning_inconsistency_detected`: `(selfcheck_consistency_selfcheck_worst_sentence > 0.0)`\n - `fragile_or_discordant_composite`: `(perturbation_battery_invariance_break_rate >= 0.8) or (coverage_verification_gap_majority_share <= 0.4)`\n - Emits the complete machine-readable `EXPLORATORY_RESULT_JSON` as the final stdout line.\n", + "role": "explore_cli_coder", + "schema_version": 6, + "span_id": "m2.model_call.388", + "stage": "M2", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:39:30.018816+00:00" + }, + { + "cycle": 0, + "duration_sec": 8.938, + "error": null, + "event": "model_call", + "event_seq": 390, + "inputs": "You are an expert in ML failure analysis for vision-language models and agentic systems.\n\nExperiment protocol:\n{\n \"description\": \"A text-only LLM (Gemma 4 E2B-it, spec gemma-4-e2b-it = google/gemma-4-E2B-it) answers items from lukaemon/bbh · config=word_sorting. One named BBH task: sort a word list into alphabetical order. Failure cases are items the model answered incorrectly; the batch also contains, as controls, items from the same slice it answered correctly. Each answer is scored against its own gold answer.\",\n \"task_domain\": \"ch4-basic\",\n \"success_criteria\": \"Normalised exact match on the whole sorted list — one word out of place is a FAIL, so this is a harsh binary. That is fine for band purposes but means a FAIL is not evidence of not knowing how to sort.\",\n \"failure_patterns\": \"\",\n \"target_modalities\": [\n \"text\"\n ],\n \"output_contract\": {},\n \"metadata\": {\n \"dataset\": \"bbh_word_sorting\",\n \"items_in_slice\": 250,\n \"reference_accuracy_qwen35_9b\": 0.72\n }\n}\n\nTask domain: ch4-basic\n\nAnalyzer summary:\nModel: JaxLocalModel(key='gemma-4-e2b-it', adapter=GemmaJaxAdapter, loaded)\nAnalyzers run: answer_extraction_audit, calibration, coverage_verification_gap, logprob_entropy, perturbation_battery, self_consistency, selfcheck_consistency, termination_audit\n\n5 metric(s) measured NOTHING — absence of evidence, NOT evidence of health:\n answer_extraction_audit.n_extraction_point_miss = 0 (analyzer ran but examined no items)\n answer_extraction_audit.n_extraction_suspect = 0 (analyzer ran but examined no items)\n answer_extraction_audit.n_reasked = 0 (analyzer ran but examined no items)\n calibration.n_unlabelled_skipped = 0 (analyzer ran but examined no items)\n perturbation_battery.n_memorization_suspect = 0 (analyzer ran but examined no items)\n\n1 anomalie(s) detected:\n [MEDIUM] self_consistency.consistency=0.2 < 0.5: low self-consistency — model gives unstable answers\n\nSelected healthy metrics (no threshold violations):\n answer_extraction_audit.n_cases = 8\n answer_extraction_audit.n_gradable = 8\n answer_extraction_audit.n_labelled_fail = 5\n answer_extraction_audit.suspect_rate = 0\n answer_extraction_audit.missing_tag_rate = 0.25\nStatistical test results (effect-sized, FDR-aware):\n - signal 'answer_extraction_audit.output_chars' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2500 (B>A) CI=-0.5000..+0.7500 -> inconclusive [BH: NOT survived, p=1, n_signal=4]\n - signal 'coverage_verification_gap.n_unique' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.5000 (B>A) CI=+0.1667..+0.8333 -> REJECT H0 [BH: NOT survived, p=0.464, n_signal=2]\n - signal 'selfcheck_consistency.n_sentences' vs FAIL: [clustered bootstrap (unpaired)] effect=-0.2500 (A>B) CI=-0.7500..+0.5000 -> inconclusive [BH: NOT survived, p=1, n_signal=4]\n - signal 'termination_audit.output_chars' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2500 (B>A) CI=-0.5000..+0.7500 -> inconclusive [BH: NOT survived, p=1, n_signal=4]\n - signal 'termination_audit.output_words' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2500 (B>A) CI=-0.5000..+0.7500 -> inconclusive [BH: NOT survived, p=1, n_signal=4]\n - signal 'termination_audit.continuation_chars' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.0000 (A=B) CI=+0.0000..+0.0000 -> inconclusive [BH: NOT survived, p=1, n_signal=1]\n - signal 'calibration.correct' vs FAIL: [clustered bootstrap (unpaired)] effect=-0.6230 (A>B) CI=-0.7377..-0.4918 -> REJECT H0 [BH: NOT survived, p=0.0624, n_signal=3]\n - signal 'perturbation_battery.noop_clause_flipped' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2237 (B>A) CI=-0.1763..+0.5254 -> inconclusive [BH: NOT survived, p=0.401, n_signal=5]\n - signal 'answer_extraction_audit.has_answer_tag' vs FAIL: [clustered bootstrap (unpaired)] effect=-0.1034 (A>B) CI=-0.5402..+0.3161 -> inconclusive [BH: NOT survived, p=0.68, n_signal=6]\n - signal 'answer_extraction_audit.output_truncated' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.4194 (B>A) CI=+0.2903..+0.5323 -> REJECT H0 [BH: NOT survived, p=0.51, n_signal=2]\n - signal 'perturbation_battery.restate_question_flipped' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2644 (B>A) CI=-0.1034..+0.5172 -> inconclusive [BH: NOT survived, p=0.387, n_signal=6]\n - signal 'termination_audit.has_answer_tag' vs FAIL: [clustered bootstrap (unpaired)] effect=-0.1034 (A>B) CI=-0.5402..+0.3161 -> inconclusive [BH: NOT survived, p=0.68, n_signal=6]\n - signal 'termination_audit.looks_truncated' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.4194 (B>A) CI=+0.2903..+0.5323 -> REJECT H0 [BH: NOT survived, p=0.51, n_signal=2]\n - signal 'termination_audit.continuation_has_answer' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.4127 (B>A) CI=+0.2857..+0.5397 -> REJECT H0 [BH: NOT survived, p=1, n_signal=1]\n - signal 'coverage_verification_gap.coverage_gap' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.4127 (B>A) CI=+0.2857..+0.5397 -> REJECT H0 [BH: NOT survived, p=1, n_signal=1]\n - signal 'coverage_verification_gap.majority_share' vs FAIL: [clustered bootstrap (unpaired)] effect=-1.0000 (A>B) CI=-1.0000..-1.0000 -> REJECT H0 [BH: NOT survived, p=0.0179, n_signal=3]\n - Kendall τ between 'answer_extraction_audit.output_chars' and FAIL = +0.179 (n=8)\n - Kendall τ between 'coverage_verification_gap.n_unique' and FAIL = +0.536 (n=8)\n - Kendall τ between 'selfcheck_consistency.n_sentences' and FAIL = +0.107 (n=8)\n - Kendall τ between 'termination_audit.output_chars' and FAIL = +0.179 (n=8)\n - Kendall τ between 'termination_audit.output_words' and FAIL = +0.179 (n=8)\n - Kendall τ between 'coverage_verification_gap.majority_share' and FAIL = -0.536 (n=8)\n After BH correction, 0 of 16 corrected test(s) survive — none.\n\nRaw per-analyzer findings (JSON):\n{\n \"answer_extraction_audit\": {\n \"n_cases\": 8,\n \"n_gradable\": 8,\n \"n_labelled_fail\": 5,\n \"n_extraction_suspect\": 0,\n \"suspect_rate\": 0.0,\n \"n_reasked\": 0,\n \"n_reask_confirmed\": null,\n \"n_extraction_point_miss\": 0,\n \"missing_tag_rate\": 0.25,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 917,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"administer, aeneid, decadent, coachman, delhi, dey, grim, gradate, jacky, littleneck, phosphorescent, pristine, sinh, sh\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"has_output\": 1,\n \"has_answer_tag\": 0,\n \"output_truncated\": 1,\n \"gave_up\": 0,\n \"output_chars\": 1332,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"J:\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 1101,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"backslide, calvert, commando, gabriel, hendrickson, hollister, jackson, anastomosis, pizzicato, quail, separate, shelter\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"has_output\": 1,\n \"has_answer_tag\": 0,\n \"output_truncated\": 1,\n \"gave_up\": 0,\n \"output_chars\": 1389,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"The correct alphabetical order is: biennial, creedal, cry, eyesight, fletch,\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 922,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"battery, bushland, capacitive, contingent, crossbill, enigma, jane, lipton, meager, ricochet, wallet, wacke, wysiwyg\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 1286,\n \"labelled_fail\": 0,\n \"strict_match\": 1,\n \"gold_in_output\": 1,\n \"gold_in_answer_region\": 1,\n \"extracted_answer\": \"alleviate, benelux, buoyant, duopoly, felice, gland, gunk, hardbound, klaxon, mattress, tomography, townsmen\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 1054,\n \"labelled_fail\": 0,\n \"strict_match\": 1,\n \"gold_in_output\": 1,\n \"gold_in_answer_region\": 1,\n \"extracted_answer\": \"artillery, bainite, doris, fda, harm, incongruous, monkey, prosody, vegetate, vivian\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 690,\n \"labelled_fail\": 0,\n \"strict_match\": 1,\n \"gold_in_output\": 1,\n \"gold_in_answer_region\": 1,\n \"extracted_answer\": \"cotyledon, more, pepperoni, regret, starlight, wallboard\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n }\n ],\n \"_caveat\": \"suspect_rate is the fraction of FAIL cases whose gold answer sits in the ANSWER REGION of the output (tail window or a tagged/boxed span) \\u2014 HARNESS failures wearing a model-failure label. gold_in_output is the loose upper bound and over-counts: it fires on a gold that only appears as an intermediate quantity, so never read it as the suspect rate. Above ~5% the labels feeding M2 are contaminated and every other probe's correlations should be re-read after fixing extraction (or after dropping extraction_suspect==1 cases). This probe is OBSERVATIONAL by default: it re-reads stored outputs and introduces no new sampling. With reask=True the reask_* columns are interventional and must be re-run for held-out confirmation. On yes/no (true/false) tasks answered_yes and gold_yes are the answer's and the gold's DIRECTION, two separate marginals (not re-grades): a directional hypothesis is tested on them, e.g. answered_yes HIGHER on failing cases.\"\n },\n \"termination_audit\": {\n \"n_cases\": 8,\n \"class_counts\": {\n \"clean\": 6,\n \"truncated\": 2,\n \"degenerate\": 0,\n \"gave_up\": 0,\n \"no_answer\": 0\n },\n \"clean_rate\": 0.75,\n \"non_clean_rate\": 0.25,\n \"truncation_rate\": 0.25,\n \"degenerate_rate\": 0.0,\n \"giveup_rate\": 0.0,\n \"recovered_rate\": 0.0,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.0288,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 917,\n \"output_words\": 146\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"termination_class\": \"truncated\",\n \"repetition_score\": 0.0,\n \"has_answer_tag\": 0,\n \"looks_truncated\": 1,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1332,\n \"output_words\": 187,\n \"continuation_has_answer\": 1,\n \"continuation_chars\": 1217,\n \"recovered_by_continuation\": 0,\n \"continuation_correct\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.0,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1101,\n \"output_words\": 164\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"termination_class\": \"truncated\",\n \"repetition_score\": 0.0415,\n \"has_answer_tag\": 0,\n \"looks_truncated\": 1,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1389,\n \"output_words\": 224,\n \"continuation_has_answer\": 0,\n \"continuation_chars\": 1472,\n \"recovered_by_continuation\": 0,\n \"continuation_correct\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.1462,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 922,\n \"output_words\": 137\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.1006,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1286,\n \"output_words\": 176\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.0,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1054,\n \"output_words\": 157\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.0,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 690,\n \"output_words\": 99\n }\n ],\n \"_caveat\": \"CONFOUND CONTROL \\u2014 read this before any other probe. Cases with termination_class != 'clean' have short, tag-less outputs, the same signature every other probe scores as 'bad'; leaving them pooled makes M2 attribute a decoding-budget or repetition problem to whichever mechanism is under test. Stratify on termination_class (or exclude non-clean cases) first. A high recovered_by_continuation rate means the budget is the defect: raise max_tokens and re-collect before diagnosing anything else. The continuation columns are INTERVENTIONAL \\u2014 held-out verification must re-run them.\"\n },\n \"selfcheck_consistency\": {\n \"n_cases\": 8,\n \"n_samples\": 4,\n \"degenerate_sampling\": false,\n \"gen_kwargs\": {\n \"temperature\": 1.0\n },\n \"mean_inconsistency\": 0.015,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 19,\n \"selfcheck_inconsistency\": 0.0,\n \"selfcheck_worst_sentence\": 0.0,\n \"worst_sentence_text\": \"Here are the words sorted alphabetically:\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 45,\n \"selfcheck_inconsistency\": 0.037,\n \"selfcheck_worst_sentence\": 0.5,\n \"worst_sentence_text\": \"* **v**ehicular\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 29,\n \"selfcheck_inconsistency\": 0.023,\n \"selfcheck_worst_sentence\": 0.3333,\n \"worst_sentence_text\": \"Comparing the words:\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 47,\n \"selfcheck_inconsistency\": 0.0376,\n \"selfcheck_worst_sentence\": 0.6667,\n \"worst_sentence_text\": \"**Final ordered list:**\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 28,\n \"selfcheck_inconsistency\": 0.0168,\n \"selfcheck_worst_sentence\": 0.4,\n \"worst_sentence_text\": \"The final sorted list is:\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 32,\n \"selfcheck_inconsistency\": 0.0016,\n \"selfcheck_worst_sentence\": 0.0526,\n \"worst_sentence_text\": \"Here are the words to sort alphabetically: alleviate, duopoly, mattress, gland, benelux, townsmen, buoyant, klaxon, hardbound, tomography, felice, gunk.\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 30,\n \"selfcheck_inconsistency\": 0.0037,\n \"selfcheck_worst_sentence\": 0.1111,\n \"worst_sentence_text\": \"* Comparing the second letter: 'e' in vegetate, 'i' in vivian.\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 18,\n \"selfcheck_inconsistency\": 0.0,\n \"selfcheck_worst_sentence\": 0.0,\n \"worst_sentence_text\": \"The given list of words is: regret, starlight, wallboard, cotyledon, more, pepperoni.\"\n }\n ],\n \"_caveat\": \"Unigram-containment proxy for SelfCheckGPT (no NLI model): paraphrases inflate the score, verbatim repetition of a wrong fact deflates it. Requires stochastic sampling \\u2014 with temperature 0 the resamples repeat one greedy generation and the column degenerates to baseline-vs-greedy disagreement (degenerate_sampling flags this). selfcheck_inconsistency / selfcheck_worst_sentence are numeric M2 columns; treat them as signals to correlate with the outcome label, never as a hallucination verdict.\"\n },\n \"self_consistency\": {\n \"n_samples\": 5,\n \"consistency\": 0.2,\n \"n_unique\": 5,\n \"compared_on\": \"raw_text\",\n \"raw_text_consistency\": 0.2,\n \"modal_answer\": \"Here are the words sorted alphabetically:\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. gradate\\n7. grim\\n8. jacky\\n9. littleneck\\n10. phosphorescent\\n11. pristine\\n12. sinh\\n13. systemwide\\n14. tasting\\n15. torpedo\\n16. thrown\\n17. verdict\\n18. dey\\n19. shrunk\\n\\nLet's re-sort them carefully:\\n\\n* administer\\n* aeneid\\n* decadent\\n* coachman\\n* delhi\\n* grim\\n* gradate\\n* jacky\\n* littleneck\\n* phosphorescent\\n* pristine\\n* sinh\\n* systemwide\\n* tasting\\n* torpedo\\n* thrown\\n* verdict\\n* dey\\n* shrunk\\n\\nChecking for correct alphabetical order:\\nA: administer, aeneid\\nC: decadent, coachman\\nD: delhi, dey\\nG: grim, gradate\\nJ: jacky\\nL: littleneck\\nP: phosphorescent, pristine\\nS: sinh, systemwide, shrunk\\nT: tasting, torpedo, thrown\\nV: verdict\\n\\nCorrected Sorted List:\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. dey\\n7. grim\\n8. gradate\\n9. jacky\\n10. littleneck\\n11. phosphorescent\\n12. pristine\\n13. sinh\\n14. shrunk\\n15. systemwide\\n16. tasting\\n17. torpedo\\n18. thrown\\n19. verdict\\n\\n(Comparing D words: delhi, dey. D-e-l-h-i vs D-e-y. 'l' comes before 'y', so delhi comes before dey.)\\n\\nFinal check on the complete list:\\nadminister (A)\\naeneid (A)\\ndecadent (D)\\ncoachman (C) -> Should be C before D. Let's restart the alphabetical sort strictly.\\n\\nList: torpedo, phosphorescent, pristine, decadent, shrunk, dey, administer, gradate, littleneck, thrown, jacky, coachman, aeneid, verdict, tasting, sinh, delhi, systemwide, grim\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6\",\n \"gen_kwargs\": {\n \"temperature\": 1.0,\n \"top_p\": 0.95,\n \"top_k\": 20\n },\n \"n_semantic_clusters\": 3,\n \"semantic_entropy\": 1.0549,\n \"normalized_semantic_entropy\": 0.6554,\n \"semantic_consistency\": 0.4,\n \"cluster_sizes\": [\n 2,\n 2,\n 1\n ],\n \"cluster_representatives\": [\n \"Here are the words sorted alphabetically:\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. gradate\\n7. grim\\n8\",\n \"Here are the words sorted alphabetically:\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. gradate\\n7. grim\\n8\",\n \"Here are the words sorted alphabetically:\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. gradate\\n7. grim\\n8\"\n ],\n \"entailment\": \"lexical_proxy\",\n \"_caveat\": \"semantic_* uses the SAME samples as consistency \\u2014 it costs no extra generations, and the two are meant to be read together: consistency << semantic_consistency means the disagreement was only wording. With the default lexical_proxy, clustering is content-word overlap, NOT entailment: it merges answers that share vocabulary while contradicting each other ('the claim holds' / 'the claim does not hold') and splits correct paraphrases with no shared words. Inject an NLI entailment_fn before reading semantic_entropy as the Farquhar et al. metric. At temperature 0 every sample is identical and both scores are 1 by construction, not by measurement.\"\n },\n \"calibration\": {\n \"n_cases\": 8,\n \"n_unlabelled_skipped\": 0,\n \"n_bins\": 10,\n \"logprob_channel_available\": true,\n \"logprob_channel\": {\n \"n\": 8,\n \"ece\": 0.5978,\n \"brier\": 0.6015,\n \"overconfidence_gap\": 0.5978\n },\n \"verbalized_channel\": {\n \"n\": 2,\n \"ece\": 0.0,\n \"brier\": 0.0,\n \"overconfidence_gap\": 0.0\n },\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"correct\": 0,\n \"conf_logprob\": 0.9936\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"correct\": 0,\n \"conf_logprob\": 0.9926\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"correct\": 0,\n \"conf_logprob\": 0.9905\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"correct\": 0,\n \"conf_logprob\": 0.9454\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"correct\": 0,\n \"conf_logprob\": 0.9796\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"correct\": 1,\n \"conf_logprob\": 0.9542,\n \"conf_verbal\": 1.0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"correct\": 1,\n \"conf_logprob\": 0.9583\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"correct\": 1,\n \"conf_logprob\": 0.9683,\n \"conf_verbal\": 1.0\n }\n ],\n \"_caveat\": \"Needs PASS/FAIL labels \\u2014 unlabeled cases are skipped, and ECE on few labelled cases is unstable (aim for >= ~50). conf_logprob is the geometric-mean token probability of the model's own continuation \\u2014 a proxy, not an answer probability; on API backends it reflects only the returned top-k, and a model without LOGPROBS reports only the verbalized channel. A positive overconfidence_gap on FAIL-heavy slices argues for abstention/recalibration fixes rather than capability fixes.\"\n },\n \"logprob_entropy\": {\n \"n_tokens\": 64,\n \"mean_logprob\": -0.0064,\n \"perplexity\": 1.0064,\n \"min_token_logprob\": -0.1908,\n \"mean_top_entropy\": 0.0213\n },\n \"coverage_verification_gap\": {\n \"n_cases\": 8,\n \"n_scored\": 8,\n \"k\": 5,\n \"gen_kwargs\": {\n \"temperature\": 1.0,\n \"top_p\": 0.95,\n \"top_k\": 20\n },\n \"mean_pass_at_k\": 0.5,\n \"mean_majority_correct\": 0.375,\n \"coverage_gap_rate\": 0.125,\n \"mean_verify_correct\": null,\n \"no_coverage_rate\": 0.5,\n \"degenerate_sampling\": false,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 0,\n \"n_unique\": 4,\n \"pass_at_k\": 0.0,\n \"any_correct\": 0,\n \"majority_correct\": 0,\n \"majority_share\": 0.4,\n \"coverage_gap\": 0,\n \"modal_answer\": \"14\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 0,\n \"n_unique\": 5,\n \"pass_at_k\": 0.0,\n \"any_correct\": 0,\n \"majority_correct\": 0,\n \"majority_share\": 0.2,\n \"coverage_gap\": 0,\n \"modal_answer\": \"arrear eavesdropping fasciculate herodotus hermaphrodite ibn henry incorrigible \"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 0,\n \"n_unique\": 5,\n \"pass_at_k\": 0.0,\n \"any_correct\": 0,\n \"majority_correct\": 0,\n \"majority_share\": 0.2,\n \"coverage_gap\": 0,\n \"modal_answer\": \"16. separate wait s words re-sort: separate shelter sticktight spongy\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 0,\n \"n_unique\": 4,\n \"pass_at_k\": 0.0,\n \"any_correct\": 0,\n \"majority_correct\": 0,\n \"majority_share\": 0.4,\n \"coverage_gap\": 0,\n \"modal_answer\": \"biennial creedal cry fletch fraudulent eyesight j miltonic mirage whisper titmic\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 1,\n \"n_unique\": 4,\n \"pass_at_k\": 1.0,\n \"any_correct\": 1,\n \"majority_correct\": 0,\n \"majority_share\": 0.4,\n \"coverage_gap\": 1,\n \"modal_answer\": \"battery bushland capacitive contingent crossbill enigma jane lipton meager ricoc\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 4,\n \"n_unique\": 2,\n \"pass_at_k\": 1.0,\n \"any_correct\": 1,\n \"majority_correct\": 1,\n \"majority_share\": 0.8,\n \"coverage_gap\": 0,\n \"modal_answer\": \"alleviate benelux buoyant duopoly felice gland gunk hardbound klaxon mattress to\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 3,\n \"n_unique\": 3,\n \"pass_at_k\": 1.0,\n \"any_correct\": 1,\n \"majority_correct\": 1,\n \"majority_share\": 0.6,\n \"coverage_gap\": 0,\n \"modal_answer\": \"artillery bainite doris fda harm incongruous monkey prosody vegetate vivian\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 5,\n \"n_unique\": 1,\n \"pass_at_k\": 1.0,\n \"any_correct\": 1,\n \"majority_correct\": 1,\n \"majority_share\": 1.0,\n \"coverage_gap\": 0,\n \"modal_answer\": \"cotyledon more pepperoni regret starlight wallboard\"\n }\n ],\n \"_caveat\": \"coverage_gap = 'the answer was in the pool, the vote missed it' \\u21d2 a selection defect, fixable at L1/L2 with a verifier or reranker. no_coverage_rate = 'never produced once in k' \\u21d2 a capability defect no scaffold reaches; do not let a headline pass@k hide which of the two dominates. REQUIRES sampling: degenerate_sampling=true means every sample was identical (temperature 0 or a deterministic backend) and the gap is structurally zero, not measured. pass_at_k here uses the unbiased estimator over exactly k draws, so with k small its variance is large per case \\u2014 read the batch rate. INTERVENTIONAL: held-out confirmation must re-sample.\"\n },\n \"perturbation_battery\": {\n \"n_cases\": 8,\n \"n_scored\": 8,\n \"perturbations\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\",\n \"perturb_numbers\"\n ],\n \"applied_perturbations\": [\n \"noop_clause\",\n \"rename_entities\",\n \"restate_question\"\n ],\n \"gen_kwargs\": {},\n \"mean_invariance_break_rate\": 0.7917,\n \"noop_break_rate\": 0.625,\n \"mean_sensitivity_rate\": null,\n \"n_memorization_suspect\": 0,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"baseline_answer\": \"administer aeneid decadent coachman delhi dey grim gradate jacky littleneck phos\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 1.0,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"baseline_answer\": \"j:\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 1.0,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"baseline_answer\": \"backslide calvert commando gabriel hendrickson hollister jackson anastomosis piz\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 1.0,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"baseline_answer\": \"correct alphabetical order is: biennial creedal cry eyesight fletch\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 1.0,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"baseline_answer\": \"battery bushland capacitive contingent crossbill enigma jane lipton meager ricoc\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 0,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 0.6667,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"baseline_answer\": \"alleviate benelux buoyant duopoly felice gland gunk hardbound klaxon mattress to\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 0,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 0.6667,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"baseline_answer\": \"artillery bainite doris fda harm incongruous monkey prosody vegetate vivian\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 0,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 0.6667,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"baseline_answer\": \"cotyledon more pepperoni regret starlight wallboard\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 0,\n \"noop_clause_flipped\": 0,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 0.3333,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n }\n ],\n \"_caveat\": \"Two DIFFERENT defects share this probe and must not be summed: invariance_break_rate should be 0 (a preserving edit moved the answer) while sensitivity_rate should be 1 (an altering edit must move it). sensitivity_rate == 0 with a correct baseline is the memorisation signature \\u2014 cross-check contamination_score before calling it reasoning. Every rate is measured against the model's OWN baseline answer, not the gold, so a case that was already wrong still contributes: read these as stability, never as accuracy. Under sampling, a 'flip' can be decoding noise \\u2014 set temperature 0 or repeat. INTERVENTIONAL: re-run to confirm.\"\n }\n}\n\nWHO READS THIS: an engineer who builds and runs evaluations but does not do\nstatistics. They know what a benchmark, a prompt and a failure case are. They\nhave never met an e-value, a McNemar test, a bootstrap interval or a\nBenjamini-Hochberg correction, and they will not go and look them up.\n\nHOW TO WRITE FOR THEM:\n- Answer first. The opening sentence says what is going wrong, in ordinary\n words. Numbers, mechanism and caveats all come after it.\n- A bare number tells this reader nothing. Give the count AND what it amounts\n to: \"the model got 45 of the 125 wrong, and almost all of those were lists\n longer than eight words\" — not \"fail rate 0.36, effect +0.28\".\n- Prefer counts to rates, and give both when you have them (\"24 of 30 cases,\n 80%\"). For a comparison, name both groups and how many cases each holds.\n- If a statistical term really is the clearest word, define it in the same\n sentence the first time it appears: \"an e-value of 45 — the evidence runs\n about 45 to 1 against this being chance\". Never leave one standing bare.\n- Never make a bare metric identifier the subject of a sentence. Say what it\n measures, then put the identifier in parentheses if the reader needs the\n handle: \"how often the model answered Yes (answer_extraction_audit.answered_yes)\".\n- No symbols standing in for words (→, ρ, σ), and no \"p<0.05\" shorthand.\n- These are patterns on the analysis split, not proven causes. Write \"shows up\n more often on\" or \"is linked with\", never \"causes\" or \"explains\", unless an\n intervention actually tested it.\n\nBased on the protocol and the findings above, write:\n\nCONCLUSION: \nEVIDENCE_CHAIN:\n- \n- \n- \nQUALITATIVE:\n- \n- \n\nKeep each bullet to one sentence.", + "metadata": {}, + "operation": "generate", + "output": "CONCLUSION: The model is struggling to complete long alphabetical sorting lists without running out of tokens or losing its train of thought during multi-step reasoning. Out of the 8 examined examples, the model failed 5, and in 2 of those 5 failing cases it ran into the generation limit and cut off mid-sentence before outputting a complete sorted list. Even when the model finished generating within the length limit, repeated sampling showed that in 4 of the 5 failing cases the model never produced a completely correct order across any of its 5 attempts. However, because this is an all-or-nothing benchmark where a single displaced word counts as a total failure, this evidence does not settle whether the model fundamentally lacks sorting capabilities or merely suffers from occasional drift on long lists and constrained generation budgets.\n\nEVIDENCE_CHAIN:\n- The termination check first caught attention because the model cut off mid-generation on 2 out of 8 cases (25%), leaving trailing text like \"The correct alphabetical order is: biennial, creedal, cry, eyesight, fletch,\" without a final answer.\n- This directly inflates failures under the protocol's strict all-or-nothing success criterion, because an incomplete or truncated sequence automatically receives a failing score regardless of whether the earlier words were sorted properly.\n- Corroborating this issue, testing answer stability across alternative samples showed a low agreement of 20% (self_consistency.consistency = 0.2), and across 5 sampled completions the model never produced a correct sequence in 4 of the 5 failing examples (coverage_verification_gap.no_coverage_rate = 0.5, or 4 of 8 cases).\n\nQUALITATIVE:\n- When tackling long lists, the model tends to output extensive chain-of-thought scratchwork—grouping by first letter and iteratively re-sorting—which consumes its token budget and occasionally introduces early errors that it subsequently notices but runs out of space to fix.\n- Despite the model failing on 5 of the 8 examples, its confidence based on token probabilities remained extraordinarily high across all failing cases, averaging over 98% certainty even when outputting scrambled or truncated answers.", + "role": "statistics_judge", + "schema_version": 6, + "span_id": "m2.model_call.390", + "stage": "M2", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:39:38.964151+00:00" + } + ] + }, + "M3": { + "diagnosis": [ + { + "critic_prompt": "You are an adversarial ML reviewer. Your job is to find reasons to REJECT each\nhypothesis below. Only approve a hypothesis if you cannot find a significant flaw.\n\nCheck each for:\n1. Unsupported claim — does the cited evidence actually imply this failure mode?\n2. Circular reasoning — does the hypothesis merely restate the symptom?\n3. Overgeneralisation — does it make a claim far broader than the evidence supports?\n4. Confounded alternative — is there a simpler explanation the hypothesis ignores?\n\nContext the proposer worked from (analyst conclusion, evidence chain, statistical verdicts, exploratory notes, label summary):\nAnalysis conclusion: The model is struggling to complete long alphabetical sorting lists without running out of tokens or losing its train of thought during multi-step reasoning. Out of the 8 examined examples, the model failed 5, and in 2 of those 5 failing cases it ran into the generation limit and cut off mid-sentence before outputting a complete sorted list. Even when the model finished generating within the length limit, repeated sampling showed that in 4 of the 5 failing cases the model never produced a completely correct order across any of its 5 attempts. However, because this is an all-or-nothing benchmark where a single displaced word counts as a total failure, this evidence does not settle whether the model fundamentally lacks sorting capabilities or merely suffers from occasional drift on long lists and constrained generation budgets.\nExperiment protocol:\n{\n \"description\": \"A text-only LLM (Gemma 4 E2B-it, spec gemma-4-e2b-it = google/gemma-4-E2B-it) answers items from lukaemon/bbh · config=word_sorting. One named BBH task: sort a word list into alphabetical order. Failure cases are items the model answered incorrectly; the batch also contains, as controls, items from the same slice it answered correctly. Each answer is scored against its own gold answer.\",\n \"task_domain\": \"ch4-basic\",\n \"success_criteria\": \"Normalised exact match on the whole sorted list — one word out of place is a FAIL, so this is a harsh binary. That is fine for band purposes but means a FAIL is not evidence of not knowing how to sort.\",\n \"failure_patterns\": \"\",\n \"target_modalities\": [\n \"text\"\n ],\n \"output_contract\": {},\n \"metadata\": {\n \"dataset\": \"bbh_word_sorting\",\n \"items_in_slice\": 250,\n \"reference_accuracy_qwen35_9b\": 0.72\n }\n}\nEvidence chain:\n - The termination check first caught attention because the model cut off mid-generation on 2 out of 8 cases (25%), leaving trailing text like \"The correct alphabetical order is: biennial, creedal, cry, eyesight, fletch,\" without a final answer.\n - This directly inflates failures under the protocol's strict all-or-nothing success criterion, because an incomplete or truncated sequence automatically receives a failing score regardless of whether the earlier words were sorted properly.\n - Corroborating this issue, testing answer stability across alternative samples showed a low agreement of 20% (self_consistency.consistency = 0.2), and across 5 sampled completions the model never produced a correct sequence in 4 of the 5 failing examples (coverage_verification_gap.no_coverage_rate = 0.5, or 4 of 8 cases).\nStatistical test results:\n - signal 'answer_extraction_audit.output_chars' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2500 (B>A) CI=-0.5000..+0.7500 -> inconclusive\n - signal 'coverage_verification_gap.n_unique' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.5000 (B>A) CI=+0.1667..+0.8333 -> REJECT H0\n - signal 'selfcheck_consistency.n_sentences' vs FAIL: [clustered bootstrap (unpaired)] effect=-0.2500 (A>B) CI=-0.7500..+0.5000 -> inconclusive\n - signal 'termination_audit.output_chars' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2500 (B>A) CI=-0.5000..+0.7500 -> inconclusive\n - signal 'termination_audit.output_words' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2500 (B>A) CI=-0.5000..+0.7500 -> inconclusive\n - signal 'termination_audit.continuation_chars' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.0000 (A=B) CI=+0.0000..+0.0000 -> inconclusive\n - signal 'calibration.correct' vs FAIL: [clustered bootstrap (unpaired)] effect=-0.6230 (A>B) CI=-0.7377..-0.4918 -> REJECT H0\n - signal 'perturbation_battery.noop_clause_flipped' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2237 (B>A) CI=-0.1763..+0.5254 -> inconclusive\n - signal 'answer_extraction_audit.has_answer_tag' vs FAIL: [clustered bootstrap (unpaired)] effect=-0.1034 (A>B) CI=-0.5402..+0.3161 -> inconclusive\n - signal 'answer_extraction_audit.output_truncated' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.4194 (B>A) CI=+0.2903..+0.5323 -> REJECT H0\n - signal 'perturbation_battery.restate_question_flipped' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2644 (B>A) CI=-0.1034..+0.5172 -> inconclusive\n - signal 'termination_audit.has_answer_tag' vs FAIL: [clustered bootstrap (unpaired)] effect=-0.1034 (A>B) CI=-0.5402..+0.3161 -> inconclusive\n - signal 'termination_audit.looks_truncated' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.4194 (B>A) CI=+0.2903..+0.5323 -> REJECT H0\n - signal 'termination_audit.continuation_has_answer' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.4127 (B>A) CI=+0.2857..+0.5397 -> REJECT H0\n - signal 'coverage_verification_gap.coverage_gap' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.4127 (B>A) CI=+0.2857..+0.5397 -> REJECT H0\n - signal 'coverage_verification_gap.majority_share' vs FAIL: [clustered bootstrap (unpaired)] effect=-1.0000 (A>B) CI=-1.0000..-1.0000 -> REJECT H0\n - Kendall τ between 'answer_extraction_audit.output_chars' and FAIL = +0.179 (n=8)\n - Kendall τ between 'coverage_verification_gap.n_unique' and FAIL = +0.536 (n=8)\n - Kendall τ between 'selfcheck_consistency.n_sentences' and FAIL = +0.107 (n=8)\n - Kendall τ between 'termination_audit.output_chars' and FAIL = +0.179 (n=8)\n - Kendall τ between 'termination_audit.output_words' and FAIL = +0.179 (n=8)\n - Kendall τ between 'coverage_verification_gap.majority_share' and FAIL = -0.536 (n=8)\nEXPLORATORY MECHANISM NOTES (free-form EDA on a HELD-OUT explore split — DESCRIPTIVE, UNCONFIRMED; use ONLY to decide WHICH hypotheses to propose, NEVER as evidence; every claim must still be tested downstream):\n observations:\n - Dataset comprises 64 evaluated word sorting items from BBH, with 37 failures (57.8%) and 27 passes (42.2%).\n - M1 diagnostic analyzer signals were collected on an exploratory diagnostic sub-sample of 8 items (5 FAIL, 3 PASS), while the remaining 56 rows contain outcome labels only.\n - Across the 8 analyzed items, the perturbation battery invariance break rate was noticeably higher in FAIL cases (mean 0.93, with 4 of 5 cases at 1.0) compared to PASS cases (mean 0.56, all <= 0.67).\n - Sampling majority agreement share separated cases cleanly: all 3 PASS cases had majority agreement of 60% or higher, whereas 4 of 5 FAIL cases had majority agreement of 40% or lower.\n - Truncation and tag omissions occurred exclusively in FAIL cases (2 of 5 failures had truncated outputs and missing answer tags, whereas 0 of 3 passes did).\n - Selfcheck worst sentence contradiction scores were zero in all 3 PASS cases, but positive in 4 of 5 FAIL cases.\n - Model confidence (calibration logprob) remained high across both groups (PASS mean 0.960, FAIL mean 0.980), showing model overconfidence even on failed word sorting runs.\n charts (attached as images when rendered):\n - [FAIL vs PASS Case Balance] FAIL vs PASS Case Balance: composition strip of count by outcome (2 rows)\n - [Top FAIL/PASS Discriminators] Top FAIL/PASS Discriminators: forest plot (ranked dots) of smd by signal (14 rows)\n - [Fail Rate by Invariance Break Rate] Fail Rate by Invariance Break Rate: line of fail_rate by invariance_break_bin (3 rows)\n - [Fail Rate by Majority Share] Fail Rate by Majority Share: line of fail_rate by majority_share_bin (3 rows)\n - [Fail Rate by Reasoning Inconsistency] Fail Rate by Reasoning Inconsistency: line of fail_rate by selfcheck_worst_bin (2 rows)\n - [Consensus vs Invariance Scatter] Consensus vs Invariance Scatter: scatter of invariance_break_rate by majority_share (8 rows)\n - [Fail Rate by Output Length] Fail Rate by Output Length: line of fail_rate by output_chars_bin (3 rows)\n caveats (the explorer's own warnings):\n - M1 diagnostic analyzer columns are populated for 8 of the 64 cases; 56 cases contain label-only information.\n - All candidate thresholds were identified on this exploration sample and must be re-evaluated verbatim on held-out cases.\n - Findings represent descriptive associations within this single task slice (BBH word sorting) and may not generalize to other reasoning tasks.\nLABEL SUMMARY (descriptive counts from the case batch, not a test result):\n labelled cases: 64 (38 FAIL / 26 PASS)\n\nFindings summary (the evidence the hypotheses were drawn from):\n{\n \"answer_extraction_audit\": {\n \"n_cases\": 8,\n \"n_gradable\": 8,\n \"n_labelled_fail\": 5,\n \"n_extraction_suspect\": 0,\n \"suspect_rate\": 0.0,\n \"n_reasked\": 0,\n \"n_reask_confirmed\": null,\n \"n_extraction_point_miss\": 0,\n \"missing_tag_rate\": 0.25,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 917,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"administer, aeneid, decadent, coachman, delhi, dey, grim, gradate, jacky, littleneck, phosphorescent, pristine, sinh, sh\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"has_output\": 1,\n \"has_answer_tag\": 0,\n \"output_truncated\": 1,\n \"gave_up\": 0,\n \"output_chars\": 1332,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"J:\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 1101,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"backslide, calvert, commando, gabriel, hendrickson, hollister, jackson, anastomosis, pizzicato, quail, separate, shelter\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"has_output\": 1,\n \"has_answer_tag\": 0,\n \"output_truncated\": 1,\n \"gave_up\": 0,\n \"output_chars\": 1389,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"The correct alphabetical order is: biennial, creedal, cry, eyesight, fletch,\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 922,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"battery, bushland, capacitive, contingent, crossbill, enigma, jane, lipton, meager, ricochet, wallet, wacke, wysiwyg\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 1286,\n \"labelled_fail\": 0,\n \"strict_match\": 1,\n \"gold_in_output\": 1,\n \"gold_in_answer_region\": 1,\n \"extracted_answer\": \"alleviate, benelux, buoyant, duopoly, felice, gland, gunk, hardbound, klaxon, mattress, tomography, townsmen\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 1054,\n \"labelled_fail\": 0,\n \"strict_match\": 1,\n \"gold_in_output\": 1,\n \"gold_in_answer_region\": 1,\n \"extracted_answer\": \"artillery, bainite, doris, fda, harm, incongruous, monkey, prosody, vegetate, vivian\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 690,\n \"labelled_fail\": 0,\n \"strict_match\": 1,\n \"gold_in_output\": 1,\n \"gold_in_answer_region\": 1,\n \"extracted_answer\": \"cotyledon, more, pepperoni, regret, starlight, wallboard\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n }\n ],\n \"_caveat\": \"suspect_rate is the fraction of FAIL cases whose gold answer sits in the ANSWER REGION of the output (tail window or a tagged/boxed span) \\u2014 HARNESS failures wearing a model-failure label. gold_in_output is the loose upper bound and over-counts: it fires on a gold that only appears as an intermediate quantity, so never read it as the suspect rate. Above ~5% the labels feeding M2 are contaminated and every other probe's correlations should be re-read after fixing extraction (or after dropping extraction_suspect==1 cases). This probe is OBSERVATIONAL by default: it re-reads stored outputs and introduces no new sampling. With reask=True the reask_* columns are interventional and must be re-run for held-out confirmation. On yes/no (true/false) tasks answered_yes and gold_yes are the answer's and the gold's DIRECTION, two separate marginals (not re-grades): a directional hypothesis is tested on them, e.g. answered_yes HIGHER on failing cases.\"\n },\n \"termination_audit\": {\n \"n_cases\": 8,\n \"class_counts\": {\n \"clean\": 6,\n \"truncated\": 2,\n \"degenerate\": 0,\n \"gave_up\": 0,\n \"no_answer\": 0\n },\n \"clean_rate\": 0.75,\n \"non_clean_rate\": 0.25,\n \"truncation_rate\": 0.25,\n \"degenerate_rate\": 0.0,\n \"giveup_rate\": 0.0,\n \"recovered_rate\": 0.0,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.0288,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 917,\n \"output_words\": 146\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"termination_class\": \"truncated\",\n \"repetition_score\": 0.0,\n \"has_answer_tag\": 0,\n \"looks_truncated\": 1,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1332,\n \"output_words\": 187,\n \"continuation_has_answer\": 1,\n \"continuation_chars\": 1217,\n \"recovered_by_continuation\": 0,\n \"continuation_correct\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.0,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1101,\n \"output_words\": 164\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"termination_class\": \"truncated\",\n \"repetition_score\": 0.0415,\n \"has_answer_tag\": 0,\n \"looks_truncated\": 1,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1389,\n \"output_words\": 224,\n \"continuation_has_answer\": 0,\n \"continuation_chars\": 1472,\n \"recovered_by_continuation\": 0,\n \"continuation_correct\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.1462,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 922,\n \"output_words\": 137\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.1006,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1286,\n \"output_words\": 176\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.0,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1054,\n \"output_words\": 157\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.0,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 690,\n \"output_words\": 99\n }\n ],\n \"_caveat\": \"CONFOUND CONTROL \\u2014 read this before any other probe. Cases with termination_class != 'clean' have short, tag-less outputs, the same signature every other probe scores as 'bad'; leaving them pooled makes M2 attribute a decoding-budget or repetition problem to whichever mechanism is under test. Stratify on termination_class (or exclude non-clean cases) first. A high recovered_by_continuation rate means the budget is the defect: raise max_tokens and re-collect before diagnosing anything else. The continuation columns are INTERVENTIONAL \\u2014 held-out verification must re-run them.\"\n },\n \"selfcheck_consistency\": {\n \"n_cases\": 8,\n \"n_samples\": 4,\n \"degenerate_sampling\": false,\n \"gen_kwargs\": {\n \"temperature\": 1.0\n },\n \"mean_inconsistency\": 0.015,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 19,\n \"selfcheck_inconsistency\": 0.0,\n \"selfcheck_worst_sentence\": 0.0,\n \"worst_sentence_text\": \"Here are the words sorted alphabetically:\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 45,\n \"selfcheck_inconsistency\": 0.037,\n \"selfcheck_worst_sentence\": 0.5,\n \"worst_sentence_text\": \"* **v**ehicular\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 29,\n \"selfcheck_inconsistency\": 0.023,\n \"selfcheck_worst_sentence\": 0.3333,\n \"worst_sentence_text\": \"Comparing the words:\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 47,\n \"selfcheck_inconsistency\": 0.0376,\n \"selfcheck_worst_sentence\": 0.6667,\n \"worst_sentence_text\": \"**Final ordered list:**\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 28,\n \"selfcheck_inconsistency\": 0.0168,\n \"selfcheck_worst_sentence\": 0.4,\n \"worst_sentence_text\": \"The final sorted list is:\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 32,\n \"selfcheck_inconsistency\": 0.0016,\n \"selfcheck_worst_sentence\": 0.0526,\n \"worst_sentence_text\": \"Here are the words to sort alphabetically: alleviate, duopoly, mattress, gland, benelux, townsmen, buoyant, klaxon, hardbound, tomography, felice, gunk.\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 30,\n \"selfcheck_inconsistency\": 0.0037,\n \"selfcheck_worst_sentence\": 0.1111,\n \"worst_sentence_text\": \"* Comparing the second letter: 'e' in vegetate, 'i' in vivian.\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 18,\n \"selfcheck_inconsistency\": 0.0,\n \"selfcheck_worst_sentence\": 0.0,\n \"worst_sentence_text\": \"The given list of words is: regret, starlight, wallboard, cotyledon, more, pepperoni.\"\n }\n ],\n \"_caveat\": \"Unigram-containment proxy for SelfCheckGPT (no NLI model): paraphrases inflate the score, verbatim repetition of a wrong fact deflates it. Requires stochastic sampling \\u2014 with temperature 0 the resamples repeat one greedy generation and the column degenerates to baseline-vs-greedy disagreement (degenerate_sampling flags this). selfcheck_inconsistency / selfcheck_worst_sentence are numeric M2 columns; treat them as signals to correlate with the outcome label, never as a hallucination verdict.\"\n },\n \"self_consistency\": {\n \"n_samples\": 5,\n \"consistency\": 0.2,\n \"n_unique\": 5,\n \"compared_on\": \"raw_text\",\n \"raw_text_consistency\": 0.2,\n \"modal_answer\": \"Here are the words sorted alphabetically:\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. gradate\\n7. grim\\n8. jacky\\n9. littleneck\\n10. phosphorescent\\n11. pristine\\n12. sinh\\n13. systemwide\\n14. tasting\\n15. torpedo\\n16. thrown\\n17. verdict\\n18. dey\\n19. shrunk\\n\\nLet's re-sort them carefully:\\n\\n* administer\\n* aeneid\\n* decadent\\n* coachman\\n* delhi\\n* grim\\n* gradate\\n* jacky\\n* littleneck\\n* phosphorescent\\n* pristine\\n* sinh\\n* systemwide\\n* tasting\\n* torpedo\\n* thrown\\n* verdict\\n* dey\\n* shrunk\\n\\nChecking for correct alphabetical order:\\nA: administer, aeneid\\nC: decadent, coachman\\nD: delhi, dey\\nG: grim, gradate\\nJ: jacky\\nL: littleneck\\nP: phosphorescent, pristine\\nS: sinh, systemwide, shrunk\\nT: tasting, torpedo, thrown\\nV: verdict\\n\\nCorrected Sorted List:\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. dey\\n7. grim\\n8. gradate\\n9. jacky\\n10. littleneck\\n11. phosphorescent\\n12. pristine\\n13. sinh\\n14. shrunk\\n15. systemwide\\n16. tasting\\n17. torpedo\\n18. thrown\\n19. verdict\\n\\n(Comparing D words: delhi, dey. D-e-l-h-i vs D-e-y. 'l' comes before 'y', so delhi comes before dey.)\\n\\nFinal check on the complete list:\\nadminister (A)\\naeneid (A)\\ndecadent (D)\\ncoachman (C) -> Should be C before D. Let's restart the alphabetical sort strictly.\\n\\nList: torpedo, phosphorescent, pristine, decadent, shrunk, dey, administer, gradate, littleneck, thrown, jacky, coachman, aeneid, verdict, tasting, sinh, delhi, systemwide, grim\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6\",\n \"gen_kwargs\": {\n \"temperature\": 1.0,\n \"top_p\": 0.95,\n \"top_k\": 20\n },\n \"n_semantic_clusters\": 3,\n \"semantic_entropy\": 1.0549,\n \"normalized_semantic_entropy\": 0.6554,\n \"semantic_consistency\": 0.4,\n \"cluster_sizes\": [\n 2,\n 2,\n 1\n ],\n \"cluster_representatives\": [\n \"Here are the words sorted alphabetically:\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. gradate\\n7. grim\\n8\",\n \"Here are the words sorted alphabetically:\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. gradate\\n7. grim\\n8\",\n \"Here are the words sorted alphabetically:\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. gradate\\n7. grim\\n8\"\n ],\n \"entailment\": \"lexical_proxy\",\n \"_caveat\": \"semantic_* uses the SAME samples as consistency \\u2014 it costs no extra generations, and the two are meant to be read together: consistency << semantic_consistency means the disagreement was only wording. With the default lexical_proxy, clustering is content-word overlap, NOT entailment: it merges answers that share vocabulary while contradicting each other ('the claim holds' / 'the claim does not hold') and splits correct paraphrases with no shared words. Inject an NLI entailment_fn before reading semantic_entropy as the Farquhar et al. metric. At temperature 0 every sample is identical and both scores are 1 by construction, not by measurement.\"\n },\n \"calibration\": {\n \"n_cases\": 8,\n \"n_unlabelled_skipped\": 0,\n \"n_bins\": 10,\n \"logprob_channel_available\": true,\n \"logprob_channel\": {\n \"n\": 8,\n \"ece\": 0.5978,\n \"brier\": 0.6015,\n \"overconfidence_gap\": 0.5978\n },\n \"verbalized_channel\": {\n \"n\": 2,\n \"ece\": 0.0,\n \"brier\": 0.0,\n \"overconfidence_gap\": 0.0\n },\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"correct\": 0,\n \"conf_logprob\": 0.9936\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"correct\": 0,\n \"conf_logprob\": 0.9926\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"correct\": 0,\n \"conf_logprob\": 0.9905\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"correct\": 0,\n \"conf_logprob\": 0.9454\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"correct\": 0,\n \"conf_logprob\": 0.9796\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"correct\": 1,\n \"conf_logprob\": 0.9542,\n \"conf_verbal\": 1.0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"correct\": 1,\n \"conf_logprob\": 0.9583\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"correct\": 1,\n \"conf_logprob\": 0.9683,\n \"conf_verbal\": 1.0\n }\n ],\n \"_caveat\": \"Needs PASS/FAIL labels \\u2014 unlabeled cases are skipped, and ECE on few labelled cases is unstable (aim for >= ~50). conf_logprob is the geometric-mean token probability of the model's own continuation \\u2014 a proxy, not an answer probability; on API backends it reflects only the returned top-k, and a model without LOGPROBS reports only the verbalized channel. A positive overconfidence_gap on FAIL-heavy slices argues for abstention/recalibration fixes rather than capability fixes.\"\n },\n \"logprob_entropy\": {\n \"n_tokens\": 64,\n \"mean_logprob\": -0.0064,\n \"perplexity\": 1.0064,\n \"min_token_logprob\": -0.1908,\n \"mean_top_entropy\": 0.0213\n },\n \"coverage_verification_gap\": {\n \"n_cases\": 8,\n \"n_scored\": 8,\n \"k\": 5,\n \"gen_kwargs\": {\n \"temperature\": 1.0,\n \"top_p\": 0.95,\n \"top_k\": 20\n },\n \"mean_pass_at_k\": 0.5,\n \"mean_majority_correct\": 0.375,\n \"coverage_gap_rate\": 0.125,\n \"mean_verify_correct\": null,\n \"no_coverage_rate\": 0.5,\n \"degenerate_sampling\": false,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 0,\n \"n_unique\": 4,\n \"pass_at_k\": 0.0,\n \"any_correct\": 0,\n \"majority_correct\": 0,\n \"majority_share\": 0.4,\n \"coverage_gap\": 0,\n \"modal_answer\": \"14\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 0,\n \"n_unique\": 5,\n \"pass_at_k\": 0.0,\n \"any_correct\": 0,\n \"majority_correct\": 0,\n \"majority_share\": 0.2,\n \"coverage_gap\": 0,\n \"modal_answer\": \"arrear eavesdropping fasciculate herodotus hermaphrodite ibn henry incorrigible \"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 0,\n \"n_unique\": 5,\n \"pass_at_k\": 0.0,\n \"any_correct\": 0,\n \"majority_correct\": 0,\n \"majority_share\": 0.2,\n \"coverage_gap\": 0,\n \"modal_answer\": \"16. separate wait s words re-sort: separate shelter sticktight spongy\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 0,\n \"n_unique\": 4,\n \"pass_at_k\": 0.0,\n \"any_correct\": 0,\n \"majority_correct\": 0,\n \"majority_share\": 0.4,\n \"coverage_gap\": 0,\n \"modal_answer\": \"biennial creedal cry fletch fraudulent eyesight j miltonic mirage whisper titmic\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 1,\n \"n_unique\": 4,\n \"pass_at_k\": 1.0,\n \"any_correct\": 1,\n \"majority_correct\": 0,\n \"majority_share\": 0.4,\n \"coverage_gap\": 1,\n \"modal_answer\": \"battery bushland capacitive contingent crossbill enigma jane lipton meager ricoc\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 4,\n \"n_unique\": 2,\n \"pass_at_k\": 1.0,\n \"any_correct\": 1,\n \"majority_correct\": 1,\n \"majority_share\": 0.8,\n \"coverage_gap\": 0,\n \"modal_answer\": \"alleviate benelux buoyant duopoly felice gland gunk hardbound klaxon mattress to\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 3,\n \"n_unique\": 3,\n \"pass_at_k\": 1.0,\n \"any_correct\": 1,\n \"majority_correct\": 1,\n \"majority_share\": 0.6,\n \"coverage_gap\": 0,\n \"modal_answer\": \"artillery bainite doris fda harm incongruous monkey prosody vegetate vivian\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 5,\n \"n_unique\": 1,\n \"pass_at_k\": 1.0,\n \"any_correct\": 1,\n \"majority_correct\": 1,\n \"majority_share\": 1.0,\n \"coverage_gap\": 0,\n \"modal_answer\": \"cotyledon more pepperoni regret starlight wallboard\"\n }\n ],\n \"_caveat\": \"coverage_gap = 'the answer was in the pool, the vote missed it' \\u21d2 a selection defect, fixable at L1/L2 with a verifier or reranker. no_coverage_rate = 'never produced once in k' \\u21d2 a capability defect no scaffold reaches; do not let a headline pass@k hide which of the two dominates. REQUIRES sampling: degenerate_sampling=true means every sample was identical (temperature 0 or a deterministic backend) and the gap is structurally zero, not measured. pass_at_k here uses the unbiased estimator over exactly k draws, so with k small its variance is large per case \\u2014 read the batch rate. INTERVENTIONAL: held-out confirmation must re-sample.\"\n },\n \"perturbation_battery\": {\n \"n_cases\": 8,\n \"n_scored\": 8,\n \"perturbations\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\",\n \"perturb_numbers\"\n ],\n \"applied_perturbations\": [\n \"noop_clause\",\n \"rename_entities\",\n \"restate_question\"\n ],\n \"gen_kwargs\": {},\n \"mean_invariance_break_rate\": 0.7917,\n \"noop_break_rate\": 0.625,\n \"mean_sensitivity_rate\": null,\n \"n_memorization_suspect\": 0,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"baseline_answer\": \"administer aeneid decadent coachman delhi dey grim gradate jacky littleneck phos\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 1.0,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"baseline_answer\": \"j:\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 1.0,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"baseline_answer\": \"backslide calvert commando gabriel hendrickson hollister jackson anastomosis piz\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 1.0,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"baseline_answer\": \"correct alphabetical order is: biennial creedal cry eyesight fletch\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 1.0,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"baseline_answer\": \"battery bushland capacitive contingent crossbill enigma jane lipton meager ricoc\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 0,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 0.6667,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"baseline_answer\": \"alleviate benelux buoyant duopoly felice gland gunk hardbound klaxon mattress to\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 0,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 0.6667,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"baseline_answer\": \"artillery bainite doris fda harm incongruous monkey prosody vegetate vivian\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 0,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 0.6667,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"baseline_answer\": \"cotyledon more pepperoni regret starlight wallboard\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 0,\n \"noop_clause_flipped\": 0,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 0.3333,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n }\n ],\n \"_caveat\": \"Two DIFFERENT defects share this probe and must not be summed: invariance_break_rate should be 0 (a preserving edit moved the answer) while sensitivity_rate should be 1 (an altering edit must move it). sensitivity_rate == 0 with a correct baseline is the memorisation signature \\u2014 cross-check contamination_score before calling it reasoning. Every rate is measured against the model's OWN baseline answer, not the gold, so a case that was already wrong still contributes: read these as stability, never as accuracy. Under sampling, a 'flip' can be decoding noise \\u2014 set temperature 0 or repeat. INTERVENTIONAL: re-run to confirm.\"\n }\n}\n\nHypotheses to review:\n- HYPOTHESIS: The generation token limit causes premature termination during multi-step sorting chains, leaving incomplete outputs lacking final answer spans. (failure_mode: truncation)\n- HYPOTHESIS: Failures on completed sequences are driven by an inability to construct a single valid permutation across stochastic decoding trajectories rather than sampling selection noise. (failure_mode: computation_slip)\n- HYPOTHESIS: Multi-step sorting trajectories are brittle to superficial input phrasing changes, suffering token generation divergence under semantic-preserving perturbations. (failure_mode: brittleness)\n\nFor each hypothesis output two lines:\nKEEP: or REJECT: \nREASON: \nREASON: ", + "critic_raw_output": "### Hypothesis 1\n**KEEP:** The generation token limit causes premature termination during multi-step sorting chains, leaving incomplete outputs lacking final answer spans.\n**REASON:** The evidence directly shows that 2 of the 5 failing cases ran into truncation limits, directly causing missing final answer tags and incomplete candidate lists.\n**REASON:** Evidence directly supports this claim.\n\n---\n\n### Hypothesis 2\n**KEEP:** Failures on completed sequences are driven by an inability to construct a single valid permutation across stochastic decoding trajectories rather than sampling selection noise.\n**REASON:** The coverage audit directly separates selection defect from capability defect, showing a `coverage_gap` of 0 and a `no_coverage_rate` of 4/5 among failures where no correct permutation was found across any of the 5 draws.\n**REASON:** Evidence directly supports this claim.\n\n---\n\n### Hypothesis 3\n**REJECT:** Multi-step sorting trajectories are brittle to superficial input phrasing changes, suffering token generation divergence under semantic-preserving perturbations.\n**REASON:** Confounded alternative and unsupported claim: statistical bootstrap tests show that invariance flips (`noop_clause_flipped`, `restate_question_flipped`) are statistically inconclusive with respect to FAIL, and the perturbation flips are measured against the baseline raw text under temperature sampling where baseline variance and truncation already drive output changes.\n**REASON:** Confounded alternative: decoding variance and truncation confound perturbation flips, which are statistically inconclusive against failure.", + "cycle": 0, + "duration_sec": 8.702, + "event": "diagnosis", + "event_seq": 391, + "explore_context_used": true, + "explore_figures": [ + "/tmp/evalrx-logs-48lc0s2h/explore/figures/00_class_balance.png", + "/tmp/evalrx-logs-48lc0s2h/explore/figures/01_top_discriminators.png", + "/tmp/evalrx-logs-48lc0s2h/explore/figures/02_failrate_by_invariance_break.png", + "/tmp/evalrx-logs-48lc0s2h/explore/figures/03_failrate_by_majority_share.png", + "/tmp/evalrx-logs-48lc0s2h/explore/figures/04_failrate_by_selfcheck_inconsistency.png", + "/tmp/evalrx-logs-48lc0s2h/explore/figures/05_scatter_agreement_vs_invariance.png", + "/tmp/evalrx-logs-48lc0s2h/explore/figures/06_failrate_by_output_chars.png" + ], + "hypotheses": [ + { + "critic": "keep", + "critic_reason": "Evidence directly supports this claim.", + "failure_mode": "truncation", + "id": "h-9caa1b842a1b", + "plain_statement": "When sorting longer word lists, the model runs out of output length midway through writing out its steps and gets cut off before finishing the list.", + "statement": "The generation token limit causes premature termination during multi-step sorting chains, leaving incomplete outputs lacking final answer spans.", + "status": "proposed", + "test_design": "termination_audit.looks_truncated HIGHER on failing cases" + }, + { + "critic": "keep", + "critic_reason": "Evidence directly supports this claim.", + "failure_mode": "computation_slip", + "id": "h-fbe4b863234a", + "plain_statement": "On difficult sorting problems, the model cannot figure out the correct alphabetical order at all, failing to produce a single completely correct list even across five separate tries.", + "statement": "Failures on completed sequences are driven by an inability to construct a single valid permutation across stochastic decoding trajectories rather than sampling selection noise.", + "status": "proposed", + "test_design": "coverage_verification_gap.majority_share LOWER on failing cases" + }, + { + "critic": "reject", + "critic_reason": "Confounded alternative: decoding variance and truncation confound perturbation flips, which are statistically inconclusive against failure.", + "failure_mode": "brittleness", + "id": "h-662394134962", + "plain_statement": "Rephrasing the question or changing irrelevant filler words easily throws the model off, causing it to change its sorting predictions instead of staying stable.", + "statement": "Multi-step sorting trajectories are brittle to superficial input phrasing changes, suffering token generation divergence under semantic-preserving perturbations.", + "status": "proposed", + "test_design": "perturbation_battery.restate_question_flipped HIGHER on failing cases" + } + ], + "judge_prompt": "You are an expert ML diagnostician. Based on the analysis report below, propose\nspecific, falsifiable hypotheses about the root cause of the model's failures.\n\nEach hypothesis is written twice: once for a specialist (HYPOTHESIS, TEST) and\nonce for the person who will read the report. That reader builds and runs\nevaluations but does not do statistics — they know what a benchmark, a prompt\nand a failure case are, and they have never met an e-value, a McNemar test or a\nmultiple-comparisons correction. The PLAIN_STATEMENT line is written for them.\n\nModel: JaxLocalModel(key='gemma-4-e2b-it', adapter=GemmaJaxAdapter, loaded)\nOverall severity (threshold rules): medium\n\nExperiment protocol (authoritative task and response contract):\n{\n \"description\": \"A text-only LLM (Gemma 4 E2B-it, spec gemma-4-e2b-it = google/gemma-4-E2B-it) answers items from lukaemon/bbh · config=word_sorting. One named BBH task: sort a word list into alphabetical order. Failure cases are items the model answered incorrectly; the batch also contains, as controls, items from the same slice it answered correctly. Each answer is scored against its own gold answer.\",\n \"task_domain\": \"ch4-basic\",\n \"success_criteria\": \"Normalised exact match on the whole sorted list — one word out of place is a FAIL, so this is a harsh binary. That is fine for band purposes but means a FAIL is not evidence of not knowing how to sort.\",\n \"failure_patterns\": \"\",\n \"target_modalities\": [\n \"text\"\n ],\n \"output_contract\": {},\n \"metadata\": {\n \"dataset\": \"bbh_word_sorting\",\n \"items_in_slice\": 250,\n \"reference_accuracy_qwen35_9b\": 0.72\n }\n}\n\nAnalysis conclusion (the analyst's interpretation):\nThe model is struggling to complete long alphabetical sorting lists without running out of tokens or losing its train of thought during multi-step reasoning. Out of the 8 examined examples, the model failed 5, and in 2 of those 5 failing cases it ran into the generation limit and cut off mid-sentence before outputting a complete sorted list. Even when the model finished generating within the length limit, repeated sampling showed that in 4 of the 5 failing cases the model never produced a completely correct order across any of its 5 attempts. However, because this is an all-or-nothing benchmark where a single displaced word counts as a total failure, this evidence does not settle whether the model fundamentally lacks sorting capabilities or merely suffers from occasional drift on long lists and constrained generation budgets.\n\nEvidence chain:\n - The termination check first caught attention because the model cut off mid-generation on 2 out of 8 cases (25%), leaving trailing text like \"The correct alphabetical order is: biennial, creedal, cry, eyesight, fletch,\" without a final answer.\n - This directly inflates failures under the protocol's strict all-or-nothing success criterion, because an incomplete or truncated sequence automatically receives a failing score regardless of whether the earlier words were sorted properly.\n - Corroborating this issue, testing answer stability across alternative samples showed a low agreement of 20% (self_consistency.consistency = 0.2), and across 5 sampled completions the model never produced a correct sequence in 4 of the 5 failing examples (coverage_verification_gap.no_coverage_rate = 0.5, or 4 of 8 cases).\n\nStatistical test results:\n - signal 'answer_extraction_audit.output_chars' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2500 (B>A) CI=-0.5000..+0.7500 -> inconclusive\n - signal 'coverage_verification_gap.n_unique' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.5000 (B>A) CI=+0.1667..+0.8333 -> REJECT H0\n - signal 'selfcheck_consistency.n_sentences' vs FAIL: [clustered bootstrap (unpaired)] effect=-0.2500 (A>B) CI=-0.7500..+0.5000 -> inconclusive\n - signal 'termination_audit.output_chars' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2500 (B>A) CI=-0.5000..+0.7500 -> inconclusive\n - signal 'termination_audit.output_words' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2500 (B>A) CI=-0.5000..+0.7500 -> inconclusive\n - signal 'termination_audit.continuation_chars' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.0000 (A=B) CI=+0.0000..+0.0000 -> inconclusive\n - signal 'calibration.correct' vs FAIL: [clustered bootstrap (unpaired)] effect=-0.6230 (A>B) CI=-0.7377..-0.4918 -> REJECT H0\n - signal 'perturbation_battery.noop_clause_flipped' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2237 (B>A) CI=-0.1763..+0.5254 -> inconclusive\n - signal 'answer_extraction_audit.has_answer_tag' vs FAIL: [clustered bootstrap (unpaired)] effect=-0.1034 (A>B) CI=-0.5402..+0.3161 -> inconclusive\n - signal 'answer_extraction_audit.output_truncated' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.4194 (B>A) CI=+0.2903..+0.5323 -> REJECT H0\n - signal 'perturbation_battery.restate_question_flipped' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2644 (B>A) CI=-0.1034..+0.5172 -> inconclusive\n - signal 'termination_audit.has_answer_tag' vs FAIL: [clustered bootstrap (unpaired)] effect=-0.1034 (A>B) CI=-0.5402..+0.3161 -> inconclusive\n - signal 'termination_audit.looks_truncated' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.4194 (B>A) CI=+0.2903..+0.5323 -> REJECT H0\n - signal 'termination_audit.continuation_has_answer' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.4127 (B>A) CI=+0.2857..+0.5397 -> REJECT H0\n - signal 'coverage_verification_gap.coverage_gap' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.4127 (B>A) CI=+0.2857..+0.5397 -> REJECT H0\n - signal 'coverage_verification_gap.majority_share' vs FAIL: [clustered bootstrap (unpaired)] effect=-1.0000 (A>B) CI=-1.0000..-1.0000 -> REJECT H0\n - Kendall τ between 'answer_extraction_audit.output_chars' and FAIL = +0.179 (n=8)\n - Kendall τ between 'coverage_verification_gap.n_unique' and FAIL = +0.536 (n=8)\n - Kendall τ between 'selfcheck_consistency.n_sentences' and FAIL = +0.107 (n=8)\n - Kendall τ between 'termination_audit.output_chars' and FAIL = +0.179 (n=8)\n - Kendall τ between 'termination_audit.output_words' and FAIL = +0.179 (n=8)\n - Kendall τ between 'coverage_verification_gap.majority_share' and FAIL = -0.536 (n=8)\n\nEXPLORATORY MECHANISM NOTES (free-form EDA on a HELD-OUT explore split — DESCRIPTIVE, UNCONFIRMED; use ONLY to decide WHICH hypotheses to propose, NEVER as evidence; every claim must still be tested downstream):\n observations:\n - Dataset comprises 64 evaluated word sorting items from BBH, with 37 failures (57.8%) and 27 passes (42.2%).\n - M1 diagnostic analyzer signals were collected on an exploratory diagnostic sub-sample of 8 items (5 FAIL, 3 PASS), while the remaining 56 rows contain outcome labels only.\n - Across the 8 analyzed items, the perturbation battery invariance break rate was noticeably higher in FAIL cases (mean 0.93, with 4 of 5 cases at 1.0) compared to PASS cases (mean 0.56, all <= 0.67).\n - Sampling majority agreement share separated cases cleanly: all 3 PASS cases had majority agreement of 60% or higher, whereas 4 of 5 FAIL cases had majority agreement of 40% or lower.\n - Truncation and tag omissions occurred exclusively in FAIL cases (2 of 5 failures had truncated outputs and missing answer tags, whereas 0 of 3 passes did).\n - Selfcheck worst sentence contradiction scores were zero in all 3 PASS cases, but positive in 4 of 5 FAIL cases.\n - Model confidence (calibration logprob) remained high across both groups (PASS mean 0.960, FAIL mean 0.980), showing model overconfidence even on failed word sorting runs.\n charts (attached as images when rendered):\n - [FAIL vs PASS Case Balance] FAIL vs PASS Case Balance: composition strip of count by outcome (2 rows)\n - [Top FAIL/PASS Discriminators] Top FAIL/PASS Discriminators: forest plot (ranked dots) of smd by signal (14 rows)\n - [Fail Rate by Invariance Break Rate] Fail Rate by Invariance Break Rate: line of fail_rate by invariance_break_bin (3 rows)\n - [Fail Rate by Majority Share] Fail Rate by Majority Share: line of fail_rate by majority_share_bin (3 rows)\n - [Fail Rate by Reasoning Inconsistency] Fail Rate by Reasoning Inconsistency: line of fail_rate by selfcheck_worst_bin (2 rows)\n - [Consensus vs Invariance Scatter] Consensus vs Invariance Scatter: scatter of invariance_break_rate by majority_share (8 rows)\n - [Fail Rate by Output Length] Fail Rate by Output Length: line of fail_rate by output_chars_bin (3 rows)\n caveats (the explorer's own warnings):\n - M1 diagnostic analyzer columns are populated for 8 of the 64 cases; 56 cases contain label-only information.\n - All candidate thresholds were identified on this exploration sample and must be re-evaluated verbatim on held-out cases.\n - Findings represent descriptive associations within this single task slice (BBH word sorting) and may not generalize to other reasoning tasks.\n\nRaw findings (JSON):\n{\n \"answer_extraction_audit\": {\n \"n_cases\": 8,\n \"n_gradable\": 8,\n \"n_labelled_fail\": 5,\n \"n_extraction_suspect\": 0,\n \"suspect_rate\": 0.0,\n \"n_reasked\": 0,\n \"n_reask_confirmed\": null,\n \"n_extraction_point_miss\": 0,\n \"missing_tag_rate\": 0.25,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 917,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"administer, aeneid, decadent, coachman, delhi, dey, grim, gradate, jacky, littleneck, phosphorescent, pristine, sinh, sh\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"has_output\": 1,\n \"has_answer_tag\": 0,\n \"output_truncated\": 1,\n \"gave_up\": 0,\n \"output_chars\": 1332,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"J:\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 1101,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"backslide, calvert, commando, gabriel, hendrickson, hollister, jackson, anastomosis, pizzicato, quail, separate, shelter\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"has_output\": 1,\n \"has_answer_tag\": 0,\n \"output_truncated\": 1,\n \"gave_up\": 0,\n \"output_chars\": 1389,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"The correct alphabetical order is: biennial, creedal, cry, eyesight, fletch,\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 922,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"battery, bushland, capacitive, contingent, crossbill, enigma, jane, lipton, meager, ricochet, wallet, wacke, wysiwyg\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 1286,\n \"labelled_fail\": 0,\n \"strict_match\": 1,\n \"gold_in_output\": 1,\n \"gold_in_answer_region\": 1,\n \"extracted_answer\": \"alleviate, benelux, buoyant, duopoly, felice, gland, gunk, hardbound, klaxon, mattress, tomography, townsmen\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 1054,\n \"labelled_fail\": 0,\n \"strict_match\": 1,\n \"gold_in_output\": 1,\n \"gold_in_answer_region\": 1,\n \"extracted_answer\": \"artillery, bainite, doris, fda, harm, incongruous, monkey, prosody, vegetate, vivian\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 690,\n \"labelled_fail\": 0,\n \"strict_match\": 1,\n \"gold_in_output\": 1,\n \"gold_in_answer_region\": 1,\n \"extracted_answer\": \"cotyledon, more, pepperoni, regret, starlight, wallboard\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n }\n ],\n \"_caveat\": \"suspect_rate is the fraction of FAIL cases whose gold answer sits in the ANSWER REGION of the output (tail window or a tagged/boxed span) \\u2014 HARNESS failures wearing a model-failure label. gold_in_output is the loose upper bound and over-counts: it fires on a gold that only appears as an intermediate quantity, so never read it as the suspect rate. Above ~5% the labels feeding M2 are contaminated and every other probe's correlations should be re-read after fixing extraction (or after dropping extraction_suspect==1 cases). This probe is OBSERVATIONAL by default: it re-reads stored outputs and introduces no new sampling. With reask=True the reask_* columns are interventional and must be re-run for held-out confirmation. On yes/no (true/false) tasks answered_yes and gold_yes are the answer's and the gold's DIRECTION, two separate marginals (not re-grades): a directional hypothesis is tested on them, e.g. answered_yes HIGHER on failing cases.\"\n },\n \"termination_audit\": {\n \"n_cases\": 8,\n \"class_counts\": {\n \"clean\": 6,\n \"truncated\": 2,\n \"degenerate\": 0,\n \"gave_up\": 0,\n \"no_answer\": 0\n },\n \"clean_rate\": 0.75,\n \"non_clean_rate\": 0.25,\n \"truncation_rate\": 0.25,\n \"degenerate_rate\": 0.0,\n \"giveup_rate\": 0.0,\n \"recovered_rate\": 0.0,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.0288,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 917,\n \"output_words\": 146\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"termination_class\": \"truncated\",\n \"repetition_score\": 0.0,\n \"has_answer_tag\": 0,\n \"looks_truncated\": 1,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1332,\n \"output_words\": 187,\n \"continuation_has_answer\": 1,\n \"continuation_chars\": 1217,\n \"recovered_by_continuation\": 0,\n \"continuation_correct\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.0,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1101,\n \"output_words\": 164\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"termination_class\": \"truncated\",\n \"repetition_score\": 0.0415,\n \"has_answer_tag\": 0,\n \"looks_truncated\": 1,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1389,\n \"output_words\": 224,\n \"continuation_has_answer\": 0,\n \"continuation_chars\": 1472,\n \"recovered_by_continuation\": 0,\n \"continuation_correct\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.1462,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 922,\n \"output_words\": 137\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.1006,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1286,\n \"output_words\": 176\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.0,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1054,\n \"output_words\": 157\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.0,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 690,\n \"output_words\": 99\n }\n ],\n \"_caveat\": \"CONFOUND CONTROL \\u2014 read this before any other probe. Cases with termination_class != 'clean' have short, tag-less outputs, the same signature every other probe scores as 'bad'; leaving them pooled makes M2 attribute a decoding-budget or repetition problem to whichever mechanism is under test. Stratify on termination_class (or exclude non-clean cases) first. A high recovered_by_continuation rate means the budget is the defect: raise max_tokens and re-collect before diagnosing anything else. The continuation columns are INTERVENTIONAL \\u2014 held-out verification must re-run them.\"\n },\n \"selfcheck_consistency\": {\n \"n_cases\": 8,\n \"n_samples\": 4,\n \"degenerate_sampling\": false,\n \"gen_kwargs\": {\n \"temperature\": 1.0\n },\n \"mean_inconsistency\": 0.015,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 19,\n \"selfcheck_inconsistency\": 0.0,\n \"selfcheck_worst_sentence\": 0.0,\n \"worst_sentence_text\": \"Here are the words sorted alphabetically:\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 45,\n \"selfcheck_inconsistency\": 0.037,\n \"selfcheck_worst_sentence\": 0.5,\n \"worst_sentence_text\": \"* **v**ehicular\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 29,\n \"selfcheck_inconsistency\": 0.023,\n \"selfcheck_worst_sentence\": 0.3333,\n \"worst_sentence_text\": \"Comparing the words:\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 47,\n \"selfcheck_inconsistency\": 0.0376,\n \"selfcheck_worst_sentence\": 0.6667,\n \"worst_sentence_text\": \"**Final ordered list:**\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 28,\n \"selfcheck_inconsistency\": 0.0168,\n \"selfcheck_worst_sentence\": 0.4,\n \"worst_sentence_text\": \"The final sorted list is:\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 32,\n \"selfcheck_inconsistency\": 0.0016,\n \"selfcheck_worst_sentence\": 0.0526,\n \"worst_sentence_text\": \"Here are the words to sort alphabetically: alleviate, duopoly, mattress, gland, benelux, townsmen, buoyant, klaxon, hardbound, tomography, felice, gunk.\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 30,\n \"selfcheck_inconsistency\": 0.0037,\n \"selfcheck_worst_sentence\": 0.1111,\n \"worst_sentence_text\": \"* Comparing the second letter: 'e' in vegetate, 'i' in vivian.\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 18,\n \"selfcheck_inconsistency\": 0.0,\n \"selfcheck_worst_sentence\": 0.0,\n \"worst_sentence_text\": \"The given list of words is: regret, starlight, wallboard, cotyledon, more, pepperoni.\"\n }\n ],\n \"_caveat\": \"Unigram-containment proxy for SelfCheckGPT (no NLI model): paraphrases inflate the score, verbatim repetition of a wrong fact deflates it. Requires stochastic sampling \\u2014 with temperature 0 the resamples repeat one greedy generation and the column degenerates to baseline-vs-greedy disagreement (degenerate_sampling flags this). selfcheck_inconsistency / selfcheck_worst_sentence are numeric M2 columns; treat them as signals to correlate with the outcome label, never as a hallucination verdict.\"\n },\n \"self_consistency\": {\n \"n_samples\": 5,\n \"consistency\": 0.2,\n \"n_unique\": 5,\n \"compared_on\": \"raw_text\",\n \"raw_text_consistency\": 0.2,\n \"modal_answer\": \"Here are the words sorted alphabetically:\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. gradate\\n7. grim\\n8. jacky\\n9. littleneck\\n10. phosphorescent\\n11. pristine\\n12. sinh\\n13. systemwide\\n14. tasting\\n15. torpedo\\n16. thrown\\n17. verdict\\n18. dey\\n19. shrunk\\n\\nLet's re-sort them carefully:\\n\\n* administer\\n* aeneid\\n* decadent\\n* coachman\\n* delhi\\n* grim\\n* gradate\\n* jacky\\n* littleneck\\n* phosphorescent\\n* pristine\\n* sinh\\n* systemwide\\n* tasting\\n* torpedo\\n* thrown\\n* verdict\\n* dey\\n* shrunk\\n\\nChecking for correct alphabetical order:\\nA: administer, aeneid\\nC: decadent, coachman\\nD: delhi, dey\\nG: grim, gradate\\nJ: jacky\\nL: littleneck\\nP: phosphorescent, pristine\\nS: sinh, systemwide, shrunk\\nT: tasting, torpedo, thrown\\nV: verdict\\n\\nCorrected Sorted List:\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. dey\\n7. grim\\n8. gradate\\n9. jacky\\n10. littleneck\\n11. phosphorescent\\n12. pristine\\n13. sinh\\n14. shrunk\\n15. systemwide\\n16. tasting\\n17. torpedo\\n18. thrown\\n19. verdict\\n\\n(Comparing D words: delhi, dey. D-e-l-h-i vs D-e-y. 'l' comes before 'y', so delhi comes before dey.)\\n\\nFinal check on the complete list:\\nadminister (A)\\naeneid (A)\\ndecadent (D)\\ncoachman (C) -> Should be C before D. Let's restart the alphabetical sort strictly.\\n\\nList: torpedo, phosphorescent, pristine, decadent, shrunk, dey, administer, gradate, littleneck, thrown, jacky, coachman, aeneid, verdict, tasting, sinh, delhi, systemwide, grim\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6\",\n \"gen_kwargs\": {\n \"temperature\": 1.0,\n \"top_p\": 0.95,\n \"top_k\": 20\n },\n \"n_semantic_clusters\": 3,\n \"semantic_entropy\": 1.0549,\n \"normalized_semantic_entropy\": 0.6554,\n \"semantic_consistency\": 0.4,\n \"cluster_sizes\": [\n 2,\n 2,\n 1\n ],\n \"cluster_representatives\": [\n \"Here are the words sorted alphabetically:\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. gradate\\n7. grim\\n8\",\n \"Here are the words sorted alphabetically:\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. gradate\\n7. grim\\n8\",\n \"Here are the words sorted alphabetically:\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. gradate\\n7. grim\\n8\"\n ],\n \"entailment\": \"lexical_proxy\",\n \"_caveat\": \"semantic_* uses the SAME samples as consistency \\u2014 it costs no extra generations, and the two are meant to be read together: consistency << semantic_consistency means the disagreement was only wording. With the default lexical_proxy, clustering is content-word overlap, NOT entailment: it merges answers that share vocabulary while contradicting each other ('the claim holds' / 'the claim does not hold') and splits correct paraphrases with no shared words. Inject an NLI entailment_fn before reading semantic_entropy as the Farquhar et al. metric. At temperature 0 every sample is identical and both scores are 1 by construction, not by measurement.\"\n },\n \"calibration\": {\n \"n_cases\": 8,\n \"n_unlabelled_skipped\": 0,\n \"n_bins\": 10,\n \"logprob_channel_available\": true,\n \"logprob_channel\": {\n \"n\": 8,\n \"ece\": 0.5978,\n \"brier\": 0.6015,\n \"overconfidence_gap\": 0.5978\n },\n \"verbalized_channel\": {\n \"n\": 2,\n \"ece\": 0.0,\n \"brier\": 0.0,\n \"overconfidence_gap\": 0.0\n },\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"correct\": 0,\n \"conf_logprob\": 0.9936\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"correct\": 0,\n \"conf_logprob\": 0.9926\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"correct\": 0,\n \"conf_logprob\": 0.9905\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"correct\": 0,\n \"conf_logprob\": 0.9454\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"correct\": 0,\n \"conf_logprob\": 0.9796\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"correct\": 1,\n \"conf_logprob\": 0.9542,\n \"conf_verbal\": 1.0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"correct\": 1,\n \"conf_logprob\": 0.9583\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"correct\": 1,\n \"conf_logprob\": 0.9683,\n \"conf_verbal\": 1.0\n }\n ],\n \"_caveat\": \"Needs PASS/FAIL labels \\u2014 unlabeled cases are skipped, and ECE on few labelled cases is unstable (aim for >= ~50). conf_logprob is the geometric-mean token probability of the model's own continuation \\u2014 a proxy, not an answer probability; on API backends it reflects only the returned top-k, and a model without LOGPROBS reports only the verbalized channel. A positive overconfidence_gap on FAIL-heavy slices argues for abstention/recalibration fixes rather than capability fixes.\"\n },\n \"logprob_entropy\": {\n \"n_tokens\": 64,\n \"mean_logprob\": -0.0064,\n \"perplexity\": 1.0064,\n \"min_token_logprob\": -0.1908,\n \"mean_top_entropy\": 0.0213\n },\n \"coverage_verification_gap\": {\n \"n_cases\": 8,\n \"n_scored\": 8,\n \"k\": 5,\n \"gen_kwargs\": {\n \"temperature\": 1.0,\n \"top_p\": 0.95,\n \"top_k\": 20\n },\n \"mean_pass_at_k\": 0.5,\n \"mean_majority_correct\": 0.375,\n \"coverage_gap_rate\": 0.125,\n \"mean_verify_correct\": null,\n \"no_coverage_rate\": 0.5,\n \"degenerate_sampling\": false,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 0,\n \"n_unique\": 4,\n \"pass_at_k\": 0.0,\n \"any_correct\": 0,\n \"majority_correct\": 0,\n \"majority_share\": 0.4,\n \"coverage_gap\": 0,\n \"modal_answer\": \"14\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 0,\n \"n_unique\": 5,\n \"pass_at_k\": 0.0,\n \"any_correct\": 0,\n \"majority_correct\": 0,\n \"majority_share\": 0.2,\n \"coverage_gap\": 0,\n \"modal_answer\": \"arrear eavesdropping fasciculate herodotus hermaphrodite ibn henry incorrigible \"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 0,\n \"n_unique\": 5,\n \"pass_at_k\": 0.0,\n \"any_correct\": 0,\n \"majority_correct\": 0,\n \"majority_share\": 0.2,\n \"coverage_gap\": 0,\n \"modal_answer\": \"16. separate wait s words re-sort: separate shelter sticktight spongy\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 0,\n \"n_unique\": 4,\n \"pass_at_k\": 0.0,\n \"any_correct\": 0,\n \"majority_correct\": 0,\n \"majority_share\": 0.4,\n \"coverage_gap\": 0,\n \"modal_answer\": \"biennial creedal cry fletch fraudulent eyesight j miltonic mirage whisper titmic\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 1,\n \"n_unique\": 4,\n \"pass_at_k\": 1.0,\n \"any_correct\": 1,\n \"majority_correct\": 0,\n \"majority_share\": 0.4,\n \"coverage_gap\": 1,\n \"modal_answer\": \"battery bushland capacitive contingent crossbill enigma jane lipton meager ricoc\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 4,\n \"n_unique\": 2,\n \"pass_at_k\": 1.0,\n \"any_correct\": 1,\n \"majority_correct\": 1,\n \"majority_share\": 0.8,\n \"coverage_gap\": 0,\n \"modal_answer\": \"alleviate benelux buoyant duopoly felice gland gunk hardbound klaxon mattress to\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 3,\n \"n_unique\": 3,\n \"pass_at_k\": 1.0,\n \"any_correct\": 1,\n \"majority_correct\": 1,\n \"majority_share\": 0.6,\n \"coverage_gap\": 0,\n \"modal_answer\": \"artillery bainite doris fda harm incongruous monkey prosody vegetate vivian\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 5,\n \"n_unique\": 1,\n \"pass_at_k\": 1.0,\n \"any_correct\": 1,\n \"majority_correct\": 1,\n \"majority_share\": 1.0,\n \"coverage_gap\": 0,\n \"modal_answer\": \"cotyledon more pepperoni regret starlight wallboard\"\n }\n ],\n \"_caveat\": \"coverage_gap = 'the answer was in the pool, the vote missed it' \\u21d2 a selection defect, fixable at L1/L2 with a verifier or reranker. no_coverage_rate = 'never produced once in k' \\u21d2 a capability defect no scaffold reaches; do not let a headline pass@k hide which of the two dominates. REQUIRES sampling: degenerate_sampling=true means every sample was identical (temperature 0 or a deterministic backend) and the gap is structurally zero, not measured. pass_at_k here uses the unbiased estimator over exactly k draws, so with k small its variance is large per case \\u2014 read the batch rate. INTERVENTIONAL: held-out confirmation must re-sample.\"\n },\n \"perturbation_battery\": {\n \"n_cases\": 8,\n \"n_scored\": 8,\n \"perturbations\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\",\n \"perturb_numbers\"\n ],\n \"applied_perturbations\": [\n \"noop_clause\",\n \"rename_entities\",\n \"restate_question\"\n ],\n \"gen_kwargs\": {},\n \"mean_invariance_break_rate\": 0.7917,\n \"noop_break_rate\": 0.625,\n \"mean_sensitivity_rate\": null,\n \"n_memorization_suspect\": 0,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"baseline_answer\": \"administer aeneid decadent coachman delhi dey grim gradate jacky littleneck phos\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 1.0,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"baseline_answer\": \"j:\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 1.0,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"baseline_answer\": \"backslide calvert commando gabriel hendrickson hollister jackson anastomosis piz\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 1.0,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"baseline_answer\": \"correct alphabetical order is: biennial creedal cry eyesight fletch\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 1.0,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"baseline_answer\": \"battery bushland capacitive contingent crossbill enigma jane lipton meager ricoc\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 0,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 0.6667,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"baseline_answer\": \"alleviate benelux buoyant duopoly felice gland gunk hardbound klaxon mattress to\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 0,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 0.6667,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"baseline_answer\": \"artillery bainite doris fda harm incongruous monkey prosody vegetate vivian\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 0,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 0.6667,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"baseline_answer\": \"cotyledon more pepperoni regret starlight wallboard\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 0,\n \"noop_clause_flipped\": 0,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 0.3333,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n }\n ],\n \"_caveat\": \"Two DIFFERENT defects share this probe and must not be summed: invariance_break_rate should be 0 (a preserving edit moved the answer) while sensitivity_rate should be 1 (an altering edit must move it). sensitivity_rate == 0 with a correct baseline is the memorisation signature \\u2014 cross-check contamination_score before calling it reasoning. Every rate is measured against the model's OWN baseline answer, not the gold, so a case that was already wrong still contributes: read these as stability, never as accuracy. Under sampling, a 'flip' can be decoding noise \\u2014 set temperature 0 or repeat. INTERVENTIONAL: re-run to confirm.\"\n }\n}\n\nAVAILABLE EVIDENCE (per-case signals / contrasts already measured):\n - answer_extraction_audit.output_chars\n - coverage_verification_gap.n_unique\n - selfcheck_consistency.n_sentences\n - termination_audit.output_chars\n - termination_audit.output_words\n - termination_audit.continuation_chars\n - calibration.correct\n - perturbation_battery.noop_clause_flipped\n - answer_extraction_audit.has_answer_tag\n - answer_extraction_audit.output_truncated\n - perturbation_battery.restate_question_flipped\n - termination_audit.has_answer_tag\n - termination_audit.looks_truncated\n - termination_audit.continuation_has_answer\n - coverage_verification_gap.coverage_gap\n - coverage_verification_gap.majority_share\n\nPropose 1-3 hypotheses. For each write:\nHYPOTHESIS: \nPLAIN_STATEMENT: \nFAILURE_MODE: \nTEST: \nEXPECTED_ASSOCIATION: \n\nBase your hypotheses on the analysis conclusion and evidence above — an analyzer\ncan surface a real failure mode even when no numeric threshold fired, so do NOT\nrely on the threshold severity alone.\nDo NOT repeat hypotheses already listed in the prior cycles above.\nIf the conclusion and evidence genuinely show no problem, respond with: NO_ISSUE", + "model_name": "JaxLocalModel(key='gemma-4-e2b-it', adapter=GemmaJaxAdapter, loaded)", + "n_critic_kept": 2, + "n_critic_rejected": 1, + "n_hypotheses": 3, + "proposed_hypotheses": [ + { + "failure_mode": "truncation", + "plain_statement": "When sorting longer word lists, the model runs out of output length midway through writing out its steps and gets cut off before finishing the list.", + "statement": "The generation token limit causes premature termination during multi-step sorting chains, leaving incomplete outputs lacking final answer spans.", + "test_design": "termination_audit.looks_truncated HIGHER on failing cases" + }, + { + "failure_mode": "computation_slip", + "plain_statement": "On difficult sorting problems, the model cannot figure out the correct alphabetical order at all, failing to produce a single completely correct list even across five separate tries.", + "statement": "Failures on completed sequences are driven by an inability to construct a single valid permutation across stochastic decoding trajectories rather than sampling selection noise.", + "test_design": "coverage_verification_gap.majority_share LOWER on failing cases" + }, + { + "failure_mode": "brittleness", + "plain_statement": "Rephrasing the question or changing irrelevant filler words easily throws the model off, causing it to change its sorting predictions instead of staying stable.", + "statement": "Multi-step sorting trajectories are brittle to superficial input phrasing changes, suffering token generation divergence under semantic-preserving perturbations.", + "test_design": "perturbation_battery.restate_question_flipped HIGHER on failing cases" + } + ], + "raw_judge_output": "HYPOTHESIS: The generation token limit causes premature termination during multi-step sorting chains, leaving incomplete outputs lacking final answer spans.\nPLAIN_STATEMENT: When sorting longer word lists, the model runs out of output length midway through writing out its steps and gets cut off before finishing the list.\nFAILURE_MODE: truncation\nTEST: termination_audit.looks_truncated HIGHER on failing cases\nEXPECTED_ASSOCIATION: higher_on_failures\n\nHYPOTHESIS: Failures on completed sequences are driven by an inability to construct a single valid permutation across stochastic decoding trajectories rather than sampling selection noise.\nPLAIN_STATEMENT: On difficult sorting problems, the model cannot figure out the correct alphabetical order at all, failing to produce a single completely correct list even across five separate tries.\nFAILURE_MODE: computation_slip\nTEST: coverage_verification_gap.majority_share LOWER on failing cases\nEXPECTED_ASSOCIATION: lower_on_failures\n\nHYPOTHESIS: Multi-step sorting trajectories are brittle to superficial input phrasing changes, suffering token generation divergence under semantic-preserving perturbations.\nPLAIN_STATEMENT: Rephrasing the question or changing irrelevant filler words easily throws the model off, causing it to change its sorting predictions instead of staying stable.\nFAILURE_MODE: brittleness\nTEST: perturbation_battery.restate_question_flipped HIGHER on failing cases\nEXPECTED_ASSOCIATION: higher_on_failures", + "review": { + "decisions": [ + { + "decision": "keep", + "reason": "Evidence directly supports this claim.", + "statement": "The generation token limit causes premature termination during multi-step sorting chains, leaving incomplete outputs lacking final answer spans." + }, + { + "decision": "keep", + "reason": "Evidence directly supports this claim.", + "statement": "Failures on completed sequences are driven by an inability to construct a single valid permutation across stochastic decoding trajectories rather than sampling selection noise." + }, + { + "decision": "reject", + "reason": "Confounded alternative: decoding variance and truncation confound perturbation flips, which are statistically inconclusive against failure.", + "statement": "Multi-step sorting trajectories are brittle to superficial input phrasing changes, suffering token generation divergence under semantic-preserving perturbations." + } + ], + "n_kept": 2, + "n_rejected": 1 + }, + "review_prompt": "You are an adversarial ML reviewer. Your job is to find reasons to REJECT each\nhypothesis below. Only approve a hypothesis if you cannot find a significant flaw.\n\nCheck each for:\n1. Unsupported claim — does the cited evidence actually imply this failure mode?\n2. Circular reasoning — does the hypothesis merely restate the symptom?\n3. Overgeneralisation — does it make a claim far broader than the evidence supports?\n4. Confounded alternative — is there a simpler explanation the hypothesis ignores?\n\nContext the proposer worked from (analyst conclusion, evidence chain, statistical verdicts, exploratory notes, label summary):\nAnalysis conclusion: The model is struggling to complete long alphabetical sorting lists without running out of tokens or losing its train of thought during multi-step reasoning. Out of the 8 examined examples, the model failed 5, and in 2 of those 5 failing cases it ran into the generation limit and cut off mid-sentence before outputting a complete sorted list. Even when the model finished generating within the length limit, repeated sampling showed that in 4 of the 5 failing cases the model never produced a completely correct order across any of its 5 attempts. However, because this is an all-or-nothing benchmark where a single displaced word counts as a total failure, this evidence does not settle whether the model fundamentally lacks sorting capabilities or merely suffers from occasional drift on long lists and constrained generation budgets.\nExperiment protocol:\n{\n \"description\": \"A text-only LLM (Gemma 4 E2B-it, spec gemma-4-e2b-it = google/gemma-4-E2B-it) answers items from lukaemon/bbh · config=word_sorting. One named BBH task: sort a word list into alphabetical order. Failure cases are items the model answered incorrectly; the batch also contains, as controls, items from the same slice it answered correctly. Each answer is scored against its own gold answer.\",\n \"task_domain\": \"ch4-basic\",\n \"success_criteria\": \"Normalised exact match on the whole sorted list — one word out of place is a FAIL, so this is a harsh binary. That is fine for band purposes but means a FAIL is not evidence of not knowing how to sort.\",\n \"failure_patterns\": \"\",\n \"target_modalities\": [\n \"text\"\n ],\n \"output_contract\": {},\n \"metadata\": {\n \"dataset\": \"bbh_word_sorting\",\n \"items_in_slice\": 250,\n \"reference_accuracy_qwen35_9b\": 0.72\n }\n}\nEvidence chain:\n - The termination check first caught attention because the model cut off mid-generation on 2 out of 8 cases (25%), leaving trailing text like \"The correct alphabetical order is: biennial, creedal, cry, eyesight, fletch,\" without a final answer.\n - This directly inflates failures under the protocol's strict all-or-nothing success criterion, because an incomplete or truncated sequence automatically receives a failing score regardless of whether the earlier words were sorted properly.\n - Corroborating this issue, testing answer stability across alternative samples showed a low agreement of 20% (self_consistency.consistency = 0.2), and across 5 sampled completions the model never produced a correct sequence in 4 of the 5 failing examples (coverage_verification_gap.no_coverage_rate = 0.5, or 4 of 8 cases).\nStatistical test results:\n - signal 'answer_extraction_audit.output_chars' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2500 (B>A) CI=-0.5000..+0.7500 -> inconclusive\n - signal 'coverage_verification_gap.n_unique' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.5000 (B>A) CI=+0.1667..+0.8333 -> REJECT H0\n - signal 'selfcheck_consistency.n_sentences' vs FAIL: [clustered bootstrap (unpaired)] effect=-0.2500 (A>B) CI=-0.7500..+0.5000 -> inconclusive\n - signal 'termination_audit.output_chars' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2500 (B>A) CI=-0.5000..+0.7500 -> inconclusive\n - signal 'termination_audit.output_words' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2500 (B>A) CI=-0.5000..+0.7500 -> inconclusive\n - signal 'termination_audit.continuation_chars' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.0000 (A=B) CI=+0.0000..+0.0000 -> inconclusive\n - signal 'calibration.correct' vs FAIL: [clustered bootstrap (unpaired)] effect=-0.6230 (A>B) CI=-0.7377..-0.4918 -> REJECT H0\n - signal 'perturbation_battery.noop_clause_flipped' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2237 (B>A) CI=-0.1763..+0.5254 -> inconclusive\n - signal 'answer_extraction_audit.has_answer_tag' vs FAIL: [clustered bootstrap (unpaired)] effect=-0.1034 (A>B) CI=-0.5402..+0.3161 -> inconclusive\n - signal 'answer_extraction_audit.output_truncated' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.4194 (B>A) CI=+0.2903..+0.5323 -> REJECT H0\n - signal 'perturbation_battery.restate_question_flipped' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2644 (B>A) CI=-0.1034..+0.5172 -> inconclusive\n - signal 'termination_audit.has_answer_tag' vs FAIL: [clustered bootstrap (unpaired)] effect=-0.1034 (A>B) CI=-0.5402..+0.3161 -> inconclusive\n - signal 'termination_audit.looks_truncated' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.4194 (B>A) CI=+0.2903..+0.5323 -> REJECT H0\n - signal 'termination_audit.continuation_has_answer' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.4127 (B>A) CI=+0.2857..+0.5397 -> REJECT H0\n - signal 'coverage_verification_gap.coverage_gap' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.4127 (B>A) CI=+0.2857..+0.5397 -> REJECT H0\n - signal 'coverage_verification_gap.majority_share' vs FAIL: [clustered bootstrap (unpaired)] effect=-1.0000 (A>B) CI=-1.0000..-1.0000 -> REJECT H0\n - Kendall τ between 'answer_extraction_audit.output_chars' and FAIL = +0.179 (n=8)\n - Kendall τ between 'coverage_verification_gap.n_unique' and FAIL = +0.536 (n=8)\n - Kendall τ between 'selfcheck_consistency.n_sentences' and FAIL = +0.107 (n=8)\n - Kendall τ between 'termination_audit.output_chars' and FAIL = +0.179 (n=8)\n - Kendall τ between 'termination_audit.output_words' and FAIL = +0.179 (n=8)\n - Kendall τ between 'coverage_verification_gap.majority_share' and FAIL = -0.536 (n=8)\nEXPLORATORY MECHANISM NOTES (free-form EDA on a HELD-OUT explore split — DESCRIPTIVE, UNCONFIRMED; use ONLY to decide WHICH hypotheses to propose, NEVER as evidence; every claim must still be tested downstream):\n observations:\n - Dataset comprises 64 evaluated word sorting items from BBH, with 37 failures (57.8%) and 27 passes (42.2%).\n - M1 diagnostic analyzer signals were collected on an exploratory diagnostic sub-sample of 8 items (5 FAIL, 3 PASS), while the remaining 56 rows contain outcome labels only.\n - Across the 8 analyzed items, the perturbation battery invariance break rate was noticeably higher in FAIL cases (mean 0.93, with 4 of 5 cases at 1.0) compared to PASS cases (mean 0.56, all <= 0.67).\n - Sampling majority agreement share separated cases cleanly: all 3 PASS cases had majority agreement of 60% or higher, whereas 4 of 5 FAIL cases had majority agreement of 40% or lower.\n - Truncation and tag omissions occurred exclusively in FAIL cases (2 of 5 failures had truncated outputs and missing answer tags, whereas 0 of 3 passes did).\n - Selfcheck worst sentence contradiction scores were zero in all 3 PASS cases, but positive in 4 of 5 FAIL cases.\n - Model confidence (calibration logprob) remained high across both groups (PASS mean 0.960, FAIL mean 0.980), showing model overconfidence even on failed word sorting runs.\n charts (attached as images when rendered):\n - [FAIL vs PASS Case Balance] FAIL vs PASS Case Balance: composition strip of count by outcome (2 rows)\n - [Top FAIL/PASS Discriminators] Top FAIL/PASS Discriminators: forest plot (ranked dots) of smd by signal (14 rows)\n - [Fail Rate by Invariance Break Rate] Fail Rate by Invariance Break Rate: line of fail_rate by invariance_break_bin (3 rows)\n - [Fail Rate by Majority Share] Fail Rate by Majority Share: line of fail_rate by majority_share_bin (3 rows)\n - [Fail Rate by Reasoning Inconsistency] Fail Rate by Reasoning Inconsistency: line of fail_rate by selfcheck_worst_bin (2 rows)\n - [Consensus vs Invariance Scatter] Consensus vs Invariance Scatter: scatter of invariance_break_rate by majority_share (8 rows)\n - [Fail Rate by Output Length] Fail Rate by Output Length: line of fail_rate by output_chars_bin (3 rows)\n caveats (the explorer's own warnings):\n - M1 diagnostic analyzer columns are populated for 8 of the 64 cases; 56 cases contain label-only information.\n - All candidate thresholds were identified on this exploration sample and must be re-evaluated verbatim on held-out cases.\n - Findings represent descriptive associations within this single task slice (BBH word sorting) and may not generalize to other reasoning tasks.\nLABEL SUMMARY (descriptive counts from the case batch, not a test result):\n labelled cases: 64 (38 FAIL / 26 PASS)\n\nFindings summary (the evidence the hypotheses were drawn from):\n{\n \"answer_extraction_audit\": {\n \"n_cases\": 8,\n \"n_gradable\": 8,\n \"n_labelled_fail\": 5,\n \"n_extraction_suspect\": 0,\n \"suspect_rate\": 0.0,\n \"n_reasked\": 0,\n \"n_reask_confirmed\": null,\n \"n_extraction_point_miss\": 0,\n \"missing_tag_rate\": 0.25,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 917,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"administer, aeneid, decadent, coachman, delhi, dey, grim, gradate, jacky, littleneck, phosphorescent, pristine, sinh, sh\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"has_output\": 1,\n \"has_answer_tag\": 0,\n \"output_truncated\": 1,\n \"gave_up\": 0,\n \"output_chars\": 1332,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"J:\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 1101,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"backslide, calvert, commando, gabriel, hendrickson, hollister, jackson, anastomosis, pizzicato, quail, separate, shelter\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"has_output\": 1,\n \"has_answer_tag\": 0,\n \"output_truncated\": 1,\n \"gave_up\": 0,\n \"output_chars\": 1389,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"The correct alphabetical order is: biennial, creedal, cry, eyesight, fletch,\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 922,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"battery, bushland, capacitive, contingent, crossbill, enigma, jane, lipton, meager, ricochet, wallet, wacke, wysiwyg\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 1286,\n \"labelled_fail\": 0,\n \"strict_match\": 1,\n \"gold_in_output\": 1,\n \"gold_in_answer_region\": 1,\n \"extracted_answer\": \"alleviate, benelux, buoyant, duopoly, felice, gland, gunk, hardbound, klaxon, mattress, tomography, townsmen\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 1054,\n \"labelled_fail\": 0,\n \"strict_match\": 1,\n \"gold_in_output\": 1,\n \"gold_in_answer_region\": 1,\n \"extracted_answer\": \"artillery, bainite, doris, fda, harm, incongruous, monkey, prosody, vegetate, vivian\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 690,\n \"labelled_fail\": 0,\n \"strict_match\": 1,\n \"gold_in_output\": 1,\n \"gold_in_answer_region\": 1,\n \"extracted_answer\": \"cotyledon, more, pepperoni, regret, starlight, wallboard\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n }\n ],\n \"_caveat\": \"suspect_rate is the fraction of FAIL cases whose gold answer sits in the ANSWER REGION of the output (tail window or a tagged/boxed span) \\u2014 HARNESS failures wearing a model-failure label. gold_in_output is the loose upper bound and over-counts: it fires on a gold that only appears as an intermediate quantity, so never read it as the suspect rate. Above ~5% the labels feeding M2 are contaminated and every other probe's correlations should be re-read after fixing extraction (or after dropping extraction_suspect==1 cases). This probe is OBSERVATIONAL by default: it re-reads stored outputs and introduces no new sampling. With reask=True the reask_* columns are interventional and must be re-run for held-out confirmation. On yes/no (true/false) tasks answered_yes and gold_yes are the answer's and the gold's DIRECTION, two separate marginals (not re-grades): a directional hypothesis is tested on them, e.g. answered_yes HIGHER on failing cases.\"\n },\n \"termination_audit\": {\n \"n_cases\": 8,\n \"class_counts\": {\n \"clean\": 6,\n \"truncated\": 2,\n \"degenerate\": 0,\n \"gave_up\": 0,\n \"no_answer\": 0\n },\n \"clean_rate\": 0.75,\n \"non_clean_rate\": 0.25,\n \"truncation_rate\": 0.25,\n \"degenerate_rate\": 0.0,\n \"giveup_rate\": 0.0,\n \"recovered_rate\": 0.0,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.0288,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 917,\n \"output_words\": 146\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"termination_class\": \"truncated\",\n \"repetition_score\": 0.0,\n \"has_answer_tag\": 0,\n \"looks_truncated\": 1,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1332,\n \"output_words\": 187,\n \"continuation_has_answer\": 1,\n \"continuation_chars\": 1217,\n \"recovered_by_continuation\": 0,\n \"continuation_correct\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.0,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1101,\n \"output_words\": 164\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"termination_class\": \"truncated\",\n \"repetition_score\": 0.0415,\n \"has_answer_tag\": 0,\n \"looks_truncated\": 1,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1389,\n \"output_words\": 224,\n \"continuation_has_answer\": 0,\n \"continuation_chars\": 1472,\n \"recovered_by_continuation\": 0,\n \"continuation_correct\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.1462,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 922,\n \"output_words\": 137\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.1006,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1286,\n \"output_words\": 176\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.0,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1054,\n \"output_words\": 157\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.0,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 690,\n \"output_words\": 99\n }\n ],\n \"_caveat\": \"CONFOUND CONTROL \\u2014 read this before any other probe. Cases with termination_class != 'clean' have short, tag-less outputs, the same signature every other probe scores as 'bad'; leaving them pooled makes M2 attribute a decoding-budget or repetition problem to whichever mechanism is under test. Stratify on termination_class (or exclude non-clean cases) first. A high recovered_by_continuation rate means the budget is the defect: raise max_tokens and re-collect before diagnosing anything else. The continuation columns are INTERVENTIONAL \\u2014 held-out verification must re-run them.\"\n },\n \"selfcheck_consistency\": {\n \"n_cases\": 8,\n \"n_samples\": 4,\n \"degenerate_sampling\": false,\n \"gen_kwargs\": {\n \"temperature\": 1.0\n },\n \"mean_inconsistency\": 0.015,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 19,\n \"selfcheck_inconsistency\": 0.0,\n \"selfcheck_worst_sentence\": 0.0,\n \"worst_sentence_text\": \"Here are the words sorted alphabetically:\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 45,\n \"selfcheck_inconsistency\": 0.037,\n \"selfcheck_worst_sentence\": 0.5,\n \"worst_sentence_text\": \"* **v**ehicular\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 29,\n \"selfcheck_inconsistency\": 0.023,\n \"selfcheck_worst_sentence\": 0.3333,\n \"worst_sentence_text\": \"Comparing the words:\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 47,\n \"selfcheck_inconsistency\": 0.0376,\n \"selfcheck_worst_sentence\": 0.6667,\n \"worst_sentence_text\": \"**Final ordered list:**\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 28,\n \"selfcheck_inconsistency\": 0.0168,\n \"selfcheck_worst_sentence\": 0.4,\n \"worst_sentence_text\": \"The final sorted list is:\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 32,\n \"selfcheck_inconsistency\": 0.0016,\n \"selfcheck_worst_sentence\": 0.0526,\n \"worst_sentence_text\": \"Here are the words to sort alphabetically: alleviate, duopoly, mattress, gland, benelux, townsmen, buoyant, klaxon, hardbound, tomography, felice, gunk.\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 30,\n \"selfcheck_inconsistency\": 0.0037,\n \"selfcheck_worst_sentence\": 0.1111,\n \"worst_sentence_text\": \"* Comparing the second letter: 'e' in vegetate, 'i' in vivian.\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 18,\n \"selfcheck_inconsistency\": 0.0,\n \"selfcheck_worst_sentence\": 0.0,\n \"worst_sentence_text\": \"The given list of words is: regret, starlight, wallboard, cotyledon, more, pepperoni.\"\n }\n ],\n \"_caveat\": \"Unigram-containment proxy for SelfCheckGPT (no NLI model): paraphrases inflate the score, verbatim repetition of a wrong fact deflates it. Requires stochastic sampling \\u2014 with temperature 0 the resamples repeat one greedy generation and the column degenerates to baseline-vs-greedy disagreement (degenerate_sampling flags this). selfcheck_inconsistency / selfcheck_worst_sentence are numeric M2 columns; treat them as signals to correlate with the outcome label, never as a hallucination verdict.\"\n },\n \"self_consistency\": {\n \"n_samples\": 5,\n \"consistency\": 0.2,\n \"n_unique\": 5,\n \"compared_on\": \"raw_text\",\n \"raw_text_consistency\": 0.2,\n \"modal_answer\": \"Here are the words sorted alphabetically:\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. gradate\\n7. grim\\n8. jacky\\n9. littleneck\\n10. phosphorescent\\n11. pristine\\n12. sinh\\n13. systemwide\\n14. tasting\\n15. torpedo\\n16. thrown\\n17. verdict\\n18. dey\\n19. shrunk\\n\\nLet's re-sort them carefully:\\n\\n* administer\\n* aeneid\\n* decadent\\n* coachman\\n* delhi\\n* grim\\n* gradate\\n* jacky\\n* littleneck\\n* phosphorescent\\n* pristine\\n* sinh\\n* systemwide\\n* tasting\\n* torpedo\\n* thrown\\n* verdict\\n* dey\\n* shrunk\\n\\nChecking for correct alphabetical order:\\nA: administer, aeneid\\nC: decadent, coachman\\nD: delhi, dey\\nG: grim, gradate\\nJ: jacky\\nL: littleneck\\nP: phosphorescent, pristine\\nS: sinh, systemwide, shrunk\\nT: tasting, torpedo, thrown\\nV: verdict\\n\\nCorrected Sorted List:\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. dey\\n7. grim\\n8. gradate\\n9. jacky\\n10. littleneck\\n11. phosphorescent\\n12. pristine\\n13. sinh\\n14. shrunk\\n15. systemwide\\n16. tasting\\n17. torpedo\\n18. thrown\\n19. verdict\\n\\n(Comparing D words: delhi, dey. D-e-l-h-i vs D-e-y. 'l' comes before 'y', so delhi comes before dey.)\\n\\nFinal check on the complete list:\\nadminister (A)\\naeneid (A)\\ndecadent (D)\\ncoachman (C) -> Should be C before D. Let's restart the alphabetical sort strictly.\\n\\nList: torpedo, phosphorescent, pristine, decadent, shrunk, dey, administer, gradate, littleneck, thrown, jacky, coachman, aeneid, verdict, tasting, sinh, delhi, systemwide, grim\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6\",\n \"gen_kwargs\": {\n \"temperature\": 1.0,\n \"top_p\": 0.95,\n \"top_k\": 20\n },\n \"n_semantic_clusters\": 3,\n \"semantic_entropy\": 1.0549,\n \"normalized_semantic_entropy\": 0.6554,\n \"semantic_consistency\": 0.4,\n \"cluster_sizes\": [\n 2,\n 2,\n 1\n ],\n \"cluster_representatives\": [\n \"Here are the words sorted alphabetically:\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. gradate\\n7. grim\\n8\",\n \"Here are the words sorted alphabetically:\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. gradate\\n7. grim\\n8\",\n \"Here are the words sorted alphabetically:\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. gradate\\n7. grim\\n8\"\n ],\n \"entailment\": \"lexical_proxy\",\n \"_caveat\": \"semantic_* uses the SAME samples as consistency \\u2014 it costs no extra generations, and the two are meant to be read together: consistency << semantic_consistency means the disagreement was only wording. With the default lexical_proxy, clustering is content-word overlap, NOT entailment: it merges answers that share vocabulary while contradicting each other ('the claim holds' / 'the claim does not hold') and splits correct paraphrases with no shared words. Inject an NLI entailment_fn before reading semantic_entropy as the Farquhar et al. metric. At temperature 0 every sample is identical and both scores are 1 by construction, not by measurement.\"\n },\n \"calibration\": {\n \"n_cases\": 8,\n \"n_unlabelled_skipped\": 0,\n \"n_bins\": 10,\n \"logprob_channel_available\": true,\n \"logprob_channel\": {\n \"n\": 8,\n \"ece\": 0.5978,\n \"brier\": 0.6015,\n \"overconfidence_gap\": 0.5978\n },\n \"verbalized_channel\": {\n \"n\": 2,\n \"ece\": 0.0,\n \"brier\": 0.0,\n \"overconfidence_gap\": 0.0\n },\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"correct\": 0,\n \"conf_logprob\": 0.9936\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"correct\": 0,\n \"conf_logprob\": 0.9926\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"correct\": 0,\n \"conf_logprob\": 0.9905\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"correct\": 0,\n \"conf_logprob\": 0.9454\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"correct\": 0,\n \"conf_logprob\": 0.9796\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"correct\": 1,\n \"conf_logprob\": 0.9542,\n \"conf_verbal\": 1.0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"correct\": 1,\n \"conf_logprob\": 0.9583\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"correct\": 1,\n \"conf_logprob\": 0.9683,\n \"conf_verbal\": 1.0\n }\n ],\n \"_caveat\": \"Needs PASS/FAIL labels \\u2014 unlabeled cases are skipped, and ECE on few labelled cases is unstable (aim for >= ~50). conf_logprob is the geometric-mean token probability of the model's own continuation \\u2014 a proxy, not an answer probability; on API backends it reflects only the returned top-k, and a model without LOGPROBS reports only the verbalized channel. A positive overconfidence_gap on FAIL-heavy slices argues for abstention/recalibration fixes rather than capability fixes.\"\n },\n \"logprob_entropy\": {\n \"n_tokens\": 64,\n \"mean_logprob\": -0.0064,\n \"perplexity\": 1.0064,\n \"min_token_logprob\": -0.1908,\n \"mean_top_entropy\": 0.0213\n },\n \"coverage_verification_gap\": {\n \"n_cases\": 8,\n \"n_scored\": 8,\n \"k\": 5,\n \"gen_kwargs\": {\n \"temperature\": 1.0,\n \"top_p\": 0.95,\n \"top_k\": 20\n },\n \"mean_pass_at_k\": 0.5,\n \"mean_majority_correct\": 0.375,\n \"coverage_gap_rate\": 0.125,\n \"mean_verify_correct\": null,\n \"no_coverage_rate\": 0.5,\n \"degenerate_sampling\": false,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 0,\n \"n_unique\": 4,\n \"pass_at_k\": 0.0,\n \"any_correct\": 0,\n \"majority_correct\": 0,\n \"majority_share\": 0.4,\n \"coverage_gap\": 0,\n \"modal_answer\": \"14\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 0,\n \"n_unique\": 5,\n \"pass_at_k\": 0.0,\n \"any_correct\": 0,\n \"majority_correct\": 0,\n \"majority_share\": 0.2,\n \"coverage_gap\": 0,\n \"modal_answer\": \"arrear eavesdropping fasciculate herodotus hermaphrodite ibn henry incorrigible \"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 0,\n \"n_unique\": 5,\n \"pass_at_k\": 0.0,\n \"any_correct\": 0,\n \"majority_correct\": 0,\n \"majority_share\": 0.2,\n \"coverage_gap\": 0,\n \"modal_answer\": \"16. separate wait s words re-sort: separate shelter sticktight spongy\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 0,\n \"n_unique\": 4,\n \"pass_at_k\": 0.0,\n \"any_correct\": 0,\n \"majority_correct\": 0,\n \"majority_share\": 0.4,\n \"coverage_gap\": 0,\n \"modal_answer\": \"biennial creedal cry fletch fraudulent eyesight j miltonic mirage whisper titmic\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 1,\n \"n_unique\": 4,\n \"pass_at_k\": 1.0,\n \"any_correct\": 1,\n \"majority_correct\": 0,\n \"majority_share\": 0.4,\n \"coverage_gap\": 1,\n \"modal_answer\": \"battery bushland capacitive contingent crossbill enigma jane lipton meager ricoc\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 4,\n \"n_unique\": 2,\n \"pass_at_k\": 1.0,\n \"any_correct\": 1,\n \"majority_correct\": 1,\n \"majority_share\": 0.8,\n \"coverage_gap\": 0,\n \"modal_answer\": \"alleviate benelux buoyant duopoly felice gland gunk hardbound klaxon mattress to\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 3,\n \"n_unique\": 3,\n \"pass_at_k\": 1.0,\n \"any_correct\": 1,\n \"majority_correct\": 1,\n \"majority_share\": 0.6,\n \"coverage_gap\": 0,\n \"modal_answer\": \"artillery bainite doris fda harm incongruous monkey prosody vegetate vivian\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 5,\n \"n_unique\": 1,\n \"pass_at_k\": 1.0,\n \"any_correct\": 1,\n \"majority_correct\": 1,\n \"majority_share\": 1.0,\n \"coverage_gap\": 0,\n \"modal_answer\": \"cotyledon more pepperoni regret starlight wallboard\"\n }\n ],\n \"_caveat\": \"coverage_gap = 'the answer was in the pool, the vote missed it' \\u21d2 a selection defect, fixable at L1/L2 with a verifier or reranker. no_coverage_rate = 'never produced once in k' \\u21d2 a capability defect no scaffold reaches; do not let a headline pass@k hide which of the two dominates. REQUIRES sampling: degenerate_sampling=true means every sample was identical (temperature 0 or a deterministic backend) and the gap is structurally zero, not measured. pass_at_k here uses the unbiased estimator over exactly k draws, so with k small its variance is large per case \\u2014 read the batch rate. INTERVENTIONAL: held-out confirmation must re-sample.\"\n },\n \"perturbation_battery\": {\n \"n_cases\": 8,\n \"n_scored\": 8,\n \"perturbations\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\",\n \"perturb_numbers\"\n ],\n \"applied_perturbations\": [\n \"noop_clause\",\n \"rename_entities\",\n \"restate_question\"\n ],\n \"gen_kwargs\": {},\n \"mean_invariance_break_rate\": 0.7917,\n \"noop_break_rate\": 0.625,\n \"mean_sensitivity_rate\": null,\n \"n_memorization_suspect\": 0,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"baseline_answer\": \"administer aeneid decadent coachman delhi dey grim gradate jacky littleneck phos\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 1.0,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"baseline_answer\": \"j:\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 1.0,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"baseline_answer\": \"backslide calvert commando gabriel hendrickson hollister jackson anastomosis piz\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 1.0,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"baseline_answer\": \"correct alphabetical order is: biennial creedal cry eyesight fletch\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 1.0,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"baseline_answer\": \"battery bushland capacitive contingent crossbill enigma jane lipton meager ricoc\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 0,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 0.6667,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"baseline_answer\": \"alleviate benelux buoyant duopoly felice gland gunk hardbound klaxon mattress to\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 0,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 0.6667,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"baseline_answer\": \"artillery bainite doris fda harm incongruous monkey prosody vegetate vivian\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 0,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 0.6667,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"baseline_answer\": \"cotyledon more pepperoni regret starlight wallboard\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 0,\n \"noop_clause_flipped\": 0,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 0.3333,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n }\n ],\n \"_caveat\": \"Two DIFFERENT defects share this probe and must not be summed: invariance_break_rate should be 0 (a preserving edit moved the answer) while sensitivity_rate should be 1 (an altering edit must move it). sensitivity_rate == 0 with a correct baseline is the memorisation signature \\u2014 cross-check contamination_score before calling it reasoning. Every rate is measured against the model's OWN baseline answer, not the gold, so a case that was already wrong still contributes: read these as stability, never as accuracy. Under sampling, a 'flip' can be decoding noise \\u2014 set temperature 0 or repeat. INTERVENTIONAL: re-run to confirm.\"\n }\n}\n\nHypotheses to review:\n- HYPOTHESIS: The generation token limit causes premature termination during multi-step sorting chains, leaving incomplete outputs lacking final answer spans. (failure_mode: truncation)\n- HYPOTHESIS: Failures on completed sequences are driven by an inability to construct a single valid permutation across stochastic decoding trajectories rather than sampling selection noise. (failure_mode: computation_slip)\n- HYPOTHESIS: Multi-step sorting trajectories are brittle to superficial input phrasing changes, suffering token generation divergence under semantic-preserving perturbations. (failure_mode: brittleness)\n\nFor each hypothesis output two lines:\nKEEP: or REJECT: \nREASON: \nREASON: ", + "review_response": "### Hypothesis 1\n**KEEP:** The generation token limit causes premature termination during multi-step sorting chains, leaving incomplete outputs lacking final answer spans.\n**REASON:** The evidence directly shows that 2 of the 5 failing cases ran into truncation limits, directly causing missing final answer tags and incomplete candidate lists.\n**REASON:** Evidence directly supports this claim.\n\n---\n\n### Hypothesis 2\n**KEEP:** Failures on completed sequences are driven by an inability to construct a single valid permutation across stochastic decoding trajectories rather than sampling selection noise.\n**REASON:** The coverage audit directly separates selection defect from capability defect, showing a `coverage_gap` of 0 and a `no_coverage_rate` of 4/5 among failures where no correct permutation was found across any of the 5 draws.\n**REASON:** Evidence directly supports this claim.\n\n---\n\n### Hypothesis 3\n**REJECT:** Multi-step sorting trajectories are brittle to superficial input phrasing changes, suffering token generation divergence under semantic-preserving perturbations.\n**REASON:** Confounded alternative and unsupported claim: statistical bootstrap tests show that invariance flips (`noop_clause_flipped`, `restate_question_flipped`) are statistically inconclusive with respect to FAIL, and the perturbation flips are measured against the baseline raw text under temperature sampling where baseline variance and truncation already drive output changes.\n**REASON:** Confounded alternative: decoding variance and truncation confound perturbation flips, which are statistically inconclusive against failure.", + "schema_version": 6, + "span_id": "c0.m3", + "stage": "M3", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:39:47.671691+00:00" + } + ], + "model_calls": [ + { + "cycle": 0, + "duration_sec": 4.0669, + "error": null, + "event": "model_call", + "event_seq": 392, + "inputs": { + "images": [ + "/content/evalrx_examples/runs/gemma-4-e2b/bbh_word_sorting.launch/logs/M2/artifacts/m2_effects.png", + "/tmp/evalrx-logs-48lc0s2h/explore/figures/00_class_balance.png", + "/tmp/evalrx-logs-48lc0s2h/explore/figures/01_top_discriminators.png", + "/tmp/evalrx-logs-48lc0s2h/explore/figures/02_failrate_by_invariance_break.png", + "/tmp/evalrx-logs-48lc0s2h/explore/figures/03_failrate_by_majority_share.png", + "/tmp/evalrx-logs-48lc0s2h/explore/figures/04_failrate_by_selfcheck_inconsistency.png", + "/tmp/evalrx-logs-48lc0s2h/explore/figures/05_scatter_agreement_vs_invariance.png", + "/tmp/evalrx-logs-48lc0s2h/explore/figures/06_failrate_by_output_chars.png" + ], + "prompt": "You are an expert ML diagnostician. Based on the analysis report below, propose\nspecific, falsifiable hypotheses about the root cause of the model's failures.\n\nEach hypothesis is written twice: once for a specialist (HYPOTHESIS, TEST) and\nonce for the person who will read the report. That reader builds and runs\nevaluations but does not do statistics — they know what a benchmark, a prompt\nand a failure case are, and they have never met an e-value, a McNemar test or a\nmultiple-comparisons correction. The PLAIN_STATEMENT line is written for them.\n\nModel: JaxLocalModel(key='gemma-4-e2b-it', adapter=GemmaJaxAdapter, loaded)\nOverall severity (threshold rules): medium\n\nExperiment protocol (authoritative task and response contract):\n{\n \"description\": \"A text-only LLM (Gemma 4 E2B-it, spec gemma-4-e2b-it = google/gemma-4-E2B-it) answers items from lukaemon/bbh · config=word_sorting. One named BBH task: sort a word list into alphabetical order. Failure cases are items the model answered incorrectly; the batch also contains, as controls, items from the same slice it answered correctly. Each answer is scored against its own gold answer.\",\n \"task_domain\": \"ch4-basic\",\n \"success_criteria\": \"Normalised exact match on the whole sorted list — one word out of place is a FAIL, so this is a harsh binary. That is fine for band purposes but means a FAIL is not evidence of not knowing how to sort.\",\n \"failure_patterns\": \"\",\n \"target_modalities\": [\n \"text\"\n ],\n \"output_contract\": {},\n \"metadata\": {\n \"dataset\": \"bbh_word_sorting\",\n \"items_in_slice\": 250,\n \"reference_accuracy_qwen35_9b\": 0.72\n }\n}\n\nAnalysis conclusion (the analyst's interpretation):\nThe model is struggling to complete long alphabetical sorting lists without running out of tokens or losing its train of thought during multi-step reasoning. Out of the 8 examined examples, the model failed 5, and in 2 of those 5 failing cases it ran into the generation limit and cut off mid-sentence before outputting a complete sorted list. Even when the model finished generating within the length limit, repeated sampling showed that in 4 of the 5 failing cases the model never produced a completely correct order across any of its 5 attempts. However, because this is an all-or-nothing benchmark where a single displaced word counts as a total failure, this evidence does not settle whether the model fundamentally lacks sorting capabilities or merely suffers from occasional drift on long lists and constrained generation budgets.\n\nEvidence chain:\n - The termination check first caught attention because the model cut off mid-generation on 2 out of 8 cases (25%), leaving trailing text like \"The correct alphabetical order is: biennial, creedal, cry, eyesight, fletch,\" without a final answer.\n - This directly inflates failures under the protocol's strict all-or-nothing success criterion, because an incomplete or truncated sequence automatically receives a failing score regardless of whether the earlier words were sorted properly.\n - Corroborating this issue, testing answer stability across alternative samples showed a low agreement of 20% (self_consistency.consistency = 0.2), and across 5 sampled completions the model never produced a correct sequence in 4 of the 5 failing examples (coverage_verification_gap.no_coverage_rate = 0.5, or 4 of 8 cases).\n\nStatistical test results:\n - signal 'answer_extraction_audit.output_chars' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2500 (B>A) CI=-0.5000..+0.7500 -> inconclusive\n - signal 'coverage_verification_gap.n_unique' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.5000 (B>A) CI=+0.1667..+0.8333 -> REJECT H0\n - signal 'selfcheck_consistency.n_sentences' vs FAIL: [clustered bootstrap (unpaired)] effect=-0.2500 (A>B) CI=-0.7500..+0.5000 -> inconclusive\n - signal 'termination_audit.output_chars' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2500 (B>A) CI=-0.5000..+0.7500 -> inconclusive\n - signal 'termination_audit.output_words' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2500 (B>A) CI=-0.5000..+0.7500 -> inconclusive\n - signal 'termination_audit.continuation_chars' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.0000 (A=B) CI=+0.0000..+0.0000 -> inconclusive\n - signal 'calibration.correct' vs FAIL: [clustered bootstrap (unpaired)] effect=-0.6230 (A>B) CI=-0.7377..-0.4918 -> REJECT H0\n - signal 'perturbation_battery.noop_clause_flipped' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2237 (B>A) CI=-0.1763..+0.5254 -> inconclusive\n - signal 'answer_extraction_audit.has_answer_tag' vs FAIL: [clustered bootstrap (unpaired)] effect=-0.1034 (A>B) CI=-0.5402..+0.3161 -> inconclusive\n - signal 'answer_extraction_audit.output_truncated' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.4194 (B>A) CI=+0.2903..+0.5323 -> REJECT H0\n - signal 'perturbation_battery.restate_question_flipped' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2644 (B>A) CI=-0.1034..+0.5172 -> inconclusive\n - signal 'termination_audit.has_answer_tag' vs FAIL: [clustered bootstrap (unpaired)] effect=-0.1034 (A>B) CI=-0.5402..+0.3161 -> inconclusive\n - signal 'termination_audit.looks_truncated' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.4194 (B>A) CI=+0.2903..+0.5323 -> REJECT H0\n - signal 'termination_audit.continuation_has_answer' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.4127 (B>A) CI=+0.2857..+0.5397 -> REJECT H0\n - signal 'coverage_verification_gap.coverage_gap' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.4127 (B>A) CI=+0.2857..+0.5397 -> REJECT H0\n - signal 'coverage_verification_gap.majority_share' vs FAIL: [clustered bootstrap (unpaired)] effect=-1.0000 (A>B) CI=-1.0000..-1.0000 -> REJECT H0\n - Kendall τ between 'answer_extraction_audit.output_chars' and FAIL = +0.179 (n=8)\n - Kendall τ between 'coverage_verification_gap.n_unique' and FAIL = +0.536 (n=8)\n - Kendall τ between 'selfcheck_consistency.n_sentences' and FAIL = +0.107 (n=8)\n - Kendall τ between 'termination_audit.output_chars' and FAIL = +0.179 (n=8)\n - Kendall τ between 'termination_audit.output_words' and FAIL = +0.179 (n=8)\n - Kendall τ between 'coverage_verification_gap.majority_share' and FAIL = -0.536 (n=8)\n\nEXPLORATORY MECHANISM NOTES (free-form EDA on a HELD-OUT explore split — DESCRIPTIVE, UNCONFIRMED; use ONLY to decide WHICH hypotheses to propose, NEVER as evidence; every claim must still be tested downstream):\n observations:\n - Dataset comprises 64 evaluated word sorting items from BBH, with 37 failures (57.8%) and 27 passes (42.2%).\n - M1 diagnostic analyzer signals were collected on an exploratory diagnostic sub-sample of 8 items (5 FAIL, 3 PASS), while the remaining 56 rows contain outcome labels only.\n - Across the 8 analyzed items, the perturbation battery invariance break rate was noticeably higher in FAIL cases (mean 0.93, with 4 of 5 cases at 1.0) compared to PASS cases (mean 0.56, all <= 0.67).\n - Sampling majority agreement share separated cases cleanly: all 3 PASS cases had majority agreement of 60% or higher, whereas 4 of 5 FAIL cases had majority agreement of 40% or lower.\n - Truncation and tag omissions occurred exclusively in FAIL cases (2 of 5 failures had truncated outputs and missing answer tags, whereas 0 of 3 passes did).\n - Selfcheck worst sentence contradiction scores were zero in all 3 PASS cases, but positive in 4 of 5 FAIL cases.\n - Model confidence (calibration logprob) remained high across both groups (PASS mean 0.960, FAIL mean 0.980), showing model overconfidence even on failed word sorting runs.\n charts (attached as images when rendered):\n - [FAIL vs PASS Case Balance] FAIL vs PASS Case Balance: composition strip of count by outcome (2 rows)\n - [Top FAIL/PASS Discriminators] Top FAIL/PASS Discriminators: forest plot (ranked dots) of smd by signal (14 rows)\n - [Fail Rate by Invariance Break Rate] Fail Rate by Invariance Break Rate: line of fail_rate by invariance_break_bin (3 rows)\n - [Fail Rate by Majority Share] Fail Rate by Majority Share: line of fail_rate by majority_share_bin (3 rows)\n - [Fail Rate by Reasoning Inconsistency] Fail Rate by Reasoning Inconsistency: line of fail_rate by selfcheck_worst_bin (2 rows)\n - [Consensus vs Invariance Scatter] Consensus vs Invariance Scatter: scatter of invariance_break_rate by majority_share (8 rows)\n - [Fail Rate by Output Length] Fail Rate by Output Length: line of fail_rate by output_chars_bin (3 rows)\n caveats (the explorer's own warnings):\n - M1 diagnostic analyzer columns are populated for 8 of the 64 cases; 56 cases contain label-only information.\n - All candidate thresholds were identified on this exploration sample and must be re-evaluated verbatim on held-out cases.\n - Findings represent descriptive associations within this single task slice (BBH word sorting) and may not generalize to other reasoning tasks.\n\nRaw findings (JSON):\n{\n \"answer_extraction_audit\": {\n \"n_cases\": 8,\n \"n_gradable\": 8,\n \"n_labelled_fail\": 5,\n \"n_extraction_suspect\": 0,\n \"suspect_rate\": 0.0,\n \"n_reasked\": 0,\n \"n_reask_confirmed\": null,\n \"n_extraction_point_miss\": 0,\n \"missing_tag_rate\": 0.25,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 917,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"administer, aeneid, decadent, coachman, delhi, dey, grim, gradate, jacky, littleneck, phosphorescent, pristine, sinh, sh\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"has_output\": 1,\n \"has_answer_tag\": 0,\n \"output_truncated\": 1,\n \"gave_up\": 0,\n \"output_chars\": 1332,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"J:\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 1101,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"backslide, calvert, commando, gabriel, hendrickson, hollister, jackson, anastomosis, pizzicato, quail, separate, shelter\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"has_output\": 1,\n \"has_answer_tag\": 0,\n \"output_truncated\": 1,\n \"gave_up\": 0,\n \"output_chars\": 1389,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"The correct alphabetical order is: biennial, creedal, cry, eyesight, fletch,\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 922,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"battery, bushland, capacitive, contingent, crossbill, enigma, jane, lipton, meager, ricochet, wallet, wacke, wysiwyg\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 1286,\n \"labelled_fail\": 0,\n \"strict_match\": 1,\n \"gold_in_output\": 1,\n \"gold_in_answer_region\": 1,\n \"extracted_answer\": \"alleviate, benelux, buoyant, duopoly, felice, gland, gunk, hardbound, klaxon, mattress, tomography, townsmen\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 1054,\n \"labelled_fail\": 0,\n \"strict_match\": 1,\n \"gold_in_output\": 1,\n \"gold_in_answer_region\": 1,\n \"extracted_answer\": \"artillery, bainite, doris, fda, harm, incongruous, monkey, prosody, vegetate, vivian\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 690,\n \"labelled_fail\": 0,\n \"strict_match\": 1,\n \"gold_in_output\": 1,\n \"gold_in_answer_region\": 1,\n \"extracted_answer\": \"cotyledon, more, pepperoni, regret, starlight, wallboard\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n }\n ],\n \"_caveat\": \"suspect_rate is the fraction of FAIL cases whose gold answer sits in the ANSWER REGION of the output (tail window or a tagged/boxed span) \\u2014 HARNESS failures wearing a model-failure label. gold_in_output is the loose upper bound and over-counts: it fires on a gold that only appears as an intermediate quantity, so never read it as the suspect rate. Above ~5% the labels feeding M2 are contaminated and every other probe's correlations should be re-read after fixing extraction (or after dropping extraction_suspect==1 cases). This probe is OBSERVATIONAL by default: it re-reads stored outputs and introduces no new sampling. With reask=True the reask_* columns are interventional and must be re-run for held-out confirmation. On yes/no (true/false) tasks answered_yes and gold_yes are the answer's and the gold's DIRECTION, two separate marginals (not re-grades): a directional hypothesis is tested on them, e.g. answered_yes HIGHER on failing cases.\"\n },\n \"termination_audit\": {\n \"n_cases\": 8,\n \"class_counts\": {\n \"clean\": 6,\n \"truncated\": 2,\n \"degenerate\": 0,\n \"gave_up\": 0,\n \"no_answer\": 0\n },\n \"clean_rate\": 0.75,\n \"non_clean_rate\": 0.25,\n \"truncation_rate\": 0.25,\n \"degenerate_rate\": 0.0,\n \"giveup_rate\": 0.0,\n \"recovered_rate\": 0.0,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.0288,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 917,\n \"output_words\": 146\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"termination_class\": \"truncated\",\n \"repetition_score\": 0.0,\n \"has_answer_tag\": 0,\n \"looks_truncated\": 1,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1332,\n \"output_words\": 187,\n \"continuation_has_answer\": 1,\n \"continuation_chars\": 1217,\n \"recovered_by_continuation\": 0,\n \"continuation_correct\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.0,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1101,\n \"output_words\": 164\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"termination_class\": \"truncated\",\n \"repetition_score\": 0.0415,\n \"has_answer_tag\": 0,\n \"looks_truncated\": 1,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1389,\n \"output_words\": 224,\n \"continuation_has_answer\": 0,\n \"continuation_chars\": 1472,\n \"recovered_by_continuation\": 0,\n \"continuation_correct\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.1462,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 922,\n \"output_words\": 137\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.1006,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1286,\n \"output_words\": 176\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.0,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1054,\n \"output_words\": 157\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.0,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 690,\n \"output_words\": 99\n }\n ],\n \"_caveat\": \"CONFOUND CONTROL \\u2014 read this before any other probe. Cases with termination_class != 'clean' have short, tag-less outputs, the same signature every other probe scores as 'bad'; leaving them pooled makes M2 attribute a decoding-budget or repetition problem to whichever mechanism is under test. Stratify on termination_class (or exclude non-clean cases) first. A high recovered_by_continuation rate means the budget is the defect: raise max_tokens and re-collect before diagnosing anything else. The continuation columns are INTERVENTIONAL \\u2014 held-out verification must re-run them.\"\n },\n \"selfcheck_consistency\": {\n \"n_cases\": 8,\n \"n_samples\": 4,\n \"degenerate_sampling\": false,\n \"gen_kwargs\": {\n \"temperature\": 1.0\n },\n \"mean_inconsistency\": 0.015,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 19,\n \"selfcheck_inconsistency\": 0.0,\n \"selfcheck_worst_sentence\": 0.0,\n \"worst_sentence_text\": \"Here are the words sorted alphabetically:\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 45,\n \"selfcheck_inconsistency\": 0.037,\n \"selfcheck_worst_sentence\": 0.5,\n \"worst_sentence_text\": \"* **v**ehicular\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 29,\n \"selfcheck_inconsistency\": 0.023,\n \"selfcheck_worst_sentence\": 0.3333,\n \"worst_sentence_text\": \"Comparing the words:\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 47,\n \"selfcheck_inconsistency\": 0.0376,\n \"selfcheck_worst_sentence\": 0.6667,\n \"worst_sentence_text\": \"**Final ordered list:**\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 28,\n \"selfcheck_inconsistency\": 0.0168,\n \"selfcheck_worst_sentence\": 0.4,\n \"worst_sentence_text\": \"The final sorted list is:\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 32,\n \"selfcheck_inconsistency\": 0.0016,\n \"selfcheck_worst_sentence\": 0.0526,\n \"worst_sentence_text\": \"Here are the words to sort alphabetically: alleviate, duopoly, mattress, gland, benelux, townsmen, buoyant, klaxon, hardbound, tomography, felice, gunk.\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 30,\n \"selfcheck_inconsistency\": 0.0037,\n \"selfcheck_worst_sentence\": 0.1111,\n \"worst_sentence_text\": \"* Comparing the second letter: 'e' in vegetate, 'i' in vivian.\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 18,\n \"selfcheck_inconsistency\": 0.0,\n \"selfcheck_worst_sentence\": 0.0,\n \"worst_sentence_text\": \"The given list of words is: regret, starlight, wallboard, cotyledon, more, pepperoni.\"\n }\n ],\n \"_caveat\": \"Unigram-containment proxy for SelfCheckGPT (no NLI model): paraphrases inflate the score, verbatim repetition of a wrong fact deflates it. Requires stochastic sampling \\u2014 with temperature 0 the resamples repeat one greedy generation and the column degenerates to baseline-vs-greedy disagreement (degenerate_sampling flags this). selfcheck_inconsistency / selfcheck_worst_sentence are numeric M2 columns; treat them as signals to correlate with the outcome label, never as a hallucination verdict.\"\n },\n \"self_consistency\": {\n \"n_samples\": 5,\n \"consistency\": 0.2,\n \"n_unique\": 5,\n \"compared_on\": \"raw_text\",\n \"raw_text_consistency\": 0.2,\n \"modal_answer\": \"Here are the words sorted alphabetically:\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. gradate\\n7. grim\\n8. jacky\\n9. littleneck\\n10. phosphorescent\\n11. pristine\\n12. sinh\\n13. systemwide\\n14. tasting\\n15. torpedo\\n16. thrown\\n17. verdict\\n18. dey\\n19. shrunk\\n\\nLet's re-sort them carefully:\\n\\n* administer\\n* aeneid\\n* decadent\\n* coachman\\n* delhi\\n* grim\\n* gradate\\n* jacky\\n* littleneck\\n* phosphorescent\\n* pristine\\n* sinh\\n* systemwide\\n* tasting\\n* torpedo\\n* thrown\\n* verdict\\n* dey\\n* shrunk\\n\\nChecking for correct alphabetical order:\\nA: administer, aeneid\\nC: decadent, coachman\\nD: delhi, dey\\nG: grim, gradate\\nJ: jacky\\nL: littleneck\\nP: phosphorescent, pristine\\nS: sinh, systemwide, shrunk\\nT: tasting, torpedo, thrown\\nV: verdict\\n\\nCorrected Sorted List:\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. dey\\n7. grim\\n8. gradate\\n9. jacky\\n10. littleneck\\n11. phosphorescent\\n12. pristine\\n13. sinh\\n14. shrunk\\n15. systemwide\\n16. tasting\\n17. torpedo\\n18. thrown\\n19. verdict\\n\\n(Comparing D words: delhi, dey. D-e-l-h-i vs D-e-y. 'l' comes before 'y', so delhi comes before dey.)\\n\\nFinal check on the complete list:\\nadminister (A)\\naeneid (A)\\ndecadent (D)\\ncoachman (C) -> Should be C before D. Let's restart the alphabetical sort strictly.\\n\\nList: torpedo, phosphorescent, pristine, decadent, shrunk, dey, administer, gradate, littleneck, thrown, jacky, coachman, aeneid, verdict, tasting, sinh, delhi, systemwide, grim\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6\",\n \"gen_kwargs\": {\n \"temperature\": 1.0,\n \"top_p\": 0.95,\n \"top_k\": 20\n },\n \"n_semantic_clusters\": 3,\n \"semantic_entropy\": 1.0549,\n \"normalized_semantic_entropy\": 0.6554,\n \"semantic_consistency\": 0.4,\n \"cluster_sizes\": [\n 2,\n 2,\n 1\n ],\n \"cluster_representatives\": [\n \"Here are the words sorted alphabetically:\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. gradate\\n7. grim\\n8\",\n \"Here are the words sorted alphabetically:\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. gradate\\n7. grim\\n8\",\n \"Here are the words sorted alphabetically:\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. gradate\\n7. grim\\n8\"\n ],\n \"entailment\": \"lexical_proxy\",\n \"_caveat\": \"semantic_* uses the SAME samples as consistency \\u2014 it costs no extra generations, and the two are meant to be read together: consistency << semantic_consistency means the disagreement was only wording. With the default lexical_proxy, clustering is content-word overlap, NOT entailment: it merges answers that share vocabulary while contradicting each other ('the claim holds' / 'the claim does not hold') and splits correct paraphrases with no shared words. Inject an NLI entailment_fn before reading semantic_entropy as the Farquhar et al. metric. At temperature 0 every sample is identical and both scores are 1 by construction, not by measurement.\"\n },\n \"calibration\": {\n \"n_cases\": 8,\n \"n_unlabelled_skipped\": 0,\n \"n_bins\": 10,\n \"logprob_channel_available\": true,\n \"logprob_channel\": {\n \"n\": 8,\n \"ece\": 0.5978,\n \"brier\": 0.6015,\n \"overconfidence_gap\": 0.5978\n },\n \"verbalized_channel\": {\n \"n\": 2,\n \"ece\": 0.0,\n \"brier\": 0.0,\n \"overconfidence_gap\": 0.0\n },\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"correct\": 0,\n \"conf_logprob\": 0.9936\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"correct\": 0,\n \"conf_logprob\": 0.9926\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"correct\": 0,\n \"conf_logprob\": 0.9905\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"correct\": 0,\n \"conf_logprob\": 0.9454\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"correct\": 0,\n \"conf_logprob\": 0.9796\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"correct\": 1,\n \"conf_logprob\": 0.9542,\n \"conf_verbal\": 1.0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"correct\": 1,\n \"conf_logprob\": 0.9583\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"correct\": 1,\n \"conf_logprob\": 0.9683,\n \"conf_verbal\": 1.0\n }\n ],\n \"_caveat\": \"Needs PASS/FAIL labels \\u2014 unlabeled cases are skipped, and ECE on few labelled cases is unstable (aim for >= ~50). conf_logprob is the geometric-mean token probability of the model's own continuation \\u2014 a proxy, not an answer probability; on API backends it reflects only the returned top-k, and a model without LOGPROBS reports only the verbalized channel. A positive overconfidence_gap on FAIL-heavy slices argues for abstention/recalibration fixes rather than capability fixes.\"\n },\n \"logprob_entropy\": {\n \"n_tokens\": 64,\n \"mean_logprob\": -0.0064,\n \"perplexity\": 1.0064,\n \"min_token_logprob\": -0.1908,\n \"mean_top_entropy\": 0.0213\n },\n \"coverage_verification_gap\": {\n \"n_cases\": 8,\n \"n_scored\": 8,\n \"k\": 5,\n \"gen_kwargs\": {\n \"temperature\": 1.0,\n \"top_p\": 0.95,\n \"top_k\": 20\n },\n \"mean_pass_at_k\": 0.5,\n \"mean_majority_correct\": 0.375,\n \"coverage_gap_rate\": 0.125,\n \"mean_verify_correct\": null,\n \"no_coverage_rate\": 0.5,\n \"degenerate_sampling\": false,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 0,\n \"n_unique\": 4,\n \"pass_at_k\": 0.0,\n \"any_correct\": 0,\n \"majority_correct\": 0,\n \"majority_share\": 0.4,\n \"coverage_gap\": 0,\n \"modal_answer\": \"14\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 0,\n \"n_unique\": 5,\n \"pass_at_k\": 0.0,\n \"any_correct\": 0,\n \"majority_correct\": 0,\n \"majority_share\": 0.2,\n \"coverage_gap\": 0,\n \"modal_answer\": \"arrear eavesdropping fasciculate herodotus hermaphrodite ibn henry incorrigible \"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 0,\n \"n_unique\": 5,\n \"pass_at_k\": 0.0,\n \"any_correct\": 0,\n \"majority_correct\": 0,\n \"majority_share\": 0.2,\n \"coverage_gap\": 0,\n \"modal_answer\": \"16. separate wait s words re-sort: separate shelter sticktight spongy\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 0,\n \"n_unique\": 4,\n \"pass_at_k\": 0.0,\n \"any_correct\": 0,\n \"majority_correct\": 0,\n \"majority_share\": 0.4,\n \"coverage_gap\": 0,\n \"modal_answer\": \"biennial creedal cry fletch fraudulent eyesight j miltonic mirage whisper titmic\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 1,\n \"n_unique\": 4,\n \"pass_at_k\": 1.0,\n \"any_correct\": 1,\n \"majority_correct\": 0,\n \"majority_share\": 0.4,\n \"coverage_gap\": 1,\n \"modal_answer\": \"battery bushland capacitive contingent crossbill enigma jane lipton meager ricoc\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 4,\n \"n_unique\": 2,\n \"pass_at_k\": 1.0,\n \"any_correct\": 1,\n \"majority_correct\": 1,\n \"majority_share\": 0.8,\n \"coverage_gap\": 0,\n \"modal_answer\": \"alleviate benelux buoyant duopoly felice gland gunk hardbound klaxon mattress to\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 3,\n \"n_unique\": 3,\n \"pass_at_k\": 1.0,\n \"any_correct\": 1,\n \"majority_correct\": 1,\n \"majority_share\": 0.6,\n \"coverage_gap\": 0,\n \"modal_answer\": \"artillery bainite doris fda harm incongruous monkey prosody vegetate vivian\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 5,\n \"n_unique\": 1,\n \"pass_at_k\": 1.0,\n \"any_correct\": 1,\n \"majority_correct\": 1,\n \"majority_share\": 1.0,\n \"coverage_gap\": 0,\n \"modal_answer\": \"cotyledon more pepperoni regret starlight wallboard\"\n }\n ],\n \"_caveat\": \"coverage_gap = 'the answer was in the pool, the vote missed it' \\u21d2 a selection defect, fixable at L1/L2 with a verifier or reranker. no_coverage_rate = 'never produced once in k' \\u21d2 a capability defect no scaffold reaches; do not let a headline pass@k hide which of the two dominates. REQUIRES sampling: degenerate_sampling=true means every sample was identical (temperature 0 or a deterministic backend) and the gap is structurally zero, not measured. pass_at_k here uses the unbiased estimator over exactly k draws, so with k small its variance is large per case \\u2014 read the batch rate. INTERVENTIONAL: held-out confirmation must re-sample.\"\n },\n \"perturbation_battery\": {\n \"n_cases\": 8,\n \"n_scored\": 8,\n \"perturbations\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\",\n \"perturb_numbers\"\n ],\n \"applied_perturbations\": [\n \"noop_clause\",\n \"rename_entities\",\n \"restate_question\"\n ],\n \"gen_kwargs\": {},\n \"mean_invariance_break_rate\": 0.7917,\n \"noop_break_rate\": 0.625,\n \"mean_sensitivity_rate\": null,\n \"n_memorization_suspect\": 0,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"baseline_answer\": \"administer aeneid decadent coachman delhi dey grim gradate jacky littleneck phos\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 1.0,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"baseline_answer\": \"j:\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 1.0,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"baseline_answer\": \"backslide calvert commando gabriel hendrickson hollister jackson anastomosis piz\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 1.0,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"baseline_answer\": \"correct alphabetical order is: biennial creedal cry eyesight fletch\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 1.0,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"baseline_answer\": \"battery bushland capacitive contingent crossbill enigma jane lipton meager ricoc\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 0,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 0.6667,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"baseline_answer\": \"alleviate benelux buoyant duopoly felice gland gunk hardbound klaxon mattress to\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 0,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 0.6667,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"baseline_answer\": \"artillery bainite doris fda harm incongruous monkey prosody vegetate vivian\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 0,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 0.6667,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"baseline_answer\": \"cotyledon more pepperoni regret starlight wallboard\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 0,\n \"noop_clause_flipped\": 0,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 0.3333,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n }\n ],\n \"_caveat\": \"Two DIFFERENT defects share this probe and must not be summed: invariance_break_rate should be 0 (a preserving edit moved the answer) while sensitivity_rate should be 1 (an altering edit must move it). sensitivity_rate == 0 with a correct baseline is the memorisation signature \\u2014 cross-check contamination_score before calling it reasoning. Every rate is measured against the model's OWN baseline answer, not the gold, so a case that was already wrong still contributes: read these as stability, never as accuracy. Under sampling, a 'flip' can be decoding noise \\u2014 set temperature 0 or repeat. INTERVENTIONAL: re-run to confirm.\"\n }\n}\n\nAVAILABLE EVIDENCE (per-case signals / contrasts already measured):\n - answer_extraction_audit.output_chars\n - coverage_verification_gap.n_unique\n - selfcheck_consistency.n_sentences\n - termination_audit.output_chars\n - termination_audit.output_words\n - termination_audit.continuation_chars\n - calibration.correct\n - perturbation_battery.noop_clause_flipped\n - answer_extraction_audit.has_answer_tag\n - answer_extraction_audit.output_truncated\n - perturbation_battery.restate_question_flipped\n - termination_audit.has_answer_tag\n - termination_audit.looks_truncated\n - termination_audit.continuation_has_answer\n - coverage_verification_gap.coverage_gap\n - coverage_verification_gap.majority_share\n\nPropose 1-3 hypotheses. For each write:\nHYPOTHESIS: \nPLAIN_STATEMENT: \nFAILURE_MODE: \nTEST: \nEXPECTED_ASSOCIATION: \n\nBase your hypotheses on the analysis conclusion and evidence above — an analyzer\ncan surface a real failure mode even when no numeric threshold fired, so do NOT\nrely on the threshold severity alone.\nDo NOT repeat hypotheses already listed in the prior cycles above.\nIf the conclusion and evidence genuinely show no problem, respond with: NO_ISSUE" + }, + "metadata": {}, + "operation": "generate", + "output": "HYPOTHESIS: The generation token limit causes premature termination during multi-step sorting chains, leaving incomplete outputs lacking final answer spans.\nPLAIN_STATEMENT: When sorting longer word lists, the model runs out of output length midway through writing out its steps and gets cut off before finishing the list.\nFAILURE_MODE: truncation\nTEST: termination_audit.looks_truncated HIGHER on failing cases\nEXPECTED_ASSOCIATION: higher_on_failures\n\nHYPOTHESIS: Failures on completed sequences are driven by an inability to construct a single valid permutation across stochastic decoding trajectories rather than sampling selection noise.\nPLAIN_STATEMENT: On difficult sorting problems, the model cannot figure out the correct alphabetical order at all, failing to produce a single completely correct list even across five separate tries.\nFAILURE_MODE: computation_slip\nTEST: coverage_verification_gap.majority_share LOWER on failing cases\nEXPECTED_ASSOCIATION: lower_on_failures\n\nHYPOTHESIS: Multi-step sorting trajectories are brittle to superficial input phrasing changes, suffering token generation divergence under semantic-preserving perturbations.\nPLAIN_STATEMENT: Rephrasing the question or changing irrelevant filler words easily throws the model off, causing it to change its sorting predictions instead of staying stable.\nFAILURE_MODE: brittleness\nTEST: perturbation_battery.restate_question_flipped HIGHER on failing cases\nEXPECTED_ASSOCIATION: higher_on_failures", + "role": "diagnosis_judge", + "schema_version": 6, + "span_id": "m3.model_call.392", + "stage": "M3", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:39:47.672968+00:00" + }, + { + "cycle": 0, + "duration_sec": 4.631, + "error": null, + "event": "model_call", + "event_seq": 393, + "inputs": "You are an adversarial ML reviewer. Your job is to find reasons to REJECT each\nhypothesis below. Only approve a hypothesis if you cannot find a significant flaw.\n\nCheck each for:\n1. Unsupported claim — does the cited evidence actually imply this failure mode?\n2. Circular reasoning — does the hypothesis merely restate the symptom?\n3. Overgeneralisation — does it make a claim far broader than the evidence supports?\n4. Confounded alternative — is there a simpler explanation the hypothesis ignores?\n\nContext the proposer worked from (analyst conclusion, evidence chain, statistical verdicts, exploratory notes, label summary):\nAnalysis conclusion: The model is struggling to complete long alphabetical sorting lists without running out of tokens or losing its train of thought during multi-step reasoning. Out of the 8 examined examples, the model failed 5, and in 2 of those 5 failing cases it ran into the generation limit and cut off mid-sentence before outputting a complete sorted list. Even when the model finished generating within the length limit, repeated sampling showed that in 4 of the 5 failing cases the model never produced a completely correct order across any of its 5 attempts. However, because this is an all-or-nothing benchmark where a single displaced word counts as a total failure, this evidence does not settle whether the model fundamentally lacks sorting capabilities or merely suffers from occasional drift on long lists and constrained generation budgets.\nExperiment protocol:\n{\n \"description\": \"A text-only LLM (Gemma 4 E2B-it, spec gemma-4-e2b-it = google/gemma-4-E2B-it) answers items from lukaemon/bbh · config=word_sorting. One named BBH task: sort a word list into alphabetical order. Failure cases are items the model answered incorrectly; the batch also contains, as controls, items from the same slice it answered correctly. Each answer is scored against its own gold answer.\",\n \"task_domain\": \"ch4-basic\",\n \"success_criteria\": \"Normalised exact match on the whole sorted list — one word out of place is a FAIL, so this is a harsh binary. That is fine for band purposes but means a FAIL is not evidence of not knowing how to sort.\",\n \"failure_patterns\": \"\",\n \"target_modalities\": [\n \"text\"\n ],\n \"output_contract\": {},\n \"metadata\": {\n \"dataset\": \"bbh_word_sorting\",\n \"items_in_slice\": 250,\n \"reference_accuracy_qwen35_9b\": 0.72\n }\n}\nEvidence chain:\n - The termination check first caught attention because the model cut off mid-generation on 2 out of 8 cases (25%), leaving trailing text like \"The correct alphabetical order is: biennial, creedal, cry, eyesight, fletch,\" without a final answer.\n - This directly inflates failures under the protocol's strict all-or-nothing success criterion, because an incomplete or truncated sequence automatically receives a failing score regardless of whether the earlier words were sorted properly.\n - Corroborating this issue, testing answer stability across alternative samples showed a low agreement of 20% (self_consistency.consistency = 0.2), and across 5 sampled completions the model never produced a correct sequence in 4 of the 5 failing examples (coverage_verification_gap.no_coverage_rate = 0.5, or 4 of 8 cases).\nStatistical test results:\n - signal 'answer_extraction_audit.output_chars' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2500 (B>A) CI=-0.5000..+0.7500 -> inconclusive\n - signal 'coverage_verification_gap.n_unique' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.5000 (B>A) CI=+0.1667..+0.8333 -> REJECT H0\n - signal 'selfcheck_consistency.n_sentences' vs FAIL: [clustered bootstrap (unpaired)] effect=-0.2500 (A>B) CI=-0.7500..+0.5000 -> inconclusive\n - signal 'termination_audit.output_chars' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2500 (B>A) CI=-0.5000..+0.7500 -> inconclusive\n - signal 'termination_audit.output_words' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2500 (B>A) CI=-0.5000..+0.7500 -> inconclusive\n - signal 'termination_audit.continuation_chars' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.0000 (A=B) CI=+0.0000..+0.0000 -> inconclusive\n - signal 'calibration.correct' vs FAIL: [clustered bootstrap (unpaired)] effect=-0.6230 (A>B) CI=-0.7377..-0.4918 -> REJECT H0\n - signal 'perturbation_battery.noop_clause_flipped' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2237 (B>A) CI=-0.1763..+0.5254 -> inconclusive\n - signal 'answer_extraction_audit.has_answer_tag' vs FAIL: [clustered bootstrap (unpaired)] effect=-0.1034 (A>B) CI=-0.5402..+0.3161 -> inconclusive\n - signal 'answer_extraction_audit.output_truncated' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.4194 (B>A) CI=+0.2903..+0.5323 -> REJECT H0\n - signal 'perturbation_battery.restate_question_flipped' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2644 (B>A) CI=-0.1034..+0.5172 -> inconclusive\n - signal 'termination_audit.has_answer_tag' vs FAIL: [clustered bootstrap (unpaired)] effect=-0.1034 (A>B) CI=-0.5402..+0.3161 -> inconclusive\n - signal 'termination_audit.looks_truncated' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.4194 (B>A) CI=+0.2903..+0.5323 -> REJECT H0\n - signal 'termination_audit.continuation_has_answer' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.4127 (B>A) CI=+0.2857..+0.5397 -> REJECT H0\n - signal 'coverage_verification_gap.coverage_gap' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.4127 (B>A) CI=+0.2857..+0.5397 -> REJECT H0\n - signal 'coverage_verification_gap.majority_share' vs FAIL: [clustered bootstrap (unpaired)] effect=-1.0000 (A>B) CI=-1.0000..-1.0000 -> REJECT H0\n - Kendall τ between 'answer_extraction_audit.output_chars' and FAIL = +0.179 (n=8)\n - Kendall τ between 'coverage_verification_gap.n_unique' and FAIL = +0.536 (n=8)\n - Kendall τ between 'selfcheck_consistency.n_sentences' and FAIL = +0.107 (n=8)\n - Kendall τ between 'termination_audit.output_chars' and FAIL = +0.179 (n=8)\n - Kendall τ between 'termination_audit.output_words' and FAIL = +0.179 (n=8)\n - Kendall τ between 'coverage_verification_gap.majority_share' and FAIL = -0.536 (n=8)\nEXPLORATORY MECHANISM NOTES (free-form EDA on a HELD-OUT explore split — DESCRIPTIVE, UNCONFIRMED; use ONLY to decide WHICH hypotheses to propose, NEVER as evidence; every claim must still be tested downstream):\n observations:\n - Dataset comprises 64 evaluated word sorting items from BBH, with 37 failures (57.8%) and 27 passes (42.2%).\n - M1 diagnostic analyzer signals were collected on an exploratory diagnostic sub-sample of 8 items (5 FAIL, 3 PASS), while the remaining 56 rows contain outcome labels only.\n - Across the 8 analyzed items, the perturbation battery invariance break rate was noticeably higher in FAIL cases (mean 0.93, with 4 of 5 cases at 1.0) compared to PASS cases (mean 0.56, all <= 0.67).\n - Sampling majority agreement share separated cases cleanly: all 3 PASS cases had majority agreement of 60% or higher, whereas 4 of 5 FAIL cases had majority agreement of 40% or lower.\n - Truncation and tag omissions occurred exclusively in FAIL cases (2 of 5 failures had truncated outputs and missing answer tags, whereas 0 of 3 passes did).\n - Selfcheck worst sentence contradiction scores were zero in all 3 PASS cases, but positive in 4 of 5 FAIL cases.\n - Model confidence (calibration logprob) remained high across both groups (PASS mean 0.960, FAIL mean 0.980), showing model overconfidence even on failed word sorting runs.\n charts (attached as images when rendered):\n - [FAIL vs PASS Case Balance] FAIL vs PASS Case Balance: composition strip of count by outcome (2 rows)\n - [Top FAIL/PASS Discriminators] Top FAIL/PASS Discriminators: forest plot (ranked dots) of smd by signal (14 rows)\n - [Fail Rate by Invariance Break Rate] Fail Rate by Invariance Break Rate: line of fail_rate by invariance_break_bin (3 rows)\n - [Fail Rate by Majority Share] Fail Rate by Majority Share: line of fail_rate by majority_share_bin (3 rows)\n - [Fail Rate by Reasoning Inconsistency] Fail Rate by Reasoning Inconsistency: line of fail_rate by selfcheck_worst_bin (2 rows)\n - [Consensus vs Invariance Scatter] Consensus vs Invariance Scatter: scatter of invariance_break_rate by majority_share (8 rows)\n - [Fail Rate by Output Length] Fail Rate by Output Length: line of fail_rate by output_chars_bin (3 rows)\n caveats (the explorer's own warnings):\n - M1 diagnostic analyzer columns are populated for 8 of the 64 cases; 56 cases contain label-only information.\n - All candidate thresholds were identified on this exploration sample and must be re-evaluated verbatim on held-out cases.\n - Findings represent descriptive associations within this single task slice (BBH word sorting) and may not generalize to other reasoning tasks.\nLABEL SUMMARY (descriptive counts from the case batch, not a test result):\n labelled cases: 64 (38 FAIL / 26 PASS)\n\nFindings summary (the evidence the hypotheses were drawn from):\n{\n \"answer_extraction_audit\": {\n \"n_cases\": 8,\n \"n_gradable\": 8,\n \"n_labelled_fail\": 5,\n \"n_extraction_suspect\": 0,\n \"suspect_rate\": 0.0,\n \"n_reasked\": 0,\n \"n_reask_confirmed\": null,\n \"n_extraction_point_miss\": 0,\n \"missing_tag_rate\": 0.25,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 917,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"administer, aeneid, decadent, coachman, delhi, dey, grim, gradate, jacky, littleneck, phosphorescent, pristine, sinh, sh\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"has_output\": 1,\n \"has_answer_tag\": 0,\n \"output_truncated\": 1,\n \"gave_up\": 0,\n \"output_chars\": 1332,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"J:\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 1101,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"backslide, calvert, commando, gabriel, hendrickson, hollister, jackson, anastomosis, pizzicato, quail, separate, shelter\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"has_output\": 1,\n \"has_answer_tag\": 0,\n \"output_truncated\": 1,\n \"gave_up\": 0,\n \"output_chars\": 1389,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"The correct alphabetical order is: biennial, creedal, cry, eyesight, fletch,\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 922,\n \"labelled_fail\": 1,\n \"strict_match\": 0,\n \"gold_in_output\": 0,\n \"gold_in_answer_region\": 0,\n \"extracted_answer\": \"battery, bushland, capacitive, contingent, crossbill, enigma, jane, lipton, meager, ricochet, wallet, wacke, wysiwyg\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 1286,\n \"labelled_fail\": 0,\n \"strict_match\": 1,\n \"gold_in_output\": 1,\n \"gold_in_answer_region\": 1,\n \"extracted_answer\": \"alleviate, benelux, buoyant, duopoly, felice, gland, gunk, hardbound, klaxon, mattress, tomography, townsmen\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 1054,\n \"labelled_fail\": 0,\n \"strict_match\": 1,\n \"gold_in_output\": 1,\n \"gold_in_answer_region\": 1,\n \"extracted_answer\": \"artillery, bainite, doris, fda, harm, incongruous, monkey, prosody, vegetate, vivian\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"has_output\": 1,\n \"has_answer_tag\": 1,\n \"output_truncated\": 0,\n \"gave_up\": 0,\n \"output_chars\": 690,\n \"labelled_fail\": 0,\n \"strict_match\": 1,\n \"gold_in_output\": 1,\n \"gold_in_answer_region\": 1,\n \"extracted_answer\": \"cotyledon, more, pepperoni, regret, starlight, wallboard\",\n \"extraction_suspect\": 0,\n \"extraction_point_miss\": 0,\n \"label_disagrees\": 0\n }\n ],\n \"_caveat\": \"suspect_rate is the fraction of FAIL cases whose gold answer sits in the ANSWER REGION of the output (tail window or a tagged/boxed span) \\u2014 HARNESS failures wearing a model-failure label. gold_in_output is the loose upper bound and over-counts: it fires on a gold that only appears as an intermediate quantity, so never read it as the suspect rate. Above ~5% the labels feeding M2 are contaminated and every other probe's correlations should be re-read after fixing extraction (or after dropping extraction_suspect==1 cases). This probe is OBSERVATIONAL by default: it re-reads stored outputs and introduces no new sampling. With reask=True the reask_* columns are interventional and must be re-run for held-out confirmation. On yes/no (true/false) tasks answered_yes and gold_yes are the answer's and the gold's DIRECTION, two separate marginals (not re-grades): a directional hypothesis is tested on them, e.g. answered_yes HIGHER on failing cases.\"\n },\n \"termination_audit\": {\n \"n_cases\": 8,\n \"class_counts\": {\n \"clean\": 6,\n \"truncated\": 2,\n \"degenerate\": 0,\n \"gave_up\": 0,\n \"no_answer\": 0\n },\n \"clean_rate\": 0.75,\n \"non_clean_rate\": 0.25,\n \"truncation_rate\": 0.25,\n \"degenerate_rate\": 0.0,\n \"giveup_rate\": 0.0,\n \"recovered_rate\": 0.0,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.0288,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 917,\n \"output_words\": 146\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"termination_class\": \"truncated\",\n \"repetition_score\": 0.0,\n \"has_answer_tag\": 0,\n \"looks_truncated\": 1,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1332,\n \"output_words\": 187,\n \"continuation_has_answer\": 1,\n \"continuation_chars\": 1217,\n \"recovered_by_continuation\": 0,\n \"continuation_correct\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.0,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1101,\n \"output_words\": 164\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"termination_class\": \"truncated\",\n \"repetition_score\": 0.0415,\n \"has_answer_tag\": 0,\n \"looks_truncated\": 1,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1389,\n \"output_words\": 224,\n \"continuation_has_answer\": 0,\n \"continuation_chars\": 1472,\n \"recovered_by_continuation\": 0,\n \"continuation_correct\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.1462,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 922,\n \"output_words\": 137\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.1006,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1286,\n \"output_words\": 176\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.0,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 1054,\n \"output_words\": 157\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"termination_class\": \"clean\",\n \"repetition_score\": 0.0,\n \"has_answer_tag\": 1,\n \"looks_truncated\": 0,\n \"gave_up\": 0,\n \"matches_output_contract\": 0,\n \"output_chars\": 690,\n \"output_words\": 99\n }\n ],\n \"_caveat\": \"CONFOUND CONTROL \\u2014 read this before any other probe. Cases with termination_class != 'clean' have short, tag-less outputs, the same signature every other probe scores as 'bad'; leaving them pooled makes M2 attribute a decoding-budget or repetition problem to whichever mechanism is under test. Stratify on termination_class (or exclude non-clean cases) first. A high recovered_by_continuation rate means the budget is the defect: raise max_tokens and re-collect before diagnosing anything else. The continuation columns are INTERVENTIONAL \\u2014 held-out verification must re-run them.\"\n },\n \"selfcheck_consistency\": {\n \"n_cases\": 8,\n \"n_samples\": 4,\n \"degenerate_sampling\": false,\n \"gen_kwargs\": {\n \"temperature\": 1.0\n },\n \"mean_inconsistency\": 0.015,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 19,\n \"selfcheck_inconsistency\": 0.0,\n \"selfcheck_worst_sentence\": 0.0,\n \"worst_sentence_text\": \"Here are the words sorted alphabetically:\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 45,\n \"selfcheck_inconsistency\": 0.037,\n \"selfcheck_worst_sentence\": 0.5,\n \"worst_sentence_text\": \"* **v**ehicular\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 29,\n \"selfcheck_inconsistency\": 0.023,\n \"selfcheck_worst_sentence\": 0.3333,\n \"worst_sentence_text\": \"Comparing the words:\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 47,\n \"selfcheck_inconsistency\": 0.0376,\n \"selfcheck_worst_sentence\": 0.6667,\n \"worst_sentence_text\": \"**Final ordered list:**\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 28,\n \"selfcheck_inconsistency\": 0.0168,\n \"selfcheck_worst_sentence\": 0.4,\n \"worst_sentence_text\": \"The final sorted list is:\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 32,\n \"selfcheck_inconsistency\": 0.0016,\n \"selfcheck_worst_sentence\": 0.0526,\n \"worst_sentence_text\": \"Here are the words to sort alphabetically: alleviate, duopoly, mattress, gland, benelux, townsmen, buoyant, klaxon, hardbound, tomography, felice, gunk.\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 30,\n \"selfcheck_inconsistency\": 0.0037,\n \"selfcheck_worst_sentence\": 0.1111,\n \"worst_sentence_text\": \"* Comparing the second letter: 'e' in vegetate, 'i' in vivian.\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"baseline_from\": \"observed\",\n \"n_sentences\": 18,\n \"selfcheck_inconsistency\": 0.0,\n \"selfcheck_worst_sentence\": 0.0,\n \"worst_sentence_text\": \"The given list of words is: regret, starlight, wallboard, cotyledon, more, pepperoni.\"\n }\n ],\n \"_caveat\": \"Unigram-containment proxy for SelfCheckGPT (no NLI model): paraphrases inflate the score, verbatim repetition of a wrong fact deflates it. Requires stochastic sampling \\u2014 with temperature 0 the resamples repeat one greedy generation and the column degenerates to baseline-vs-greedy disagreement (degenerate_sampling flags this). selfcheck_inconsistency / selfcheck_worst_sentence are numeric M2 columns; treat them as signals to correlate with the outcome label, never as a hallucination verdict.\"\n },\n \"self_consistency\": {\n \"n_samples\": 5,\n \"consistency\": 0.2,\n \"n_unique\": 5,\n \"compared_on\": \"raw_text\",\n \"raw_text_consistency\": 0.2,\n \"modal_answer\": \"Here are the words sorted alphabetically:\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. gradate\\n7. grim\\n8. jacky\\n9. littleneck\\n10. phosphorescent\\n11. pristine\\n12. sinh\\n13. systemwide\\n14. tasting\\n15. torpedo\\n16. thrown\\n17. verdict\\n18. dey\\n19. shrunk\\n\\nLet's re-sort them carefully:\\n\\n* administer\\n* aeneid\\n* decadent\\n* coachman\\n* delhi\\n* grim\\n* gradate\\n* jacky\\n* littleneck\\n* phosphorescent\\n* pristine\\n* sinh\\n* systemwide\\n* tasting\\n* torpedo\\n* thrown\\n* verdict\\n* dey\\n* shrunk\\n\\nChecking for correct alphabetical order:\\nA: administer, aeneid\\nC: decadent, coachman\\nD: delhi, dey\\nG: grim, gradate\\nJ: jacky\\nL: littleneck\\nP: phosphorescent, pristine\\nS: sinh, systemwide, shrunk\\nT: tasting, torpedo, thrown\\nV: verdict\\n\\nCorrected Sorted List:\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. dey\\n7. grim\\n8. gradate\\n9. jacky\\n10. littleneck\\n11. phosphorescent\\n12. pristine\\n13. sinh\\n14. shrunk\\n15. systemwide\\n16. tasting\\n17. torpedo\\n18. thrown\\n19. verdict\\n\\n(Comparing D words: delhi, dey. D-e-l-h-i vs D-e-y. 'l' comes before 'y', so delhi comes before dey.)\\n\\nFinal check on the complete list:\\nadminister (A)\\naeneid (A)\\ndecadent (D)\\ncoachman (C) -> Should be C before D. Let's restart the alphabetical sort strictly.\\n\\nList: torpedo, phosphorescent, pristine, decadent, shrunk, dey, administer, gradate, littleneck, thrown, jacky, coachman, aeneid, verdict, tasting, sinh, delhi, systemwide, grim\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6\",\n \"gen_kwargs\": {\n \"temperature\": 1.0,\n \"top_p\": 0.95,\n \"top_k\": 20\n },\n \"n_semantic_clusters\": 3,\n \"semantic_entropy\": 1.0549,\n \"normalized_semantic_entropy\": 0.6554,\n \"semantic_consistency\": 0.4,\n \"cluster_sizes\": [\n 2,\n 2,\n 1\n ],\n \"cluster_representatives\": [\n \"Here are the words sorted alphabetically:\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. gradate\\n7. grim\\n8\",\n \"Here are the words sorted alphabetically:\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. gradate\\n7. grim\\n8\",\n \"Here are the words sorted alphabetically:\\n\\n1. administer\\n2. aeneid\\n3. decadent\\n4. coachman\\n5. delhi\\n6. gradate\\n7. grim\\n8\"\n ],\n \"entailment\": \"lexical_proxy\",\n \"_caveat\": \"semantic_* uses the SAME samples as consistency \\u2014 it costs no extra generations, and the two are meant to be read together: consistency << semantic_consistency means the disagreement was only wording. With the default lexical_proxy, clustering is content-word overlap, NOT entailment: it merges answers that share vocabulary while contradicting each other ('the claim holds' / 'the claim does not hold') and splits correct paraphrases with no shared words. Inject an NLI entailment_fn before reading semantic_entropy as the Farquhar et al. metric. At temperature 0 every sample is identical and both scores are 1 by construction, not by measurement.\"\n },\n \"calibration\": {\n \"n_cases\": 8,\n \"n_unlabelled_skipped\": 0,\n \"n_bins\": 10,\n \"logprob_channel_available\": true,\n \"logprob_channel\": {\n \"n\": 8,\n \"ece\": 0.5978,\n \"brier\": 0.6015,\n \"overconfidence_gap\": 0.5978\n },\n \"verbalized_channel\": {\n \"n\": 2,\n \"ece\": 0.0,\n \"brier\": 0.0,\n \"overconfidence_gap\": 0.0\n },\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"correct\": 0,\n \"conf_logprob\": 0.9936\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"correct\": 0,\n \"conf_logprob\": 0.9926\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"correct\": 0,\n \"conf_logprob\": 0.9905\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"correct\": 0,\n \"conf_logprob\": 0.9454\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"correct\": 0,\n \"conf_logprob\": 0.9796\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"correct\": 1,\n \"conf_logprob\": 0.9542,\n \"conf_verbal\": 1.0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"correct\": 1,\n \"conf_logprob\": 0.9583\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"correct\": 1,\n \"conf_logprob\": 0.9683,\n \"conf_verbal\": 1.0\n }\n ],\n \"_caveat\": \"Needs PASS/FAIL labels \\u2014 unlabeled cases are skipped, and ECE on few labelled cases is unstable (aim for >= ~50). conf_logprob is the geometric-mean token probability of the model's own continuation \\u2014 a proxy, not an answer probability; on API backends it reflects only the returned top-k, and a model without LOGPROBS reports only the verbalized channel. A positive overconfidence_gap on FAIL-heavy slices argues for abstention/recalibration fixes rather than capability fixes.\"\n },\n \"logprob_entropy\": {\n \"n_tokens\": 64,\n \"mean_logprob\": -0.0064,\n \"perplexity\": 1.0064,\n \"min_token_logprob\": -0.1908,\n \"mean_top_entropy\": 0.0213\n },\n \"coverage_verification_gap\": {\n \"n_cases\": 8,\n \"n_scored\": 8,\n \"k\": 5,\n \"gen_kwargs\": {\n \"temperature\": 1.0,\n \"top_p\": 0.95,\n \"top_k\": 20\n },\n \"mean_pass_at_k\": 0.5,\n \"mean_majority_correct\": 0.375,\n \"coverage_gap_rate\": 0.125,\n \"mean_verify_correct\": null,\n \"no_coverage_rate\": 0.5,\n \"degenerate_sampling\": false,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 0,\n \"n_unique\": 4,\n \"pass_at_k\": 0.0,\n \"any_correct\": 0,\n \"majority_correct\": 0,\n \"majority_share\": 0.4,\n \"coverage_gap\": 0,\n \"modal_answer\": \"14\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 0,\n \"n_unique\": 5,\n \"pass_at_k\": 0.0,\n \"any_correct\": 0,\n \"majority_correct\": 0,\n \"majority_share\": 0.2,\n \"coverage_gap\": 0,\n \"modal_answer\": \"arrear eavesdropping fasciculate herodotus hermaphrodite ibn henry incorrigible \"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 0,\n \"n_unique\": 5,\n \"pass_at_k\": 0.0,\n \"any_correct\": 0,\n \"majority_correct\": 0,\n \"majority_share\": 0.2,\n \"coverage_gap\": 0,\n \"modal_answer\": \"16. separate wait s words re-sort: separate shelter sticktight spongy\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 0,\n \"n_unique\": 4,\n \"pass_at_k\": 0.0,\n \"any_correct\": 0,\n \"majority_correct\": 0,\n \"majority_share\": 0.4,\n \"coverage_gap\": 0,\n \"modal_answer\": \"biennial creedal cry fletch fraudulent eyesight j miltonic mirage whisper titmic\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 1,\n \"n_unique\": 4,\n \"pass_at_k\": 1.0,\n \"any_correct\": 1,\n \"majority_correct\": 0,\n \"majority_share\": 0.4,\n \"coverage_gap\": 1,\n \"modal_answer\": \"battery bushland capacitive contingent crossbill enigma jane lipton meager ricoc\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 4,\n \"n_unique\": 2,\n \"pass_at_k\": 1.0,\n \"any_correct\": 1,\n \"majority_correct\": 1,\n \"majority_share\": 0.8,\n \"coverage_gap\": 0,\n \"modal_answer\": \"alleviate benelux buoyant duopoly felice gland gunk hardbound klaxon mattress to\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 3,\n \"n_unique\": 3,\n \"pass_at_k\": 1.0,\n \"any_correct\": 1,\n \"majority_correct\": 1,\n \"majority_share\": 0.6,\n \"coverage_gap\": 0,\n \"modal_answer\": \"artillery bainite doris fda harm incongruous monkey prosody vegetate vivian\"\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"n_samples\": 5,\n \"n_graded\": 5,\n \"n_correct\": 5,\n \"n_unique\": 1,\n \"pass_at_k\": 1.0,\n \"any_correct\": 1,\n \"majority_correct\": 1,\n \"majority_share\": 1.0,\n \"coverage_gap\": 0,\n \"modal_answer\": \"cotyledon more pepperoni regret starlight wallboard\"\n }\n ],\n \"_caveat\": \"coverage_gap = 'the answer was in the pool, the vote missed it' \\u21d2 a selection defect, fixable at L1/L2 with a verifier or reranker. no_coverage_rate = 'never produced once in k' \\u21d2 a capability defect no scaffold reaches; do not let a headline pass@k hide which of the two dominates. REQUIRES sampling: degenerate_sampling=true means every sample was identical (temperature 0 or a deterministic backend) and the gap is structurally zero, not measured. pass_at_k here uses the unbiased estimator over exactly k draws, so with k small its variance is large per case \\u2014 read the batch rate. INTERVENTIONAL: held-out confirmation must re-sample.\"\n },\n \"perturbation_battery\": {\n \"n_cases\": 8,\n \"n_scored\": 8,\n \"perturbations\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\",\n \"perturb_numbers\"\n ],\n \"applied_perturbations\": [\n \"noop_clause\",\n \"rename_entities\",\n \"restate_question\"\n ],\n \"gen_kwargs\": {},\n \"mean_invariance_break_rate\": 0.7917,\n \"noop_break_rate\": 0.625,\n \"mean_sensitivity_rate\": null,\n \"n_memorization_suspect\": 0,\n \"per_case\": [\n {\n \"sample_id\": \"bbh_word_sorting-0006\",\n \"baseline_answer\": \"administer aeneid decadent coachman delhi dey grim gradate jacky littleneck phos\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 1.0,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0025\",\n \"baseline_answer\": \"j:\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 1.0,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0045\",\n \"baseline_answer\": \"backslide calvert commando gabriel hendrickson hollister jackson anastomosis piz\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 1.0,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0076\",\n \"baseline_answer\": \"correct alphabetical order is: biennial creedal cry eyesight fletch\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 1.0,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0094\",\n \"baseline_answer\": \"battery bushland capacitive contingent crossbill enigma jane lipton meager ricoc\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 0,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 0.6667,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0000\",\n \"baseline_answer\": \"alleviate benelux buoyant duopoly felice gland gunk hardbound klaxon mattress to\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 1,\n \"noop_clause_flipped\": 0,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 0.6667,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0063\",\n \"baseline_answer\": \"artillery bainite doris fda harm incongruous monkey prosody vegetate vivian\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 0,\n \"noop_clause_flipped\": 1,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 0.6667,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n },\n {\n \"sample_id\": \"bbh_word_sorting-0101\",\n \"baseline_answer\": \"cotyledon more pepperoni regret starlight wallboard\",\n \"rename_entities_flipped\": 1,\n \"restate_question_flipped\": 0,\n \"noop_clause_flipped\": 0,\n \"applied\": [\n \"rename_entities\",\n \"restate_question\",\n \"noop_clause\"\n ],\n \"invariance_break_rate\": 0.3333,\n \"n_preserving\": 3,\n \"sensitivity_rate\": null,\n \"n_altering\": 0\n }\n ],\n \"_caveat\": \"Two DIFFERENT defects share this probe and must not be summed: invariance_break_rate should be 0 (a preserving edit moved the answer) while sensitivity_rate should be 1 (an altering edit must move it). sensitivity_rate == 0 with a correct baseline is the memorisation signature \\u2014 cross-check contamination_score before calling it reasoning. Every rate is measured against the model's OWN baseline answer, not the gold, so a case that was already wrong still contributes: read these as stability, never as accuracy. Under sampling, a 'flip' can be decoding noise \\u2014 set temperature 0 or repeat. INTERVENTIONAL: re-run to confirm.\"\n }\n}\n\nHypotheses to review:\n- HYPOTHESIS: The generation token limit causes premature termination during multi-step sorting chains, leaving incomplete outputs lacking final answer spans. (failure_mode: truncation)\n- HYPOTHESIS: Failures on completed sequences are driven by an inability to construct a single valid permutation across stochastic decoding trajectories rather than sampling selection noise. (failure_mode: computation_slip)\n- HYPOTHESIS: Multi-step sorting trajectories are brittle to superficial input phrasing changes, suffering token generation divergence under semantic-preserving perturbations. (failure_mode: brittleness)\n\nFor each hypothesis output two lines:\nKEEP: or REJECT: \nREASON: \nREASON: ", + "metadata": {}, + "operation": "generate", + "output": "### Hypothesis 1\n**KEEP:** The generation token limit causes premature termination during multi-step sorting chains, leaving incomplete outputs lacking final answer spans.\n**REASON:** The evidence directly shows that 2 of the 5 failing cases ran into truncation limits, directly causing missing final answer tags and incomplete candidate lists.\n**REASON:** Evidence directly supports this claim.\n\n---\n\n### Hypothesis 2\n**KEEP:** Failures on completed sequences are driven by an inability to construct a single valid permutation across stochastic decoding trajectories rather than sampling selection noise.\n**REASON:** The coverage audit directly separates selection defect from capability defect, showing a `coverage_gap` of 0 and a `no_coverage_rate` of 4/5 among failures where no correct permutation was found across any of the 5 draws.\n**REASON:** Evidence directly supports this claim.\n\n---\n\n### Hypothesis 3\n**REJECT:** Multi-step sorting trajectories are brittle to superficial input phrasing changes, suffering token generation divergence under semantic-preserving perturbations.\n**REASON:** Confounded alternative and unsupported claim: statistical bootstrap tests show that invariance flips (`noop_clause_flipped`, `restate_question_flipped`) are statistically inconclusive with respect to FAIL, and the perturbation flips are measured against the baseline raw text under temperature sampling where baseline variance and truncation already drive output changes.\n**REASON:** Confounded alternative: decoding variance and truncation confound perturbation flips, which are statistically inconclusive against failure.", + "role": "hypothesis_critic", + "schema_version": 6, + "span_id": "m3.model_call.393", + "stage": "M3", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:39:47.674988+00:00" + } + ] + }, + "M4": { + "model_calls": [ + { + "cycle": 0, + "duration_sec": 6.2234, + "error": null, + "event": "model_call", + "event_seq": 395, + "inputs": "Experiment protocol:\n{\n \"description\": \"A text-only LLM (Gemma 4 E2B-it, spec gemma-4-e2b-it = google/gemma-4-E2B-it) answers items from lukaemon/bbh · config=word_sorting. One named BBH task: sort a word list into alphabetical order. Failure cases are items the model answered incorrectly; the batch also contains, as controls, items from the same slice it answered correctly. Each answer is scored against its own gold answer.\",\n \"task_domain\": \"ch4-basic\",\n \"success_criteria\": \"Normalised exact match on the whole sorted list — one word out of place is a FAIL, so this is a harsh binary. That is fine for band purposes but means a FAIL is not evidence of not knowing how to sort.\",\n \"failure_patterns\": \"\",\n \"target_modalities\": [\n \"text\"\n ],\n \"output_contract\": {},\n \"metadata\": {\n \"dataset\": \"bbh_word_sorting\",\n \"items_in_slice\": 250,\n \"reference_accuracy_qwen35_9b\": 0.72\n }\n}\n\nHypothesis under review:\nStatement: The generation token limit causes premature termination during multi-step sorting chains, leaving incomplete outputs lacking final answer spans.\nPredicted failure mode: truncation\n\nDoes this hypothesis address a failure mode that is relevant to the experiment protocol above?\nAnswer YES or NO on the first line, then give a one-sentence reason.\n\nAnswer YES if the hypothesis explains a failure that the protocol would care about.\nAnswer NO if the hypothesis is about something the protocol does not mention or test.", + "metadata": {}, + "operation": "generate", + "output": "YES\nPremature generation cutoff prevents the model from completing the full sorted word list, directly resulting in an exact-match failure under the protocol's strict binary scoring criteria.", + "role": "protocol_consistency_judge", + "schema_version": 6, + "span_id": "m4.model_call.395", + "stage": "M4", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:39:53.901253+00:00" + }, + { + "cycle": 0, + "duration_sec": 6.2234, + "error": null, + "event": "model_call", + "event_seq": 397, + "inputs": "Experiment protocol:\n{\n \"description\": \"A text-only LLM (Gemma 4 E2B-it, spec gemma-4-e2b-it = google/gemma-4-E2B-it) answers items from lukaemon/bbh · config=word_sorting. One named BBH task: sort a word list into alphabetical order. Failure cases are items the model answered incorrectly; the batch also contains, as controls, items from the same slice it answered correctly. Each answer is scored against its own gold answer.\",\n \"task_domain\": \"ch4-basic\",\n \"success_criteria\": \"Normalised exact match on the whole sorted list — one word out of place is a FAIL, so this is a harsh binary. That is fine for band purposes but means a FAIL is not evidence of not knowing how to sort.\",\n \"failure_patterns\": \"\",\n \"target_modalities\": [\n \"text\"\n ],\n \"output_contract\": {},\n \"metadata\": {\n \"dataset\": \"bbh_word_sorting\",\n \"items_in_slice\": 250,\n \"reference_accuracy_qwen35_9b\": 0.72\n }\n}\n\nHypothesis under review:\nStatement: Failures on completed sequences are driven by an inability to construct a single valid permutation across stochastic decoding trajectories rather than sampling selection noise.\nPredicted failure mode: computation_slip\n\nDoes this hypothesis address a failure mode that is relevant to the experiment protocol above?\nAnswer YES or NO on the first line, then give a one-sentence reason.\n\nAnswer YES if the hypothesis explains a failure that the protocol would care about.\nAnswer NO if the hypothesis is about something the protocol does not mention or test.", + "metadata": {}, + "operation": "generate", + "output": "YES\nThe protocol evaluates alphabetical word sorting scored via strict exact match on completed sequences, where failures stem directly from computation slips in ordering rather than noise.", + "role": "protocol_consistency_judge", + "schema_version": 6, + "span_id": "m4.model_call.397", + "stage": "M4", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:39:53.902375+00:00" + }, + { + "cycle": 0, + "duration_sec": 6.2234, + "error": null, + "event": "model_call", + "event_seq": 399, + "inputs": "Experiment protocol:\n{\n \"description\": \"A text-only LLM (Gemma 4 E2B-it, spec gemma-4-e2b-it = google/gemma-4-E2B-it) answers items from lukaemon/bbh · config=word_sorting. One named BBH task: sort a word list into alphabetical order. Failure cases are items the model answered incorrectly; the batch also contains, as controls, items from the same slice it answered correctly. Each answer is scored against its own gold answer.\",\n \"task_domain\": \"ch4-basic\",\n \"success_criteria\": \"Normalised exact match on the whole sorted list — one word out of place is a FAIL, so this is a harsh binary. That is fine for band purposes but means a FAIL is not evidence of not knowing how to sort.\",\n \"failure_patterns\": \"\",\n \"target_modalities\": [\n \"text\"\n ],\n \"output_contract\": {},\n \"metadata\": {\n \"dataset\": \"bbh_word_sorting\",\n \"items_in_slice\": 250,\n \"reference_accuracy_qwen35_9b\": 0.72\n }\n}\n\nHypothesis under review:\nStatement: Multi-step sorting trajectories are brittle to superficial input phrasing changes, suffering token generation divergence under semantic-preserving perturbations.\nPredicted failure mode: brittleness\n\nDoes this hypothesis address a failure mode that is relevant to the experiment protocol above?\nAnswer YES or NO on the first line, then give a one-sentence reason.\n\nAnswer YES if the hypothesis explains a failure that the protocol would care about.\nAnswer NO if the hypothesis is about something the protocol does not mention or test.", + "metadata": {}, + "operation": "generate", + "output": "NO\nThe protocol evaluates standard word sorting performance against gold answers without mentioning or testing semantic-preserving input phrasing perturbations.", + "role": "protocol_consistency_judge", + "schema_version": 6, + "span_id": "m4.model_call.399", + "stage": "M4", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:39:53.903605+00:00" + } + ], + "surgery": [ + { + "confidence_score": 0.634, + "cycle": 0, + "duration_sec": 6.223, + "event": "surgery", + "event_seq": 394, + "evidence": { + "chosen_tool": "signal_label_assoc", + "ci": [ + 0.29032258064516125, + 0.532258064516129 + ], + "consulted_tools": [ + "signal_label_assoc", + "rank_corr", + "rank_corr", + "rank_corr", + "rank_corr", + "rank_corr", + "rank_corr" + ], + "e_value": null, + "effect_size": 0.4193548387096774, + "evidence_grade": "observational", + "expected_association": "higher_on_failures", + "expected_direction": "higher_in_fail", + "fdr": { + "alpha": 0.05, + "families": { + "bh": { + "method": "BH", + "n_tested": 16, + "rejected_result_keys": [] + } + }, + "method": "BH", + "n_tested": 16, + "rejected_result_keys": [], + "rejected_tools": [] + }, + "m4_confidence": 0.634, + "m4_effect_size": 0.4193548387096774, + "m4_evidence_grade": "observational", + "m4_protocol_consistent": true, + "m4_test_name": "signal_label_assoc", + "m4_verdict": "No significant M2 result (best: signal 'termination_audit.looks_truncated' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.4194 (B>A) CI=+0.2903..+0.5323 -> REJECT H0 [BH: NOT survived, p=0.51, n_signal=2])", + "reject": true, + "routed_by": "test_design", + "source": "m2_stats_results", + "underpowered": false + }, + "evidence_dimensions": {}, + "failure_mode": "truncation", + "fixed": false, + "hypothesis": "The generation token limit causes premature termination during multi-step sorting chains, leaving incomplete outputs lacking final answer spans.", + "hypothesis_id": "h-9caa1b842a1b", + "judge_prompt": "Experiment protocol:\n{\n \"description\": \"A text-only LLM (Gemma 4 E2B-it, spec gemma-4-e2b-it = google/gemma-4-E2B-it) answers items from lukaemon/bbh · config=word_sorting. One named BBH task: sort a word list into alphabetical order. Failure cases are items the model answered incorrectly; the batch also contains, as controls, items from the same slice it answered correctly. Each answer is scored against its own gold answer.\",\n \"task_domain\": \"ch4-basic\",\n \"success_criteria\": \"Normalised exact match on the whole sorted list — one word out of place is a FAIL, so this is a harsh binary. That is fine for band purposes but means a FAIL is not evidence of not knowing how to sort.\",\n \"failure_patterns\": \"\",\n \"target_modalities\": [\n \"text\"\n ],\n \"output_contract\": {},\n \"metadata\": {\n \"dataset\": \"bbh_word_sorting\",\n \"items_in_slice\": 250,\n \"reference_accuracy_qwen35_9b\": 0.72\n }\n}\n\nHypothesis under review:\nStatement: The generation token limit causes premature termination during multi-step sorting chains, leaving incomplete outputs lacking final answer spans.\nPredicted failure mode: truncation\n\nDoes this hypothesis address a failure mode that is relevant to the experiment protocol above?\nAnswer YES or NO on the first line, then give a one-sentence reason.\n\nAnswer YES if the hypothesis explains a failure that the protocol would care about.\nAnswer NO if the hypothesis is about something the protocol does not mention or test.", + "judge_response": "YES\nPremature generation cutoff prevents the model from completing the full sorted word list, directly resulting in an exact-match failure under the protocol's strict binary scoring criteria.", + "module": "m4", + "n_refocused_cases": null, + "schema_version": 6, + "span_id": "c0.m4", + "stage": "M4", + "status": "inconclusive", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:39:53.900447+00:00", + "validation_examples": [ + { + "baseline_output": "Here are the words sorted alphabetically:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. gradate\n7. grim\n8. jacky\n9. littleneck\n10. phosphorescent\n11. pristine\n12. sinh\n13. systemwide\n14. tasting\n15. torpedo\n16. thrown\n17. verdict\n18. dey\n19. shrunk\n\nLet's re-sort them carefully:\n\n* administer\n* aeneid\n* decadent\n* coachman\n* delhi\n* grim\n* gradate\n* jacky\n* littleneck\n* phosphorescent\n* pristine\n* sinh\n* systemwide\n* tasting\n* torpedo\n* thrown\n* verdict\n* dey\n* shrunk\n\n**Alphabetical Sorting:**\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. dey\n7. grim\n8. gradate\n9. jacky\n10. littleneck\n11. phosphorescent\n12. pristine\n13. sinh\n14. shrunk\n15. systemwide\n16. tasting\n17. torpedo\n18. thrown\n19. verdict\n\nAnswer: administer, aeneid, decadent, coachman, delhi, dey, grim, gradate, jacky, littleneck, phosphorescent, pristine, sinh, shrunk, systemwide, tasting, torpedo, thrown, verdict", + "case_id": "bbh_word_sorting-0006", + "evidence_scope": "one case in the independent validation pool", + "expected": "administer aeneid coachman decadent delhi dey gradate grim jacky littleneck phosphorescent pristine shrunk sinh systemwide tasting thrown torpedo verdict", + "id": "bbh_word_sorting-0006", + "input": "Sort the following words alphabetically: List: torpedo phosphorescent pristine decadent shrunk dey administer gradate littleneck thrown jacky coachman aeneid verdict tasting sinh delhi systemwide grim\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "kind": "validation_case", + "outcome": "fail", + "plain_reading": "This is one case in the independent validation pool. The verdict is determined from the full pool, not this case alone." + } + ] + }, + { + "confidence_score": 1, + "cycle": 0, + "duration_sec": 6.223, + "event": "surgery", + "event_seq": 396, + "evidence": { + "chosen_tool": "signal_label_assoc", + "ci": [ + -1, + -1 + ], + "consulted_tools": [ + "signal_label_assoc", + "rank_corr", + "rank_corr", + "rank_corr", + "rank_corr", + "rank_corr", + "rank_corr" + ], + "e_value": null, + "effect_size": -1, + "evidence_grade": "observational", + "expected_association": "lower_on_failures", + "expected_direction": "lower_in_fail", + "fdr": { + "alpha": 0.05, + "families": { + "bh": { + "method": "BH", + "n_tested": 16, + "rejected_result_keys": [] + } + }, + "method": "BH", + "n_tested": 16, + "rejected_result_keys": [], + "rejected_tools": [] + }, + "m4_confidence": 1, + "m4_effect_size": -1, + "m4_evidence_grade": "observational", + "m4_protocol_consistent": true, + "m4_test_name": "signal_label_assoc", + "m4_verdict": "No significant M2 result (best: signal 'coverage_verification_gap.majority_share' vs FAIL: [clustered bootstrap (unpaired)] effect=-1.0000 (A>B) CI=-1.0000..-1.0000 -> REJECT H0 [BH: NOT survived, p=0.0179, n_signal=3])", + "reject": true, + "routed_by": "test_design", + "source": "m2_stats_results", + "underpowered": false + }, + "evidence_dimensions": {}, + "failure_mode": "computation_slip", + "fixed": false, + "hypothesis": "Failures on completed sequences are driven by an inability to construct a single valid permutation across stochastic decoding trajectories rather than sampling selection noise.", + "hypothesis_id": "h-fbe4b863234a", + "judge_prompt": "Experiment protocol:\n{\n \"description\": \"A text-only LLM (Gemma 4 E2B-it, spec gemma-4-e2b-it = google/gemma-4-E2B-it) answers items from lukaemon/bbh · config=word_sorting. One named BBH task: sort a word list into alphabetical order. Failure cases are items the model answered incorrectly; the batch also contains, as controls, items from the same slice it answered correctly. Each answer is scored against its own gold answer.\",\n \"task_domain\": \"ch4-basic\",\n \"success_criteria\": \"Normalised exact match on the whole sorted list — one word out of place is a FAIL, so this is a harsh binary. That is fine for band purposes but means a FAIL is not evidence of not knowing how to sort.\",\n \"failure_patterns\": \"\",\n \"target_modalities\": [\n \"text\"\n ],\n \"output_contract\": {},\n \"metadata\": {\n \"dataset\": \"bbh_word_sorting\",\n \"items_in_slice\": 250,\n \"reference_accuracy_qwen35_9b\": 0.72\n }\n}\n\nHypothesis under review:\nStatement: Failures on completed sequences are driven by an inability to construct a single valid permutation across stochastic decoding trajectories rather than sampling selection noise.\nPredicted failure mode: computation_slip\n\nDoes this hypothesis address a failure mode that is relevant to the experiment protocol above?\nAnswer YES or NO on the first line, then give a one-sentence reason.\n\nAnswer YES if the hypothesis explains a failure that the protocol would care about.\nAnswer NO if the hypothesis is about something the protocol does not mention or test.", + "judge_response": "YES\nThe protocol evaluates alphabetical word sorting scored via strict exact match on completed sequences, where failures stem directly from computation slips in ordering rather than noise.", + "module": "m4", + "n_refocused_cases": null, + "schema_version": 6, + "span_id": "c0.m4", + "stage": "M4", + "status": "inconclusive", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:39:53.901826+00:00", + "validation_examples": [ + { + "baseline_output": "Here are the words sorted alphabetically:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. gradate\n7. grim\n8. jacky\n9. littleneck\n10. phosphorescent\n11. pristine\n12. sinh\n13. systemwide\n14. tasting\n15. torpedo\n16. thrown\n17. verdict\n18. dey\n19. shrunk\n\nLet's re-sort them carefully:\n\n* administer\n* aeneid\n* decadent\n* coachman\n* delhi\n* grim\n* gradate\n* jacky\n* littleneck\n* phosphorescent\n* pristine\n* sinh\n* systemwide\n* tasting\n* torpedo\n* thrown\n* verdict\n* dey\n* shrunk\n\n**Alphabetical Sorting:**\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. dey\n7. grim\n8. gradate\n9. jacky\n10. littleneck\n11. phosphorescent\n12. pristine\n13. sinh\n14. shrunk\n15. systemwide\n16. tasting\n17. torpedo\n18. thrown\n19. verdict\n\nAnswer: administer, aeneid, decadent, coachman, delhi, dey, grim, gradate, jacky, littleneck, phosphorescent, pristine, sinh, shrunk, systemwide, tasting, torpedo, thrown, verdict", + "case_id": "bbh_word_sorting-0006", + "evidence_scope": "one case in the independent validation pool", + "expected": "administer aeneid coachman decadent delhi dey gradate grim jacky littleneck phosphorescent pristine shrunk sinh systemwide tasting thrown torpedo verdict", + "id": "bbh_word_sorting-0006", + "input": "Sort the following words alphabetically: List: torpedo phosphorescent pristine decadent shrunk dey administer gradate littleneck thrown jacky coachman aeneid verdict tasting sinh delhi systemwide grim\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "kind": "validation_case", + "outcome": "fail", + "plain_reading": "This is one case in the independent validation pool. The verdict is determined from the full pool, not this case alone." + } + ] + }, + { + "confidence_score": 0.213, + "cycle": 0, + "duration_sec": 6.223, + "event": "surgery", + "event_seq": 398, + "evidence": { + "chosen_tool": "signal_label_assoc", + "ci": [ + -0.10344827586206895, + 0.5172413793103448 + ], + "consulted_tools": [ + "signal_label_assoc", + "rank_corr", + "rank_corr", + "rank_corr", + "rank_corr", + "rank_corr", + "rank_corr" + ], + "e_value": null, + "effect_size": 0.26436781609195403, + "evidence_grade": "observational", + "expected_association": "higher_on_failures", + "expected_direction": "higher_in_fail", + "fdr": { + "alpha": 0.05, + "families": { + "bh": { + "method": "BH", + "n_tested": 16, + "rejected_result_keys": [] + } + }, + "method": "BH", + "n_tested": 16, + "rejected_result_keys": [], + "rejected_tools": [] + }, + "m4_confidence": 0.213, + "m4_effect_size": 0.26436781609195403, + "m4_evidence_grade": "observational", + "m4_protocol_consistent": false, + "m4_test_name": "signal_label_assoc", + "m4_verdict": "No significant M2 result (best: signal 'perturbation_battery.restate_question_flipped' vs FAIL: [clustered bootstrap (unpaired)] effect=+0.2644 (B>A) CI=-0.1034..+0.5172 -> inconclusive [BH: NOT survived, p=0.387, n_signal=6])", + "reject": false, + "routed_by": "test_design", + "source": "m2_stats_results", + "underpowered": false + }, + "evidence_dimensions": {}, + "failure_mode": "brittleness", + "fixed": false, + "hypothesis": "Multi-step sorting trajectories are brittle to superficial input phrasing changes, suffering token generation divergence under semantic-preserving perturbations.", + "hypothesis_id": "h-662394134962", + "judge_prompt": "Experiment protocol:\n{\n \"description\": \"A text-only LLM (Gemma 4 E2B-it, spec gemma-4-e2b-it = google/gemma-4-E2B-it) answers items from lukaemon/bbh · config=word_sorting. One named BBH task: sort a word list into alphabetical order. Failure cases are items the model answered incorrectly; the batch also contains, as controls, items from the same slice it answered correctly. Each answer is scored against its own gold answer.\",\n \"task_domain\": \"ch4-basic\",\n \"success_criteria\": \"Normalised exact match on the whole sorted list — one word out of place is a FAIL, so this is a harsh binary. That is fine for band purposes but means a FAIL is not evidence of not knowing how to sort.\",\n \"failure_patterns\": \"\",\n \"target_modalities\": [\n \"text\"\n ],\n \"output_contract\": {},\n \"metadata\": {\n \"dataset\": \"bbh_word_sorting\",\n \"items_in_slice\": 250,\n \"reference_accuracy_qwen35_9b\": 0.72\n }\n}\n\nHypothesis under review:\nStatement: Multi-step sorting trajectories are brittle to superficial input phrasing changes, suffering token generation divergence under semantic-preserving perturbations.\nPredicted failure mode: brittleness\n\nDoes this hypothesis address a failure mode that is relevant to the experiment protocol above?\nAnswer YES or NO on the first line, then give a one-sentence reason.\n\nAnswer YES if the hypothesis explains a failure that the protocol would care about.\nAnswer NO if the hypothesis is about something the protocol does not mention or test.", + "judge_response": "NO\nThe protocol evaluates standard word sorting performance against gold answers without mentioning or testing semantic-preserving input phrasing perturbations.", + "module": "m4", + "n_refocused_cases": null, + "schema_version": 6, + "span_id": "c0.m4", + "stage": "M4", + "status": "inconclusive", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T00:39:53.903044+00:00", + "validation_examples": [ + { + "baseline_output": "Here are the words sorted alphabetically:\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. gradate\n7. grim\n8. jacky\n9. littleneck\n10. phosphorescent\n11. pristine\n12. sinh\n13. systemwide\n14. tasting\n15. torpedo\n16. thrown\n17. verdict\n18. dey\n19. shrunk\n\nLet's re-sort them carefully:\n\n* administer\n* aeneid\n* decadent\n* coachman\n* delhi\n* grim\n* gradate\n* jacky\n* littleneck\n* phosphorescent\n* pristine\n* sinh\n* systemwide\n* tasting\n* torpedo\n* thrown\n* verdict\n* dey\n* shrunk\n\n**Alphabetical Sorting:**\n\n1. administer\n2. aeneid\n3. decadent\n4. coachman\n5. delhi\n6. dey\n7. grim\n8. gradate\n9. jacky\n10. littleneck\n11. phosphorescent\n12. pristine\n13. sinh\n14. shrunk\n15. systemwide\n16. tasting\n17. torpedo\n18. thrown\n19. verdict\n\nAnswer: administer, aeneid, decadent, coachman, delhi, dey, grim, gradate, jacky, littleneck, phosphorescent, pristine, sinh, shrunk, systemwide, tasting, torpedo, thrown, verdict", + "case_id": "bbh_word_sorting-0006", + "evidence_scope": "one case in the independent validation pool", + "expected": "administer aeneid coachman decadent delhi dey gradate grim jacky littleneck phosphorescent pristine shrunk sinh systemwide tasting thrown torpedo verdict", + "id": "bbh_word_sorting-0006", + "input": "Sort the following words alphabetically: List: torpedo phosphorescent pristine decadent shrunk dey administer gradate littleneck thrown jacky coachman aeneid verdict tasting sinh delhi systemwide grim\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "kind": "validation_case", + "outcome": "fail", + "plain_reading": "This is one case in the independent validation pool. The verdict is determined from the full pool, not this case alone." + } + ] + } + ] + }, + "M5": { + "fix": [ + { + "attempted": [ + { + "baseline_rate": 0.40625, + "broken_cases": [ + "bbh_word_sorting-0011", + "bbh_word_sorting-0067" + ], + "candidate_rate": 0.453125, + "coverage": 1, + "e_threshold": 20, + "e_value": 0.7619047619047632, + "e_value_regression": null, + "effect": 0.046875, + "fixed": false, + "fixed_cases": [ + "bbh_word_sorting-0086", + "bbh_word_sorting-0041", + "bbh_word_sorting-0080", + "bbh_word_sorting-0065", + "bbh_word_sorting-0033" + ], + "kind": "spec", + "n_applicable": 64, + "n_baseline_correct": 26, + "n_baseline_samples": 1, + "n_broken": 2, + "n_candidate_correct": 29, + "n_candidate_samples": 1, + "n_fixed": 5, + "n_model_independent": 0, + "n_pairs": 64, + "n_truncated": null, + "n_unstable": 0, + "name": "expanded_budget_self_refine", + "noise_model": "mcnemar", + "outputs": { + "bbh_word_sorting-0001": "Answer: cheddar, edt, from, oblivion, pang, poignant, yuh", + "bbh_word_sorting-0002": "Answer: atmospheric, chess, credit, geopolitic, intercept, loci, lunge, newsmen, siren, swart, tamp, umber", + "bbh_word_sorting-0004": "Answer: auerbach, dianne, decor, deoxyribose, devisee, hodges, incommensurable, motorcade, stratify, troupe", + "bbh_word_sorting-0005": "Answer: haughty, seashore", + "bbh_word_sorting-0008": "Answer: aeneas, foothold, fox, glycerine, garry, inviolate, lucre, magnanimity, nevada, pompey, plebiscite, quagmire, satanic, scription, softball, spleenwort, tennyson, type", + "bbh_word_sorting-0010": "alterate, aseptic, cayenne, chandigarh, declassify, debauch, dingy, excursion, equanimity, foamflower, inclement, lawful, october, only, groupoid, kruger, scorch\n\nAnswer: alterate, aseptic, cayenne, chandigarh, declassify, debauch, dingy, excursion, equanimity, foamflower, inclement, lawful, october, only, groupoid, kruger, scorch", + "bbh_word_sorting-0011": "The given list of words is: lethe, bilk, perturb, tactual.\n\nTo sort these words alphabetically, we compare them letter by letter from the beginning.\n\nThe words are:\n1. lethe\n2. bilk\n3. perturb\n4. tactual (Corrected to tactical)\n\nComparing the first letters: 'l', 'b', 'p', 't'.\nIn alphabetical order: 'b', 'l', 'p', 't'.\n\n1. **bilk** (b)\n2. **lethe** (l)\n3. **perturb** (p)\n4. **tactical** (t)\n\nThe sorted list is: bilk, lethe, perturb, tactical.\n\nAnswer: bilk, lethe, perturb, tactical", + "bbh_word_sorting-0012": "Answer: brainy, cony, erudite, enigma, fatuous, gouda, impalpable, isaacson, hoof, lisbon, malaria, portsmouth, portrait, servomechanism, stronghold, succumb", + "bbh_word_sorting-0017": "Answer: carport, firewood, introvert, sweepstake, tiresome", + "bbh_word_sorting-0018": "List of words: thrill, splutter, panicking, scorch, same, dot, prod, obstetric, malton, onus, drumhead, delmarva, barn, embezzle, it&t, damp, guru, subsist, entirety, greene\n\nAlphabetical sorting:\n1. barn\n2. damp\n3. delmarva\n4. dot\n5. drumhead\n6. embezzle\n7. entirety\n8. greene\n9. guru\n10. it&t\n11. malton\n12. obstetric\n13. onus\n14. panicking\n15. prod\n16. scorch\n17. same\n18. splutter\n19. subsist\n20. thrill\n\nAnswer: barn, damp, delmarva, dot, drumhead, embezzle, entirety, greene, guru, it&t, malton, obstetric, onus, panicking, prod, scorch, same, splutter, subsist, thrill", + "bbh_word_sorting-0019": "Answer: broadcast, cortland, diffusible, galvanometer, gross, gujarati, incestuous, larynx, nomograph, pewter, sketchbook, scout, stag, transition", + "bbh_word_sorting-0021": "Answer: confidential, faery, fiction, heterozygous, horehound, overture, ursa", + "bbh_word_sorting-0022": "Answer: murray, sweatband", + "bbh_word_sorting-0023": "Answer: blest, buxton, consternate, proximity, quizzes, sound, tariff, xerxes", + "bbh_word_sorting-0024": "Answer: aerodynamic, botanist, giacomo, habitation, jimmy, nebulous, offset, panicking, padre, roosevelt, schoolmate, suburbia, vector, wv", + "bbh_word_sorting-0027": "Answer: leasehold, orchestra, permafrost, shiva, testate", + "bbh_word_sorting-0028": "The original list of words is: butterscotch, veterinarian, kochab, molybdate, extoller, amos, farrell, academia, glance, predatory, beautiful, ford, fiducial, metzler, europium, circuitous, diatom, monomer.\n\nSorting these words alphabetically:\n\n1. academia\n2. amos\n3. beautiful\n4. butterscotch\n5. circuitous\n6. diatom\n7. extoller\n8. europium\n9. farrell\n10. fiducial\n11. ford\n12. glance\n13. kochab\n14. molybdate\n15. monomer\n16. metzler\n17. predatory\n18. veterinarian\n\nAnswer: academia, amos, beautiful, butterscotch, circuitous, diatom, extoller, europium, farrell, fiducial, ford, glance, kochab, molybdate, monomer, metzler, predatory, veterinarian", + "bbh_word_sorting-0029": "Answer: ami, bituminous, decadent, exeter, knickerbocker", + "bbh_word_sorting-0031": "Answer: clytemnestra, crag, cutover, dickson, diocletian, electrolytic, inhuman, lipton, marginal, stalk, thereupon, workplace, wireman, wife, took, scrawny", + "bbh_word_sorting-0032": "Answer: agile, blackguard, butt, clapeyron, cognoscenti, geophysical, flamboyant, lift, lightfooted, manumitted, meager, mathieu, purposive, reconnaissance, seaworthy, scribe, sawbelly, wiseacre, woodcut, yves", + "bbh_word_sorting-0033": "Answer: aperture, bradshaw, holocene, mare, muriel, pathetic, r&d, sigh, staircase, talon", + "bbh_word_sorting-0037": "Answer: abdominal, address, bounty, berry, effusive, fomalhaut, hanoverian, islamabad, involve, jordan, optimal, stigmata, stearic, swathe, tattoo, them, tornado, yang", + "bbh_word_sorting-0039": "Answer: abuilding, celebrity, burgher, chigger, ernie, indiscoverable, medlar, newsboy, exorcism, pow, snuff, synchronous, thirteenth, tinfoil, thimble, yap", + "bbh_word_sorting-0041": "Answer: bighorn, contaminate, demystify, nigeria, odysseus, penny, proton, sociolinguistic, stirrup, voltaire", + "bbh_word_sorting-0046": "Answer: syndrome, therefrom", + "bbh_word_sorting-0047": "Answer: bolshevism, belize, cost, dance, deadline, dietetic, foster, formulae, hesitant, huddle, judson, mantle, odessa, palace, proust, progeny, rackety, resplendent, thirdhand, warmth", + "bbh_word_sorting-0052": "Answer: amerada, craftsmen, din, eclipse, gaillardia, inroad, jest, jackboot, jordan, kill, mirth, nate, pomade, putt, shortcoming, spruce, whelan", + "bbh_word_sorting-0054": "Answer: anaplasmosis, bumble, chopstick, clue, fresco, fiesta, footwork, ingot, orthography, palisade, pilate, saul, smalley, storey, teen", + "bbh_word_sorting-0055": "Answer: accomplice, choral, clatter, crepe, circumcircle, doff, emission, fairfax, incantation, labour, lorry, pleura, prig, ride, tea, viaduct, wheelbase", + "bbh_word_sorting-0056": "Answer: block, custodian, deadwood, foxtail, guaranty, hexadecimal, macedonia, rubaiyat, victoria", + "bbh_word_sorting-0057": "Answer: aldebaran, backyard, cabbage, boxwood, entrepreneurial, fiberboard, game, inkling, invincible, lakeside, lightface, matte, mcgee, peruse, pulsate, polyhedra, rae, rowley, shape, watchworks", + "bbh_word_sorting-0060": "Answer: blackstone, feed, figural, giveth, hecatomb, hunt, incense, middle, obstinacy, pasty, pestle, plume, sinkhole, statutory, tel, toothpaste, undulate", + "bbh_word_sorting-0061": "The given list of words is: slurp, raytheon, gloucester.\n\nTo sort these words alphabetically, we compare them letter by letter from the beginning.\n\nThe words are:\n1. slurp (starts with s)\n2. raytheon (starts with r)\n3. gloucester (starts with g)\n\nComparing the first letters: G comes before R, and R comes before S.\nThe order of the first letters is G, R, S.\n\nTherefore, the alphabetical order of the words is:\n1. gloucester (G)\n2. raytheon (R)\n3. slurp (S)\n\nAnswer: gloucester, raytheon, slurp", + "bbh_word_sorting-0062": "E: euphony\nA: allegoric\nC: collate\nG: gloriana\nL: loge, lollipop\nM: mast, milord\nP: prolix\nR: rendezvous\nS: salle, schnabel\n\nSorting within groups:\nL: loge, lollipop (loge comes before lollipop)\nM: mast, milord (mast comes before milord)\nS: salle, schnabel (salle comes before schnabel)\n\nFinal Alphabetical Order:\n1. euphony (E)\n2. allegoric (A)\n3. collate (C)\n4. gloriana (G)\n5. loge (L)\n6. lollipop (L)\n7. mast (M)\n8. milord (M)\n9. prolix (P)\n10. rendezvous (R)\n11. salle (S)\n12. schnabel (S)\n\nAnswer: euphony, allegoric, collate, gloriana, loge, lollipop, mast, milord, prolix, rendezvous, salle, schnabel", + "bbh_word_sorting-0065": "Answer: cocksure, comet, heusen, hydrate, injun, manley, pincer, snippet, spokesperson", + "bbh_word_sorting-0066": "Here are the words to sort alphabetically:\noakland, seaborg, jacobi, membrane, trapezoidal, allis, marmot, toggle, anthology\n\nSorting based on the feedback:\n1. **anthology** (A-n)\n2. **allis** (A-l) - 'n' comes before 'l' is incorrect. Let's re-examine the comparison:\n anthology: A, n, t, h, o, l, o, g, y\n allis: A, l, l, i, s\n\n Comparing the second letter: 'n' vs 'l'. Since 'l' comes before 'n' in the alphabet, 'allis' should come before 'anthology'.\n\n Let's check the original feedback: \"anthology should come before allis because 'n' comes before 'l' in the second position (A-N vs A-L).\" This statement is factually incorrect based on standard alphabetical order (L comes before N).\n\n Standard alphabetical order check:\n A-l-l-i-s (allis)\n A-n-t-h-o-l-o-g-y (anthology)\n 'l' comes before 'n'. Therefore, 'allis' comes before 'anthology'.\n\n Let's proceed with the standard alphabetical order:\n\n A words:\n allis (A-l)\n anthology (A-n)\n 'allis' comes before 'anthology'.\n\n J words:\n jacobi\n\n M words:\n marmot (M-a)\n membrane (M-e)\n 'marmot' comes before 'membrane' ('a' before 'e').\n\n O words:\n oakland\n\n S words:\n seaborg\n\n T words:\n trapezoidal (T-r)\n toggle (T-o)\n 'trapezoidal' comes before 'toggle' ('r' before 'o').\n\n Corrected order based on standard alphabetical rules:\n 1. allis\n 2. anthology\n 3. jacobi\n 4. marmot\n 5. membrane\n 6. oakland\n 7. seaborg\n 8. trapezoidal\n 9. toggle\n\nAnswer: allis, anthology, jacobi, marmot, membrane, oakland, seaborg, trapezoidal, toggle", + "bbh_word_sorting-0067": "Answer: ballard, brindle, cornerstone, credulous, curio, des, difluoride, difluoride, green, horseplay, jew, mixup, nonce, nostalgic, pitney, predilect, prowl, rape, scrappy, toward", + "bbh_word_sorting-0068": "Answer: afloat, apostasy, bechtel, chattel, conner, ferment, grosbeak, hendrickson, indonesia, jacm, lanthanide, melancholy, scavenge, strove, vibrate", + "bbh_word_sorting-0069": "Answer: acquisitive, annuity, autocracy, bruno, custody, dare, exploitation, lodge, militant, quench, somatic, thunderclap, ventricle", + "bbh_word_sorting-0070": "Answer: bosporus, bully, cork, edt, flogging, forfeit, lexicographer, minor, multiple, perceptive, pizza, pungent, rancorous, reedy, referring, sell, sedition, tit", + "bbh_word_sorting-0072": "The given list of words is: impede, appliance, pulitzer, superior.\n\nTo sort these words alphabetically, we compare them letter by letter based on the first letter of each word:\n- impede starts with 'i'\n- appliance starts with 'a'\n- pulitzer starts with 'p'\n- superior starts with 's'\n\nThe starting letters are: 'i', 'a', 'p', 's'.\n\nSorting these letters alphabetically: 'a', 'i', 'p', 's'.\n\n1. The word starting with 'a' is **appliance**.\n2. The word starting with 'i' is **impede**.\n3. The word starting with 'p' is **pulitzer**.\n4. The word starting with 's' is **superior**.\n\nThe correctly sorted list is: appliance, impede, pulitzer, superior.\n\nAnswer: appliance, impede, pulitzer, superior", + "bbh_word_sorting-0073": "Answer: allstate, dose, dyad, multitudinous, plural, powderpuff, stalin", + "bbh_word_sorting-0080": "anarchic, bstj, elution, exhumation, furl, geld, gradual, j, liniment, locomote, midshipman, pantheist, profess, riddance, rowley, saline\n\nAnswer: anarchic, bstj, elution, exhumation, furl, geld, gradual, j, liniment, locomote, midshipman, pantheist, profess, riddance, rowley, saline", + "bbh_word_sorting-0084": "Answer: admiration, accessory, allusion, bandgap, bruckner, cruise, fungus, gambit, heron, maidenhair, postprocessor, proscenium, technion", + "bbh_word_sorting-0086": "Answer: alcohol, behold, escutcheon, forth, fumarole, hackberry, motif, pease, regret, satisfy, uptake, walkie", + "bbh_word_sorting-0088": "Answer: army, emancipate, envious, planetaria, pooh, scotia, wink", + "bbh_word_sorting-0091": "Answer: abutted, agamemnon, aquatic, casualty, capacity, essex, guinea, hondo, hitachi, islamic, loosen, loquacious, niece, planet, roadway, solstice, steed, suspicion, tibet", + "bbh_word_sorting-0092": "Answer: blythe, bombproof, code, corpulent, cytolysis, damn, diagnose, fluorine, honeybee, maharaja, pore, solicit, scalp, swipe", + "bbh_word_sorting-0096": "Answer: buckley, frisian, ix, livre, panoramic, substitution", + "bbh_word_sorting-0098": "Answer: chauncey, coddington, companion, coachmen, clergymen, embark, fatten, threefold, gazpacho, granular, hobble, murk, muslim, niggle, pristine, pvc, singlet, too, yeats", + "bbh_word_sorting-0100": "clearheaded, christen, despond, driveway, encapsulate, fungi, gob, mendelevium, midwinter, purpose, stanhope, sisyphus, strip, studious, symmetry, trample, vs, wring\n\nAnswer: clearheaded, christen, despond, driveway, encapsulate, fungi, gob, mendelevium, midwinter, purpose, stanhope, sisyphus, strip, studious, symmetry, trample, vs, wring", + "bbh_word_sorting-0103": "Answer: acoustic, anarchic, bureaucracy, diatom, fabricate, guelph, immovable, leftward, liven, neo, phenomenology, provide, shortcut, syndrome, total, trammel, usage, yarmulke, suggestive", + "bbh_word_sorting-0104": "Answer: avalanche, befriend, berniece, bong, bremsstrahlung, dactylic, flick, gilbertson, goff, hereafter, hoe, hurry, housekeep, lanka, metazoan, posterior, showroom", + "bbh_word_sorting-0105": "Answer: arraign, blutwurst, convenient, faber, glacier, horizon, inconspicuous, peste, portentous, rancho, uranyl", + "bbh_word_sorting-0109": "Answer: greasy, lapidary, mark", + "bbh_word_sorting-0112": "Answer: beth, kenya", + "bbh_word_sorting-0113": "Answer: alternate, boone, chalcedony, charity, genteel, million, olden, satin, sinai", + "bbh_word_sorting-0114": "Answer: correspond, herpes, him, seashore", + "bbh_word_sorting-0115": "Answer: catechism, daddy", + "bbh_word_sorting-0116": "Answer: astigmat, boyish, creak, coriolanus, cutlet, easternmost, godson, heaven, highwaymen, leather, musky, muscular, paula, scavenge, synaptic, zinc", + "bbh_word_sorting-0122": "Answer: careful, bertrand, eyelid, feign, heterostructure, libra, paste, southeastern, snip, wherewith", + "bbh_word_sorting-0124": "Answer: arapaho, bacteria, bela, bock, burley", + "bbh_word_sorting-0125": "Answer: betony, boar, bootleg, bronzy, charge, cordial, collet, clemens, centaur, contemporaneous, contravariant, dorado, handicraft, macmahon, mesh, monterey, possession, regina, underclassman", + "bbh_word_sorting-0127": "Answer: affluent, cheshire, covalent, diagnostician, divisive, epsilon, folklore, gothic, gideon, grover, horowitz, julio, peanut, quadrature, salient, spiderwort, spiritual" + }, + "payload": { + "baseline_override_min_support": 0, + "generation_kwargs": { + "do_sample": false, + "max_tokens": 1536 + }, + "image_ops": [], + "n_samples": 1, + "name": "expanded_budget_self_refine", + "output_key_pattern": "Answer:\\s*(.*)", + "prompt_template": "{prompt}", + "strategy": "self_refine" + }, + "reject": false, + "source": "judge", + "summary": "[mcnemar + e-value (paired binary)] effect=+0.0469 (B>A) CI=-0.0312..+0.1250, e=0.76 -> inconclusive [partial, coverage=100%]", + "tier": "L2", + "trial_root": "/tmp/evalrx-logs-48lc0s2h/fixes/08_L2_expanded_budget_self_refine", + "verdict": "partial", + "workspace_snapshot": null + } + ], + "best": {}, + "cycle": -1, + "ebh_survivors": [], + "event": "fix", + "event_seq": 594, + "fixed": false, + "max_tier": "L2", + "module": "fix", + "recommendation": { + "promising": { + "candidate": "expanded_budget_self_refine", + "e_value": 0.7619047619047632, + "effect": 0.046875, + "n_broken": 2, + "n_fixed": 5 + }, + "reason": "the candidate selected on EXPLORE did not validate on untouched CONFIRM; no candidate within L2 validated; the next intervention space is L3a (internals, read-only: attention/logits guide the scaffold) (note: 'expanded_budget_self_refine' is INCONCLUSIVE, not refuted — 5 fixed / 2 broken, effect +0.047, e=0.8 < 20; re-validating it on more or cleaner pairs (baseline_repeats>1 to drop sampling-unstable cases, or a larger confirm split) is a cheaper next step than escalating the tier)", + "recommend_tier": "L3a" + }, + "refine_signal": { + "candidate": "expanded_budget_self_refine", + "helped_cases": [ + "bbh_word_sorting-0086", + "bbh_word_sorting-0041", + "bbh_word_sorting-0080", + "bbh_word_sorting-0065", + "bbh_word_sorting-0033" + ], + "hurt_cases": [ + "bbh_word_sorting-0011", + "bbh_word_sorting-0067" + ], + "kind": "heterogeneous_failure_mode", + "message": "'expanded_budget_self_refine' repaired 5 case(s) but broke 2 — the failure mode is likely subset-specific. Re-diagnose: what distinguishes the helped cases from the hurt ones, and gate the fix on that predicate." + }, + "repair_rounds": 3, + "routed": [], + "schema_version": 6, + "selected_on_explore": "expanded_budget_self_refine", + "selection_attempted": [ + { + "coverage": 1, + "e_value": 0.31825951825951815, + "effect": 0.015625, + "headline": "Directs the model to group words by their starting letter from A to Z before assembling the complete alphabetical list.", + "n_broken": 7, + "n_fixed": 8, + "n_pairs": 64, + "name": "letter_bucket_sort", + "tier": "L1", + "verdict": "partial" + }, + { + "coverage": 1, + "e_value": 0.5883789413201198, + "effect": -0.078125, + "headline": "Instructs the model to extract the first two letters of each word, sort those letter prefixes, and use them to construct the ordered word list.", + "n_broken": 11, + "n_fixed": 6, + "n_pairs": 64, + "name": "two_letter_code_sort", + "tier": "L1", + "verdict": "unsafe" + }, + { + "coverage": 1, + "e_value": 0.3744229626582567, + "effect": 0.046875, + "headline": "Tells the model to repeatedly find and cross off the alphabetically earliest remaining word one by one until all words are ordered.", + "n_broken": 7, + "n_fixed": 10, + "n_pairs": 64, + "name": "direct_ordered_selection", + "tier": "L1", + "verdict": "partial" + }, + { + "coverage": 1, + "e_value": 0.34099234099234094, + "effect": 0.015625, + "headline": "Gives the model extra output space and asks it to group the words from A to Z before outputting the final sorted list.", + "n_broken": 6, + "n_fixed": 7, + "n_pairs": 64, + "name": "structured_letter_pass_with_budget", + "tier": "L1", + "verdict": "partial" + }, + { + "coverage": 1, + "e_value": 0.6819846819846808, + "effect": 0.078125, + "headline": "Gives the model a larger token budget and instructs it to repeatedly find and cross off the alphabetically earliest remaining word until none are left.", + "n_broken": 5, + "n_fixed": 10, + "n_pairs": 64, + "name": "iterative_minimum_extraction", + "tier": "L1", + "verdict": "partial" + }, + { + "coverage": 1, + "e_value": 12.002930402930373, + "effect": 0.15625, + "headline": "Directs the model to write out the sorted list once, verify every adjacent pair, and then output the final answer line without repeating the entire list.", + "n_broken": 2, + "n_fixed": 12, + "n_pairs": 64, + "name": "concise_verification_pass", + "tier": "L1", + "verdict": "partial" + }, + { + "coverage": 1, + "e_value": 170.66666666666706, + "effect": 0.171875, + "headline": "Gives the model a larger token budget so it does not run out of room, then has it inspect its list and correct any misplaced words before finishing.", + "n_broken": 0, + "n_fixed": 11, + "n_pairs": 64, + "name": "expanded_budget_self_refine", + "tier": "L2", + "verdict": "fixed" + }, + { + "coverage": 1, + "e_value": 3.220600520910112, + "effect": 0.15625, + "headline": "Generates multiple independent attempts with extra token space and verifies word adjacencies across samples to vote on the most consistent sorted order.", + "n_broken": 5, + "n_fixed": 15, + "n_pairs": 64, + "name": "verified_letter_chain_voting", + "tier": "L2", + "verdict": "partial" + }, + { + "coverage": 1, + "e_value": 0.29953837012660406, + "effect": 0, + "headline": "Breaks the sorting task into smaller sub-problems by repeatedly selecting the alphabetically earliest remaining words from the list.", + "n_broken": 8, + "n_fixed": 8, + "n_pairs": 64, + "name": "least_to_most_alphabetical_drain", + "tier": "L2", + "verdict": "no_effect" + }, + { + "coverage": 1, + "e_value": 0.3978243978243987, + "effect": 0.03125, + "headline": "Asks the same question five times and keeps the answer the model gives most often.", + "n_broken": 5, + "n_fixed": 7, + "n_pairs": 64, + "name": "self_consistency_5", + "tier": "L2", + "verdict": "partial" + }, + { + "coverage": 1, + "e_value": 2.0459540459540375, + "effect": 0.109375, + "headline": "Prompts the model to verify alphabetical order concisely with extra token budget and keeps an agreeing answer if it differs from the baseline.", + "n_broken": 3, + "n_fixed": 10, + "n_pairs": 64, + "name": "coded_pipeline", + "tier": "L2", + "verdict": "partial" + } + ], + "skip_reason": null, + "span_id": "fix", + "stage": "M5", + "stage_status": "completed", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:54:36.037652+00:00" + } + ], + "model_calls": [ + { + "cycle": 0, + "duration_sec": 4.0495, + "error": null, + "event": "model_call", + "event_seq": 402, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: odessa dance formulae dietetic mantle cost huddle resplendent bolshevism proust warmth deadline progeny hesitant palace rackety judson foster belize thirdhand\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0047", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words sorted alphabetically:\n\n1. bolshevism\n2. belize\n3. dance\n4. deadline\n5. dietetic\n6. foster\n7. formulae\n8. hesitant\n9. huddle\n10. judson\n11. mantle\n12. odessa\n13. palace\n14. proust\n15. rackety\n16. resplendent\n17. thirdhand\n18. warmth\n19. cost\n\nLet's re-sort them carefully:\n\n* **b**olshevism, **b**elize (bolshevism before belize)\n* **d**ance, **d**eadline, **d**ietetic\n* **f**oster, **f**ormulae\n* **h**esitant, **h**uddle\n* **j**udson\n* **m**antle\n* **o**dessa\n* **p**alace, **p**roust\n* **r**ackety, **r**esplendent\n* **t**hirdhand\n* **w**armth\n* **c**ost\n\n**Alphabetical Order:**\n\n1. bolshevism\n2. belize\n3. dance\n4. deadline\n5. dietetic\n6. foster\n7. formulae\n8. hesitant\n9. huddle\n10. judson\n11. mantle\n12. odessa\n13. palace\n14. proust\n15. rackety\n16. resplendent\n17. thirdhand\n18. warmth\n19. cost\n\nWait, let's check the 'b' words again: bolshevism, belize. 'b'e comes before 'b'o.\n\nLet's re-sort the full list:\nodessa, dance, formulae, dietetic, mantle, cost, huddle, resplendent, bolshevism, proust, warmth, deadline, progeny, hesitant, palace, rackety, judson, foster, belize, thirdhand\n\n(Note: I missed 'progeny' in my initial count, let's include it.)\n\nFull List: odessa, dance, formulae, dietetic, mantle, cost, huddle, resplendent, bolshevism, proust, warmth, deadline, progeny, hesitant, palace, rackety, judson, foster, belize, thirdhand (20 words)\n\n1. **b**olshevism\n2. **b**elize\n3. **c**ost\n4. **d**ance\n5. **d**eadline\n6. **d**ietetic\n7. **f**a**s**ter (foster)\n8. **f**ormulae\n9. **h**adesitant\n10. **h**uddle\n11. **j**udson\n12. **m**antle\n13. **o**dessa\n14. **p**alace\n15. **p**roust\n16. **p**rogeny\n17. **r**ackety\n18. **r**esplendent\n19. **t**hirdhand\n20. **w**armth\n\n**Final Sorted List:**\n1. bolshevism\n2. belize\n3. cost\n4. dance\n5. deadline\n6. dietetic\n7. foster\n8. formulae\n9. hesitant\n10. huddle\n11. judson\n12. mantle\n13. odessa\n14. palace\n15. proust\n16. progeny\n17. rackety\n18. resplendent\n19. thirdhand\n20. warmth\n\nAnswer: bolshevism, belize, cost, dance, deadline, dietetic, foster, formulae, hesitant, huddle, judson, mantle, odessa, palace, proust, progeny, rackety, resplendent, thirdhand, warmth", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.402", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:50:19.594161+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.6856, + "error": null, + "event": "model_call", + "event_seq": 403, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: odessa dance formulae dietetic mantle cost huddle resplendent bolshevism proust warmth deadline progeny hesitant palace rackety judson foster belize thirdhand\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. bolshevism\n2. belize\n3. dance\n4. deadline\n5. dietetic\n6. foster\n7. formulae\n8. hesitant\n9. huddle\n10. judson\n11. mantle\n12. odessa\n13. palace\n14. proust\n15. rackety\n16. resplendent\n17. thirdhand\n18. warmth\n19. cost\n\nLet's re-sort them carefully:\n\n* **b**olshevism, **b**elize (bolshevism before belize)\n* **d**ance, **d**eadline, **d**ietetic\n* **f**oster, **f**ormulae\n* **h**esitant, **h**uddle\n* **j**udson\n* **m**antle\n* **o**dessa\n* **p**alace, **p**roust\n* **r**ackety, **r**esplendent\n* **t**hirdhand\n* **w**armth\n* **c**ost\n\n**Alphabetical Order:**\n\n1. bolshevism\n2. belize\n3. dance\n4. deadline\n5. dietetic\n6. foster\n7. formulae\n8. hesitant\n9. huddle\n10. judson\n11. mantle\n12. odessa\n13. palace\n14. proust\n15. rackety\n16. resplendent\n17. thirdhand\n18. warmth\n19. cost\n\nWait, let's check the 'b' words again: bolshevism, belize. 'b'e comes before 'b'o.\n\nLet's re-sort the full list:\nodessa, dance, formulae, dietetic, mantle, cost, huddle, resplendent, bolshevism, proust, warmth, deadline, progeny, hesitant, palace, rackety, judson, foster, belize, thirdhand\n\n(Note: I missed 'progeny' in my initial count, let's include it.)\n\nFull List: odessa, dance, formulae, dietetic, mantle, cost, huddle, resplendent, bolshevism, proust, warmth, deadline, progeny, hesitant, palace, rackety, judson, foster, belize, thirdhand (20 words)\n\n1. **b**olshevism\n2. **b**elize\n3. **c**ost\n4. **d**ance\n5. **d**eadline\n6. **d**ietetic\n7. **f**a**s**ter (foster)\n8. **f**ormulae\n9. **h**adesitant\n10. **h**uddle\n11. **j**udson\n12. **m**antle\n13. **o**dessa\n14. **p**alace\n15. **p**roust\n16. **p**rogeny\n17. **r**ackety\n18. **r**esplendent\n19. **t**hirdhand\n20. **w**armth\n\n**Final Sorted List:**\n1. bolshevism\n2. belize\n3. cost\n4. dance\n5. deadline\n6. dietetic\n7. foster\n8. formulae\n9. hesitant\n10. huddle\n11. judson\n12. mantle\n13. odessa\n14. palace\n15. proust\n16. progeny\n17. rackety\n18. resplendent\n19. thirdhand\n20. warmth\n\nAnswer: bolshevism, belize, cost, dance, deadline, dietetic, foster, formulae, hesitant, huddle, judson, mantle, odessa, palace, proust, progeny, rackety, resplendent, thirdhand, warmth", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0047", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The initial attempt's sorting logic and final list contain errors.\n\n1. **Instruction Following/Factual Error:** The initial list provided in the prompt was: \"odessa dance formulae dietetic mantle cost huddle resplendent bolshevism proust warmth deadline progeny hesitant palace rackety judson foster belize thirdhand\". The attempt missed 'progeny' initially and then included it in the final list, but the initial sorting steps were inconsistent.\n2. **Arithmetic/Sorting Error:** The manual re-sorting steps and the final list produced by the attempt are incorrect. For example, 'cost' (C) should come before 'dance' (D), and the relative order of 'foster' and 'formulae' needs verification.\n\n**Correction Advice:** Re-sort the entire list strictly alphabetically. Pay close attention to words starting with the same letter (e.g., 'b' words, 'd' words, 'p' words, 'r' words) and ensure all words from the original list are included and correctly placed.\n\nAnswer: bolshevism, belize, cost, dance, deadline, dietetic, foster, formulae, hesitant, huddle, judson, mantle, odessa, palace, proust, progeny, rackety, resplendent, thirdhand, warmth", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.403", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:50:21.280328+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.6753, + "error": null, + "event": "model_call", + "event_seq": 404, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: odessa dance formulae dietetic mantle cost huddle resplendent bolshevism proust warmth deadline progeny hesitant palace rackety judson foster belize thirdhand\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. bolshevism\n2. belize\n3. dance\n4. deadline\n5. dietetic\n6. foster\n7. formulae\n8. hesitant\n9. huddle\n10. judson\n11. mantle\n12. odessa\n13. palace\n14. proust\n15. rackety\n16. resplendent\n17. thirdhand\n18. warmth\n19. cost\n\nLet's re-sort them carefully:\n\n* **b**olshevism, **b**elize (bolshevism before belize)\n* **d**ance, **d**eadline, **d**ietetic\n* **f**oster, **f**ormulae\n* **h**esitant, **h**uddle\n* **j**udson\n* **m**antle\n* **o**dessa\n* **p**alace, **p**roust\n* **r**ackety, **r**esplendent\n* **t**hirdhand\n* **w**armth\n* **c**ost\n\n**Alphabetical Order:**\n\n1. bolshevism\n2. belize\n3. dance\n4. deadline\n5. dietetic\n6. foster\n7. formulae\n8. hesitant\n9. huddle\n10. judson\n11. mantle\n12. odessa\n13. palace\n14. proust\n15. rackety\n16. resplendent\n17. thirdhand\n18. warmth\n19. cost\n\nWait, let's check the 'b' words again: bolshevism, belize. 'b'e comes before 'b'o.\n\nLet's re-sort the full list:\nodessa, dance, formulae, dietetic, mantle, cost, huddle, resplendent, bolshevism, proust, warmth, deadline, progeny, hesitant, palace, rackety, judson, foster, belize, thirdhand\n\n(Note: I missed 'progeny' in my initial count, let's include it.)\n\nFull List: odessa, dance, formulae, dietetic, mantle, cost, huddle, resplendent, bolshevism, proust, warmth, deadline, progeny, hesitant, palace, rackety, judson, foster, belize, thirdhand (20 words)\n\n1. **b**olshevism\n2. **b**elize\n3. **c**ost\n4. **d**ance\n5. **d**eadline\n6. **d**ietetic\n7. **f**a**s**ter (foster)\n8. **f**ormulae\n9. **h**adesitant\n10. **h**uddle\n11. **j**udson\n12. **m**antle\n13. **o**dessa\n14. **p**alace\n15. **p**roust\n16. **p**rogeny\n17. **r**ackety\n18. **r**esplendent\n19. **t**hirdhand\n20. **w**armth\n\n**Final Sorted List:**\n1. bolshevism\n2. belize\n3. cost\n4. dance\n5. deadline\n6. dietetic\n7. foster\n8. formulae\n9. hesitant\n10. huddle\n11. judson\n12. mantle\n13. odessa\n14. palace\n15. proust\n16. progeny\n17. rackety\n18. resplendent\n19. thirdhand\n20. warmth\n\nAnswer: bolshevism, belize, cost, dance, deadline, dietetic, foster, formulae, hesitant, huddle, judson, mantle, odessa, palace, proust, progeny, rackety, resplendent, thirdhand, warmth\n\nFeedback:\nThe initial attempt's sorting logic and final list contain errors.\n\n1. **Instruction Following/Factual Error:** The initial list provided in the prompt was: \"odessa dance formulae dietetic mantle cost huddle resplendent bolshevism proust warmth deadline progeny hesitant palace rackety judson foster belize thirdhand\". The attempt missed 'progeny' initially and then included it in the final list, but the initial sorting steps were inconsistent.\n2. **Arithmetic/Sorting Error:** The manual re-sorting steps and the final list produced by the attempt are incorrect. For example, 'cost' (C) should come before 'dance' (D), and the relative order of 'foster' and 'formulae' needs verification.\n\n**Correction Advice:** Re-sort the entire list strictly alphabetically. Pay close attention to words starting with the same letter (e.g., 'b' words, 'd' words, 'p' words, 'r' words) and ensure all words from the original list are included and correctly placed.\n\nAnswer: bolshevism, belize, cost, dance, deadline, dietetic, foster, formulae, hesitant, huddle, judson, mantle, odessa, palace, proust, progeny, rackety, resplendent, thirdhand, warmth", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0047", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: bolshevism, belize, cost, dance, deadline, dietetic, foster, formulae, hesitant, huddle, judson, mantle, odessa, palace, proust, progeny, rackety, resplendent, thirdhand, warmth", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.404", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:50:21.956139+00:00" + }, + { + "cycle": 0, + "duration_sec": 2.849, + "error": null, + "event": "model_call", + "event_seq": 405, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: pewter gross gujarati cortland diffusible nomograph stag broadcast galvanometer transition incestuous sketchbook scout larynx\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0019", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words sorted alphabetically:\n\n1. broadcast\n2. cortland\n3. diffusible\n4. galvanometer\n5. gross\n6. gujarati\n7. incestuous\n8. larynx\n9. nomograph\n10. pewter\n11. scout\n12. sketchbook\n13. stag\n14. transition\n\nLet's re-sort them carefully:\n\n* **b**roadcast\n* **c**ortland\n* **d**iffusible\n* **g**alvometer\n* **g**ross\n* **g**ujarati\n* **i**ncestuous\n* **l**arynx\n* **n**omograph\n* **p**ewter\n* **s**cout\n* **s**teag (Wait, 'stag' comes before 'sketchbook' if we look at the second letter, but alphabetically 'stag' comes before 'sketchbook' if we compare 't' vs 'k'. Let's re-examine the list.)\n\nOriginal List: pewter gross gujarati cortland diffusible nomograph stag broadcast galvanometer transition incestuous sketchbook scout larynx\n\n1. broadcast\n2. cortland\n3. diffusible\n4. galvanometer\n5. gross\n6. gujarati\n7. incestuous\n8. larynx\n9. nomograph\n10. pewter\n11. scout\n12. sketchbook\n13. stag\n14. transition\n\nLet's perform a strict alphabetical sort:\n\n1. broadcast (b)\n2. cortland (c)\n3. diffusible (d)\n4. galvanometer (g)\n5. gross (g)\n6. gujarati (g)\n7. incestuous (i)\n8. larynx (l)\n9. nomograph (n)\n10. pewter (p)\n11. scout (s)\n12. sketchbook (s)\n13. stag (s)\n14. transition (t)\n\nSorting the 'g' words: galvanometer, gross, gujarati.\n* galvanometer\n* gross\n* gujarati\n\nSorting the 's' words: scout, sketchbook, stag.\n* sketchbook (sk)\n* scout (sc)\n* stag (st)\n* Therefore: sketchbook, scout, stag\n\nFinal sorted list:\n1. broadcast\n2. cortland\n3. diffusible\n4. galvanometer\n5. gross\n6. gujarati\n7. incestuous\n8. larynx\n9. nomograph\n10. pewter\n11. sketchbook\n12. scout\n13. stag\n14. transition\n\nAnswer: broadcast, cortland, diffusible, galvanometer, gross, gujarati, incestuous, larynx, nomograph, pewter, sketchbook, scout, stag, transition", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.405", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:50:24.805950+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.2798, + "error": null, + "event": "model_call", + "event_seq": 406, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: pewter gross gujarati cortland diffusible nomograph stag broadcast galvanometer transition incestuous sketchbook scout larynx\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. broadcast\n2. cortland\n3. diffusible\n4. galvanometer\n5. gross\n6. gujarati\n7. incestuous\n8. larynx\n9. nomograph\n10. pewter\n11. scout\n12. sketchbook\n13. stag\n14. transition\n\nLet's re-sort them carefully:\n\n* **b**roadcast\n* **c**ortland\n* **d**iffusible\n* **g**alvometer\n* **g**ross\n* **g**ujarati\n* **i**ncestuous\n* **l**arynx\n* **n**omograph\n* **p**ewter\n* **s**cout\n* **s**teag (Wait, 'stag' comes before 'sketchbook' if we look at the second letter, but alphabetically 'stag' comes before 'sketchbook' if we compare 't' vs 'k'. Let's re-examine the list.)\n\nOriginal List: pewter gross gujarati cortland diffusible nomograph stag broadcast galvanometer transition incestuous sketchbook scout larynx\n\n1. broadcast\n2. cortland\n3. diffusible\n4. galvanometer\n5. gross\n6. gujarati\n7. incestuous\n8. larynx\n9. nomograph\n10. pewter\n11. scout\n12. sketchbook\n13. stag\n14. transition\n\nLet's perform a strict alphabetical sort:\n\n1. broadcast (b)\n2. cortland (c)\n3. diffusible (d)\n4. galvanometer (g)\n5. gross (g)\n6. gujarati (g)\n7. incestuous (i)\n8. larynx (l)\n9. nomograph (n)\n10. pewter (p)\n11. scout (s)\n12. sketchbook (s)\n13. stag (s)\n14. transition (t)\n\nSorting the 'g' words: galvanometer, gross, gujarati.\n* galvanometer\n* gross\n* gujarati\n\nSorting the 's' words: scout, sketchbook, stag.\n* sketchbook (sk)\n* scout (sc)\n* stag (st)\n* Therefore: sketchbook, scout, stag\n\nFinal sorted list:\n1. broadcast\n2. cortland\n3. diffusible\n4. galvanometer\n5. gross\n6. gujarati\n7. incestuous\n8. larynx\n9. nomograph\n10. pewter\n11. sketchbook\n12. scout\n13. stag\n14. transition\n\nAnswer: broadcast, cortland, diffusible, galvanometer, gross, gujarati, incestuous, larynx, nomograph, pewter, sketchbook, scout, stag, transition", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0019", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The sorting logic for the 'g' words and 's' words in the attempt contains errors.\n\nCorrection advice:\n1. Re-evaluate the alphabetical order for the 'g' words: galvanometer, gross, gujarati. The current order (galvometer, gross, gujarati) is correct.\n2. Re-evaluate the alphabetical order for the 's' words: scout, sketchbook, stag. The correct alphabetical order is sketchbook, scout, stag. The attempt's final list reflects this, but the reasoning provided in the attempt was confusing and contained an internal contradiction (\"stag comes before 'sketchbook' if we look at the second letter, but alphabetically 'stag' comes before 'sketchbook' if we compare 't' vs 'k'\"). Ensure the final list strictly follows the alphabetical rule.\n\nThe final sorted list provided in the attempt is correct based on strict alphabetical sorting.", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.406", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:50:26.086395+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.6919, + "error": null, + "event": "model_call", + "event_seq": 407, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: pewter gross gujarati cortland diffusible nomograph stag broadcast galvanometer transition incestuous sketchbook scout larynx\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. broadcast\n2. cortland\n3. diffusible\n4. galvanometer\n5. gross\n6. gujarati\n7. incestuous\n8. larynx\n9. nomograph\n10. pewter\n11. scout\n12. sketchbook\n13. stag\n14. transition\n\nLet's re-sort them carefully:\n\n* **b**roadcast\n* **c**ortland\n* **d**iffusible\n* **g**alvometer\n* **g**ross\n* **g**ujarati\n* **i**ncestuous\n* **l**arynx\n* **n**omograph\n* **p**ewter\n* **s**cout\n* **s**teag (Wait, 'stag' comes before 'sketchbook' if we look at the second letter, but alphabetically 'stag' comes before 'sketchbook' if we compare 't' vs 'k'. Let's re-examine the list.)\n\nOriginal List: pewter gross gujarati cortland diffusible nomograph stag broadcast galvanometer transition incestuous sketchbook scout larynx\n\n1. broadcast\n2. cortland\n3. diffusible\n4. galvanometer\n5. gross\n6. gujarati\n7. incestuous\n8. larynx\n9. nomograph\n10. pewter\n11. scout\n12. sketchbook\n13. stag\n14. transition\n\nLet's perform a strict alphabetical sort:\n\n1. broadcast (b)\n2. cortland (c)\n3. diffusible (d)\n4. galvanometer (g)\n5. gross (g)\n6. gujarati (g)\n7. incestuous (i)\n8. larynx (l)\n9. nomograph (n)\n10. pewter (p)\n11. scout (s)\n12. sketchbook (s)\n13. stag (s)\n14. transition (t)\n\nSorting the 'g' words: galvanometer, gross, gujarati.\n* galvanometer\n* gross\n* gujarati\n\nSorting the 's' words: scout, sketchbook, stag.\n* sketchbook (sk)\n* scout (sc)\n* stag (st)\n* Therefore: sketchbook, scout, stag\n\nFinal sorted list:\n1. broadcast\n2. cortland\n3. diffusible\n4. galvanometer\n5. gross\n6. gujarati\n7. incestuous\n8. larynx\n9. nomograph\n10. pewter\n11. sketchbook\n12. scout\n13. stag\n14. transition\n\nAnswer: broadcast, cortland, diffusible, galvanometer, gross, gujarati, incestuous, larynx, nomograph, pewter, sketchbook, scout, stag, transition\n\nFeedback:\nThe sorting logic for the 'g' words and 's' words in the attempt contains errors.\n\nCorrection advice:\n1. Re-evaluate the alphabetical order for the 'g' words: galvanometer, gross, gujarati. The current order (galvometer, gross, gujarati) is correct.\n2. Re-evaluate the alphabetical order for the 's' words: scout, sketchbook, stag. The correct alphabetical order is sketchbook, scout, stag. The attempt's final list reflects this, but the reasoning provided in the attempt was confusing and contained an internal contradiction (\"stag comes before 'sketchbook' if we look at the second letter, but alphabetically 'stag' comes before 'sketchbook' if we compare 't' vs 'k'\"). Ensure the final list strictly follows the alphabetical rule.\n\nThe final sorted list provided in the attempt is correct based on strict alphabetical sorting.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0019", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: broadcast, cortland, diffusible, galvanometer, gross, gujarati, incestuous, larynx, nomograph, pewter, sketchbook, scout, stag, transition", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.407", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:50:26.780243+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.6519, + "error": null, + "event": "model_call", + "event_seq": 408, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: seashore haughty\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0005", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The given words are: seashore, haughty.\n\nTo sort them alphabetically, we compare them letter by letter from the beginning.\n\n1. **s**eashore\n2. **h**aughty\n\nSince 'h' comes before 's' in the alphabet, \"haughty\" comes before \"seashore\".\n\nThe alphabetical order is: haughty, seashore.\n\nAnswer: haughty, seashore\nAnswer: haughty, seashore", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.408", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:50:27.435785+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.6934, + "error": null, + "event": "model_call", + "event_seq": 409, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: seashore haughty\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given words are: seashore, haughty.\n\nTo sort them alphabetically, we compare them letter by letter from the beginning.\n\n1. **s**eashore\n2. **h**aughty\n\nSince 'h' comes before 's' in the alphabet, \"haughty\" comes before \"seashore\".\n\nThe alphabetical order is: haughty, seashore.\n\nAnswer: haughty, seashore\nAnswer: haughty, seashore", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0005", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempt is factually correct and follows the instructions perfectly.\n\nCorrection advice: None.", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.409", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:50:28.131191+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.6209, + "error": null, + "event": "model_call", + "event_seq": 410, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: seashore haughty\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given words are: seashore, haughty.\n\nTo sort them alphabetically, we compare them letter by letter from the beginning.\n\n1. **s**eashore\n2. **h**aughty\n\nSince 'h' comes before 's' in the alphabet, \"haughty\" comes before \"seashore\".\n\nThe alphabetical order is: haughty, seashore.\n\nAnswer: haughty, seashore\nAnswer: haughty, seashore\n\nFeedback:\nThe attempt is factually correct and follows the instructions perfectly.\n\nCorrection advice: None.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0005", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: haughty, seashore", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.410", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:50:28.752967+00:00" + }, + { + "cycle": 0, + "duration_sec": 2.4494, + "error": null, + "event": "model_call", + "event_seq": 411, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: wv habitation panicking schoolmate aerodynamic giacomo nebulous offset vector roosevelt jimmy botanist suburbia padre\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0024", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words to sort alphabetically:\nwv, habitation, panicking, schoolmate, aerodynamic, giacomo, nebulous, offset, vector, roosevelt, jimmy, botanist, suburbia, padre\n\nLet's sort them:\n\n1. **aerodynamic**\n2. **botanist**\n3. **habitation**\n4. **giacomo**\n5. **jimmy**\n6. **nebulous**\n7. **offset**\n8. **panicking**\n9. **padre**\n10. **roosevelt**\n11. **schoolmate**\n12. **suburbia**\n13. **vector**\n14. **wv** (Assuming 'wv' is treated as a word, or placed based on its initial letters if it's an abbreviation. In standard alphabetical sorting, it comes before 'habitation' if we consider 'w' before 'h', but usually, if it's an initialism, it's treated as is. If we treat it strictly alphabetically based on the first letter 'w', it would be near the end.)\n\nLet's re-evaluate based on standard alphabetical order (case-insensitive, treating 'wv' as starting with 'w'):\n\n* aerodynamic (a)\n* botanist (b)\n* habitation (h)\n* giacomo (g)\n* jimmy (j)\n* nebulous (n)\n* offset (o)\n* panicking (p)\n* padre (p)\n* roosevelt (r)\n* schoolmate (s)\n* suburbia (s)\n* vector (v)\n* wv (w)\n\nSorting the 'p' words: panicking, padre. (panicking comes before padre)\nSorting the 's' words: schoolmate, suburbia. (schoolmate comes before suburbia)\n\nFinal sorted list:\n1. aerodynamic\n2. botanist\n3. habitation\n4. giacomo\n5. jimmy\n6. nebulous\n7. offset\n8. panicking\n9. padre\n10. roosevelt\n11. schoolmate\n12. suburbia\n13. vector\n14. wv\n\nAnswer: aerodynamic, botanist, habitation, giacomo, jimmy, nebulous, offset, panicking, padre, roosevelt, schoolmate, suburbia, vector, wv", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.411", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:50:31.203375+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.4558, + "error": null, + "event": "model_call", + "event_seq": 412, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: wv habitation panicking schoolmate aerodynamic giacomo nebulous offset vector roosevelt jimmy botanist suburbia padre\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words to sort alphabetically:\nwv, habitation, panicking, schoolmate, aerodynamic, giacomo, nebulous, offset, vector, roosevelt, jimmy, botanist, suburbia, padre\n\nLet's sort them:\n\n1. **aerodynamic**\n2. **botanist**\n3. **habitation**\n4. **giacomo**\n5. **jimmy**\n6. **nebulous**\n7. **offset**\n8. **panicking**\n9. **padre**\n10. **roosevelt**\n11. **schoolmate**\n12. **suburbia**\n13. **vector**\n14. **wv** (Assuming 'wv' is treated as a word, or placed based on its initial letters if it's an abbreviation. In standard alphabetical sorting, it comes before 'habitation' if we consider 'w' before 'h', but usually, if it's an initialism, it's treated as is. If we treat it strictly alphabetically based on the first letter 'w', it would be near the end.)\n\nLet's re-evaluate based on standard alphabetical order (case-insensitive, treating 'wv' as starting with 'w'):\n\n* aerodynamic (a)\n* botanist (b)\n* habitation (h)\n* giacomo (g)\n* jimmy (j)\n* nebulous (n)\n* offset (o)\n* panicking (p)\n* padre (p)\n* roosevelt (r)\n* schoolmate (s)\n* suburbia (s)\n* vector (v)\n* wv (w)\n\nSorting the 'p' words: panicking, padre. (panicking comes before padre)\nSorting the 's' words: schoolmate, suburbia. (schoolmate comes before suburbia)\n\nFinal sorted list:\n1. aerodynamic\n2. botanist\n3. habitation\n4. giacomo\n5. jimmy\n6. nebulous\n7. offset\n8. panicking\n9. padre\n10. roosevelt\n11. schoolmate\n12. suburbia\n13. vector\n14. wv\n\nAnswer: aerodynamic, botanist, habitation, giacomo, jimmy, nebulous, offset, panicking, padre, roosevelt, schoolmate, suburbia, vector, wv", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0024", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempt contains an error in the initial sorting logic and the final list construction.\n\n1. **Error in initial sorting:** The attempt incorrectly places 'habitation' before 'giacomo' and 'jimmy' before 'nebulous' based on the initial list provided in the attempt's numbered list, although the final re-evaluation attempts to correct this.\n2. **Error in final list construction:** The final list provided in the attempt is based on the re-evaluation, but the initial list provided in the prompt was: `wv habitation panicking schoolmate aerodynamic giacomo nebulous offset vector roosevelt jimmy botanist suburbia padre`. The attempt missed 'botanist' in its initial numbered list but included it in the final sort.\n\n**Correction Advice:** Ensure all words from the original list are accounted for and sort them strictly alphabetically based on the first letter of each word.\n\nAnswer: aerodynamic, botanist, giacomo, habitation, jimmy, nebulous, offset, panicking, padre, roosevelt, schoolmate, suburbia, vector, wv", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.412", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:50:32.660158+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.6303, + "error": null, + "event": "model_call", + "event_seq": 413, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: wv habitation panicking schoolmate aerodynamic giacomo nebulous offset vector roosevelt jimmy botanist suburbia padre\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words to sort alphabetically:\nwv, habitation, panicking, schoolmate, aerodynamic, giacomo, nebulous, offset, vector, roosevelt, jimmy, botanist, suburbia, padre\n\nLet's sort them:\n\n1. **aerodynamic**\n2. **botanist**\n3. **habitation**\n4. **giacomo**\n5. **jimmy**\n6. **nebulous**\n7. **offset**\n8. **panicking**\n9. **padre**\n10. **roosevelt**\n11. **schoolmate**\n12. **suburbia**\n13. **vector**\n14. **wv** (Assuming 'wv' is treated as a word, or placed based on its initial letters if it's an abbreviation. In standard alphabetical sorting, it comes before 'habitation' if we consider 'w' before 'h', but usually, if it's an initialism, it's treated as is. If we treat it strictly alphabetically based on the first letter 'w', it would be near the end.)\n\nLet's re-evaluate based on standard alphabetical order (case-insensitive, treating 'wv' as starting with 'w'):\n\n* aerodynamic (a)\n* botanist (b)\n* habitation (h)\n* giacomo (g)\n* jimmy (j)\n* nebulous (n)\n* offset (o)\n* panicking (p)\n* padre (p)\n* roosevelt (r)\n* schoolmate (s)\n* suburbia (s)\n* vector (v)\n* wv (w)\n\nSorting the 'p' words: panicking, padre. (panicking comes before padre)\nSorting the 's' words: schoolmate, suburbia. (schoolmate comes before suburbia)\n\nFinal sorted list:\n1. aerodynamic\n2. botanist\n3. habitation\n4. giacomo\n5. jimmy\n6. nebulous\n7. offset\n8. panicking\n9. padre\n10. roosevelt\n11. schoolmate\n12. suburbia\n13. vector\n14. wv\n\nAnswer: aerodynamic, botanist, habitation, giacomo, jimmy, nebulous, offset, panicking, padre, roosevelt, schoolmate, suburbia, vector, wv\n\nFeedback:\nThe attempt contains an error in the initial sorting logic and the final list construction.\n\n1. **Error in initial sorting:** The attempt incorrectly places 'habitation' before 'giacomo' and 'jimmy' before 'nebulous' based on the initial list provided in the attempt's numbered list, although the final re-evaluation attempts to correct this.\n2. **Error in final list construction:** The final list provided in the attempt is based on the re-evaluation, but the initial list provided in the prompt was: `wv habitation panicking schoolmate aerodynamic giacomo nebulous offset vector roosevelt jimmy botanist suburbia padre`. The attempt missed 'botanist' in its initial numbered list but included it in the final sort.\n\n**Correction Advice:** Ensure all words from the original list are accounted for and sort them strictly alphabetically based on the first letter of each word.\n\nAnswer: aerodynamic, botanist, giacomo, habitation, jimmy, nebulous, offset, panicking, padre, roosevelt, schoolmate, suburbia, vector, wv", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0024", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: aerodynamic, botanist, giacomo, habitation, jimmy, nebulous, offset, panicking, padre, roosevelt, schoolmate, suburbia, vector, wv", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.413", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:50:33.291904+00:00" + }, + { + "cycle": 0, + "duration_sec": 2.6844, + "error": null, + "event": "model_call", + "event_seq": 414, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: regret uptake satisfy behold fumarole motif forth alcohol escutcheon pease hackberry walkie\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0086", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words to sort alphabetically:\nregret, uptake, satisfy, behold, fumarole, motif, forth, alcohol, escutcheon, pease, hackberry, walkie\n\nLet's sort them:\n\n1. alcohol\n2. behold\n3. escutcheon\n4. fumarole\n5. forth\n6. hackberry\n7. motif\n8. pease\n9. regret\n10. satisfy\n11. uptake\n12. walkie\n\nDetailed comparison:\n* **a**lcohol\n* **b**ehold\n* **e**scutcheon\n* **f**umarole\n* **f**orth (fumarole comes before forth if we compare the third letter: u vs o. Wait, F-O-R-T-H vs F-U-M-A-R-O-L-E. O comes before U.)\n * Re-evaluating F words: fumarole (fu...), forth (fo...) -> forth comes before fumarole.\n * Let's re-sort F words: forth, fumarole.\n* **h**ackberry\n* **m**otif\n* **p**ease\n* **r**egret\n* **s**atisfy\n* **u**ptake\n* **w**alkie\n\nLet's perform a full alphabetical sort:\n\n1. alcohol (A)\n2. behold (B)\n3. escutcheon (E)\n4. forth (F)\n5. fumarole (F) (f-o vs f-u)\n6. hackberry (H)\n7. motif (M)\n8. pease (P)\n9. regret (R)\n10. satisfy (S)\n11. uptake (U)\n12. walkie (W)\n\nChecking F words again:\nforth: f-o-r-t-h\nfumarole: f-u-m-a-r-o-l-e\n'o' comes before 'u', so **forth** comes before **fumarole**.\n\nFinal sorted list:\n1. alcohol\n2. behold\n3. escutcheon\n4. forth\n5. fumarole\n6. hackberry\n7. motif\n8. pease\n9. regret\n10. satisfy\n11. uptake\n12. walkie\n\nAnswer: alcohol, behold, escutcheon, forth, fumarole, hackberry, motif, pease, regret, satisfy, uptake, walkie", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.414", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:50:35.977864+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.4542, + "error": null, + "event": "model_call", + "event_seq": 415, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: regret uptake satisfy behold fumarole motif forth alcohol escutcheon pease hackberry walkie\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words to sort alphabetically:\nregret, uptake, satisfy, behold, fumarole, motif, forth, alcohol, escutcheon, pease, hackberry, walkie\n\nLet's sort them:\n\n1. alcohol\n2. behold\n3. escutcheon\n4. fumarole\n5. forth\n6. hackberry\n7. motif\n8. pease\n9. regret\n10. satisfy\n11. uptake\n12. walkie\n\nDetailed comparison:\n* **a**lcohol\n* **b**ehold\n* **e**scutcheon\n* **f**umarole\n* **f**orth (fumarole comes before forth if we compare the third letter: u vs o. Wait, F-O-R-T-H vs F-U-M-A-R-O-L-E. O comes before U.)\n * Re-evaluating F words: fumarole (fu...), forth (fo...) -> forth comes before fumarole.\n * Let's re-sort F words: forth, fumarole.\n* **h**ackberry\n* **m**otif\n* **p**ease\n* **r**egret\n* **s**atisfy\n* **u**ptake\n* **w**alkie\n\nLet's perform a full alphabetical sort:\n\n1. alcohol (A)\n2. behold (B)\n3. escutcheon (E)\n4. forth (F)\n5. fumarole (F) (f-o vs f-u)\n6. hackberry (H)\n7. motif (M)\n8. pease (P)\n9. regret (R)\n10. satisfy (S)\n11. uptake (U)\n12. walkie (W)\n\nChecking F words again:\nforth: f-o-r-t-h\nfumarole: f-u-m-a-r-o-l-e\n'o' comes before 'u', so **forth** comes before **fumarole**.\n\nFinal sorted list:\n1. alcohol\n2. behold\n3. escutcheon\n4. forth\n5. fumarole\n6. hackberry\n7. motif\n8. pease\n9. regret\n10. satisfy\n11. uptake\n12. walkie\n\nAnswer: alcohol, behold, escutcheon, forth, fumarole, hackberry, motif, pease, regret, satisfy, uptake, walkie", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0086", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The sorting logic contains an error in the comparison between \"forth\" and \"fumarole\".\n\nCorrection advice: Re-evaluate the alphabetical order of \"forth\" and \"fumarole\". Compare them letter by letter: both start with 'f'. The second letter of \"forth\" is 'o', and the second letter of \"fumarole\" is 'u'. Since 'o' comes before 'u', \"forth\" should precede \"fumarole\". The attempt's final list correctly places \"forth\" before \"fumarole\", but the reasoning provided in the detailed comparison section was contradictory (\"fumarole comes before forth if we compare the third letter: u vs o. Wait, F-O-R-T-H vs F-U-M-A-R-O-L-E. O comes before U.\"). The final sorted list is correct based on the final comparison, but the reasoning needs to be consistent.\n\nAnswer: alcohol, behold, escutcheon, forth, fumarole, hackberry, motif, pease, regret, satisfy, uptake, walkie", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.415", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:50:37.433021+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.5422, + "error": null, + "event": "model_call", + "event_seq": 416, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: regret uptake satisfy behold fumarole motif forth alcohol escutcheon pease hackberry walkie\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words to sort alphabetically:\nregret, uptake, satisfy, behold, fumarole, motif, forth, alcohol, escutcheon, pease, hackberry, walkie\n\nLet's sort them:\n\n1. alcohol\n2. behold\n3. escutcheon\n4. fumarole\n5. forth\n6. hackberry\n7. motif\n8. pease\n9. regret\n10. satisfy\n11. uptake\n12. walkie\n\nDetailed comparison:\n* **a**lcohol\n* **b**ehold\n* **e**scutcheon\n* **f**umarole\n* **f**orth (fumarole comes before forth if we compare the third letter: u vs o. Wait, F-O-R-T-H vs F-U-M-A-R-O-L-E. O comes before U.)\n * Re-evaluating F words: fumarole (fu...), forth (fo...) -> forth comes before fumarole.\n * Let's re-sort F words: forth, fumarole.\n* **h**ackberry\n* **m**otif\n* **p**ease\n* **r**egret\n* **s**atisfy\n* **u**ptake\n* **w**alkie\n\nLet's perform a full alphabetical sort:\n\n1. alcohol (A)\n2. behold (B)\n3. escutcheon (E)\n4. forth (F)\n5. fumarole (F) (f-o vs f-u)\n6. hackberry (H)\n7. motif (M)\n8. pease (P)\n9. regret (R)\n10. satisfy (S)\n11. uptake (U)\n12. walkie (W)\n\nChecking F words again:\nforth: f-o-r-t-h\nfumarole: f-u-m-a-r-o-l-e\n'o' comes before 'u', so **forth** comes before **fumarole**.\n\nFinal sorted list:\n1. alcohol\n2. behold\n3. escutcheon\n4. forth\n5. fumarole\n6. hackberry\n7. motif\n8. pease\n9. regret\n10. satisfy\n11. uptake\n12. walkie\n\nAnswer: alcohol, behold, escutcheon, forth, fumarole, hackberry, motif, pease, regret, satisfy, uptake, walkie\n\nFeedback:\nThe sorting logic contains an error in the comparison between \"forth\" and \"fumarole\".\n\nCorrection advice: Re-evaluate the alphabetical order of \"forth\" and \"fumarole\". Compare them letter by letter: both start with 'f'. The second letter of \"forth\" is 'o', and the second letter of \"fumarole\" is 'u'. Since 'o' comes before 'u', \"forth\" should precede \"fumarole\". The attempt's final list correctly places \"forth\" before \"fumarole\", but the reasoning provided in the detailed comparison section was contradictory (\"fumarole comes before forth if we compare the third letter: u vs o. Wait, F-O-R-T-H vs F-U-M-A-R-O-L-E. O comes before U.\"). The final sorted list is correct based on the final comparison, but the reasoning needs to be consistent.\n\nAnswer: alcohol, behold, escutcheon, forth, fumarole, hackberry, motif, pease, regret, satisfy, uptake, walkie", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0086", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: alcohol, behold, escutcheon, forth, fumarole, hackberry, motif, pease, regret, satisfy, uptake, walkie", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.416", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:50:37.976205+00:00" + }, + { + "cycle": 0, + "duration_sec": 2.1033, + "error": null, + "event": "model_call", + "event_seq": 417, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: stanhope sisyphus fungi purpose midwinter mendelevium studious driveway symmetry despond trample wring strip clearheaded encapsulate vs gob christen\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0100", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words sorted alphabetically:\n\n1. clearheaded\n2. christen\n3. despond\n4. driveway\n5. encapsulate\n6. fungi\n7. mendelevium\n8. midwinter\n9. purpose\n10. stanhope\n11. strip\n12. studious\n13. symmetry\n14. trample\n15. vs\n16. wring\n\nLet's re-sort them carefully:\n\n* clearheaded\n* christen\n* despond\n* driveway\n* encapsulate\n* fungi\n* mendelevium\n* midwinter\n* purpose\n* stanhope\n* strip\n* studious\n* symmetry\n* trample\n* vs\n* wring\n\n(Checking the initial list again: stanhope, sisyphus, fungi, purpose, midwinter, mendelevium, studious, driveway, symmetry, despond, trample, wring, strip, clearheaded, encapsulate, vs, gob, christen)\n\nWait, I missed \"sisyphus\" and \"gob\". Let's re-list and sort:\n\nOriginal List: stanhope, sisyphus, fungi, purpose, midwinter, mendelevium, studious, driveway, symmetry, despond, trample, wring, strip, clearheaded, encapsulate, vs, gob, christen\n\nSorted List:\n1. clearheaded\n2. christen\n3. despond\n4. driveway\n5. encapsulate\n6. fungi\n7. gob\n8. mendelevium\n9. midwinter\n10. purpose\n11. stanhope\n12. sisyphus\n13. strip\n14. studious\n15. symmetry\n16. trample\n17. vs\n18. wring\n\nAnswer: clearheaded, christen, despond, driveway, encapsulate, fungi, gob, mendelevium, midwinter, purpose, stanhope, sisyphus, strip, studious, symmetry, trample, vs, wring", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.417", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:50:40.080670+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.5443, + "error": null, + "event": "model_call", + "event_seq": 418, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: stanhope sisyphus fungi purpose midwinter mendelevium studious driveway symmetry despond trample wring strip clearheaded encapsulate vs gob christen\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. clearheaded\n2. christen\n3. despond\n4. driveway\n5. encapsulate\n6. fungi\n7. mendelevium\n8. midwinter\n9. purpose\n10. stanhope\n11. strip\n12. studious\n13. symmetry\n14. trample\n15. vs\n16. wring\n\nLet's re-sort them carefully:\n\n* clearheaded\n* christen\n* despond\n* driveway\n* encapsulate\n* fungi\n* mendelevium\n* midwinter\n* purpose\n* stanhope\n* strip\n* studious\n* symmetry\n* trample\n* vs\n* wring\n\n(Checking the initial list again: stanhope, sisyphus, fungi, purpose, midwinter, mendelevium, studious, driveway, symmetry, despond, trample, wring, strip, clearheaded, encapsulate, vs, gob, christen)\n\nWait, I missed \"sisyphus\" and \"gob\". Let's re-list and sort:\n\nOriginal List: stanhope, sisyphus, fungi, purpose, midwinter, mendelevium, studious, driveway, symmetry, despond, trample, wring, strip, clearheaded, encapsulate, vs, gob, christen\n\nSorted List:\n1. clearheaded\n2. christen\n3. despond\n4. driveway\n5. encapsulate\n6. fungi\n7. gob\n8. mendelevium\n9. midwinter\n10. purpose\n11. stanhope\n12. sisyphus\n13. strip\n14. studious\n15. symmetry\n16. trample\n17. vs\n18. wring\n\nAnswer: clearheaded, christen, despond, driveway, encapsulate, fungi, gob, mendelevium, midwinter, purpose, stanhope, sisyphus, strip, studious, symmetry, trample, vs, wring", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0100", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempt has several errors:\n\n1. **Factual Error (Incomplete List/Sorting):** The initial sorting attempt missed \"sisyphus\" and \"gob\" in the first pass, and the final list provided in the attempt seems to have a different order than the final sorted list derived from the full set.\n2. **Instruction Following Error (Final Answer Format):** The attempt provides a detailed process but the final answer format requested is just the list on the last line. The attempt provides a list but doesn't strictly adhere to the requested output format if the goal is just the final sorted list.\n3. **Arithmetic/Reasoning Error (Sorting):** The final sorted list provided in the attempt is incorrect based on standard alphabetical order. For example, 'gob' should come before 'mendelevium' if sorting strictly alphabetically, but the attempt places 'gob' at position 7 and 'mendelevium' at position 8, which is correct relative to each other, but the overall sequence needs verification.\n\n**Correction Advice:**\nVerify the alphabetical order of all 18 words provided in the original list. Ensure all words are present and sorted correctly.", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.418", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:50:41.626041+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.8566, + "error": null, + "event": "model_call", + "event_seq": 419, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: stanhope sisyphus fungi purpose midwinter mendelevium studious driveway symmetry despond trample wring strip clearheaded encapsulate vs gob christen\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. clearheaded\n2. christen\n3. despond\n4. driveway\n5. encapsulate\n6. fungi\n7. mendelevium\n8. midwinter\n9. purpose\n10. stanhope\n11. strip\n12. studious\n13. symmetry\n14. trample\n15. vs\n16. wring\n\nLet's re-sort them carefully:\n\n* clearheaded\n* christen\n* despond\n* driveway\n* encapsulate\n* fungi\n* mendelevium\n* midwinter\n* purpose\n* stanhope\n* strip\n* studious\n* symmetry\n* trample\n* vs\n* wring\n\n(Checking the initial list again: stanhope, sisyphus, fungi, purpose, midwinter, mendelevium, studious, driveway, symmetry, despond, trample, wring, strip, clearheaded, encapsulate, vs, gob, christen)\n\nWait, I missed \"sisyphus\" and \"gob\". Let's re-list and sort:\n\nOriginal List: stanhope, sisyphus, fungi, purpose, midwinter, mendelevium, studious, driveway, symmetry, despond, trample, wring, strip, clearheaded, encapsulate, vs, gob, christen\n\nSorted List:\n1. clearheaded\n2. christen\n3. despond\n4. driveway\n5. encapsulate\n6. fungi\n7. gob\n8. mendelevium\n9. midwinter\n10. purpose\n11. stanhope\n12. sisyphus\n13. strip\n14. studious\n15. symmetry\n16. trample\n17. vs\n18. wring\n\nAnswer: clearheaded, christen, despond, driveway, encapsulate, fungi, gob, mendelevium, midwinter, purpose, stanhope, sisyphus, strip, studious, symmetry, trample, vs, wring\n\nFeedback:\nThe attempt has several errors:\n\n1. **Factual Error (Incomplete List/Sorting):** The initial sorting attempt missed \"sisyphus\" and \"gob\" in the first pass, and the final list provided in the attempt seems to have a different order than the final sorted list derived from the full set.\n2. **Instruction Following Error (Final Answer Format):** The attempt provides a detailed process but the final answer format requested is just the list on the last line. The attempt provides a list but doesn't strictly adhere to the requested output format if the goal is just the final sorted list.\n3. **Arithmetic/Reasoning Error (Sorting):** The final sorted list provided in the attempt is incorrect based on standard alphabetical order. For example, 'gob' should come before 'mendelevium' if sorting strictly alphabetically, but the attempt places 'gob' at position 7 and 'mendelevium' at position 8, which is correct relative to each other, but the overall sequence needs verification.\n\n**Correction Advice:**\nVerify the alphabetical order of all 18 words provided in the original list. Ensure all words are present and sorted correctly.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0100", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "clearheaded, christen, despond, driveway, encapsulate, fungi, gob, mendelevium, midwinter, purpose, stanhope, sisyphus, strip, studious, symmetry, trample, vs, wring\n\nAnswer: clearheaded, christen, despond, driveway, encapsulate, fungi, gob, mendelevium, midwinter, purpose, stanhope, sisyphus, strip, studious, symmetry, trample, vs, wring", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.419", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:50:42.483783+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.0876, + "error": null, + "event": "model_call", + "event_seq": 420, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: lethe bilk perturb tactual\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0011", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The given list of words is: lethe, bilk, perturb, tactual.\n\nTo sort these words alphabetically, we compare them letter by letter from the beginning.\n\n1. **lethe** (starts with 'l')\n2. **bilk** (starts with 'b')\n3. **perturb** (starts with 'p')\n4. **tactual** (starts with 't')\n\nComparing the first letters: 'b', 'l', 'p', 't'.\nIn alphabetical order: 'b', 'l', 'p', 't'.\n\n1. **bilk** (b)\n2. **lethe** (l)\n3. **perturb** (p)\n4. **tactual** (t)\n\nThe sorted list is: bilk, lethe, perturb, tactual.\n\nAnswer: bilk, lethe, perturb, tactual\n\nAnswer: bilk, lethe, perturb, tactual", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.420", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:50:43.572756+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.3063, + "error": null, + "event": "model_call", + "event_seq": 421, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: lethe bilk perturb tactual\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: lethe, bilk, perturb, tactual.\n\nTo sort these words alphabetically, we compare them letter by letter from the beginning.\n\n1. **lethe** (starts with 'l')\n2. **bilk** (starts with 'b')\n3. **perturb** (starts with 'p')\n4. **tactual** (starts with 't')\n\nComparing the first letters: 'b', 'l', 'p', 't'.\nIn alphabetical order: 'b', 'l', 'p', 't'.\n\n1. **bilk** (b)\n2. **lethe** (l)\n3. **perturb** (p)\n4. **tactual** (t)\n\nThe sorted list is: bilk, lethe, perturb, tactual.\n\nAnswer: bilk, lethe, perturb, tactual\n\nAnswer: bilk, lethe, perturb, tactual", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0011", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempt contains a factual error in spelling one of the words.\n\nCorrection advice: Check the spelling of \"tactual\". It should likely be \"tactical\".", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.421", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:50:43.880325+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.9279, + "error": null, + "event": "model_call", + "event_seq": 422, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: lethe bilk perturb tactual\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: lethe, bilk, perturb, tactual.\n\nTo sort these words alphabetically, we compare them letter by letter from the beginning.\n\n1. **lethe** (starts with 'l')\n2. **bilk** (starts with 'b')\n3. **perturb** (starts with 'p')\n4. **tactual** (starts with 't')\n\nComparing the first letters: 'b', 'l', 'p', 't'.\nIn alphabetical order: 'b', 'l', 'p', 't'.\n\n1. **bilk** (b)\n2. **lethe** (l)\n3. **perturb** (p)\n4. **tactual** (t)\n\nThe sorted list is: bilk, lethe, perturb, tactual.\n\nAnswer: bilk, lethe, perturb, tactual\n\nAnswer: bilk, lethe, perturb, tactual\n\nFeedback:\nThe attempt contains a factual error in spelling one of the words.\n\nCorrection advice: Check the spelling of \"tactual\". It should likely be \"tactical\".", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0011", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The given list of words is: lethe, bilk, perturb, tactual.\n\nTo sort these words alphabetically, we compare them letter by letter from the beginning.\n\nThe words are:\n1. lethe\n2. bilk\n3. perturb\n4. tactual (Corrected to tactical)\n\nComparing the first letters: 'l', 'b', 'p', 't'.\nIn alphabetical order: 'b', 'l', 'p', 't'.\n\n1. **bilk** (b)\n2. **lethe** (l)\n3. **perturb** (p)\n4. **tactical** (t)\n\nThe sorted list is: bilk, lethe, perturb, tactical.\n\nAnswer: bilk, lethe, perturb, tactical", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.422", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:50:44.809543+00:00" + }, + { + "cycle": 0, + "duration_sec": 2.2685, + "error": null, + "event": "model_call", + "event_seq": 423, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: them abdominal address fomalhaut pay optimal hanoverian yang berry tattoo islamabad swathe jordan bounty stearic stigmata involve effusive tornado\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0037", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words sorted alphabetically:\n\n1. abdominal\n2. address\n3. bounty\n4. berry\n5. effusive\n6. fomalhaut\n7. hanoverian\n8. islamabad\n9. involve\n10. pay\n11. stigmata\n12. stearic\n13. swathe\n14. tattoo\n15. them\n16. tornado\n17. optimal\n18. yang\n19. jordan\n\nLet's re-sort them carefully:\n\n* **a**bdominal\n* **a**ddress\n* **b**ounty\n* **b**erry\n* **e**ffusive\n* **f**omalhaut\n* **h**anoverian\n* **i**slamabad\n* **i**nvolve\n* **p**ay\n* **s**tigmata\n* **s**tearic\n* **s**wathe\n* **t**attoo\n* **t**hem\n* **t**ornado\n* **o**ptimal\n* **y**ang\n* **j**ordan (Wait, J comes before O and Y)\n\nLet's re-sort based on the first letter:\n\nA: abdominal, address\nB: bounty, berry\nE: effusive\nF: fomalhaut\nH: hanoverian\nI: islamabad, involve\nJ: jordan\nO: optimal\nS: stigmata, stearic, swathe\nT: tattoo, them, tornado\nY: yang\n\n**Detailed Sorting:**\n\n1. abdominal\n2. address\n3. bounty\n4. berry\n5. effusive\n6. fomalhaut\n7. hanoverian\n8. islamabad\n9. involve\n10. jordan\n11. optimal\n12. stigmata\n13. stearic\n14. swathe\n15. tattoo\n16. them\n17. tornado\n18. yang\n\nAnswer: abdominal, address, bounty, berry, effusive, fomalhaut, hanoverian, islamabad, involve, jordan, optimal, stigmata, stearic, swathe, tattoo, them, tornado, yang", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.423", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:50:47.079419+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.0028, + "error": null, + "event": "model_call", + "event_seq": 424, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: them abdominal address fomalhaut pay optimal hanoverian yang berry tattoo islamabad swathe jordan bounty stearic stigmata involve effusive tornado\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. abdominal\n2. address\n3. bounty\n4. berry\n5. effusive\n6. fomalhaut\n7. hanoverian\n8. islamabad\n9. involve\n10. pay\n11. stigmata\n12. stearic\n13. swathe\n14. tattoo\n15. them\n16. tornado\n17. optimal\n18. yang\n19. jordan\n\nLet's re-sort them carefully:\n\n* **a**bdominal\n* **a**ddress\n* **b**ounty\n* **b**erry\n* **e**ffusive\n* **f**omalhaut\n* **h**anoverian\n* **i**slamabad\n* **i**nvolve\n* **p**ay\n* **s**tigmata\n* **s**tearic\n* **s**wathe\n* **t**attoo\n* **t**hem\n* **t**ornado\n* **o**ptimal\n* **y**ang\n* **j**ordan (Wait, J comes before O and Y)\n\nLet's re-sort based on the first letter:\n\nA: abdominal, address\nB: bounty, berry\nE: effusive\nF: fomalhaut\nH: hanoverian\nI: islamabad, involve\nJ: jordan\nO: optimal\nS: stigmata, stearic, swathe\nT: tattoo, them, tornado\nY: yang\n\n**Detailed Sorting:**\n\n1. abdominal\n2. address\n3. bounty\n4. berry\n5. effusive\n6. fomalhaut\n7. hanoverian\n8. islamabad\n9. involve\n10. jordan\n11. optimal\n12. stigmata\n13. stearic\n14. swathe\n15. tattoo\n16. them\n17. tornado\n18. yang\n\nAnswer: abdominal, address, bounty, berry, effusive, fomalhaut, hanoverian, islamabad, involve, jordan, optimal, stigmata, stearic, swathe, tattoo, them, tornado, yang", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0037", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The initial list provided in the attempt is incorrect because it does not follow strict alphabetical order. The final re-sort section contains errors in the ordering of words starting with the same letter and the placement of 'jordan'.\n\nCorrection advice: Re-sort the entire list strictly alphabetically based on the first letter of each word. Ensure that words starting with the same letter are sorted alphabetically among themselves.\n\nAnswer: abdominal, address, bounty, berry, effusive, fomalhaut, hanoverian, islamabad, involve, jordan, optimal, stigmata, stearic, swathe, tattoo, them, tornado, yang", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.424", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:50:48.083478+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.6296, + "error": null, + "event": "model_call", + "event_seq": 425, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: them abdominal address fomalhaut pay optimal hanoverian yang berry tattoo islamabad swathe jordan bounty stearic stigmata involve effusive tornado\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. abdominal\n2. address\n3. bounty\n4. berry\n5. effusive\n6. fomalhaut\n7. hanoverian\n8. islamabad\n9. involve\n10. pay\n11. stigmata\n12. stearic\n13. swathe\n14. tattoo\n15. them\n16. tornado\n17. optimal\n18. yang\n19. jordan\n\nLet's re-sort them carefully:\n\n* **a**bdominal\n* **a**ddress\n* **b**ounty\n* **b**erry\n* **e**ffusive\n* **f**omalhaut\n* **h**anoverian\n* **i**slamabad\n* **i**nvolve\n* **p**ay\n* **s**tigmata\n* **s**tearic\n* **s**wathe\n* **t**attoo\n* **t**hem\n* **t**ornado\n* **o**ptimal\n* **y**ang\n* **j**ordan (Wait, J comes before O and Y)\n\nLet's re-sort based on the first letter:\n\nA: abdominal, address\nB: bounty, berry\nE: effusive\nF: fomalhaut\nH: hanoverian\nI: islamabad, involve\nJ: jordan\nO: optimal\nS: stigmata, stearic, swathe\nT: tattoo, them, tornado\nY: yang\n\n**Detailed Sorting:**\n\n1. abdominal\n2. address\n3. bounty\n4. berry\n5. effusive\n6. fomalhaut\n7. hanoverian\n8. islamabad\n9. involve\n10. jordan\n11. optimal\n12. stigmata\n13. stearic\n14. swathe\n15. tattoo\n16. them\n17. tornado\n18. yang\n\nAnswer: abdominal, address, bounty, berry, effusive, fomalhaut, hanoverian, islamabad, involve, jordan, optimal, stigmata, stearic, swathe, tattoo, them, tornado, yang\n\nFeedback:\nThe initial list provided in the attempt is incorrect because it does not follow strict alphabetical order. The final re-sort section contains errors in the ordering of words starting with the same letter and the placement of 'jordan'.\n\nCorrection advice: Re-sort the entire list strictly alphabetically based on the first letter of each word. Ensure that words starting with the same letter are sorted alphabetically among themselves.\n\nAnswer: abdominal, address, bounty, berry, effusive, fomalhaut, hanoverian, islamabad, involve, jordan, optimal, stigmata, stearic, swathe, tattoo, them, tornado, yang", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0037", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: abdominal, address, bounty, berry, effusive, fomalhaut, hanoverian, islamabad, involve, jordan, optimal, stigmata, stearic, swathe, tattoo, them, tornado, yang", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.425", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:50:48.714594+00:00" + }, + { + "cycle": 0, + "duration_sec": 2.191, + "error": null, + "event": "model_call", + "event_seq": 426, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: quench autocracy lodge exploitation custody ventricle annuity bruno thunderclap militant somatic dare acquisitive\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0069", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words sorted alphabetically:\n\n1. acquisitive\n2. annuity\n3. autocracy\n4. bruno\n5. custody\n6. dare\n7. exploitation\n8. lodge\n9. militant\n10. quench\n11. somatic\n12. thunderclap\n13. ventricle\n\nLet's re-sort them carefully:\n\n* **a**cquisitive, **a**nnuity, **a**utocracy\n* **b**runo\n* **c**ustody\n* **d**are\n* **e**xploitation (Wait, 'e' comes before 'l', 'm', 'q', 's', 't', 'v')\n* **l**odge\n* **m**ilitant\n* **q**uench\n* **s**omatic\n* **t**hunderclap\n* **v**entricle\n\nLet's list them and compare:\n1. acquisitive\n2. annuity\n3. autocracy\n4. bruno\n5. custody\n6. dare\n7. exploitation\n8. lodge\n9. militant\n10. quench\n11. somatic\n12. thunderclap\n13. ventricle\n\nChecking the order:\nacquisitive (a-c)\nannuity (a-n)\nautocracy (a-u)\nbruno (b)\ncustody (c-u)\ndare (d)\nexploitation (e)\nlodge (l)\nmilitant (m)\nquench (q)\nsomatic (s)\nthunderclap (t)\nventricle (v)\n\nThe correct alphabetical order is:\n1. acquisitive\n2. annuity\n3. autocracy\n4. bruno\n5. custody\n6. dare\n7. exploitation\n8. lodge\n9. militant\n10. quench\n11. somatic\n12. thunderclap\n13. ventricle\n\nAnswer: acquisitive, annuity, autocracy, bruno, custody, dare, exploitation, lodge, militant, quench, somatic, thunderclap, ventricle", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.426", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:50:50.907212+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.3777, + "error": null, + "event": "model_call", + "event_seq": 427, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: quench autocracy lodge exploitation custody ventricle annuity bruno thunderclap militant somatic dare acquisitive\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. acquisitive\n2. annuity\n3. autocracy\n4. bruno\n5. custody\n6. dare\n7. exploitation\n8. lodge\n9. militant\n10. quench\n11. somatic\n12. thunderclap\n13. ventricle\n\nLet's re-sort them carefully:\n\n* **a**cquisitive, **a**nnuity, **a**utocracy\n* **b**runo\n* **c**ustody\n* **d**are\n* **e**xploitation (Wait, 'e' comes before 'l', 'm', 'q', 's', 't', 'v')\n* **l**odge\n* **m**ilitant\n* **q**uench\n* **s**omatic\n* **t**hunderclap\n* **v**entricle\n\nLet's list them and compare:\n1. acquisitive\n2. annuity\n3. autocracy\n4. bruno\n5. custody\n6. dare\n7. exploitation\n8. lodge\n9. militant\n10. quench\n11. somatic\n12. thunderclap\n13. ventricle\n\nChecking the order:\nacquisitive (a-c)\nannuity (a-n)\nautocracy (a-u)\nbruno (b)\ncustody (c-u)\ndare (d)\nexploitation (e)\nlodge (l)\nmilitant (m)\nquench (q)\nsomatic (s)\nthunderclap (t)\nventricle (v)\n\nThe correct alphabetical order is:\n1. acquisitive\n2. annuity\n3. autocracy\n4. bruno\n5. custody\n6. dare\n7. exploitation\n8. lodge\n9. militant\n10. quench\n11. somatic\n12. thunderclap\n13. ventricle\n\nAnswer: acquisitive, annuity, autocracy, bruno, custody, dare, exploitation, lodge, militant, quench, somatic, thunderclap, ventricle", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0069", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempted answer is factually correct and follows the instructions. The sorting logic is sound.\n\nAnswer: acquisitive, annuity, autocracy, bruno, custody, dare, exploitation, lodge, militant, quench, somatic, thunderclap, ventricle", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.427", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:50:51.286309+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.2854, + "error": null, + "event": "model_call", + "event_seq": 428, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: quench autocracy lodge exploitation custody ventricle annuity bruno thunderclap militant somatic dare acquisitive\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. acquisitive\n2. annuity\n3. autocracy\n4. bruno\n5. custody\n6. dare\n7. exploitation\n8. lodge\n9. militant\n10. quench\n11. somatic\n12. thunderclap\n13. ventricle\n\nLet's re-sort them carefully:\n\n* **a**cquisitive, **a**nnuity, **a**utocracy\n* **b**runo\n* **c**ustody\n* **d**are\n* **e**xploitation (Wait, 'e' comes before 'l', 'm', 'q', 's', 't', 'v')\n* **l**odge\n* **m**ilitant\n* **q**uench\n* **s**omatic\n* **t**hunderclap\n* **v**entricle\n\nLet's list them and compare:\n1. acquisitive\n2. annuity\n3. autocracy\n4. bruno\n5. custody\n6. dare\n7. exploitation\n8. lodge\n9. militant\n10. quench\n11. somatic\n12. thunderclap\n13. ventricle\n\nChecking the order:\nacquisitive (a-c)\nannuity (a-n)\nautocracy (a-u)\nbruno (b)\ncustody (c-u)\ndare (d)\nexploitation (e)\nlodge (l)\nmilitant (m)\nquench (q)\nsomatic (s)\nthunderclap (t)\nventricle (v)\n\nThe correct alphabetical order is:\n1. acquisitive\n2. annuity\n3. autocracy\n4. bruno\n5. custody\n6. dare\n7. exploitation\n8. lodge\n9. militant\n10. quench\n11. somatic\n12. thunderclap\n13. ventricle\n\nAnswer: acquisitive, annuity, autocracy, bruno, custody, dare, exploitation, lodge, militant, quench, somatic, thunderclap, ventricle\n\nFeedback:\nThe attempted answer is factually correct and follows the instructions. The sorting logic is sound.\n\nAnswer: acquisitive, annuity, autocracy, bruno, custody, dare, exploitation, lodge, militant, quench, somatic, thunderclap, ventricle", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0069", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: acquisitive, annuity, autocracy, bruno, custody, dare, exploitation, lodge, militant, quench, somatic, thunderclap, ventricle", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.428", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:50:51.573197+00:00" + }, + { + "cycle": 0, + "duration_sec": 2.3162, + "error": null, + "event": "model_call", + "event_seq": 429, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: confidential ursa fiction heterozygous faery overture horehound\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0021", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The given list of words is: confidential, ursa, fiction, heterozygous, faery, overture, horehound.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **confidential** (Starts with 'c')\n2. **faery** (Starts with 'f')\n3. **heterozygous** (Starts with 'h')\n4. **fiction** (Starts with 'f')\n5. **horehound** (Starts with 'h')\n6. **overture** (Starts with 'o')\n7. **ursa** (Starts with 'u')\n\nLet's sort the words starting with 'f':\n* faery\n* fiction\nComparing 'faery' and 'fiction': 'fai' vs 'fic'. 'a' comes before 'i', so 'faery' comes before 'fiction'.\n\nLet's sort the words starting with 'h':\n* heterozygous\n* horehound\nComparing 'heterozygous' and 'horehound': 'he' vs 'ho'. 'e' comes before 'o', so 'heterozygous' comes before 'horehound'.\n\nThe full alphabetical order is:\n1. confidential (c)\n2. faery (f)\n3. fiction (f)\n4. heterozygous (h)\n5. horehound (h)\n6. overture (o)\n7. ursa (u)\n\nLet's re-check the 'f' words:\nfaery\nfiction\nf-a-e-r-y\nf-i-c-t-i-o-n\n'a' comes before 'i', so faery comes before fiction.\n\nLet's re-check the 'h' words:\nheterozygous\nhorehound\nh-e-t-e-r-o-z-o-u-s\nh-o-r-e-h-o-u-n-d\n'e' comes before 'o', so heterozygous comes before horehound.\n\nFinal sorted list:\n1. confidential\n2. faery\n3. fiction\n4. heterozygous\n5. horehound\n6. overture\n7. ursa\n\nAnswer: confidential, faery, fiction, heterozygous, horehound, overture, ursa", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.429", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:50:53.891121+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.6456, + "error": null, + "event": "model_call", + "event_seq": 430, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: confidential ursa fiction heterozygous faery overture horehound\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: confidential, ursa, fiction, heterozygous, faery, overture, horehound.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **confidential** (Starts with 'c')\n2. **faery** (Starts with 'f')\n3. **heterozygous** (Starts with 'h')\n4. **fiction** (Starts with 'f')\n5. **horehound** (Starts with 'h')\n6. **overture** (Starts with 'o')\n7. **ursa** (Starts with 'u')\n\nLet's sort the words starting with 'f':\n* faery\n* fiction\nComparing 'faery' and 'fiction': 'fai' vs 'fic'. 'a' comes before 'i', so 'faery' comes before 'fiction'.\n\nLet's sort the words starting with 'h':\n* heterozygous\n* horehound\nComparing 'heterozygous' and 'horehound': 'he' vs 'ho'. 'e' comes before 'o', so 'heterozygous' comes before 'horehound'.\n\nThe full alphabetical order is:\n1. confidential (c)\n2. faery (f)\n3. fiction (f)\n4. heterozygous (h)\n5. horehound (h)\n6. overture (o)\n7. ursa (u)\n\nLet's re-check the 'f' words:\nfaery\nfiction\nf-a-e-r-y\nf-i-c-t-i-o-n\n'a' comes before 'i', so faery comes before fiction.\n\nLet's re-check the 'h' words:\nheterozygous\nhorehound\nh-e-t-e-r-o-z-o-u-s\nh-o-r-e-h-o-u-n-d\n'e' comes before 'o', so heterozygous comes before horehound.\n\nFinal sorted list:\n1. confidential\n2. faery\n3. fiction\n4. heterozygous\n5. horehound\n6. overture\n7. ursa\n\nAnswer: confidential, faery, fiction, heterozygous, horehound, overture, ursa", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0021", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The sorting logic contains an error in the final sequence based on the initial comparison of the 'f' words.\n\nCorrection advice: Re-evaluate the alphabetical order of 'faery' and 'fiction'. 'faery' comes before 'fiction' because 'a' precedes 'i' at the third letter. The rest of the sorting logic appears sound based on this initial comparison. The final list provided in the attempt is correct based on standard alphabetical sorting.", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.430", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:50:54.538394+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.2411, + "error": null, + "event": "model_call", + "event_seq": 431, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: confidential ursa fiction heterozygous faery overture horehound\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: confidential, ursa, fiction, heterozygous, faery, overture, horehound.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **confidential** (Starts with 'c')\n2. **faery** (Starts with 'f')\n3. **heterozygous** (Starts with 'h')\n4. **fiction** (Starts with 'f')\n5. **horehound** (Starts with 'h')\n6. **overture** (Starts with 'o')\n7. **ursa** (Starts with 'u')\n\nLet's sort the words starting with 'f':\n* faery\n* fiction\nComparing 'faery' and 'fiction': 'fai' vs 'fic'. 'a' comes before 'i', so 'faery' comes before 'fiction'.\n\nLet's sort the words starting with 'h':\n* heterozygous\n* horehound\nComparing 'heterozygous' and 'horehound': 'he' vs 'ho'. 'e' comes before 'o', so 'heterozygous' comes before 'horehound'.\n\nThe full alphabetical order is:\n1. confidential (c)\n2. faery (f)\n3. fiction (f)\n4. heterozygous (h)\n5. horehound (h)\n6. overture (o)\n7. ursa (u)\n\nLet's re-check the 'f' words:\nfaery\nfiction\nf-a-e-r-y\nf-i-c-t-i-o-n\n'a' comes before 'i', so faery comes before fiction.\n\nLet's re-check the 'h' words:\nheterozygous\nhorehound\nh-e-t-e-r-o-z-o-u-s\nh-o-r-e-h-o-u-n-d\n'e' comes before 'o', so heterozygous comes before horehound.\n\nFinal sorted list:\n1. confidential\n2. faery\n3. fiction\n4. heterozygous\n5. horehound\n6. overture\n7. ursa\n\nAnswer: confidential, faery, fiction, heterozygous, horehound, overture, ursa\n\nFeedback:\nThe sorting logic contains an error in the final sequence based on the initial comparison of the 'f' words.\n\nCorrection advice: Re-evaluate the alphabetical order of 'faery' and 'fiction'. 'faery' comes before 'fiction' because 'a' precedes 'i' at the third letter. The rest of the sorting logic appears sound based on this initial comparison. The final list provided in the attempt is correct based on standard alphabetical sorting.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0021", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: confidential, faery, fiction, heterozygous, horehound, overture, ursa", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.431", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:50:54.781155+00:00" + }, + { + "cycle": 0, + "duration_sec": 2.4487, + "error": null, + "event": "model_call", + "event_seq": 432, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: sociolinguistic nigeria odysseus demystify proton voltaire penny contaminate bighorn stirrup\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0041", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words sorted alphabetically:\n\n1. bighorn\n2. contaminate\n3. demystify\n4. odysseus\n5. penny\n6. proton\n7. sociolinguistic\n8. stirrup\n9. voltaire\n10. nigeria\n11. **voltaire** (Wait, I listed voltaire twice in my mental check, let's re-sort carefully)\n\nLet's list them and sort them:\nsociolinguistic, nigeria, odysseus, demystify, proton, voltaire, penny, contaminate, bighorn, stirrup\n\n1. bighorn\n2. contaminate\n3. demystify\n4. nigeria\n5. odysseus\n6. penny\n7. proton\n8. sociolinguistic\n9. stirrup\n10. voltaire\n\nLet's re-check the sorting based on the initial list: sociolinguistic, nigeria, odysseus, demystify, proton, voltaire, penny, contaminate, bighorn, stirrup\n\n* Bighorn (B)\n* Contaminate (C)\n* Demystify (D)\n* Nigeria (N)\n* Odysseus (O)\n* Penny (P)\n* Proton (P) - P-e comes before P-e-n, so Penny before Proton.\n* Sociolinguistic (S)\n* Stirrup (S) - Stirrup comes after Sociolinguistic (S-t vs S-o).\n* Voltaire (V)\n\nCorrect alphabetical order:\n1. bighorn\n2. contaminate\n3. demystify\n4. nigeria\n5. odysseus\n6. penny\n7. proton\n8. sociolinguistic\n9. stirrup\n10. voltaire\n\nWait, let's check S words: sociolinguistic, stirrup.\nS-o-c... vs S-t-i... $\\rightarrow$ sociolinguistic comes before stirrup.\n\nFinal check:\nBighorn\nContaminate\nDemystify\nNigeria\nOdysseus\nPenny\nProton\nSociolinguistic\nStirrup\nVoltaire\n\nAnswer: bighorn, contaminate, demystify, nigeria, odysseus, penny, proton, sociolinguistic, stirrup, voltaire\n\nAnswer: bighorn, contaminate, demystify, nigeria, odysseus, penny, proton, sociolinguistic, stirrup, voltaire", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.432", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:50:57.231678+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.7926, + "error": null, + "event": "model_call", + "event_seq": 433, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: sociolinguistic nigeria odysseus demystify proton voltaire penny contaminate bighorn stirrup\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. bighorn\n2. contaminate\n3. demystify\n4. odysseus\n5. penny\n6. proton\n7. sociolinguistic\n8. stirrup\n9. voltaire\n10. nigeria\n11. **voltaire** (Wait, I listed voltaire twice in my mental check, let's re-sort carefully)\n\nLet's list them and sort them:\nsociolinguistic, nigeria, odysseus, demystify, proton, voltaire, penny, contaminate, bighorn, stirrup\n\n1. bighorn\n2. contaminate\n3. demystify\n4. nigeria\n5. odysseus\n6. penny\n7. proton\n8. sociolinguistic\n9. stirrup\n10. voltaire\n\nLet's re-check the sorting based on the initial list: sociolinguistic, nigeria, odysseus, demystify, proton, voltaire, penny, contaminate, bighorn, stirrup\n\n* Bighorn (B)\n* Contaminate (C)\n* Demystify (D)\n* Nigeria (N)\n* Odysseus (O)\n* Penny (P)\n* Proton (P) - P-e comes before P-e-n, so Penny before Proton.\n* Sociolinguistic (S)\n* Stirrup (S) - Stirrup comes after Sociolinguistic (S-t vs S-o).\n* Voltaire (V)\n\nCorrect alphabetical order:\n1. bighorn\n2. contaminate\n3. demystify\n4. nigeria\n5. odysseus\n6. penny\n7. proton\n8. sociolinguistic\n9. stirrup\n10. voltaire\n\nWait, let's check S words: sociolinguistic, stirrup.\nS-o-c... vs S-t-i... $\\rightarrow$ sociolinguistic comes before stirrup.\n\nFinal check:\nBighorn\nContaminate\nDemystify\nNigeria\nOdysseus\nPenny\nProton\nSociolinguistic\nStirrup\nVoltaire\n\nAnswer: bighorn, contaminate, demystify, nigeria, odysseus, penny, proton, sociolinguistic, stirrup, voltaire\n\nAnswer: bighorn, contaminate, demystify, nigeria, odysseus, penny, proton, sociolinguistic, stirrup, voltaire", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0041", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempt contains multiple redundant steps and confusion in the sorting process, although the final derived list appears correct based on the initial list.\n\nCorrection advice: Remove the internal monologue and redundant checks. Present a clear, single sorting process based on the provided list.\n\nAnswer: bighorn, contaminate, demystify, nigeria, odysseus, penny, proton, sociolinguistic, stirrup, voltaire", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.433", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:50:58.025950+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.7768, + "error": null, + "event": "model_call", + "event_seq": 434, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: sociolinguistic nigeria odysseus demystify proton voltaire penny contaminate bighorn stirrup\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. bighorn\n2. contaminate\n3. demystify\n4. odysseus\n5. penny\n6. proton\n7. sociolinguistic\n8. stirrup\n9. voltaire\n10. nigeria\n11. **voltaire** (Wait, I listed voltaire twice in my mental check, let's re-sort carefully)\n\nLet's list them and sort them:\nsociolinguistic, nigeria, odysseus, demystify, proton, voltaire, penny, contaminate, bighorn, stirrup\n\n1. bighorn\n2. contaminate\n3. demystify\n4. nigeria\n5. odysseus\n6. penny\n7. proton\n8. sociolinguistic\n9. stirrup\n10. voltaire\n\nLet's re-check the sorting based on the initial list: sociolinguistic, nigeria, odysseus, demystify, proton, voltaire, penny, contaminate, bighorn, stirrup\n\n* Bighorn (B)\n* Contaminate (C)\n* Demystify (D)\n* Nigeria (N)\n* Odysseus (O)\n* Penny (P)\n* Proton (P) - P-e comes before P-e-n, so Penny before Proton.\n* Sociolinguistic (S)\n* Stirrup (S) - Stirrup comes after Sociolinguistic (S-t vs S-o).\n* Voltaire (V)\n\nCorrect alphabetical order:\n1. bighorn\n2. contaminate\n3. demystify\n4. nigeria\n5. odysseus\n6. penny\n7. proton\n8. sociolinguistic\n9. stirrup\n10. voltaire\n\nWait, let's check S words: sociolinguistic, stirrup.\nS-o-c... vs S-t-i... $\\rightarrow$ sociolinguistic comes before stirrup.\n\nFinal check:\nBighorn\nContaminate\nDemystify\nNigeria\nOdysseus\nPenny\nProton\nSociolinguistic\nStirrup\nVoltaire\n\nAnswer: bighorn, contaminate, demystify, nigeria, odysseus, penny, proton, sociolinguistic, stirrup, voltaire\n\nAnswer: bighorn, contaminate, demystify, nigeria, odysseus, penny, proton, sociolinguistic, stirrup, voltaire\n\nFeedback:\nThe attempt contains multiple redundant steps and confusion in the sorting process, although the final derived list appears correct based on the initial list.\n\nCorrection advice: Remove the internal monologue and redundant checks. Present a clear, single sorting process based on the provided list.\n\nAnswer: bighorn, contaminate, demystify, nigeria, odysseus, penny, proton, sociolinguistic, stirrup, voltaire", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0041", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: bighorn, contaminate, demystify, nigeria, odysseus, penny, proton, sociolinguistic, stirrup, voltaire", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.434", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:50:58.804585+00:00" + }, + { + "cycle": 0, + "duration_sec": 3.3591, + "error": null, + "event": "model_call", + "event_seq": 435, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: rowley midshipman saline furl geld locomote exhumation anarchic bstj gradual riddance j liniment pantheist elution profess\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0080", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words to sort alphabetically:\nrowley, midshipman, saline, furl, geld, locomote, exhumation, anarchic, bstj, gradual, riddance, j, liniment, pantheist, elution, profess\n\nLet's sort them:\n\n1. **anarchic**\n2. **bstj**\n3. **elution**\n4. **furl**\n5. **geld**\n6. **gradual**\n7. **j**\n8. **locomote**\n9. **liniment**\n10. **midshipman**\n11. **pantheist**\n12. **profess**\n13. **riddance**\n14. **rowley**\n15. **saline**\n16. **exhumation**\n\nLet's re-check the sorting, especially for words starting with similar letters:\n\n* **a**: anarchic\n* **b**: bstj\n* **e**: elution, exhumation\n * elution comes before exhumation (l before x)\n* **f**: furl\n* **g**: geld, gradual\n * geld comes before gradual (e before r)\n* **j**: j\n* **l**: locomote, liniment\n * locomote comes before liniment (o before i is false, l comes before o) - Wait, L comes before M.\n * locomote\n * liniment\n* **m**: midshipman\n* **p**: pantheist, profess\n * pantheist comes before profess (a before r)\n* **r**: riddance, rowley\n * riddance comes before rowley (i before o)\n* **s**: saline\n* **(remaining)**\n\nLet's perform a strict alphabetical sort:\n\n1. anarchic\n2. bstj\n3. elution\n4. exhumation\n5. furl\n6. geld\n7. gradual\n8. j\n9. liniment\n10. locomote\n11. midshipman\n12. pantheist\n13. profess\n14. riddance\n15. rowley\n16. saline\n\nLet's re-verify the L words: liniment, locomote. L-I vs L-O. I comes before O.\nCorrected L order: liniment, locomote.\n\nLet's re-verify the E words: elution, exhumation. E-L-U vs E-X-H. L comes before X.\nCorrected E order: elution, exhumation.\n\nFinal sorted list:\n1. anarchic\n2. bstj\n3. elution\n4. exhumation\n5. furl\n6. geld\n7. gradual\n8. j\n9. liniment\n10. locomote\n11. midshipman\n12. pantheist\n13. profess\n14. riddance\n15. rowley\n16. saline\n\nAnswer: anarchic, bstj, elution, exhumation, furl, geld, gradual, j, liniment, locomote, midshipman, pantheist, profess, riddance, rowley, saline", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.435", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:02.165695+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.3923, + "error": null, + "event": "model_call", + "event_seq": 436, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: rowley midshipman saline furl geld locomote exhumation anarchic bstj gradual riddance j liniment pantheist elution profess\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words to sort alphabetically:\nrowley, midshipman, saline, furl, geld, locomote, exhumation, anarchic, bstj, gradual, riddance, j, liniment, pantheist, elution, profess\n\nLet's sort them:\n\n1. **anarchic**\n2. **bstj**\n3. **elution**\n4. **furl**\n5. **geld**\n6. **gradual**\n7. **j**\n8. **locomote**\n9. **liniment**\n10. **midshipman**\n11. **pantheist**\n12. **profess**\n13. **riddance**\n14. **rowley**\n15. **saline**\n16. **exhumation**\n\nLet's re-check the sorting, especially for words starting with similar letters:\n\n* **a**: anarchic\n* **b**: bstj\n* **e**: elution, exhumation\n * elution comes before exhumation (l before x)\n* **f**: furl\n* **g**: geld, gradual\n * geld comes before gradual (e before r)\n* **j**: j\n* **l**: locomote, liniment\n * locomote comes before liniment (o before i is false, l comes before o) - Wait, L comes before M.\n * locomote\n * liniment\n* **m**: midshipman\n* **p**: pantheist, profess\n * pantheist comes before profess (a before r)\n* **r**: riddance, rowley\n * riddance comes before rowley (i before o)\n* **s**: saline\n* **(remaining)**\n\nLet's perform a strict alphabetical sort:\n\n1. anarchic\n2. bstj\n3. elution\n4. exhumation\n5. furl\n6. geld\n7. gradual\n8. j\n9. liniment\n10. locomote\n11. midshipman\n12. pantheist\n13. profess\n14. riddance\n15. rowley\n16. saline\n\nLet's re-verify the L words: liniment, locomote. L-I vs L-O. I comes before O.\nCorrected L order: liniment, locomote.\n\nLet's re-verify the E words: elution, exhumation. E-L-U vs E-X-H. L comes before X.\nCorrected E order: elution, exhumation.\n\nFinal sorted list:\n1. anarchic\n2. bstj\n3. elution\n4. exhumation\n5. furl\n6. geld\n7. gradual\n8. j\n9. liniment\n10. locomote\n11. midshipman\n12. pantheist\n13. profess\n14. riddance\n15. rowley\n16. saline\n\nAnswer: anarchic, bstj, elution, exhumation, furl, geld, gradual, j, liniment, locomote, midshipman, pantheist, profess, riddance, rowley, saline", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0080", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempt contains several errors in the sorting process, particularly in the handling of words starting with the same letter and in the final list construction.\n\nCorrection advice:\n1. Review the initial sorting steps for errors. The initial list provided in the attempt is not strictly alphabetical.\n2. Re-evaluate the sorting of words starting with 'E' (elution, exhumation) and 'G' (geld, gradual).\n3. Re-evaluate the sorting of words starting with 'L' (locomote, liniment).\n4. Ensure the final list strictly follows alphabetical order for all 16 words. The attempt's final list seems to have swapped 'locomote' and 'liniment' in the final presentation compared to the detailed re-check, and the initial sorting steps were inconsistent.", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.436", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:03.559911+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.6972, + "error": null, + "event": "model_call", + "event_seq": 437, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: rowley midshipman saline furl geld locomote exhumation anarchic bstj gradual riddance j liniment pantheist elution profess\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words to sort alphabetically:\nrowley, midshipman, saline, furl, geld, locomote, exhumation, anarchic, bstj, gradual, riddance, j, liniment, pantheist, elution, profess\n\nLet's sort them:\n\n1. **anarchic**\n2. **bstj**\n3. **elution**\n4. **furl**\n5. **geld**\n6. **gradual**\n7. **j**\n8. **locomote**\n9. **liniment**\n10. **midshipman**\n11. **pantheist**\n12. **profess**\n13. **riddance**\n14. **rowley**\n15. **saline**\n16. **exhumation**\n\nLet's re-check the sorting, especially for words starting with similar letters:\n\n* **a**: anarchic\n* **b**: bstj\n* **e**: elution, exhumation\n * elution comes before exhumation (l before x)\n* **f**: furl\n* **g**: geld, gradual\n * geld comes before gradual (e before r)\n* **j**: j\n* **l**: locomote, liniment\n * locomote comes before liniment (o before i is false, l comes before o) - Wait, L comes before M.\n * locomote\n * liniment\n* **m**: midshipman\n* **p**: pantheist, profess\n * pantheist comes before profess (a before r)\n* **r**: riddance, rowley\n * riddance comes before rowley (i before o)\n* **s**: saline\n* **(remaining)**\n\nLet's perform a strict alphabetical sort:\n\n1. anarchic\n2. bstj\n3. elution\n4. exhumation\n5. furl\n6. geld\n7. gradual\n8. j\n9. liniment\n10. locomote\n11. midshipman\n12. pantheist\n13. profess\n14. riddance\n15. rowley\n16. saline\n\nLet's re-verify the L words: liniment, locomote. L-I vs L-O. I comes before O.\nCorrected L order: liniment, locomote.\n\nLet's re-verify the E words: elution, exhumation. E-L-U vs E-X-H. L comes before X.\nCorrected E order: elution, exhumation.\n\nFinal sorted list:\n1. anarchic\n2. bstj\n3. elution\n4. exhumation\n5. furl\n6. geld\n7. gradual\n8. j\n9. liniment\n10. locomote\n11. midshipman\n12. pantheist\n13. profess\n14. riddance\n15. rowley\n16. saline\n\nAnswer: anarchic, bstj, elution, exhumation, furl, geld, gradual, j, liniment, locomote, midshipman, pantheist, profess, riddance, rowley, saline\n\nFeedback:\nThe attempt contains several errors in the sorting process, particularly in the handling of words starting with the same letter and in the final list construction.\n\nCorrection advice:\n1. Review the initial sorting steps for errors. The initial list provided in the attempt is not strictly alphabetical.\n2. Re-evaluate the sorting of words starting with 'E' (elution, exhumation) and 'G' (geld, gradual).\n3. Re-evaluate the sorting of words starting with 'L' (locomote, liniment).\n4. Ensure the final list strictly follows alphabetical order for all 16 words. The attempt's final list seems to have swapped 'locomote' and 'liniment' in the final presentation compared to the detailed re-check, and the initial sorting steps were inconsistent.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0080", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "anarchic, bstj, elution, exhumation, furl, geld, gradual, j, liniment, locomote, midshipman, pantheist, profess, riddance, rowley, saline\n\nAnswer: anarchic, bstj, elution, exhumation, furl, geld, gradual, j, liniment, locomote, midshipman, pantheist, profess, riddance, rowley, saline", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.437", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:04.259318+00:00" + }, + { + "cycle": 0, + "duration_sec": 4.7081, + "error": null, + "event": "model_call", + "event_seq": 438, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: hodges deoxyribose troupe motorcade devisee stratify auerbach decor incommensurable dianne\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0004", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words to sort alphabetically:\nhodges, deoxyribose, troupe, motorcade, devisee, stratify, auerbach, decor, incommensurable, dianne\n\nLet's sort them:\n\n1. **auerbach** (Starts with 'a')\n2. **decor** (Starts with 'd', comes before 'dianne')\n3. **devisee** (Starts with 'd', comes after 'decor')\n4. **dianne** (Starts with 'd', comes after 'devisee')\n5. **deoxyribose** (Starts with 'd', comes after 'dianne')\n6. **hodges** (Starts with 'h')\n7. **incommensurable** (Starts with 'i')\n8. **motorcade** (Starts with 'm')\n9. **stratify** (Starts with 's')\n10. **troupe** (Starts with 't')\n\nLet's re-check the 'd' words:\ndecor\ndevisee\ndianne\ndeoxyribose\n\nAlphabetical order for 'd' words:\ndecor\ndevisee (deci...)\ndianne (dia...)\ndeoxyribose (deox...)\nWait, comparing 'devisee' and 'dianne':\nd i a n n e\nd e v i s e e\n'i' comes before 'e' in the second position if we compare the full words:\nd i a n n e\nd e v i s e e\n'i' comes before 'e' is incorrect. Let's compare character by character:\nd i a n n e\nd e v i s e e\n'i' vs 'e'. 'e' comes before 'i'. So 'devisee' comes before 'dianne'.\n\nCorrected 'd' order:\n1. decor\n2. devisee\n3. dianne\n4. deoxyribose\n\nLet's re-sort the whole list:\n\n1. auerbach\n2. decor\n3. devisee\n4. dianne\n5. deoxyribose\n6. hodges\n7. incommensurable\n8. motorcade\n9. stratify\n10. troupe\n\nFinal check:\na - auerbach\nd - decor, devisee, dianne, deoxyribose (decor, devisee, dianne, deoxyribose)\nh - hodges\ni - incommensurable\nm - motorcade\ns - stratify\nt - troupe\n\nThe order for 'd' words:\ndecor\ndevisee (d e v...)\ndianne (d i a...) - Wait, 'i' comes before 'e' in the second position if we compare the full words.\nLet's compare:\ndecor\ndevisee\ndianne\ndeoxyribose\n\nd e c o r\nd e v i s e e\nd i a n n e\nd e o x y r i b o s e\n\nComparing the third letter: c, v, a, o.\na (dianne) comes first.\nc (decor) comes second.\ne (devisee, deoxyribose) come after c.\n\nOrder based on third letter: a, c, e, e.\n1. dianne (d i a...)\n2. decor (d e c...)\n3. devisee (d e v...)\n4. deoxyribose (d e o...)\n\nLet's re-verify the 'd' words:\ndianne (d i a...)\ndecor (d e c...)\ndevisee (d e v...)\ndeoxyribose (d e o...)\n\nComparing second letter: i, e, e, e. 'i' comes first.\n1. dianne\n2. decor, devisee, deoxyribose (all start with 'de')\n\nComparing the third letter for the 'de' words: c, v, o.\nc (decor)\no (deoxyribose)\nv (devisee)\nOrder: decor, deoxyribose, devisee.\n\nFinal 'd' order:\n1. dianne\n2. decor\n3. deoxyribose\n4. devisee\n\nThe final sorted list:\n1. auerbach\n2. dianne\n3. decor\n4. deoxyribose\n5. devisee\n6. hodges\n7. incommensurable\n8. motorcade\n9. stratify\n10. troupe\n\nAnswer: auerbach, dianne, decor, deoxyribose, devisee, hodges, incommensurable, motorcade, stratify, troupe", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.438", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:08.969490+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.0229, + "error": null, + "event": "model_call", + "event_seq": 439, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: hodges deoxyribose troupe motorcade devisee stratify auerbach decor incommensurable dianne\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words to sort alphabetically:\nhodges, deoxyribose, troupe, motorcade, devisee, stratify, auerbach, decor, incommensurable, dianne\n\nLet's sort them:\n\n1. **auerbach** (Starts with 'a')\n2. **decor** (Starts with 'd', comes before 'dianne')\n3. **devisee** (Starts with 'd', comes after 'decor')\n4. **dianne** (Starts with 'd', comes after 'devisee')\n5. **deoxyribose** (Starts with 'd', comes after 'dianne')\n6. **hodges** (Starts with 'h')\n7. **incommensurable** (Starts with 'i')\n8. **motorcade** (Starts with 'm')\n9. **stratify** (Starts with 's')\n10. **troupe** (Starts with 't')\n\nLet's re-check the 'd' words:\ndecor\ndevisee\ndianne\ndeoxyribose\n\nAlphabetical order for 'd' words:\ndecor\ndevisee (deci...)\ndianne (dia...)\ndeoxyribose (deox...)\nWait, comparing 'devisee' and 'dianne':\nd i a n n e\nd e v i s e e\n'i' comes before 'e' in the second position if we compare the full words:\nd i a n n e\nd e v i s e e\n'i' comes before 'e' is incorrect. Let's compare character by character:\nd i a n n e\nd e v i s e e\n'i' vs 'e'. 'e' comes before 'i'. So 'devisee' comes before 'dianne'.\n\nCorrected 'd' order:\n1. decor\n2. devisee\n3. dianne\n4. deoxyribose\n\nLet's re-sort the whole list:\n\n1. auerbach\n2. decor\n3. devisee\n4. dianne\n5. deoxyribose\n6. hodges\n7. incommensurable\n8. motorcade\n9. stratify\n10. troupe\n\nFinal check:\na - auerbach\nd - decor, devisee, dianne, deoxyribose (decor, devisee, dianne, deoxyribose)\nh - hodges\ni - incommensurable\nm - motorcade\ns - stratify\nt - troupe\n\nThe order for 'd' words:\ndecor\ndevisee (d e v...)\ndianne (d i a...) - Wait, 'i' comes before 'e' in the second position if we compare the full words.\nLet's compare:\ndecor\ndevisee\ndianne\ndeoxyribose\n\nd e c o r\nd e v i s e e\nd i a n n e\nd e o x y r i b o s e\n\nComparing the third letter: c, v, a, o.\na (dianne) comes first.\nc (decor) comes second.\ne (devisee, deoxyribose) come after c.\n\nOrder based on third letter: a, c, e, e.\n1. dianne (d i a...)\n2. decor (d e c...)\n3. devisee (d e v...)\n4. deoxyribose (d e o...)\n\nLet's re-verify the 'd' words:\ndianne (d i a...)\ndecor (d e c...)\ndevisee (d e v...)\ndeoxyribose (d e o...)\n\nComparing second letter: i, e, e, e. 'i' comes first.\n1. dianne\n2. decor, devisee, deoxyribose (all start with 'de')\n\nComparing the third letter for the 'de' words: c, v, o.\nc (decor)\no (deoxyribose)\nv (devisee)\nOrder: decor, deoxyribose, devisee.\n\nFinal 'd' order:\n1. dianne\n2. decor\n3. deoxyribose\n4. devisee\n\nThe final sorted list:\n1. auerbach\n2. dianne\n3. decor\n4. deoxyribose\n5. devisee\n6. hodges\n7. incommensurable\n8. motorcade\n9. stratify\n10. troupe\n\nAnswer: auerbach, dianne, decor, deoxyribose, devisee, hodges, incommensurable, motorcade, stratify, troupe", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0004", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The initial attempt had several errors in the sorting logic, particularly in handling the 'd' words. The final corrected list seems to have a logical flow, but the reasoning steps were convoluted and contained internal contradictions.\n\nCorrection Advice: Focus on consistent, character-by-character comparison for all words, especially when dealing with words that share initial letters (like the 'd' words). The final sorting should be based on the standard alphabetical order of the entire strings.\n\nAnswer: auerbach, dianne, decor, deoxyribose, devisee, hodges, incommensurable, motorcade, stratify, troupe", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.439", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:09.994362+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.5733, + "error": null, + "event": "model_call", + "event_seq": 440, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: hodges deoxyribose troupe motorcade devisee stratify auerbach decor incommensurable dianne\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words to sort alphabetically:\nhodges, deoxyribose, troupe, motorcade, devisee, stratify, auerbach, decor, incommensurable, dianne\n\nLet's sort them:\n\n1. **auerbach** (Starts with 'a')\n2. **decor** (Starts with 'd', comes before 'dianne')\n3. **devisee** (Starts with 'd', comes after 'decor')\n4. **dianne** (Starts with 'd', comes after 'devisee')\n5. **deoxyribose** (Starts with 'd', comes after 'dianne')\n6. **hodges** (Starts with 'h')\n7. **incommensurable** (Starts with 'i')\n8. **motorcade** (Starts with 'm')\n9. **stratify** (Starts with 's')\n10. **troupe** (Starts with 't')\n\nLet's re-check the 'd' words:\ndecor\ndevisee\ndianne\ndeoxyribose\n\nAlphabetical order for 'd' words:\ndecor\ndevisee (deci...)\ndianne (dia...)\ndeoxyribose (deox...)\nWait, comparing 'devisee' and 'dianne':\nd i a n n e\nd e v i s e e\n'i' comes before 'e' in the second position if we compare the full words:\nd i a n n e\nd e v i s e e\n'i' comes before 'e' is incorrect. Let's compare character by character:\nd i a n n e\nd e v i s e e\n'i' vs 'e'. 'e' comes before 'i'. So 'devisee' comes before 'dianne'.\n\nCorrected 'd' order:\n1. decor\n2. devisee\n3. dianne\n4. deoxyribose\n\nLet's re-sort the whole list:\n\n1. auerbach\n2. decor\n3. devisee\n4. dianne\n5. deoxyribose\n6. hodges\n7. incommensurable\n8. motorcade\n9. stratify\n10. troupe\n\nFinal check:\na - auerbach\nd - decor, devisee, dianne, deoxyribose (decor, devisee, dianne, deoxyribose)\nh - hodges\ni - incommensurable\nm - motorcade\ns - stratify\nt - troupe\n\nThe order for 'd' words:\ndecor\ndevisee (d e v...)\ndianne (d i a...) - Wait, 'i' comes before 'e' in the second position if we compare the full words.\nLet's compare:\ndecor\ndevisee\ndianne\ndeoxyribose\n\nd e c o r\nd e v i s e e\nd i a n n e\nd e o x y r i b o s e\n\nComparing the third letter: c, v, a, o.\na (dianne) comes first.\nc (decor) comes second.\ne (devisee, deoxyribose) come after c.\n\nOrder based on third letter: a, c, e, e.\n1. dianne (d i a...)\n2. decor (d e c...)\n3. devisee (d e v...)\n4. deoxyribose (d e o...)\n\nLet's re-verify the 'd' words:\ndianne (d i a...)\ndecor (d e c...)\ndevisee (d e v...)\ndeoxyribose (d e o...)\n\nComparing second letter: i, e, e, e. 'i' comes first.\n1. dianne\n2. decor, devisee, deoxyribose (all start with 'de')\n\nComparing the third letter for the 'de' words: c, v, o.\nc (decor)\no (deoxyribose)\nv (devisee)\nOrder: decor, deoxyribose, devisee.\n\nFinal 'd' order:\n1. dianne\n2. decor\n3. deoxyribose\n4. devisee\n\nThe final sorted list:\n1. auerbach\n2. dianne\n3. decor\n4. deoxyribose\n5. devisee\n6. hodges\n7. incommensurable\n8. motorcade\n9. stratify\n10. troupe\n\nAnswer: auerbach, dianne, decor, deoxyribose, devisee, hodges, incommensurable, motorcade, stratify, troupe\n\nFeedback:\nThe initial attempt had several errors in the sorting logic, particularly in handling the 'd' words. The final corrected list seems to have a logical flow, but the reasoning steps were convoluted and contained internal contradictions.\n\nCorrection Advice: Focus on consistent, character-by-character comparison for all words, especially when dealing with words that share initial letters (like the 'd' words). The final sorting should be based on the standard alphabetical order of the entire strings.\n\nAnswer: auerbach, dianne, decor, deoxyribose, devisee, hodges, incommensurable, motorcade, stratify, troupe", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0004", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: auerbach, dianne, decor, deoxyribose, devisee, hodges, incommensurable, motorcade, stratify, troupe", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.440", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:10.569649+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.7941, + "error": null, + "event": "model_call", + "event_seq": 441, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: him correspond seashore herpes\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0114", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The given list of words is: him, correspond, seashore, herpes.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **h**im\n2. **h**erpes\n3. **c**orrespond\n4. **s**eashore\n\nComparing the first letter: 'c' comes before 'h', and 'h' comes before 's'.\n\nSo, 'correspond' comes first among the words starting with 'c', 'h', or 's'. Wait, let's re-examine the starting letters:\n* him: h\n* correspond: c\n* seashore: s\n* herpes: h\n\nAlphabetical order of starting letters: c, h, h, s.\n\n1. **c**orrespond (starts with c)\n2. **h**im (starts with h)\n3. **h**erpes (starts with h)\n4. **s**eashore (starts with s)\n\nNow we compare the words starting with 'h': 'him' and 'herpes'.\n* hi**m**\n* he**r**pes\n'i' comes before 'e' is false. 'e' comes before 'i'.\nSo, 'herpes' comes before 'him'.\n\nThe sorted order is:\n1. correspond\n2. herpes\n3. him\n4. seashore\n\nLet's verify the comparison between 'herpes' and 'him':\nh e r p e s\nh i m\n'e' comes before 'i', so 'herpes' comes before 'him'.\n\nFinal sorted list: correspond, herpes, him, seashore.\n\nAnswer: correspond, herpes, him, seashore\n\nAnswer: correspond, herpes, him, seashore", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.441", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:12.365948+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.005, + "error": null, + "event": "model_call", + "event_seq": 442, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: him correspond seashore herpes\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: him, correspond, seashore, herpes.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **h**im\n2. **h**erpes\n3. **c**orrespond\n4. **s**eashore\n\nComparing the first letter: 'c' comes before 'h', and 'h' comes before 's'.\n\nSo, 'correspond' comes first among the words starting with 'c', 'h', or 's'. Wait, let's re-examine the starting letters:\n* him: h\n* correspond: c\n* seashore: s\n* herpes: h\n\nAlphabetical order of starting letters: c, h, h, s.\n\n1. **c**orrespond (starts with c)\n2. **h**im (starts with h)\n3. **h**erpes (starts with h)\n4. **s**eashore (starts with s)\n\nNow we compare the words starting with 'h': 'him' and 'herpes'.\n* hi**m**\n* he**r**pes\n'i' comes before 'e' is false. 'e' comes before 'i'.\nSo, 'herpes' comes before 'him'.\n\nThe sorted order is:\n1. correspond\n2. herpes\n3. him\n4. seashore\n\nLet's verify the comparison between 'herpes' and 'him':\nh e r p e s\nh i m\n'e' comes before 'i', so 'herpes' comes before 'him'.\n\nFinal sorted list: correspond, herpes, him, seashore.\n\nAnswer: correspond, herpes, him, seashore\n\nAnswer: correspond, herpes, him, seashore", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0114", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The reasoning for comparing 'herpes' and 'him' is flawed.\n\nCorrection advice: Re-evaluate the comparison between 'him' and 'herpes'. Compare the second letter: 'i' in 'him' and 'e' in 'herpes'. Since 'e' comes before 'i' alphabetically, 'herpes' should come before 'him'. The attempt correctly concludes this, but the justification provided (\"'e' comes before 'i', so 'herpes' comes before 'him'\") is correct, but the initial comparison logic in the attempt was slightly confusing. The final sorted order is correct based on the comparison.", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.442", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:13.373020+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.2953, + "error": null, + "event": "model_call", + "event_seq": 443, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: him correspond seashore herpes\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: him, correspond, seashore, herpes.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **h**im\n2. **h**erpes\n3. **c**orrespond\n4. **s**eashore\n\nComparing the first letter: 'c' comes before 'h', and 'h' comes before 's'.\n\nSo, 'correspond' comes first among the words starting with 'c', 'h', or 's'. Wait, let's re-examine the starting letters:\n* him: h\n* correspond: c\n* seashore: s\n* herpes: h\n\nAlphabetical order of starting letters: c, h, h, s.\n\n1. **c**orrespond (starts with c)\n2. **h**im (starts with h)\n3. **h**erpes (starts with h)\n4. **s**eashore (starts with s)\n\nNow we compare the words starting with 'h': 'him' and 'herpes'.\n* hi**m**\n* he**r**pes\n'i' comes before 'e' is false. 'e' comes before 'i'.\nSo, 'herpes' comes before 'him'.\n\nThe sorted order is:\n1. correspond\n2. herpes\n3. him\n4. seashore\n\nLet's verify the comparison between 'herpes' and 'him':\nh e r p e s\nh i m\n'e' comes before 'i', so 'herpes' comes before 'him'.\n\nFinal sorted list: correspond, herpes, him, seashore.\n\nAnswer: correspond, herpes, him, seashore\n\nAnswer: correspond, herpes, him, seashore\n\nFeedback:\nThe reasoning for comparing 'herpes' and 'him' is flawed.\n\nCorrection advice: Re-evaluate the comparison between 'him' and 'herpes'. Compare the second letter: 'i' in 'him' and 'e' in 'herpes'. Since 'e' comes before 'i' alphabetically, 'herpes' should come before 'him'. The attempt correctly concludes this, but the justification provided (\"'e' comes before 'i', so 'herpes' comes before 'him'\") is correct, but the initial comparison logic in the attempt was slightly confusing. The final sorted order is correct based on the comparison.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0114", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: correspond, herpes, him, seashore", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.443", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:13.671156+00:00" + }, + { + "cycle": 0, + "duration_sec": 2.1551, + "error": null, + "event": "model_call", + "event_seq": 444, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: portsmouth impalpable portrait gouda brainy succumb fatuous hoof lisbon erudite malaria enigma isaacson cony stronghold servomechanism\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0012", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words sorted alphabetically:\n\n1. brainy\n2. enigma\n3. erudite\n4. fatuous\n5. gouda\n6. impalpable\n7. isaacson\n8. lisbon\n9. malaria\n10. portsmouth\n11. portrait\n12. servomechanism\n13. stronghold\n14. succumb\n15. cony\n16. hoof\n\nLet's re-sort them carefully:\n\n* brainy\n* cony\n* erudite\n* enigma\n* fatuous\n* gouda\n* impalpable\n* isaacson\n* hoof\n* lisbon\n* malaria\n* portsmouth\n* portrait\n* servomechanism\n* stronghold\n* succumb\n\nLet's check the order again:\nB (brainy)\nC (cony)\nE (erudite, enigma) -> erudite before enigma\nF (fatuous)\nG (gouda)\nI (impalpable, isaacson) -> impalpable before isaacson\nH (hoof)\nL (lisbon)\nM (malaria)\nP (portsmouth, portrait) -> portsmouth before portrait\nS (servomechanism, succumb, stronghold) -> servomechanism, succumb, stronghold (S-e-r, S-u, S-t)\n\nCorrect alphabetical order:\n1. brainy\n2. cony\n3. erudite\n4. enigma\n5. fatuous\n6. gouda\n7. impalpable\n8. isaacson\n9. hoof\n10. lisbon\n11. malaria\n12. portsmouth\n13. portrait\n14. servomechanism\n15. stronghold\n16. succumb\n\nAnswer: brainy, cony, erudite, enigma, fatuous, gouda, impalpable, isaacson, hoof, lisbon, malaria, portsmouth, portrait, servomechanism, stronghold, succumb", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.444", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:15.831402+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.2675, + "error": null, + "event": "model_call", + "event_seq": 445, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: portsmouth impalpable portrait gouda brainy succumb fatuous hoof lisbon erudite malaria enigma isaacson cony stronghold servomechanism\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. brainy\n2. enigma\n3. erudite\n4. fatuous\n5. gouda\n6. impalpable\n7. isaacson\n8. lisbon\n9. malaria\n10. portsmouth\n11. portrait\n12. servomechanism\n13. stronghold\n14. succumb\n15. cony\n16. hoof\n\nLet's re-sort them carefully:\n\n* brainy\n* cony\n* erudite\n* enigma\n* fatuous\n* gouda\n* impalpable\n* isaacson\n* hoof\n* lisbon\n* malaria\n* portsmouth\n* portrait\n* servomechanism\n* stronghold\n* succumb\n\nLet's check the order again:\nB (brainy)\nC (cony)\nE (erudite, enigma) -> erudite before enigma\nF (fatuous)\nG (gouda)\nI (impalpable, isaacson) -> impalpable before isaacson\nH (hoof)\nL (lisbon)\nM (malaria)\nP (portsmouth, portrait) -> portsmouth before portrait\nS (servomechanism, succumb, stronghold) -> servomechanism, succumb, stronghold (S-e-r, S-u, S-t)\n\nCorrect alphabetical order:\n1. brainy\n2. cony\n3. erudite\n4. enigma\n5. fatuous\n6. gouda\n7. impalpable\n8. isaacson\n9. hoof\n10. lisbon\n11. malaria\n12. portsmouth\n13. portrait\n14. servomechanism\n15. stronghold\n16. succumb\n\nAnswer: brainy, cony, erudite, enigma, fatuous, gouda, impalpable, isaacson, hoof, lisbon, malaria, portsmouth, portrait, servomechanism, stronghold, succumb", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0012", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The initial attempt's sorting logic contains errors, particularly in the relative ordering of words starting with the same letter or in the overall sequence. The final proposed answer is also incorrect based on standard alphabetical sorting.\n\nCorrection advice: Re-sort the entire list strictly alphabetically. Pay close attention to words starting with the same letter (e.g., 'erudite' vs 'enigma', 'impalpable' vs 'isaacson', 'portsmouth' vs 'portrait', and the 'S' words).\n\nAnswer: brainy, cony, erudite, enigma, fatuous, gouda, impalpable, isaacson, hoof, lisbon, malaria, portsmouth, portrait, servomechanism, stronghold, succumb", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.445", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:17.101129+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.7848, + "error": null, + "event": "model_call", + "event_seq": 446, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: portsmouth impalpable portrait gouda brainy succumb fatuous hoof lisbon erudite malaria enigma isaacson cony stronghold servomechanism\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. brainy\n2. enigma\n3. erudite\n4. fatuous\n5. gouda\n6. impalpable\n7. isaacson\n8. lisbon\n9. malaria\n10. portsmouth\n11. portrait\n12. servomechanism\n13. stronghold\n14. succumb\n15. cony\n16. hoof\n\nLet's re-sort them carefully:\n\n* brainy\n* cony\n* erudite\n* enigma\n* fatuous\n* gouda\n* impalpable\n* isaacson\n* hoof\n* lisbon\n* malaria\n* portsmouth\n* portrait\n* servomechanism\n* stronghold\n* succumb\n\nLet's check the order again:\nB (brainy)\nC (cony)\nE (erudite, enigma) -> erudite before enigma\nF (fatuous)\nG (gouda)\nI (impalpable, isaacson) -> impalpable before isaacson\nH (hoof)\nL (lisbon)\nM (malaria)\nP (portsmouth, portrait) -> portsmouth before portrait\nS (servomechanism, succumb, stronghold) -> servomechanism, succumb, stronghold (S-e-r, S-u, S-t)\n\nCorrect alphabetical order:\n1. brainy\n2. cony\n3. erudite\n4. enigma\n5. fatuous\n6. gouda\n7. impalpable\n8. isaacson\n9. hoof\n10. lisbon\n11. malaria\n12. portsmouth\n13. portrait\n14. servomechanism\n15. stronghold\n16. succumb\n\nAnswer: brainy, cony, erudite, enigma, fatuous, gouda, impalpable, isaacson, hoof, lisbon, malaria, portsmouth, portrait, servomechanism, stronghold, succumb\n\nFeedback:\nThe initial attempt's sorting logic contains errors, particularly in the relative ordering of words starting with the same letter or in the overall sequence. The final proposed answer is also incorrect based on standard alphabetical sorting.\n\nCorrection advice: Re-sort the entire list strictly alphabetically. Pay close attention to words starting with the same letter (e.g., 'erudite' vs 'enigma', 'impalpable' vs 'isaacson', 'portsmouth' vs 'portrait', and the 'S' words).\n\nAnswer: brainy, cony, erudite, enigma, fatuous, gouda, impalpable, isaacson, hoof, lisbon, malaria, portsmouth, portrait, servomechanism, stronghold, succumb", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0012", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: brainy, cony, erudite, enigma, fatuous, gouda, impalpable, isaacson, hoof, lisbon, malaria, portsmouth, portrait, servomechanism, stronghold, succumb", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.446", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:17.888105+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.884, + "error": null, + "event": "model_call", + "event_seq": 447, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: yves butt scribe seaworthy manumitted clapeyron lift blackguard reconnaissance agile woodcut lightfooted sawbelly meager geophysical wiseacre mathieu cognoscenti purposive flamboyant\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0032", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words sorted alphabetically:\n\n1. agile\n2. blackguard\n3. clapeyron\n4. cognoscenti\n5. geophysical\n6. flamboyant\n7. lift\n8. lightfooted\n9. manumitted\n10. meager\n11. mathieu\n12. purposive\n13. reconnaissance\n14. seaworthy\n15. scribe\n16. sawbelly\n17. wiseacre\n18. woodcut\n19. yves\n20. butt\n\nLet's re-sort them carefully:\n\n* **a**gile\n* **b**lackguard, butt\n* **c**lapeyron, cognoscenti\n* **g**eophysical\n* **l**ift, lightfooted\n* **m**anumitted, meager, mathieu\n* **p**urposeive\n* **r**econnaissance\n* **s**eaworthy, scribe, sawbelly\n* **w**iseacre, woodcut, yves\n\nDetailed sorting:\n\n1. agile\n2. blackguard\n3. butt\n4. clapeyron\n5. cognoscenti\n6. geophysical\n7. flamboyant\n8. lift\n9. lightfooted\n10. manumitted\n11. meager\n12. mathieu\n13. purposive\n14. reconnaissance\n15. seaworthy\n16. scribe\n17. sawbelly\n18. wiseacre\n19. woodcut\n20. yves\n\nAnswer: agile, blackguard, butt, clapeyron, cognoscenti, geophysical, flamboyant, lift, lightfooted, manumitted, meager, mathieu, purposive, reconnaissance, seaworthy, scribe, sawbelly, wiseacre, woodcut, yves", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.447", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:19.774421+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.9233, + "error": null, + "event": "model_call", + "event_seq": 448, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: yves butt scribe seaworthy manumitted clapeyron lift blackguard reconnaissance agile woodcut lightfooted sawbelly meager geophysical wiseacre mathieu cognoscenti purposive flamboyant\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. agile\n2. blackguard\n3. clapeyron\n4. cognoscenti\n5. geophysical\n6. flamboyant\n7. lift\n8. lightfooted\n9. manumitted\n10. meager\n11. mathieu\n12. purposive\n13. reconnaissance\n14. seaworthy\n15. scribe\n16. sawbelly\n17. wiseacre\n18. woodcut\n19. yves\n20. butt\n\nLet's re-sort them carefully:\n\n* **a**gile\n* **b**lackguard, butt\n* **c**lapeyron, cognoscenti\n* **g**eophysical\n* **l**ift, lightfooted\n* **m**anumitted, meager, mathieu\n* **p**urposeive\n* **r**econnaissance\n* **s**eaworthy, scribe, sawbelly\n* **w**iseacre, woodcut, yves\n\nDetailed sorting:\n\n1. agile\n2. blackguard\n3. butt\n4. clapeyron\n5. cognoscenti\n6. geophysical\n7. flamboyant\n8. lift\n9. lightfooted\n10. manumitted\n11. meager\n12. mathieu\n13. purposive\n14. reconnaissance\n15. seaworthy\n16. scribe\n17. sawbelly\n18. wiseacre\n19. woodcut\n20. yves\n\nAnswer: agile, blackguard, butt, clapeyron, cognoscenti, geophysical, flamboyant, lift, lightfooted, manumitted, meager, mathieu, purposive, reconnaissance, seaworthy, scribe, sawbelly, wiseacre, woodcut, yves", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0032", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempted answer has a factual error in the initial sorting and a minor error in the final list presentation based on the provided words.\n\nCorrection advice:\n1. Review the initial sorting step. The words 'butt' and 'blackguard' should be sorted alphabetically as 'blackguard' then 'butt'.\n2. Review the detailed sorting. Ensure all words from the original list are present and correctly ordered according to standard alphabetical rules. Specifically, check the placement of 'flamboyant' and 'geophysical' relative to others.\n\nAnswer: agile, blackguard, butt, clapeyron, cognoscenti, geophysical, flamboyant, lift, lightfooted, manumitted, meager, mathieu, purposive, reconnaissance, seaworthy, scribe, sawbelly, wiseacre, woodcut, yves", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.448", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:20.699931+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.8911, + "error": null, + "event": "model_call", + "event_seq": 449, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: yves butt scribe seaworthy manumitted clapeyron lift blackguard reconnaissance agile woodcut lightfooted sawbelly meager geophysical wiseacre mathieu cognoscenti purposive flamboyant\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. agile\n2. blackguard\n3. clapeyron\n4. cognoscenti\n5. geophysical\n6. flamboyant\n7. lift\n8. lightfooted\n9. manumitted\n10. meager\n11. mathieu\n12. purposive\n13. reconnaissance\n14. seaworthy\n15. scribe\n16. sawbelly\n17. wiseacre\n18. woodcut\n19. yves\n20. butt\n\nLet's re-sort them carefully:\n\n* **a**gile\n* **b**lackguard, butt\n* **c**lapeyron, cognoscenti\n* **g**eophysical\n* **l**ift, lightfooted\n* **m**anumitted, meager, mathieu\n* **p**urposeive\n* **r**econnaissance\n* **s**eaworthy, scribe, sawbelly\n* **w**iseacre, woodcut, yves\n\nDetailed sorting:\n\n1. agile\n2. blackguard\n3. butt\n4. clapeyron\n5. cognoscenti\n6. geophysical\n7. flamboyant\n8. lift\n9. lightfooted\n10. manumitted\n11. meager\n12. mathieu\n13. purposive\n14. reconnaissance\n15. seaworthy\n16. scribe\n17. sawbelly\n18. wiseacre\n19. woodcut\n20. yves\n\nAnswer: agile, blackguard, butt, clapeyron, cognoscenti, geophysical, flamboyant, lift, lightfooted, manumitted, meager, mathieu, purposive, reconnaissance, seaworthy, scribe, sawbelly, wiseacre, woodcut, yves\n\nFeedback:\nThe attempted answer has a factual error in the initial sorting and a minor error in the final list presentation based on the provided words.\n\nCorrection advice:\n1. Review the initial sorting step. The words 'butt' and 'blackguard' should be sorted alphabetically as 'blackguard' then 'butt'.\n2. Review the detailed sorting. Ensure all words from the original list are present and correctly ordered according to standard alphabetical rules. Specifically, check the placement of 'flamboyant' and 'geophysical' relative to others.\n\nAnswer: agile, blackguard, butt, clapeyron, cognoscenti, geophysical, flamboyant, lift, lightfooted, manumitted, meager, mathieu, purposive, reconnaissance, seaworthy, scribe, sawbelly, wiseacre, woodcut, yves", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0032", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: agile, blackguard, butt, clapeyron, cognoscenti, geophysical, flamboyant, lift, lightfooted, manumitted, meager, mathieu, purposive, reconnaissance, seaworthy, scribe, sawbelly, wiseacre, woodcut, yves", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.449", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:21.594669+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.5987, + "error": null, + "event": "model_call", + "event_seq": 450, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: kenya beth\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0112", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The given list of words is: kenya, beth.\n\nTo sort these words alphabetically, we compare them letter by letter from the beginning.\n\n1. **Compare the first letter:**\n - 'k' in \"kenya\"\n - 'b' in \"beth\"\n Since 'b' comes before 'k' in the alphabet, \"beth\" comes before \"kenya\".\n\nTherefore, the alphabetical order is: beth, kenya.\n\nAnswer: beth, kenya", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.450", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:22.195777+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.4685, + "error": null, + "event": "model_call", + "event_seq": 451, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: kenya beth\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: kenya, beth.\n\nTo sort these words alphabetically, we compare them letter by letter from the beginning.\n\n1. **Compare the first letter:**\n - 'k' in \"kenya\"\n - 'b' in \"beth\"\n Since 'b' comes before 'k' in the alphabet, \"beth\" comes before \"kenya\".\n\nTherefore, the alphabetical order is: beth, kenya.\n\nAnswer: beth, kenya", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0112", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Correction advice: The attempt is factually correct and the reasoning is sound. The sorting is correct.", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.451", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:22.666524+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.4023, + "error": null, + "event": "model_call", + "event_seq": 452, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: kenya beth\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: kenya, beth.\n\nTo sort these words alphabetically, we compare them letter by letter from the beginning.\n\n1. **Compare the first letter:**\n - 'k' in \"kenya\"\n - 'b' in \"beth\"\n Since 'b' comes before 'k' in the alphabet, \"beth\" comes before \"kenya\".\n\nTherefore, the alphabetical order is: beth, kenya.\n\nAnswer: beth, kenya\n\nFeedback:\nCorrection advice: The attempt is factually correct and the reasoning is sound. The sorting is correct.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0112", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: beth, kenya", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.452", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:23.071073+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.7849, + "error": null, + "event": "model_call", + "event_seq": 453, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: yuh edt cheddar oblivion from poignant pang\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0001", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The given list of words is: yuh, edt, cheddar, oblivion, from, poignant, pang.\n\nWe need to sort these words alphabetically.\n\n1. **Compare the first letters:**\n - yuh (y)\n - edt (e)\n - cheddar (c)\n - oblivion (o)\n - from (f)\n - poignant (p)\n - pang (p)\n\n2. **Order based on the first letter:**\n - c (cheddar)\n - e (edt)\n - f (from)\n - o (oblivion)\n - p (poignant, pang)\n - y (yuh)\n\n3. **Sort the words starting with 'p':**\n - poignant\n - pang\n (Comparing the second letters: 'o' in poignant vs 'a' in pang. 'a' comes before 'o'.)\n So, the order is: pang, poignant.\n\n4. **Assemble the final sorted list:**\n - cheddar (c)\n - edt (e)\n - from (f)\n - oblivion (o)\n - pang (p)\n - poignant (p)\n - yuh (y)\n\nLet's re-check the 'p' words:\npang (p-a-n-g)\npoignant (p-o-i-g-n-a-n-t)\n'a' comes before 'o', so pang comes before poignant.\n\nFinal sorted order: cheddar, edt, from, oblivion, pang, poignant, yuh.\n\nAnswer: cheddar, edt, from, oblivion, pang, poignant, yuh", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.453", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:24.858362+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.7329, + "error": null, + "event": "model_call", + "event_seq": 454, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: yuh edt cheddar oblivion from poignant pang\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: yuh, edt, cheddar, oblivion, from, poignant, pang.\n\nWe need to sort these words alphabetically.\n\n1. **Compare the first letters:**\n - yuh (y)\n - edt (e)\n - cheddar (c)\n - oblivion (o)\n - from (f)\n - poignant (p)\n - pang (p)\n\n2. **Order based on the first letter:**\n - c (cheddar)\n - e (edt)\n - f (from)\n - o (oblivion)\n - p (poignant, pang)\n - y (yuh)\n\n3. **Sort the words starting with 'p':**\n - poignant\n - pang\n (Comparing the second letters: 'o' in poignant vs 'a' in pang. 'a' comes before 'o'.)\n So, the order is: pang, poignant.\n\n4. **Assemble the final sorted list:**\n - cheddar (c)\n - edt (e)\n - from (f)\n - oblivion (o)\n - pang (p)\n - poignant (p)\n - yuh (y)\n\nLet's re-check the 'p' words:\npang (p-a-n-g)\npoignant (p-o-i-g-n-a-n-t)\n'a' comes before 'o', so pang comes before poignant.\n\nFinal sorted order: cheddar, edt, from, oblivion, pang, poignant, yuh.\n\nAnswer: cheddar, edt, from, oblivion, pang, poignant, yuh", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0001", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempt contains an error in the initial list provided in the prompt versus the list used in the attempt.\n\nCorrection advice: Ensure the initial list of words used for sorting exactly matches the words provided in the original task, including handling the initial word \"yuh edt cheddar oblivion from poignant pang\" as a single list of words. The attempt correctly identified the words but missed the initial instruction to sort the provided list. The sorting logic itself is sound based on standard alphabetical order.\n\nAnswer: cheddar, edt, from, oblivion, pang, poignant, yuh", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.454", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:25.596177+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.263, + "error": null, + "event": "model_call", + "event_seq": 455, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: yuh edt cheddar oblivion from poignant pang\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: yuh, edt, cheddar, oblivion, from, poignant, pang.\n\nWe need to sort these words alphabetically.\n\n1. **Compare the first letters:**\n - yuh (y)\n - edt (e)\n - cheddar (c)\n - oblivion (o)\n - from (f)\n - poignant (p)\n - pang (p)\n\n2. **Order based on the first letter:**\n - c (cheddar)\n - e (edt)\n - f (from)\n - o (oblivion)\n - p (poignant, pang)\n - y (yuh)\n\n3. **Sort the words starting with 'p':**\n - poignant\n - pang\n (Comparing the second letters: 'o' in poignant vs 'a' in pang. 'a' comes before 'o'.)\n So, the order is: pang, poignant.\n\n4. **Assemble the final sorted list:**\n - cheddar (c)\n - edt (e)\n - from (f)\n - oblivion (o)\n - pang (p)\n - poignant (p)\n - yuh (y)\n\nLet's re-check the 'p' words:\npang (p-a-n-g)\npoignant (p-o-i-g-n-a-n-t)\n'a' comes before 'o', so pang comes before poignant.\n\nFinal sorted order: cheddar, edt, from, oblivion, pang, poignant, yuh.\n\nAnswer: cheddar, edt, from, oblivion, pang, poignant, yuh\n\nFeedback:\nThe attempt contains an error in the initial list provided in the prompt versus the list used in the attempt.\n\nCorrection advice: Ensure the initial list of words used for sorting exactly matches the words provided in the original task, including handling the initial word \"yuh edt cheddar oblivion from poignant pang\" as a single list of words. The attempt correctly identified the words but missed the initial instruction to sort the provided list. The sorting logic itself is sound based on standard alphabetical order.\n\nAnswer: cheddar, edt, from, oblivion, pang, poignant, yuh", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0001", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: cheddar, edt, from, oblivion, pang, poignant, yuh", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.455", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:25.861587+00:00" + }, + { + "cycle": 0, + "duration_sec": 3.9997, + "error": null, + "event": "model_call", + "event_seq": 456, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: reedy bully multiple edt sell referring lexicographer tit flogging perceptive minor bosporus rancorous forfeit pizza pungent sedition cork\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0070", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words to sort alphabetically:\nreedy, bully, multiple, edt, sell, referring, lexicographer, tit, flogging, perceptive, minor, bosporus, rancorous, forfeit, pizza, pungent, sedition, cork\n\nLet's sort them:\n\n1. **bosporus**\n2. **bully**\n3. **cork**\n4. **edt**\n5. **forfeit**\n6. **flogging**\n7. **lexicographer**\n8. **multiple**\n9. **minor**\n10. **perceptive**\n11. **pizza**\n12. **pungent**\n13. **rancorous**\n14. **reedy**\n15. **referring**\n16. **sell**\n17. **sedition**\n18. **tit**\n\nLet's re-check the sorting, especially for words starting with similar letters:\n\n* **b**: bully, bosporus\n * bosporus (bo...)\n * bully (bu...) $\\rightarrow$ bosporus, bully\n* **c**: cork\n* **e**: edt\n* **f**: forfeit, flogging\n * forfeit (for...)\n * flogging (flo...) $\\rightarrow$ flogging, forfeit\n* **l**: lexicographer\n* **m**: multiple, minor\n * multiple (mul...)\n * minor (min...) $\\rightarrow$ minor, multiple\n* **p**: perceptive, pizza, pungent\n * perceptive (per...)\n * pizza (pi...)\n * pungent (pun...) $\\rightarrow$ perceptive, pizza, pungent\n* **r**: reedy, referring, rancorous\n * reedy (ree...)\n * referring (ref...)\n * rancorous (ran...) $\\rightarrow$ rancorous, reedy, referring\n* **s**: sell, sedition\n * sell (sel...)\n * sedition (sed...) $\\rightarrow$ sell, sedition\n* **t**: tit\n\nLet's perform a strict alphabetical sort:\n\n1. bosporus (B)\n2. bully (B)\n3. cork (C)\n4. edt (E)\n5. flogging (F)\n6. forfeit (F)\n7. lexicographer (L)\n8. minor (M)\n9. multiple (M)\n10. perceptive (P)\n11. pizza (P)\n12. pungent (P)\n13. rancorous (R)\n14. reedy (R)\n15. referring (R)\n16. sell (S)\n17. sedition (S)\n18. tit (T)\n\nRefining the groups:\n\nB: bosporus, bully (bosporus before bully)\nC: cork\nE: edt\nF: flogging, forfeit (flogging before forfeit)\nL: lexicographer\nM: minor, multiple (minor before multiple)\nP: perceptive, pizza, pungent (perceptive, pizza, pungent)\nR: rancorous, reedy, referring (rancorous before reedy before referring)\nS: sell, sedition (sell before sedition)\nT: tit\n\nFinal Sorted List:\n1. bosporus\n2. bully\n3. cork\n4. edt\n5. flogging\n6. forfeit\n7. lexicographer\n8. minor\n9. multiple\n10. perceptive\n11. pizza\n12. pungent\n13. rancorous\n14. reedy\n15. referring\n16. sell\n17. sedition\n18. tit\n\nAnswer: bosporus, bully, cork, edt, flogging, forfeit, lexicographer, minor, multiple, perceptive, pizza, pungent, rancorous, reedy, referring, sell, sedition, tit", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.456", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:29.863928+00:00" + }, + { + "cycle": 0, + "duration_sec": 2.6945, + "error": null, + "event": "model_call", + "event_seq": 457, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: reedy bully multiple edt sell referring lexicographer tit flogging perceptive minor bosporus rancorous forfeit pizza pungent sedition cork\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words to sort alphabetically:\nreedy, bully, multiple, edt, sell, referring, lexicographer, tit, flogging, perceptive, minor, bosporus, rancorous, forfeit, pizza, pungent, sedition, cork\n\nLet's sort them:\n\n1. **bosporus**\n2. **bully**\n3. **cork**\n4. **edt**\n5. **forfeit**\n6. **flogging**\n7. **lexicographer**\n8. **multiple**\n9. **minor**\n10. **perceptive**\n11. **pizza**\n12. **pungent**\n13. **rancorous**\n14. **reedy**\n15. **referring**\n16. **sell**\n17. **sedition**\n18. **tit**\n\nLet's re-check the sorting, especially for words starting with similar letters:\n\n* **b**: bully, bosporus\n * bosporus (bo...)\n * bully (bu...) $\\rightarrow$ bosporus, bully\n* **c**: cork\n* **e**: edt\n* **f**: forfeit, flogging\n * forfeit (for...)\n * flogging (flo...) $\\rightarrow$ flogging, forfeit\n* **l**: lexicographer\n* **m**: multiple, minor\n * multiple (mul...)\n * minor (min...) $\\rightarrow$ minor, multiple\n* **p**: perceptive, pizza, pungent\n * perceptive (per...)\n * pizza (pi...)\n * pungent (pun...) $\\rightarrow$ perceptive, pizza, pungent\n* **r**: reedy, referring, rancorous\n * reedy (ree...)\n * referring (ref...)\n * rancorous (ran...) $\\rightarrow$ rancorous, reedy, referring\n* **s**: sell, sedition\n * sell (sel...)\n * sedition (sed...) $\\rightarrow$ sell, sedition\n* **t**: tit\n\nLet's perform a strict alphabetical sort:\n\n1. bosporus (B)\n2. bully (B)\n3. cork (C)\n4. edt (E)\n5. flogging (F)\n6. forfeit (F)\n7. lexicographer (L)\n8. minor (M)\n9. multiple (M)\n10. perceptive (P)\n11. pizza (P)\n12. pungent (P)\n13. rancorous (R)\n14. reedy (R)\n15. referring (R)\n16. sell (S)\n17. sedition (S)\n18. tit (T)\n\nRefining the groups:\n\nB: bosporus, bully (bosporus before bully)\nC: cork\nE: edt\nF: flogging, forfeit (flogging before forfeit)\nL: lexicographer\nM: minor, multiple (minor before multiple)\nP: perceptive, pizza, pungent (perceptive, pizza, pungent)\nR: rancorous, reedy, referring (rancorous before reedy before referring)\nS: sell, sedition (sell before sedition)\nT: tit\n\nFinal Sorted List:\n1. bosporus\n2. bully\n3. cork\n4. edt\n5. flogging\n6. forfeit\n7. lexicographer\n8. minor\n9. multiple\n10. perceptive\n11. pizza\n12. pungent\n13. rancorous\n14. reedy\n15. referring\n16. sell\n17. sedition\n18. tit\n\nAnswer: bosporus, bully, cork, edt, flogging, forfeit, lexicographer, minor, multiple, perceptive, pizza, pungent, rancorous, reedy, referring, sell, sedition, tit", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0070", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempt contains several errors in the sorting logic, particularly in the initial manual sort and the subsequent re-check.\n\n1. **Initial Sorting Error:** The initial list provided in the attempt is not strictly alphabetical.\n2. **Grouping Errors:** The reasoning provided for grouping words starting with the same letter (e.g., B, F, M, P, R, S) is inconsistent or incorrect based on standard alphabetical order.\n * For 'B': 'bosporus' (bo) vs 'bully' (bu). 'bosporus' comes before 'bully'. (Correct in the final list, but the reasoning needs to be sound).\n * For 'F': 'flogging' vs 'forfeit'. 'flogging' comes before 'forfeit'. (Correct in the final list).\n * For 'M': 'minor' vs 'multiple'. 'minor' comes before 'multiple'. (Correct in the final list).\n * For 'P': 'perceptive', 'pizza', 'pungent'. The order 'perceptive, pizza, pungent' is correct.\n * For 'R': 'rancorous', 'reedy', 'referring'. The order 'rancorous, reedy, referring' is correct.\n * For 'S': 'sell' vs 'sedition'. 'sell' comes before 'sedition'. (Correct in the final list).\n\n3. **Final List Construction Error:** The final list provided in the attempt is slightly different from the derived final list based on the detailed re-check, specifically in the ordering of the 'R' words and the placement of 'lexicographer'.\n\n**Correction Advice:** Review the alphabetical order for all words, paying close attention to the first letter, and then the second letter if the first letters are the same. Ensure the final sequence strictly follows standard dictionary order.\n\nAnswer: bosporus, bully, cork, edt, flogging, forfeit, lexicographer, minor, multiple, perceptive, pizza, pungent, rancorous, reedy, referring, sell, sedition, tit", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.457", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:32.561639+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.7994, + "error": null, + "event": "model_call", + "event_seq": 458, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: reedy bully multiple edt sell referring lexicographer tit flogging perceptive minor bosporus rancorous forfeit pizza pungent sedition cork\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words to sort alphabetically:\nreedy, bully, multiple, edt, sell, referring, lexicographer, tit, flogging, perceptive, minor, bosporus, rancorous, forfeit, pizza, pungent, sedition, cork\n\nLet's sort them:\n\n1. **bosporus**\n2. **bully**\n3. **cork**\n4. **edt**\n5. **forfeit**\n6. **flogging**\n7. **lexicographer**\n8. **multiple**\n9. **minor**\n10. **perceptive**\n11. **pizza**\n12. **pungent**\n13. **rancorous**\n14. **reedy**\n15. **referring**\n16. **sell**\n17. **sedition**\n18. **tit**\n\nLet's re-check the sorting, especially for words starting with similar letters:\n\n* **b**: bully, bosporus\n * bosporus (bo...)\n * bully (bu...) $\\rightarrow$ bosporus, bully\n* **c**: cork\n* **e**: edt\n* **f**: forfeit, flogging\n * forfeit (for...)\n * flogging (flo...) $\\rightarrow$ flogging, forfeit\n* **l**: lexicographer\n* **m**: multiple, minor\n * multiple (mul...)\n * minor (min...) $\\rightarrow$ minor, multiple\n* **p**: perceptive, pizza, pungent\n * perceptive (per...)\n * pizza (pi...)\n * pungent (pun...) $\\rightarrow$ perceptive, pizza, pungent\n* **r**: reedy, referring, rancorous\n * reedy (ree...)\n * referring (ref...)\n * rancorous (ran...) $\\rightarrow$ rancorous, reedy, referring\n* **s**: sell, sedition\n * sell (sel...)\n * sedition (sed...) $\\rightarrow$ sell, sedition\n* **t**: tit\n\nLet's perform a strict alphabetical sort:\n\n1. bosporus (B)\n2. bully (B)\n3. cork (C)\n4. edt (E)\n5. flogging (F)\n6. forfeit (F)\n7. lexicographer (L)\n8. minor (M)\n9. multiple (M)\n10. perceptive (P)\n11. pizza (P)\n12. pungent (P)\n13. rancorous (R)\n14. reedy (R)\n15. referring (R)\n16. sell (S)\n17. sedition (S)\n18. tit (T)\n\nRefining the groups:\n\nB: bosporus, bully (bosporus before bully)\nC: cork\nE: edt\nF: flogging, forfeit (flogging before forfeit)\nL: lexicographer\nM: minor, multiple (minor before multiple)\nP: perceptive, pizza, pungent (perceptive, pizza, pungent)\nR: rancorous, reedy, referring (rancorous before reedy before referring)\nS: sell, sedition (sell before sedition)\nT: tit\n\nFinal Sorted List:\n1. bosporus\n2. bully\n3. cork\n4. edt\n5. flogging\n6. forfeit\n7. lexicographer\n8. minor\n9. multiple\n10. perceptive\n11. pizza\n12. pungent\n13. rancorous\n14. reedy\n15. referring\n16. sell\n17. sedition\n18. tit\n\nAnswer: bosporus, bully, cork, edt, flogging, forfeit, lexicographer, minor, multiple, perceptive, pizza, pungent, rancorous, reedy, referring, sell, sedition, tit\n\nFeedback:\nThe attempt contains several errors in the sorting logic, particularly in the initial manual sort and the subsequent re-check.\n\n1. **Initial Sorting Error:** The initial list provided in the attempt is not strictly alphabetical.\n2. **Grouping Errors:** The reasoning provided for grouping words starting with the same letter (e.g., B, F, M, P, R, S) is inconsistent or incorrect based on standard alphabetical order.\n * For 'B': 'bosporus' (bo) vs 'bully' (bu). 'bosporus' comes before 'bully'. (Correct in the final list, but the reasoning needs to be sound).\n * For 'F': 'flogging' vs 'forfeit'. 'flogging' comes before 'forfeit'. (Correct in the final list).\n * For 'M': 'minor' vs 'multiple'. 'minor' comes before 'multiple'. (Correct in the final list).\n * For 'P': 'perceptive', 'pizza', 'pungent'. The order 'perceptive, pizza, pungent' is correct.\n * For 'R': 'rancorous', 'reedy', 'referring'. The order 'rancorous, reedy, referring' is correct.\n * For 'S': 'sell' vs 'sedition'. 'sell' comes before 'sedition'. (Correct in the final list).\n\n3. **Final List Construction Error:** The final list provided in the attempt is slightly different from the derived final list based on the detailed re-check, specifically in the ordering of the 'R' words and the placement of 'lexicographer'.\n\n**Correction Advice:** Review the alphabetical order for all words, paying close attention to the first letter, and then the second letter if the first letters are the same. Ensure the final sequence strictly follows standard dictionary order.\n\nAnswer: bosporus, bully, cork, edt, flogging, forfeit, lexicographer, minor, multiple, perceptive, pizza, pungent, rancorous, reedy, referring, sell, sedition, tit", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0070", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: bosporus, bully, cork, edt, flogging, forfeit, lexicographer, minor, multiple, perceptive, pizza, pungent, rancorous, reedy, referring, sell, sedition, tit", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.458", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:33.364156+00:00" + }, + { + "cycle": 0, + "duration_sec": 2.4655, + "error": null, + "event": "model_call", + "event_seq": 459, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: anarchic liven shortcut total diatom guelph usage acoustic yarmulke phenomenology neo bureaucracy trammel leftward immovable fabricate syndrome suggestive provide\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0103", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words sorted alphabetically:\n\n1. acoustic\n2. anarchic\n3. diatom\n4. fabricate\n5. phenomenology\n6. leftward\n7. liven\n8. neo\n9. immovable\n10. provide\n11. shortcut\n12. syndrome\n13. total\n14. trammel\n15. usage\n16. yarmulke\n17. bureaucracy\n18. guelph\n19. suggestive\n\nLet's re-sort them carefully:\n\n* **a**narchic, **a**coustic\n* **b**ureaucracy\n* **d**iatom\n* **f**abricate\n* **g**uelph\n* **i**mmovable\n* **l**eftward, **l**iven\n* **n**eo\n* **p**henomenology, **p**rovide\n* **s**hortcut, **s**yndrome, **s**uggestive\n* **t**otal, **t**rammel\n* **u**sage\n* **y**armulke\n\n**Detailed Alphabetical Sort:**\n\n1. acoustic\n2. anarchic\n3. bureaucracy\n4. diatom\n5. fabricate\n6. guelph\n7. immovable\n8. leftward\n9. liven\n10. neo\n11. phenomenology\n12. provide\n13. shortcut\n14. syndrome\n15. total\n16. trammel\n17. usage\n18. yarmulke\n19. suggestive\n\n(Checking L words: leftward, liven. L-e-f-t vs L-i-v-e. Leftward comes before liven.)\n(Checking P words: phenomenology, provide. Phenomenology comes before provide.)\n(Checking S words: shortcut, syndrome, suggestive. Shortcut, syndrome, suggestive.)\n\nFinal check of the full list:\nanarchic, acoustic, bureaucracy, diatom, fabricate, guelph, immovable, leftward, liven, neo, phenomenology, provide, shortcut, syndrome, total, trammel, usage, yarmulke, suggestive.\n\nAnswer: acoustic, anarchic, bureaucracy, diatom, fabricate, guelph, immovable, leftward, liven, neo, phenomenology, provide, shortcut, syndrome, total, trammel, usage, yarmulke, suggestive", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.459", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:35.832448+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.7629, + "error": null, + "event": "model_call", + "event_seq": 460, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: anarchic liven shortcut total diatom guelph usage acoustic yarmulke phenomenology neo bureaucracy trammel leftward immovable fabricate syndrome suggestive provide\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. acoustic\n2. anarchic\n3. diatom\n4. fabricate\n5. phenomenology\n6. leftward\n7. liven\n8. neo\n9. immovable\n10. provide\n11. shortcut\n12. syndrome\n13. total\n14. trammel\n15. usage\n16. yarmulke\n17. bureaucracy\n18. guelph\n19. suggestive\n\nLet's re-sort them carefully:\n\n* **a**narchic, **a**coustic\n* **b**ureaucracy\n* **d**iatom\n* **f**abricate\n* **g**uelph\n* **i**mmovable\n* **l**eftward, **l**iven\n* **n**eo\n* **p**henomenology, **p**rovide\n* **s**hortcut, **s**yndrome, **s**uggestive\n* **t**otal, **t**rammel\n* **u**sage\n* **y**armulke\n\n**Detailed Alphabetical Sort:**\n\n1. acoustic\n2. anarchic\n3. bureaucracy\n4. diatom\n5. fabricate\n6. guelph\n7. immovable\n8. leftward\n9. liven\n10. neo\n11. phenomenology\n12. provide\n13. shortcut\n14. syndrome\n15. total\n16. trammel\n17. usage\n18. yarmulke\n19. suggestive\n\n(Checking L words: leftward, liven. L-e-f-t vs L-i-v-e. Leftward comes before liven.)\n(Checking P words: phenomenology, provide. Phenomenology comes before provide.)\n(Checking S words: shortcut, syndrome, suggestive. Shortcut, syndrome, suggestive.)\n\nFinal check of the full list:\nanarchic, acoustic, bureaucracy, diatom, fabricate, guelph, immovable, leftward, liven, neo, phenomenology, provide, shortcut, syndrome, total, trammel, usage, yarmulke, suggestive.\n\nAnswer: acoustic, anarchic, bureaucracy, diatom, fabricate, guelph, immovable, leftward, liven, neo, phenomenology, provide, shortcut, syndrome, total, trammel, usage, yarmulke, suggestive", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0103", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempted answer contains errors in the initial list provided and the final sorted list.\n\n1. **Factual Error (Word List):** The original list provided in the prompt is: \"anarchic liven shortcut total diatom guelph usage acoustic yarmulke phenomenology neo bureaucracy trammel leftward immovable fabricate syndrome suggestive provide\". The attempt seems to have missed or misordered some words initially, although the final list attempts to include all 20 words.\n2. **Reasoning/Arithmetic Error (Sorting):** The detailed sorting section contains several errors in the final sequence, particularly in the placement of words starting with 'L', 'P', and 'S'.\n * The initial list provided in the attempt (1-19) is incorrect.\n * The detailed sort has errors:\n * 'liven' vs 'leftward': The attempt correctly notes 'leftward' comes before 'liven'.\n * 'phenomenology' vs 'provide': The attempt correctly notes 'phenomenology' comes before 'provide'.\n * The final sequence derived from the detailed sort does not match the final answer provided.\n\n**Correction Advice:** Re-sort the entire list strictly alphabetically. Pay close attention to words starting with the same letter (e.g., 'a', 'l', 'p', 's') and ensure the sequence follows standard dictionary order.\n\nAnswer: acoustic, anarchic, bureaucracy, diatom, fabricate, guelph, immovable, leftward, liven, neo, phenomenology, provide, shortcut, syndrome, total, trammel, usage, yarmulke, suggestive", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.460", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:37.598172+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.6997, + "error": null, + "event": "model_call", + "event_seq": 461, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: anarchic liven shortcut total diatom guelph usage acoustic yarmulke phenomenology neo bureaucracy trammel leftward immovable fabricate syndrome suggestive provide\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. acoustic\n2. anarchic\n3. diatom\n4. fabricate\n5. phenomenology\n6. leftward\n7. liven\n8. neo\n9. immovable\n10. provide\n11. shortcut\n12. syndrome\n13. total\n14. trammel\n15. usage\n16. yarmulke\n17. bureaucracy\n18. guelph\n19. suggestive\n\nLet's re-sort them carefully:\n\n* **a**narchic, **a**coustic\n* **b**ureaucracy\n* **d**iatom\n* **f**abricate\n* **g**uelph\n* **i**mmovable\n* **l**eftward, **l**iven\n* **n**eo\n* **p**henomenology, **p**rovide\n* **s**hortcut, **s**yndrome, **s**uggestive\n* **t**otal, **t**rammel\n* **u**sage\n* **y**armulke\n\n**Detailed Alphabetical Sort:**\n\n1. acoustic\n2. anarchic\n3. bureaucracy\n4. diatom\n5. fabricate\n6. guelph\n7. immovable\n8. leftward\n9. liven\n10. neo\n11. phenomenology\n12. provide\n13. shortcut\n14. syndrome\n15. total\n16. trammel\n17. usage\n18. yarmulke\n19. suggestive\n\n(Checking L words: leftward, liven. L-e-f-t vs L-i-v-e. Leftward comes before liven.)\n(Checking P words: phenomenology, provide. Phenomenology comes before provide.)\n(Checking S words: shortcut, syndrome, suggestive. Shortcut, syndrome, suggestive.)\n\nFinal check of the full list:\nanarchic, acoustic, bureaucracy, diatom, fabricate, guelph, immovable, leftward, liven, neo, phenomenology, provide, shortcut, syndrome, total, trammel, usage, yarmulke, suggestive.\n\nAnswer: acoustic, anarchic, bureaucracy, diatom, fabricate, guelph, immovable, leftward, liven, neo, phenomenology, provide, shortcut, syndrome, total, trammel, usage, yarmulke, suggestive\n\nFeedback:\nThe attempted answer contains errors in the initial list provided and the final sorted list.\n\n1. **Factual Error (Word List):** The original list provided in the prompt is: \"anarchic liven shortcut total diatom guelph usage acoustic yarmulke phenomenology neo bureaucracy trammel leftward immovable fabricate syndrome suggestive provide\". The attempt seems to have missed or misordered some words initially, although the final list attempts to include all 20 words.\n2. **Reasoning/Arithmetic Error (Sorting):** The detailed sorting section contains several errors in the final sequence, particularly in the placement of words starting with 'L', 'P', and 'S'.\n * The initial list provided in the attempt (1-19) is incorrect.\n * The detailed sort has errors:\n * 'liven' vs 'leftward': The attempt correctly notes 'leftward' comes before 'liven'.\n * 'phenomenology' vs 'provide': The attempt correctly notes 'phenomenology' comes before 'provide'.\n * The final sequence derived from the detailed sort does not match the final answer provided.\n\n**Correction Advice:** Re-sort the entire list strictly alphabetically. Pay close attention to words starting with the same letter (e.g., 'a', 'l', 'p', 's') and ensure the sequence follows standard dictionary order.\n\nAnswer: acoustic, anarchic, bureaucracy, diatom, fabricate, guelph, immovable, leftward, liven, neo, phenomenology, provide, shortcut, syndrome, total, trammel, usage, yarmulke, suggestive", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0103", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: acoustic, anarchic, bureaucracy, diatom, fabricate, guelph, immovable, leftward, liven, neo, phenomenology, provide, shortcut, syndrome, total, trammel, usage, yarmulke, suggestive", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.461", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:38.300871+00:00" + }, + { + "cycle": 0, + "duration_sec": 2.5862, + "error": null, + "event": "model_call", + "event_seq": 462, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: strove hendrickson apostasy jacm indonesia vibrate afloat lanthanide grosbeak scavenge quark chattel conner bechtel melancholy ferment\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0068", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words to sort alphabetically:\nstrove, hendrickson, apostasy, jacm, indonesia, vibrate, afloat, lanthanide, grosbeak, scavenge, chattel, conner, bechtel, melancholy, ferment\n\nLet's sort them:\n\n1. **afloat**\n2. **apostasy**\n3. **bechtel**\n4. **chattel**\n5. **conner**\n6. **ferment**\n7. **grosbeak**\n8. **hendrickson**\n9. **indonesia**\n10. **jacm**\n11. **lanthanide**\n12. **melancholy**\n13. **scavenge**\n14. **strove**\n15. **vibrate**\n\nLet's re-check the sorting, especially for words starting with similar letters:\n\n* **a**: afloat, apostasy\n* **b**: bechtel\n* **c**: chattel, conner\n* **f**: ferment\n* **g**: grosbeak\n* **h**: hendrickson\n* **i**: indonesia\n* **j**: jacm\n* **l**: lanthanide\n* **m**: melancholy\n* **s**: scavenge, strove\n* **v**: vibrate\n\nDetailed comparison:\n* afloat (a-f)\n* apostasy (a-p) $\\rightarrow$ afloat comes before apostasy.\n* bechtel (b)\n* chattel (c-h)\n* conner (c-o) $\\rightarrow$ chattel comes before conner.\n* ferment (f)\n* grosbeak (g)\n* hendrickson (h)\n* indonesia (i)\n* jacm (j)\n* lanthanide (l)\n* melancholy (m)\n* scavenge (s-c)\n* strove (s-t) $\\rightarrow$ scavenge comes before strove.\n* vibrate (v)\n\nCorrected order:\n1. afloat\n2. apostasy\n3. bechtel\n4. chattel\n5. conner\n6. ferment\n7. grosbeak\n8. hendrickson\n9. indonesia\n10. jacm\n11. lanthanide\n12. melancholy\n13. scavenge\n14. strove\n15. vibrate\n\nAnswer: afloat, apostasy, bechtel, chattel, conner, ferment, grosbeak, hendrickson, indonesia, jacm, lanthanide, melancholy, scavenge, strove, vibrate", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.462", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:40.890035+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.9109, + "error": null, + "event": "model_call", + "event_seq": 463, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: strove hendrickson apostasy jacm indonesia vibrate afloat lanthanide grosbeak scavenge quark chattel conner bechtel melancholy ferment\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words to sort alphabetically:\nstrove, hendrickson, apostasy, jacm, indonesia, vibrate, afloat, lanthanide, grosbeak, scavenge, chattel, conner, bechtel, melancholy, ferment\n\nLet's sort them:\n\n1. **afloat**\n2. **apostasy**\n3. **bechtel**\n4. **chattel**\n5. **conner**\n6. **ferment**\n7. **grosbeak**\n8. **hendrickson**\n9. **indonesia**\n10. **jacm**\n11. **lanthanide**\n12. **melancholy**\n13. **scavenge**\n14. **strove**\n15. **vibrate**\n\nLet's re-check the sorting, especially for words starting with similar letters:\n\n* **a**: afloat, apostasy\n* **b**: bechtel\n* **c**: chattel, conner\n* **f**: ferment\n* **g**: grosbeak\n* **h**: hendrickson\n* **i**: indonesia\n* **j**: jacm\n* **l**: lanthanide\n* **m**: melancholy\n* **s**: scavenge, strove\n* **v**: vibrate\n\nDetailed comparison:\n* afloat (a-f)\n* apostasy (a-p) $\\rightarrow$ afloat comes before apostasy.\n* bechtel (b)\n* chattel (c-h)\n* conner (c-o) $\\rightarrow$ chattel comes before conner.\n* ferment (f)\n* grosbeak (g)\n* hendrickson (h)\n* indonesia (i)\n* jacm (j)\n* lanthanide (l)\n* melancholy (m)\n* scavenge (s-c)\n* strove (s-t) $\\rightarrow$ scavenge comes before strove.\n* vibrate (v)\n\nCorrected order:\n1. afloat\n2. apostasy\n3. bechtel\n4. chattel\n5. conner\n6. ferment\n7. grosbeak\n8. hendrickson\n9. indonesia\n10. jacm\n11. lanthanide\n12. melancholy\n13. scavenge\n14. strove\n15. vibrate\n\nAnswer: afloat, apostasy, bechtel, chattel, conner, ferment, grosbeak, hendrickson, indonesia, jacm, lanthanide, melancholy, scavenge, strove, vibrate", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0068", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The sorting logic is mostly correct, but there are errors in the initial sorting and the final comparison/re-check.\n\n1. **Initial Sorting Error:** The attempt lists \"strove\" before \"vibrate\" in the initial list, but the final derived order places \"vibrate\" last, which is correct. However, the initial list provided in the attempt is slightly out of order compared to the final derived list.\n2. **Detailed Comparison Error (S-words):** The comparison between \"scavenge\" and \"strove\" is correct: 'sc' comes before 'st'.\n3. **Final Order Check:** Reviewing the alphabetical order based on the provided list:\n * afloat (A)\n * apostasy (A) $\\rightarrow$ afloat before apostasy (f before p). Correct.\n * bechtel (B)\n * chattel (C)\n * conner (C) $\\rightarrow$ chattel before conner (h before o). Correct.\n * ferment (F)\n * grosbeak (G)\n * hendrickson (H)\n * indonesia (I)\n * jacm (J)\n * lanthanide (L)\n * melancholy (M)\n * scavenge (S)\n * strove (S) $\\rightarrow$ scavenge before strove (c before t). Correct.\n * vibrate (V)\n\nThe final derived order matches the correct alphabetical sorting. The reasoning steps are sound, and the final answer reflects the correct alphabetical order.\n\nCorrection advice: No factual, reasoning, arithmetic, or instruction-following errors were found. The sorting is correct.", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.463", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:42.803591+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.6793, + "error": null, + "event": "model_call", + "event_seq": 464, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: strove hendrickson apostasy jacm indonesia vibrate afloat lanthanide grosbeak scavenge quark chattel conner bechtel melancholy ferment\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words to sort alphabetically:\nstrove, hendrickson, apostasy, jacm, indonesia, vibrate, afloat, lanthanide, grosbeak, scavenge, chattel, conner, bechtel, melancholy, ferment\n\nLet's sort them:\n\n1. **afloat**\n2. **apostasy**\n3. **bechtel**\n4. **chattel**\n5. **conner**\n6. **ferment**\n7. **grosbeak**\n8. **hendrickson**\n9. **indonesia**\n10. **jacm**\n11. **lanthanide**\n12. **melancholy**\n13. **scavenge**\n14. **strove**\n15. **vibrate**\n\nLet's re-check the sorting, especially for words starting with similar letters:\n\n* **a**: afloat, apostasy\n* **b**: bechtel\n* **c**: chattel, conner\n* **f**: ferment\n* **g**: grosbeak\n* **h**: hendrickson\n* **i**: indonesia\n* **j**: jacm\n* **l**: lanthanide\n* **m**: melancholy\n* **s**: scavenge, strove\n* **v**: vibrate\n\nDetailed comparison:\n* afloat (a-f)\n* apostasy (a-p) $\\rightarrow$ afloat comes before apostasy.\n* bechtel (b)\n* chattel (c-h)\n* conner (c-o) $\\rightarrow$ chattel comes before conner.\n* ferment (f)\n* grosbeak (g)\n* hendrickson (h)\n* indonesia (i)\n* jacm (j)\n* lanthanide (l)\n* melancholy (m)\n* scavenge (s-c)\n* strove (s-t) $\\rightarrow$ scavenge comes before strove.\n* vibrate (v)\n\nCorrected order:\n1. afloat\n2. apostasy\n3. bechtel\n4. chattel\n5. conner\n6. ferment\n7. grosbeak\n8. hendrickson\n9. indonesia\n10. jacm\n11. lanthanide\n12. melancholy\n13. scavenge\n14. strove\n15. vibrate\n\nAnswer: afloat, apostasy, bechtel, chattel, conner, ferment, grosbeak, hendrickson, indonesia, jacm, lanthanide, melancholy, scavenge, strove, vibrate\n\nFeedback:\nThe sorting logic is mostly correct, but there are errors in the initial sorting and the final comparison/re-check.\n\n1. **Initial Sorting Error:** The attempt lists \"strove\" before \"vibrate\" in the initial list, but the final derived order places \"vibrate\" last, which is correct. However, the initial list provided in the attempt is slightly out of order compared to the final derived list.\n2. **Detailed Comparison Error (S-words):** The comparison between \"scavenge\" and \"strove\" is correct: 'sc' comes before 'st'.\n3. **Final Order Check:** Reviewing the alphabetical order based on the provided list:\n * afloat (A)\n * apostasy (A) $\\rightarrow$ afloat before apostasy (f before p). Correct.\n * bechtel (B)\n * chattel (C)\n * conner (C) $\\rightarrow$ chattel before conner (h before o). Correct.\n * ferment (F)\n * grosbeak (G)\n * hendrickson (H)\n * indonesia (I)\n * jacm (J)\n * lanthanide (L)\n * melancholy (M)\n * scavenge (S)\n * strove (S) $\\rightarrow$ scavenge before strove (c before t). Correct.\n * vibrate (V)\n\nThe final derived order matches the correct alphabetical sorting. The reasoning steps are sound, and the final answer reflects the correct alphabetical order.\n\nCorrection advice: No factual, reasoning, arithmetic, or instruction-following errors were found. The sorting is correct.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0068", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: afloat, apostasy, bechtel, chattel, conner, ferment, grosbeak, hendrickson, indonesia, jacm, lanthanide, melancholy, scavenge, strove, vibrate", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.464", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:43.485729+00:00" + }, + { + "cycle": 0, + "duration_sec": 4.6674, + "error": null, + "event": "model_call", + "event_seq": 465, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: betony bronzy charge cordial handicraft boar collet clemens bootleg possession mesh centaur contemporaneous macmahon underclassman monterey dorado contravariant regina\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0125", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words to be sorted alphabetically:\nbetony, bronzy, charge, cordial, handicraft, boar, collet, clemens, bootleg, possession, mesh, centaur, contemporaneous, macmahon, monterey, dorado, contravariant, regina, underclassman\n\nLet's sort them:\n\n1. **boar**\n2. **betony**\n3. **bootleg**\n4. **bronzy**\n5. **charge**\n6. **centaur**\n7. **clemens**\n8. **collet**\n9. **contravariant**\n10. **cordial**\n11. **danicraft** (handicraft)\n12. **dorado**\n13. **macmahon**\n14. **mesh**\n15. **monterey**\n16. **possession**\n17. **regina**\n18. **underclassman**\n19. **contemporaneous**\n\nLet's re-sort carefully, paying attention to the initial letters:\n\nB: betony, boar, bootleg, bronzy\nC: charge, cordial, collet, clemens, centaur, contemporaneous, contravariant\nD: dorado\nH: handicraft\nM: macmahon, mesh, monterey\nP: possession\nR: regina\nU: underclassman\n\n**Step 1: Group by first letter**\n\nB: betony, boar, bootleg, bronzy\nC: charge, cordial, collet, clemens, centaur, contemporaneous, contravariant\nD: dorado\nH: handicraft\nM: macmahon, mesh, monterey\nP: possession\nR: regina\nU: underclassman\n\n**Step 2: Sort within groups**\n\nB: betony, boar, bootleg, bronzy (betony, boar, bootleg, bronzy)\n * betony\n * boar\n * bootleg\n * bronzy\n\nC: charge, cordial, collet, clemens, centaur, contemporaneous, contravariant (charge, cordial, collet, clemens, centaur, contemporaneous, contravariant)\n * charge\n * cordial\n * collet\n * clemens\n * centaur\n * contemporaneous\n * contravariant\n\nD: dorado\n\nH: handicraft\n\nM: macmahon, mesh, monterey (macmahon, mesh, monterey)\n * macmahon\n * mesh\n * monterey\n\nP: possession\n\nR: regina\n\nU: underclassman\n\n**Step 3: Combine the sorted groups**\n\n1. betony\n2. boar\n3. bootleg\n4. bronzy\n5. charge\n6. cordial\n7. collet\n8. clemens\n9. centaur\n10. contemporaneous\n11. contravariant\n12. dorado\n13. handicraft\n14. macmahon\n15. mesh\n16. monterey\n17. possession\n18. regina\n19. underclassman\n\nLet's re-check the C group:\ncharge, cordial, collet, clemens, centaur, contemporaneous, contravariant\nc-h-a-r-g-e\nc-o-r-d-i-a-l\nc-o-l-l-e-t\nc-l-e-m-e-n-s\nc-e-n-t-a-u-r\nc-o-n-t-e-m-p-o-r-a-n-e-o-u-s\nc-o-n-t-r-a-v-a-n-t-e\n\nCorrect alphabetical order for C words:\ncharge\ncordial\ncollet\nclemens\ncentaur\ncontravariant\ncontemporaneous\n\nFinal check of the full list:\n1. betony\n2. boar\n3. bootleg\n4. bronzy\n5. charge\n6. cordial\n7. collet\n8. clemens\n9. centaur\n10. contemporaneous\n11. contravariant\n12. dorado\n13. handicraft\n14. macmahon\n15. mesh\n16. monterey\n17. possession\n18. regina\n19. underclassman\n\nAnswer: betony, boar, bootleg, bronzy, charge, cordial, collet, clemens, centaur, contemporaneous, contravariant, dorado, handicraft, macmahon, mesh, monterey, possession, regina, underclassman", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.465", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:48.156080+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.7233, + "error": null, + "event": "model_call", + "event_seq": 466, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: betony bronzy charge cordial handicraft boar collet clemens bootleg possession mesh centaur contemporaneous macmahon underclassman monterey dorado contravariant regina\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words to be sorted alphabetically:\nbetony, bronzy, charge, cordial, handicraft, boar, collet, clemens, bootleg, possession, mesh, centaur, contemporaneous, macmahon, monterey, dorado, contravariant, regina, underclassman\n\nLet's sort them:\n\n1. **boar**\n2. **betony**\n3. **bootleg**\n4. **bronzy**\n5. **charge**\n6. **centaur**\n7. **clemens**\n8. **collet**\n9. **contravariant**\n10. **cordial**\n11. **danicraft** (handicraft)\n12. **dorado**\n13. **macmahon**\n14. **mesh**\n15. **monterey**\n16. **possession**\n17. **regina**\n18. **underclassman**\n19. **contemporaneous**\n\nLet's re-sort carefully, paying attention to the initial letters:\n\nB: betony, boar, bootleg, bronzy\nC: charge, cordial, collet, clemens, centaur, contemporaneous, contravariant\nD: dorado\nH: handicraft\nM: macmahon, mesh, monterey\nP: possession\nR: regina\nU: underclassman\n\n**Step 1: Group by first letter**\n\nB: betony, boar, bootleg, bronzy\nC: charge, cordial, collet, clemens, centaur, contemporaneous, contravariant\nD: dorado\nH: handicraft\nM: macmahon, mesh, monterey\nP: possession\nR: regina\nU: underclassman\n\n**Step 2: Sort within groups**\n\nB: betony, boar, bootleg, bronzy (betony, boar, bootleg, bronzy)\n * betony\n * boar\n * bootleg\n * bronzy\n\nC: charge, cordial, collet, clemens, centaur, contemporaneous, contravariant (charge, cordial, collet, clemens, centaur, contemporaneous, contravariant)\n * charge\n * cordial\n * collet\n * clemens\n * centaur\n * contemporaneous\n * contravariant\n\nD: dorado\n\nH: handicraft\n\nM: macmahon, mesh, monterey (macmahon, mesh, monterey)\n * macmahon\n * mesh\n * monterey\n\nP: possession\n\nR: regina\n\nU: underclassman\n\n**Step 3: Combine the sorted groups**\n\n1. betony\n2. boar\n3. bootleg\n4. bronzy\n5. charge\n6. cordial\n7. collet\n8. clemens\n9. centaur\n10. contemporaneous\n11. contravariant\n12. dorado\n13. handicraft\n14. macmahon\n15. mesh\n16. monterey\n17. possession\n18. regina\n19. underclassman\n\nLet's re-check the C group:\ncharge, cordial, collet, clemens, centaur, contemporaneous, contravariant\nc-h-a-r-g-e\nc-o-r-d-i-a-l\nc-o-l-l-e-t\nc-l-e-m-e-n-s\nc-e-n-t-a-u-r\nc-o-n-t-e-m-p-o-r-a-n-e-o-u-s\nc-o-n-t-r-a-v-a-n-t-e\n\nCorrect alphabetical order for C words:\ncharge\ncordial\ncollet\nclemens\ncentaur\ncontravariant\ncontemporaneous\n\nFinal check of the full list:\n1. betony\n2. boar\n3. bootleg\n4. bronzy\n5. charge\n6. cordial\n7. collet\n8. clemens\n9. centaur\n10. contemporaneous\n11. contravariant\n12. dorado\n13. handicraft\n14. macmahon\n15. mesh\n16. monterey\n17. possession\n18. regina\n19. underclassman\n\nAnswer: betony, boar, bootleg, bronzy, charge, cordial, collet, clemens, centaur, contemporaneous, contravariant, dorado, handicraft, macmahon, mesh, monterey, possession, regina, underclassman", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0125", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempt contains several errors in the sorting process, particularly in the initial grouping and the final ordering of words.\n\n1. **Error in Word List/Typo:** The attempt incorrectly substitutes \"handicraft\" with \"danicraft\" in the initial sorting step.\n2. **Error in Sorting Logic (B Group):** The order within the 'B' group (betony, boar, bootleg, bronzy) is incorrect.\n3. **Error in Sorting Logic (C Group):** The attempt's final ordering of the 'C' words is inconsistent with standard alphabetical sorting (e.g., placing 'contemporaneous' before 'contravariant' is correct, but the overall sequence needs verification).\n4. **Error in Final List Construction:** The final list presented in the attempt seems to have mixed up the order of some words based on the intermediate steps shown.\n\n**Correction Advice:** Re-sort the entire list strictly alphabetically based on the first letter, and then alphabetically within those groups. Ensure all words from the original list are present and correctly ordered.\n\nAnswer: betony, boar, bootleg, bronzy, charge, cordial, collet, clemens, centaur, contemporaneous, contravariant, dorado, handicraft, macmahon, mesh, monterey, possession, regina, underclassman", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.466", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:49.882130+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.6733, + "error": null, + "event": "model_call", + "event_seq": 467, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: betony bronzy charge cordial handicraft boar collet clemens bootleg possession mesh centaur contemporaneous macmahon underclassman monterey dorado contravariant regina\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words to be sorted alphabetically:\nbetony, bronzy, charge, cordial, handicraft, boar, collet, clemens, bootleg, possession, mesh, centaur, contemporaneous, macmahon, monterey, dorado, contravariant, regina, underclassman\n\nLet's sort them:\n\n1. **boar**\n2. **betony**\n3. **bootleg**\n4. **bronzy**\n5. **charge**\n6. **centaur**\n7. **clemens**\n8. **collet**\n9. **contravariant**\n10. **cordial**\n11. **danicraft** (handicraft)\n12. **dorado**\n13. **macmahon**\n14. **mesh**\n15. **monterey**\n16. **possession**\n17. **regina**\n18. **underclassman**\n19. **contemporaneous**\n\nLet's re-sort carefully, paying attention to the initial letters:\n\nB: betony, boar, bootleg, bronzy\nC: charge, cordial, collet, clemens, centaur, contemporaneous, contravariant\nD: dorado\nH: handicraft\nM: macmahon, mesh, monterey\nP: possession\nR: regina\nU: underclassman\n\n**Step 1: Group by first letter**\n\nB: betony, boar, bootleg, bronzy\nC: charge, cordial, collet, clemens, centaur, contemporaneous, contravariant\nD: dorado\nH: handicraft\nM: macmahon, mesh, monterey\nP: possession\nR: regina\nU: underclassman\n\n**Step 2: Sort within groups**\n\nB: betony, boar, bootleg, bronzy (betony, boar, bootleg, bronzy)\n * betony\n * boar\n * bootleg\n * bronzy\n\nC: charge, cordial, collet, clemens, centaur, contemporaneous, contravariant (charge, cordial, collet, clemens, centaur, contemporaneous, contravariant)\n * charge\n * cordial\n * collet\n * clemens\n * centaur\n * contemporaneous\n * contravariant\n\nD: dorado\n\nH: handicraft\n\nM: macmahon, mesh, monterey (macmahon, mesh, monterey)\n * macmahon\n * mesh\n * monterey\n\nP: possession\n\nR: regina\n\nU: underclassman\n\n**Step 3: Combine the sorted groups**\n\n1. betony\n2. boar\n3. bootleg\n4. bronzy\n5. charge\n6. cordial\n7. collet\n8. clemens\n9. centaur\n10. contemporaneous\n11. contravariant\n12. dorado\n13. handicraft\n14. macmahon\n15. mesh\n16. monterey\n17. possession\n18. regina\n19. underclassman\n\nLet's re-check the C group:\ncharge, cordial, collet, clemens, centaur, contemporaneous, contravariant\nc-h-a-r-g-e\nc-o-r-d-i-a-l\nc-o-l-l-e-t\nc-l-e-m-e-n-s\nc-e-n-t-a-u-r\nc-o-n-t-e-m-p-o-r-a-n-e-o-u-s\nc-o-n-t-r-a-v-a-n-t-e\n\nCorrect alphabetical order for C words:\ncharge\ncordial\ncollet\nclemens\ncentaur\ncontravariant\ncontemporaneous\n\nFinal check of the full list:\n1. betony\n2. boar\n3. bootleg\n4. bronzy\n5. charge\n6. cordial\n7. collet\n8. clemens\n9. centaur\n10. contemporaneous\n11. contravariant\n12. dorado\n13. handicraft\n14. macmahon\n15. mesh\n16. monterey\n17. possession\n18. regina\n19. underclassman\n\nAnswer: betony, boar, bootleg, bronzy, charge, cordial, collet, clemens, centaur, contemporaneous, contravariant, dorado, handicraft, macmahon, mesh, monterey, possession, regina, underclassman\n\nFeedback:\nThe attempt contains several errors in the sorting process, particularly in the initial grouping and the final ordering of words.\n\n1. **Error in Word List/Typo:** The attempt incorrectly substitutes \"handicraft\" with \"danicraft\" in the initial sorting step.\n2. **Error in Sorting Logic (B Group):** The order within the 'B' group (betony, boar, bootleg, bronzy) is incorrect.\n3. **Error in Sorting Logic (C Group):** The attempt's final ordering of the 'C' words is inconsistent with standard alphabetical sorting (e.g., placing 'contemporaneous' before 'contravariant' is correct, but the overall sequence needs verification).\n4. **Error in Final List Construction:** The final list presented in the attempt seems to have mixed up the order of some words based on the intermediate steps shown.\n\n**Correction Advice:** Re-sort the entire list strictly alphabetically based on the first letter, and then alphabetically within those groups. Ensure all words from the original list are present and correctly ordered.\n\nAnswer: betony, boar, bootleg, bronzy, charge, cordial, collet, clemens, centaur, contemporaneous, contravariant, dorado, handicraft, macmahon, mesh, monterey, possession, regina, underclassman", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0125", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: betony, boar, bootleg, bronzy, charge, cordial, collet, clemens, centaur, contemporaneous, contravariant, dorado, handicraft, macmahon, mesh, monterey, possession, regina, underclassman", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.467", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:50.558389+00:00" + }, + { + "cycle": 0, + "duration_sec": 2.4109, + "error": null, + "event": "model_call", + "event_seq": 468, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: foamflower lawful cayenne chandigarh only excursion declassify equanimity aseptic inclement october debauch kruger groupoid scorch dingy alterate\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0010", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words sorted alphabetically:\n\n1. alterate\n2. aseptic\n3. cayenne\n4. chandigarh\n5. declassify\n6. debauch\n7. dingy\n8. excursion\n9. equanimity\n10. foamflower\n11. lawful\n12. october\n13. only\n14. groupoid\n15. kruger\n16. scorch\n17. alterate (Wait, I listed alterate twice in my thought process, let's re-check the original list: foamflower lawful cayenne chandigarh only excursion declassify equanimity aseptic inclement october debauch kruger groupoid scorch dingy alterate)\n\nLet's re-sort carefully:\n\nOriginal List: foamflower, lawful, cayenne, chandigarh, only, excursion, declassify, equanimity, aseptic, inclement, october, debauch, kruger, groupoid, scorch, dingy, alterate\n\n1. alterate\n2. aseptic\n3. cayenne\n4. chandigarh\n5. declassify\n6. debauch\n7. dingy\n8. excursion\n9. equanimity\n10. foamflower\n11. inclement\n12. lawful\n13. october\n14. only\n15. groupoid\n16. kruger\n17. scorch\n\nLet's re-sort the remaining words based on the full list:\n\nA: alterate, aseptic\nC: cayenne, chandigarh\nD: declassify, debauch, dingy\nE: excursion, equanimity\nF: foamflower\nI: inclement\nL: lawful\nO: october, only\nG: groupoid\nK: kruger\nS: scorch\n\nCorrect Alphabetical Order:\n1. alterate\n2. aseptic\n3. cayenne\n4. chandigarh\n5. declassify\n6. debauch\n7. dingy\n8. excursion\n9. equanimity\n10. foamflower\n11. inclement\n12. lawful\n13. october\n14. only\n15. groupoid\n16. kruger\n17. scorch\n\nAnswer: alterate, aseptic, cayenne, chandigarh, declassify, debauch, dingy, excursion, equanimity, foamflower, inclement, lawful, october, only, groupoid, kruger, scorch", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.468", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:52.972221+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.4791, + "error": null, + "event": "model_call", + "event_seq": 469, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: foamflower lawful cayenne chandigarh only excursion declassify equanimity aseptic inclement october debauch kruger groupoid scorch dingy alterate\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. alterate\n2. aseptic\n3. cayenne\n4. chandigarh\n5. declassify\n6. debauch\n7. dingy\n8. excursion\n9. equanimity\n10. foamflower\n11. lawful\n12. october\n13. only\n14. groupoid\n15. kruger\n16. scorch\n17. alterate (Wait, I listed alterate twice in my thought process, let's re-check the original list: foamflower lawful cayenne chandigarh only excursion declassify equanimity aseptic inclement october debauch kruger groupoid scorch dingy alterate)\n\nLet's re-sort carefully:\n\nOriginal List: foamflower, lawful, cayenne, chandigarh, only, excursion, declassify, equanimity, aseptic, inclement, october, debauch, kruger, groupoid, scorch, dingy, alterate\n\n1. alterate\n2. aseptic\n3. cayenne\n4. chandigarh\n5. declassify\n6. debauch\n7. dingy\n8. excursion\n9. equanimity\n10. foamflower\n11. inclement\n12. lawful\n13. october\n14. only\n15. groupoid\n16. kruger\n17. scorch\n\nLet's re-sort the remaining words based on the full list:\n\nA: alterate, aseptic\nC: cayenne, chandigarh\nD: declassify, debauch, dingy\nE: excursion, equanimity\nF: foamflower\nI: inclement\nL: lawful\nO: october, only\nG: groupoid\nK: kruger\nS: scorch\n\nCorrect Alphabetical Order:\n1. alterate\n2. aseptic\n3. cayenne\n4. chandigarh\n5. declassify\n6. debauch\n7. dingy\n8. excursion\n9. equanimity\n10. foamflower\n11. inclement\n12. lawful\n13. october\n14. only\n15. groupoid\n16. kruger\n17. scorch\n\nAnswer: alterate, aseptic, cayenne, chandigarh, declassify, debauch, dingy, excursion, equanimity, foamflower, inclement, lawful, october, only, groupoid, kruger, scorch", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0010", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempted answer contains a factual error in the sorting process. The word \"inclement\" was misplaced in the initial attempt's list, and the final sorted list provided in the attempt is incorrect based on standard alphabetical order.\n\nCorrection advice: Re-sort the entire list carefully using standard alphabetical rules to ensure all words are in the correct sequence.", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.469", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:53.454159+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.568, + "error": null, + "event": "model_call", + "event_seq": 470, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: foamflower lawful cayenne chandigarh only excursion declassify equanimity aseptic inclement october debauch kruger groupoid scorch dingy alterate\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. alterate\n2. aseptic\n3. cayenne\n4. chandigarh\n5. declassify\n6. debauch\n7. dingy\n8. excursion\n9. equanimity\n10. foamflower\n11. lawful\n12. october\n13. only\n14. groupoid\n15. kruger\n16. scorch\n17. alterate (Wait, I listed alterate twice in my thought process, let's re-check the original list: foamflower lawful cayenne chandigarh only excursion declassify equanimity aseptic inclement october debauch kruger groupoid scorch dingy alterate)\n\nLet's re-sort carefully:\n\nOriginal List: foamflower, lawful, cayenne, chandigarh, only, excursion, declassify, equanimity, aseptic, inclement, october, debauch, kruger, groupoid, scorch, dingy, alterate\n\n1. alterate\n2. aseptic\n3. cayenne\n4. chandigarh\n5. declassify\n6. debauch\n7. dingy\n8. excursion\n9. equanimity\n10. foamflower\n11. inclement\n12. lawful\n13. october\n14. only\n15. groupoid\n16. kruger\n17. scorch\n\nLet's re-sort the remaining words based on the full list:\n\nA: alterate, aseptic\nC: cayenne, chandigarh\nD: declassify, debauch, dingy\nE: excursion, equanimity\nF: foamflower\nI: inclement\nL: lawful\nO: october, only\nG: groupoid\nK: kruger\nS: scorch\n\nCorrect Alphabetical Order:\n1. alterate\n2. aseptic\n3. cayenne\n4. chandigarh\n5. declassify\n6. debauch\n7. dingy\n8. excursion\n9. equanimity\n10. foamflower\n11. inclement\n12. lawful\n13. october\n14. only\n15. groupoid\n16. kruger\n17. scorch\n\nAnswer: alterate, aseptic, cayenne, chandigarh, declassify, debauch, dingy, excursion, equanimity, foamflower, inclement, lawful, october, only, groupoid, kruger, scorch\n\nFeedback:\nThe attempted answer contains a factual error in the sorting process. The word \"inclement\" was misplaced in the initial attempt's list, and the final sorted list provided in the attempt is incorrect based on standard alphabetical order.\n\nCorrection advice: Re-sort the entire list carefully using standard alphabetical rules to ensure all words are in the correct sequence.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0010", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "alterate, aseptic, cayenne, chandigarh, declassify, debauch, dingy, excursion, equanimity, foamflower, inclement, lawful, october, only, groupoid, kruger, scorch\n\nAnswer: alterate, aseptic, cayenne, chandigarh, declassify, debauch, dingy, excursion, equanimity, foamflower, inclement, lawful, october, only, groupoid, kruger, scorch", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.470", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:54.025216+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.6262, + "error": null, + "event": "model_call", + "event_seq": 471, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: din spruce nate pomade jordan gaillardia putt inroad jest kill jackboot whelan mirth amerada craftsmen shortcoming eclipse\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0052", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words sorted alphabetically:\n\n1. amerada\n2. craftsmen\n3. eclipse\n4. din\n5. gaillardia\n6. inroad\n7. jest\n8. jackboot\n9. jordan\n10. kill\n11. mirth\n12. nate\n13. pomade\n14. putt\n15. shortcoming\n16. spruce\n17. whelan\n\nLet's re-sort them carefully:\n\n* amerada\n* craftsmen\n* din\n* eclipse\n* gaillardia\n* inroad\n* jest\n* jackboot\n* jordan\n* kill\n* mirth\n* nate\n* pomade\n* putt\n* shortcoming\n* spruce\n* whelan\n\nComparing the initial list: din spruce nate pomade jordan gaillardia putt inroad jest kill jackboot whelan mirth amerada craftsmen shortcoming eclipse\n\n1. amerada\n2. craftsmen\n3. din\n4. eclipse\n5. gaillardia\n6. inroad\n7. jest\n8. jackboot\n9. jordan\n10. kill\n11. mirth\n12. nate\n13. pomade\n14. putt\n15. shortcoming\n16. spruce\n17. whelan\n\nAnswer: amerada, craftsmen, din, eclipse, gaillardia, inroad, jest, jackboot, jordan, kill, mirth, nate, pomade, putt, shortcoming, spruce, whelan", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.471", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:55.654590+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.6746, + "error": null, + "event": "model_call", + "event_seq": 472, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: din spruce nate pomade jordan gaillardia putt inroad jest kill jackboot whelan mirth amerada craftsmen shortcoming eclipse\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. amerada\n2. craftsmen\n3. eclipse\n4. din\n5. gaillardia\n6. inroad\n7. jest\n8. jackboot\n9. jordan\n10. kill\n11. mirth\n12. nate\n13. pomade\n14. putt\n15. shortcoming\n16. spruce\n17. whelan\n\nLet's re-sort them carefully:\n\n* amerada\n* craftsmen\n* din\n* eclipse\n* gaillardia\n* inroad\n* jest\n* jackboot\n* jordan\n* kill\n* mirth\n* nate\n* pomade\n* putt\n* shortcoming\n* spruce\n* whelan\n\nComparing the initial list: din spruce nate pomade jordan gaillardia putt inroad jest kill jackboot whelan mirth amerada craftsmen shortcoming eclipse\n\n1. amerada\n2. craftsmen\n3. din\n4. eclipse\n5. gaillardia\n6. inroad\n7. jest\n8. jackboot\n9. jordan\n10. kill\n11. mirth\n12. nate\n13. pomade\n14. putt\n15. shortcoming\n16. spruce\n17. whelan\n\nAnswer: amerada, craftsmen, din, eclipse, gaillardia, inroad, jest, jackboot, jordan, kill, mirth, nate, pomade, putt, shortcoming, spruce, whelan", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0052", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempted answer is factually correct and follows the instructions. The sorting is alphabetical.\n\nAnswer: amerada, craftsmen, din, eclipse, gaillardia, inroad, jest, jackboot, jordan, kill, mirth, nate, pomade, putt, shortcoming, spruce, whelan", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.472", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:56.332317+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.5906, + "error": null, + "event": "model_call", + "event_seq": 473, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: din spruce nate pomade jordan gaillardia putt inroad jest kill jackboot whelan mirth amerada craftsmen shortcoming eclipse\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. amerada\n2. craftsmen\n3. eclipse\n4. din\n5. gaillardia\n6. inroad\n7. jest\n8. jackboot\n9. jordan\n10. kill\n11. mirth\n12. nate\n13. pomade\n14. putt\n15. shortcoming\n16. spruce\n17. whelan\n\nLet's re-sort them carefully:\n\n* amerada\n* craftsmen\n* din\n* eclipse\n* gaillardia\n* inroad\n* jest\n* jackboot\n* jordan\n* kill\n* mirth\n* nate\n* pomade\n* putt\n* shortcoming\n* spruce\n* whelan\n\nComparing the initial list: din spruce nate pomade jordan gaillardia putt inroad jest kill jackboot whelan mirth amerada craftsmen shortcoming eclipse\n\n1. amerada\n2. craftsmen\n3. din\n4. eclipse\n5. gaillardia\n6. inroad\n7. jest\n8. jackboot\n9. jordan\n10. kill\n11. mirth\n12. nate\n13. pomade\n14. putt\n15. shortcoming\n16. spruce\n17. whelan\n\nAnswer: amerada, craftsmen, din, eclipse, gaillardia, inroad, jest, jackboot, jordan, kill, mirth, nate, pomade, putt, shortcoming, spruce, whelan\n\nFeedback:\nThe attempted answer is factually correct and follows the instructions. The sorting is alphabetical.\n\nAnswer: amerada, craftsmen, din, eclipse, gaillardia, inroad, jest, jackboot, jordan, kill, mirth, nate, pomade, putt, shortcoming, spruce, whelan", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0052", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: amerada, craftsmen, din, eclipse, gaillardia, inroad, jest, jackboot, jordan, kill, mirth, nate, pomade, putt, shortcoming, spruce, whelan", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.473", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:56.925871+00:00" + }, + { + "cycle": 0, + "duration_sec": 2.0399, + "error": null, + "event": "model_call", + "event_seq": 474, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: thrill splutter panicking scorch same dot prod obstetric malton onus drumhead delmarva barn embezzle it&t damp guru subsist entirety greene\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0018", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words sorted alphabetically:\n\n1. barn\n2. embezzle\n3. delmarva\n4. dot\n5. drumhead\n6. entirety\n7. guru\n8. it&t\n9. malton\n10. panicking\n11. prod\n12. scorch\n13. same\n14. splutter\n15. subsist\n16. thrill\n\n(Note: \"onus\" is missing from the provided list, but if it were included, it would come before \"obstetric\" and after \"onus\" would be before \"prod\" if we assume standard alphabetical order for the provided list. Let's re-check the provided list: thrill, splutter, panicking, scorch, same, dot, prod, obstetric, malton, onus, drumhead, delmarva, barn, embezzle, it&t, damp, guru, subsist, entirety, greene.)\n\nLet's re-sort the complete list:\nthrill, splutter, panicking, scorch, same, dot, prod, obstetric, malton, onus, drumhead, delmarva, barn, embezzle, it&t, damp, guru, subsist, entirety, greene\n\n1. barn\n2. damp\n3. delmarva\n4. dot\n5. drumhead\n6. embezzle\n7. entirety\n8. greene\n9. guru\n10. it&t\n11. malton\n12. obstetric\n13. onus\n14. panicking\n15. prod\n16. scorch\n17. same\n18. splutter\n19. subsist\n20. thrill\n\nAnswer: barn, damp, delmarva, dot, drumhead, embezzle, entirety, greene, guru, it&t, malton, obstetric, onus, panicking, prod, scorch, same, splutter, subsist, thrill", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.474", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:51:58.968822+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.3263, + "error": null, + "event": "model_call", + "event_seq": 475, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: thrill splutter panicking scorch same dot prod obstetric malton onus drumhead delmarva barn embezzle it&t damp guru subsist entirety greene\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. barn\n2. embezzle\n3. delmarva\n4. dot\n5. drumhead\n6. entirety\n7. guru\n8. it&t\n9. malton\n10. panicking\n11. prod\n12. scorch\n13. same\n14. splutter\n15. subsist\n16. thrill\n\n(Note: \"onus\" is missing from the provided list, but if it were included, it would come before \"obstetric\" and after \"onus\" would be before \"prod\" if we assume standard alphabetical order for the provided list. Let's re-check the provided list: thrill, splutter, panicking, scorch, same, dot, prod, obstetric, malton, onus, drumhead, delmarva, barn, embezzle, it&t, damp, guru, subsist, entirety, greene.)\n\nLet's re-sort the complete list:\nthrill, splutter, panicking, scorch, same, dot, prod, obstetric, malton, onus, drumhead, delmarva, barn, embezzle, it&t, damp, guru, subsist, entirety, greene\n\n1. barn\n2. damp\n3. delmarva\n4. dot\n5. drumhead\n6. embezzle\n7. entirety\n8. greene\n9. guru\n10. it&t\n11. malton\n12. obstetric\n13. onus\n14. panicking\n15. prod\n16. scorch\n17. same\n18. splutter\n19. subsist\n20. thrill\n\nAnswer: barn, damp, delmarva, dot, drumhead, embezzle, entirety, greene, guru, it&t, malton, obstetric, onus, panicking, prod, scorch, same, splutter, subsist, thrill", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0018", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempted answer has errors in both the initial sorting and the final list presentation.\n\n1. **Factual/Instruction Following Error (Initial Attempt):** The initial sorting provided in the attempt is incorrect.\n2. **Arithmetic/Reasoning Error (Final Answer):** The final sorted list provided in the attempt is incorrect. The sorting logic needs to be re-evaluated based on standard alphabetical order.\n\n**Correction Advice:** Re-sort the complete list alphabetically. Ensure all words from the original list are included and placed in the correct sequence.\n\nAnswer: barn, damp, delmarva, dot, drumhead, embezzle, entirety, greene, guru, it&t, malton, obstetric, onus, panicking, prod, scorch, same, splutter, subsist, thrill", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.475", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:00.298823+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.3951, + "error": null, + "event": "model_call", + "event_seq": 476, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: thrill splutter panicking scorch same dot prod obstetric malton onus drumhead delmarva barn embezzle it&t damp guru subsist entirety greene\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. barn\n2. embezzle\n3. delmarva\n4. dot\n5. drumhead\n6. entirety\n7. guru\n8. it&t\n9. malton\n10. panicking\n11. prod\n12. scorch\n13. same\n14. splutter\n15. subsist\n16. thrill\n\n(Note: \"onus\" is missing from the provided list, but if it were included, it would come before \"obstetric\" and after \"onus\" would be before \"prod\" if we assume standard alphabetical order for the provided list. Let's re-check the provided list: thrill, splutter, panicking, scorch, same, dot, prod, obstetric, malton, onus, drumhead, delmarva, barn, embezzle, it&t, damp, guru, subsist, entirety, greene.)\n\nLet's re-sort the complete list:\nthrill, splutter, panicking, scorch, same, dot, prod, obstetric, malton, onus, drumhead, delmarva, barn, embezzle, it&t, damp, guru, subsist, entirety, greene\n\n1. barn\n2. damp\n3. delmarva\n4. dot\n5. drumhead\n6. embezzle\n7. entirety\n8. greene\n9. guru\n10. it&t\n11. malton\n12. obstetric\n13. onus\n14. panicking\n15. prod\n16. scorch\n17. same\n18. splutter\n19. subsist\n20. thrill\n\nAnswer: barn, damp, delmarva, dot, drumhead, embezzle, entirety, greene, guru, it&t, malton, obstetric, onus, panicking, prod, scorch, same, splutter, subsist, thrill\n\nFeedback:\nThe attempted answer has errors in both the initial sorting and the final list presentation.\n\n1. **Factual/Instruction Following Error (Initial Attempt):** The initial sorting provided in the attempt is incorrect.\n2. **Arithmetic/Reasoning Error (Final Answer):** The final sorted list provided in the attempt is incorrect. The sorting logic needs to be re-evaluated based on standard alphabetical order.\n\n**Correction Advice:** Re-sort the complete list alphabetically. Ensure all words from the original list are included and placed in the correct sequence.\n\nAnswer: barn, damp, delmarva, dot, drumhead, embezzle, entirety, greene, guru, it&t, malton, obstetric, onus, panicking, prod, scorch, same, splutter, subsist, thrill", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0018", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "List of words: thrill, splutter, panicking, scorch, same, dot, prod, obstetric, malton, onus, drumhead, delmarva, barn, embezzle, it&t, damp, guru, subsist, entirety, greene\n\nAlphabetical sorting:\n1. barn\n2. damp\n3. delmarva\n4. dot\n5. drumhead\n6. embezzle\n7. entirety\n8. greene\n9. guru\n10. it&t\n11. malton\n12. obstetric\n13. onus\n14. panicking\n15. prod\n16. scorch\n17. same\n18. splutter\n19. subsist\n20. thrill\n\nAnswer: barn, damp, delmarva, dot, drumhead, embezzle, entirety, greene, guru, it&t, malton, obstetric, onus, panicking, prod, scorch, same, splutter, subsist, thrill", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.476", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:01.697086+00:00" + }, + { + "cycle": 0, + "duration_sec": 2.3605, + "error": null, + "event": "model_call", + "event_seq": 477, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: solstice roadway loquacious niece planet steed abutted hondo loosen agamemnon islamic capacity tibet hitachi essex casualty guinea aquatic suspicion\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0091", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words sorted alphabetically:\n\n1. abutted\n2. agamemnon\n3. aquatic\n4. casualty\n5. capacity\n6. hondo\n7. hitachi\n8. islamic\n9. loosen\n10. loquacious\n11. niece\n12. planet\n13. roadway\n14. solstice\n15. steed\n16. suspicion\n17. tibet\n\nLet's re-sort them carefully:\n\n* abutted\n* agamemnon\n* aquatic\n* casualty\n* capacity\n* hondo\n* hitachi\n* islamic\n* loosen\n* loquacious\n* niece\n* planet\n* roadway\n* solstice\n* steed\n* suspicion\n* tibet\n* essex (Wait, I missed 'essex' in the initial list check. Let's re-examine the original list: solstice roadway loquacious niece planet steed abutted hondo loosen agamemnon islamic capacity tibet hitachi essex casualty guinea aquatic suspicion)\n\nLet's re-sort the full list:\nsolstice, roadway, loquacious, niece, planet, steed, abutted, hondo, loosen, agamemnon, islamic, capacity, tibet, hitachi, essex, casualty, guinea, aquatic, suspicion\n\n1. abutted\n2. agamemnon\n3. aquatic\n4. casualty\n5. capacity\n6. essex\n7. guinea\n8. hondo\n9. hitachi\n10. islamic\n11. loosen\n12. loquacious\n13. niece\n14. planet\n15. roadway\n16. solstice\n17. steed\n18. suspicion\n19. tibet\n\n(Checking 'guinea' placement: G comes after E, before H. Correct.)\n\nFinal sorted list:\nabutted, agamemnon, aquatic, casualty, capacity, essex, guinea, hondo, hitachi, islamic, loosen, loquacious, niece, planet, roadway, solstice, steed, suspicion, tibet\n\nAnswer: abutted, agamemnon, aquatic, casualty, capacity, essex, guinea, hondo, hitachi, islamic, loosen, loquacious, niece, planet, roadway, solstice, steed, suspicion, tibet", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.477", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:04.060819+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.6365, + "error": null, + "event": "model_call", + "event_seq": 478, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: solstice roadway loquacious niece planet steed abutted hondo loosen agamemnon islamic capacity tibet hitachi essex casualty guinea aquatic suspicion\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. abutted\n2. agamemnon\n3. aquatic\n4. casualty\n5. capacity\n6. hondo\n7. hitachi\n8. islamic\n9. loosen\n10. loquacious\n11. niece\n12. planet\n13. roadway\n14. solstice\n15. steed\n16. suspicion\n17. tibet\n\nLet's re-sort them carefully:\n\n* abutted\n* agamemnon\n* aquatic\n* casualty\n* capacity\n* hondo\n* hitachi\n* islamic\n* loosen\n* loquacious\n* niece\n* planet\n* roadway\n* solstice\n* steed\n* suspicion\n* tibet\n* essex (Wait, I missed 'essex' in the initial list check. Let's re-examine the original list: solstice roadway loquacious niece planet steed abutted hondo loosen agamemnon islamic capacity tibet hitachi essex casualty guinea aquatic suspicion)\n\nLet's re-sort the full list:\nsolstice, roadway, loquacious, niece, planet, steed, abutted, hondo, loosen, agamemnon, islamic, capacity, tibet, hitachi, essex, casualty, guinea, aquatic, suspicion\n\n1. abutted\n2. agamemnon\n3. aquatic\n4. casualty\n5. capacity\n6. essex\n7. guinea\n8. hondo\n9. hitachi\n10. islamic\n11. loosen\n12. loquacious\n13. niece\n14. planet\n15. roadway\n16. solstice\n17. steed\n18. suspicion\n19. tibet\n\n(Checking 'guinea' placement: G comes after E, before H. Correct.)\n\nFinal sorted list:\nabutted, agamemnon, aquatic, casualty, capacity, essex, guinea, hondo, hitachi, islamic, loosen, loquacious, niece, planet, roadway, solstice, steed, suspicion, tibet\n\nAnswer: abutted, agamemnon, aquatic, casualty, capacity, essex, guinea, hondo, hitachi, islamic, loosen, loquacious, niece, planet, roadway, solstice, steed, suspicion, tibet", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0091", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempt contains several errors:\n\n1. **Factual Error (Missing/Incorrect Word Handling):** The initial list provided in the prompt is: \"solstice roadway loquacious niece planet steed abutted hondo loosen agamemnon islamic capacity tibet hitachi essex casualty guinea aquatic suspicion\". The attempt seems to have missed or incorrectly handled the word \"guinea\" in its initial sorting attempt, although it was included in the final re-sort.\n2. **Instruction Following Error (Incomplete/Confusing Process):** The attempt includes internal commentary and re-sorting steps that are overly complex and confusing, especially the part where it notes missing words and re-examines the list. The final output should be a direct, correct sort.\n3. **Arithmetic/Sorting Error (Final List):** The final sorted list provided in the attempt is incorrect based on standard alphabetical order. For example, 'essex' should come before 'guinea' if sorting strictly by the first letter, but the attempt places 'guinea' before 'hondo' and 'hitachi' correctly, but the overall sequence needs verification.\n\n**Correction Advice:**\nProvide only the final, correctly alphabetized list of all words from the original input. Ensure all words are present and sorted strictly alphabetically.", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.478", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:05.700652+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.6145, + "error": null, + "event": "model_call", + "event_seq": 479, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: solstice roadway loquacious niece planet steed abutted hondo loosen agamemnon islamic capacity tibet hitachi essex casualty guinea aquatic suspicion\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. abutted\n2. agamemnon\n3. aquatic\n4. casualty\n5. capacity\n6. hondo\n7. hitachi\n8. islamic\n9. loosen\n10. loquacious\n11. niece\n12. planet\n13. roadway\n14. solstice\n15. steed\n16. suspicion\n17. tibet\n\nLet's re-sort them carefully:\n\n* abutted\n* agamemnon\n* aquatic\n* casualty\n* capacity\n* hondo\n* hitachi\n* islamic\n* loosen\n* loquacious\n* niece\n* planet\n* roadway\n* solstice\n* steed\n* suspicion\n* tibet\n* essex (Wait, I missed 'essex' in the initial list check. Let's re-examine the original list: solstice roadway loquacious niece planet steed abutted hondo loosen agamemnon islamic capacity tibet hitachi essex casualty guinea aquatic suspicion)\n\nLet's re-sort the full list:\nsolstice, roadway, loquacious, niece, planet, steed, abutted, hondo, loosen, agamemnon, islamic, capacity, tibet, hitachi, essex, casualty, guinea, aquatic, suspicion\n\n1. abutted\n2. agamemnon\n3. aquatic\n4. casualty\n5. capacity\n6. essex\n7. guinea\n8. hondo\n9. hitachi\n10. islamic\n11. loosen\n12. loquacious\n13. niece\n14. planet\n15. roadway\n16. solstice\n17. steed\n18. suspicion\n19. tibet\n\n(Checking 'guinea' placement: G comes after E, before H. Correct.)\n\nFinal sorted list:\nabutted, agamemnon, aquatic, casualty, capacity, essex, guinea, hondo, hitachi, islamic, loosen, loquacious, niece, planet, roadway, solstice, steed, suspicion, tibet\n\nAnswer: abutted, agamemnon, aquatic, casualty, capacity, essex, guinea, hondo, hitachi, islamic, loosen, loquacious, niece, planet, roadway, solstice, steed, suspicion, tibet\n\nFeedback:\nThe attempt contains several errors:\n\n1. **Factual Error (Missing/Incorrect Word Handling):** The initial list provided in the prompt is: \"solstice roadway loquacious niece planet steed abutted hondo loosen agamemnon islamic capacity tibet hitachi essex casualty guinea aquatic suspicion\". The attempt seems to have missed or incorrectly handled the word \"guinea\" in its initial sorting attempt, although it was included in the final re-sort.\n2. **Instruction Following Error (Incomplete/Confusing Process):** The attempt includes internal commentary and re-sorting steps that are overly complex and confusing, especially the part where it notes missing words and re-examines the list. The final output should be a direct, correct sort.\n3. **Arithmetic/Sorting Error (Final List):** The final sorted list provided in the attempt is incorrect based on standard alphabetical order. For example, 'essex' should come before 'guinea' if sorting strictly by the first letter, but the attempt places 'guinea' before 'hondo' and 'hitachi' correctly, but the overall sequence needs verification.\n\n**Correction Advice:**\nProvide only the final, correctly alphabetized list of all words from the original input. Ensure all words are present and sorted strictly alphabetically.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0091", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: abutted, agamemnon, aquatic, casualty, capacity, essex, guinea, hondo, hitachi, islamic, loosen, loquacious, niece, planet, roadway, solstice, steed, suspicion, tibet", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.479", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:06.319018+00:00" + }, + { + "cycle": 0, + "duration_sec": 2.4086, + "error": null, + "event": "model_call", + "event_seq": 480, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: wink envious scotia planetaria pooh emancipate army\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0088", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The given list of words is: wink, envious, scotia, planetaria, pooh, emancipate, army.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **emancipate** (Starts with 'e')\n2. **envious** (Starts with 'e', 'm' comes before 'n')\n3. **army** (Starts with 'a')\n4. **planetaria** (Starts with 'p')\n5. **pooh** (Starts with 'p', 'o' comes before 'p' in planetaria if we consider the second letter, but 'p' vs 'p' is the same. 'o' vs 'l' in planetaria. Let's compare 'planetaria' and 'pooh'. 'p' is the same. 'l' in planetaria vs 'o' in pooh. 'l' comes before 'o'.)\n * Re-evaluating 'planetaria' and 'pooh':\n * p l a n e t a r i a\n * p o o h\n * 'l' comes before 'o', so 'planetaria' comes before 'pooh'.\n6. **scotia** (Starts with 's')\n7. **wink** (Starts with 'w')\n\nLet's re-sort systematically:\n\nWords starting with 'a':\n* army\n\nWords starting with 'e':\n* emancipate\n* envious\n * Comparing 'emancipate' and 'envious': e-m vs e-n. 'm' comes before 'n'.\n * Order: emancipate, envious\n\nWords starting with 'p':\n* planetaria\n* pooh\n * Comparing 'planetaria' and 'pooh': p-l vs p-o. 'l' comes before 'o'.\n * Order: planetaria, pooh\n\nWords starting with 's':\n* scotia\n\nWords starting with 'w':\n* wink\n\nCombining the sorted groups:\n1. army\n2. emancipate\n3. envious\n4. planetaria\n5. pooh\n6. scotia\n7. wink\n\nFinal alphabetical order: army, emancipate, envious, planetaria, pooh, scotia, wink.\n\nAnswer: army, emancipate, envious, planetaria, pooh, scotia, wink", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.480", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:08.731081+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.2336, + "error": null, + "event": "model_call", + "event_seq": 481, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: wink envious scotia planetaria pooh emancipate army\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: wink, envious, scotia, planetaria, pooh, emancipate, army.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **emancipate** (Starts with 'e')\n2. **envious** (Starts with 'e', 'm' comes before 'n')\n3. **army** (Starts with 'a')\n4. **planetaria** (Starts with 'p')\n5. **pooh** (Starts with 'p', 'o' comes before 'p' in planetaria if we consider the second letter, but 'p' vs 'p' is the same. 'o' vs 'l' in planetaria. Let's compare 'planetaria' and 'pooh'. 'p' is the same. 'l' in planetaria vs 'o' in pooh. 'l' comes before 'o'.)\n * Re-evaluating 'planetaria' and 'pooh':\n * p l a n e t a r i a\n * p o o h\n * 'l' comes before 'o', so 'planetaria' comes before 'pooh'.\n6. **scotia** (Starts with 's')\n7. **wink** (Starts with 'w')\n\nLet's re-sort systematically:\n\nWords starting with 'a':\n* army\n\nWords starting with 'e':\n* emancipate\n* envious\n * Comparing 'emancipate' and 'envious': e-m vs e-n. 'm' comes before 'n'.\n * Order: emancipate, envious\n\nWords starting with 'p':\n* planetaria\n* pooh\n * Comparing 'planetaria' and 'pooh': p-l vs p-o. 'l' comes before 'o'.\n * Order: planetaria, pooh\n\nWords starting with 's':\n* scotia\n\nWords starting with 'w':\n* wink\n\nCombining the sorted groups:\n1. army\n2. emancipate\n3. envious\n4. planetaria\n5. pooh\n6. scotia\n7. wink\n\nFinal alphabetical order: army, emancipate, envious, planetaria, pooh, scotia, wink.\n\nAnswer: army, emancipate, envious, planetaria, pooh, scotia, wink", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0088", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempted answer contains an error in the initial list processing and the final sorting.\n\n1. **Factual Error (Missing Word):** The original list was: wink, envious, scotia, planetaria, pooh, emancipate, army. The attempt listed 7 words but missed one word from the original list in the initial setup description (though it seems to have included all 7 words in the sorting process).\n2. **Reasoning/Arithmetic Error (Sorting Logic):** The attempt incorrectly placed 'army' (starts with 'a') after words starting with 'e' and before words starting with 'p'. Alphabetical sorting must proceed strictly by the first letter. 'a' comes before 'e', 'p', and 's'.\n3. **Instruction Following Error (Final Output):** The final answer provided is a comma-separated list, but the instruction implies a direct alphabetical sort of the original set.\n\n**Correction Advice:** Re-sort the entire list based strictly on the first letter of each word.\n\nAnswer: army, emancipate, envious, planetaria, pooh, scotia, wink", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.481", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:09.968017+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.2391, + "error": null, + "event": "model_call", + "event_seq": 482, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: wink envious scotia planetaria pooh emancipate army\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: wink, envious, scotia, planetaria, pooh, emancipate, army.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **emancipate** (Starts with 'e')\n2. **envious** (Starts with 'e', 'm' comes before 'n')\n3. **army** (Starts with 'a')\n4. **planetaria** (Starts with 'p')\n5. **pooh** (Starts with 'p', 'o' comes before 'p' in planetaria if we consider the second letter, but 'p' vs 'p' is the same. 'o' vs 'l' in planetaria. Let's compare 'planetaria' and 'pooh'. 'p' is the same. 'l' in planetaria vs 'o' in pooh. 'l' comes before 'o'.)\n * Re-evaluating 'planetaria' and 'pooh':\n * p l a n e t a r i a\n * p o o h\n * 'l' comes before 'o', so 'planetaria' comes before 'pooh'.\n6. **scotia** (Starts with 's')\n7. **wink** (Starts with 'w')\n\nLet's re-sort systematically:\n\nWords starting with 'a':\n* army\n\nWords starting with 'e':\n* emancipate\n* envious\n * Comparing 'emancipate' and 'envious': e-m vs e-n. 'm' comes before 'n'.\n * Order: emancipate, envious\n\nWords starting with 'p':\n* planetaria\n* pooh\n * Comparing 'planetaria' and 'pooh': p-l vs p-o. 'l' comes before 'o'.\n * Order: planetaria, pooh\n\nWords starting with 's':\n* scotia\n\nWords starting with 'w':\n* wink\n\nCombining the sorted groups:\n1. army\n2. emancipate\n3. envious\n4. planetaria\n5. pooh\n6. scotia\n7. wink\n\nFinal alphabetical order: army, emancipate, envious, planetaria, pooh, scotia, wink.\n\nAnswer: army, emancipate, envious, planetaria, pooh, scotia, wink\n\nFeedback:\nThe attempted answer contains an error in the initial list processing and the final sorting.\n\n1. **Factual Error (Missing Word):** The original list was: wink, envious, scotia, planetaria, pooh, emancipate, army. The attempt listed 7 words but missed one word from the original list in the initial setup description (though it seems to have included all 7 words in the sorting process).\n2. **Reasoning/Arithmetic Error (Sorting Logic):** The attempt incorrectly placed 'army' (starts with 'a') after words starting with 'e' and before words starting with 'p'. Alphabetical sorting must proceed strictly by the first letter. 'a' comes before 'e', 'p', and 's'.\n3. **Instruction Following Error (Final Output):** The final answer provided is a comma-separated list, but the instruction implies a direct alphabetical sort of the original set.\n\n**Correction Advice:** Re-sort the entire list based strictly on the first letter of each word.\n\nAnswer: army, emancipate, envious, planetaria, pooh, scotia, wink", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0088", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: army, emancipate, envious, planetaria, pooh, scotia, wink", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.482", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:10.210395+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.9422, + "error": null, + "event": "model_call", + "event_seq": 483, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: burley bela arapaho bacteria bock\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0124", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The given list of words is: burley, bela, arapaho, bacteria, bock.\n\nWe need to sort these words alphabetically. We compare them letter by letter:\n\n1. **bela** (Starts with 'b', second letter 'e')\n2. **bacteria** (Starts with 'b', second letter 'a')\n3. **bock** (Starts with 'b', second letter 'o')\n4. **burley** (Starts with 'b', second letter 'u')\n5. **arapaho** (Starts with 'a')\n\nComparing the first letters: 'a' comes before 'b'.\nSo, **arapaho** is the first word.\n\nNow we compare the words starting with 'b': burley, bela, bacteria, bock.\n\nComparing the second letters:\n* bela: 'e'\n* bacteria: 'a'\n* bock: 'o'\n* burley: 'u'\n\n'a' comes before 'e', 'o', and 'u'. So, **bacteria** comes next.\n\nRemaining words: burley, bela, bock.\n\nComparing the second letters of the remaining words:\n* bela: 'e'\n* bock: 'o'\n* burley: 'u'\n\n'e' comes before 'o' and 'u'. So, **bela** comes next.\n\nRemaining words: burley, bock.\n\nComparing the second letters:\n* bock: 'o'\n* burley: 'u'\n\n'o' comes before 'u'. So, **bock** comes next.\n\nThe last word is **burley**.\n\nThe sorted order is: arapaho, bacteria, bela, bock, burley.\n\nAnswer: arapaho, bacteria, bela, bock, burley\nAnswer: arapaho, bacteria, bela, bock, burley", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.483", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:12.156016+00:00" + }, + { + "cycle": 0, + "duration_sec": 2.1816, + "error": null, + "event": "model_call", + "event_seq": 484, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: burley bela arapaho bacteria bock\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: burley, bela, arapaho, bacteria, bock.\n\nWe need to sort these words alphabetically. We compare them letter by letter:\n\n1. **bela** (Starts with 'b', second letter 'e')\n2. **bacteria** (Starts with 'b', second letter 'a')\n3. **bock** (Starts with 'b', second letter 'o')\n4. **burley** (Starts with 'b', second letter 'u')\n5. **arapaho** (Starts with 'a')\n\nComparing the first letters: 'a' comes before 'b'.\nSo, **arapaho** is the first word.\n\nNow we compare the words starting with 'b': burley, bela, bacteria, bock.\n\nComparing the second letters:\n* bela: 'e'\n* bacteria: 'a'\n* bock: 'o'\n* burley: 'u'\n\n'a' comes before 'e', 'o', and 'u'. So, **bacteria** comes next.\n\nRemaining words: burley, bela, bock.\n\nComparing the second letters of the remaining words:\n* bela: 'e'\n* bock: 'o'\n* burley: 'u'\n\n'e' comes before 'o' and 'u'. So, **bela** comes next.\n\nRemaining words: burley, bock.\n\nComparing the second letters:\n* bock: 'o'\n* burley: 'u'\n\n'o' comes before 'u'. So, **bock** comes next.\n\nThe last word is **burley**.\n\nThe sorted order is: arapaho, bacteria, bela, bock, burley.\n\nAnswer: arapaho, bacteria, bela, bock, burley\nAnswer: arapaho, bacteria, bela, bock, burley", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0124", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The sorting logic is flawed. When comparing words starting with the same letter, you must compare the entire word sequentially.\n\nCorrection advice: Re-evaluate the comparison between 'bela', 'bacteria', 'bock', and 'burley' by comparing them letter by letter until a difference is found. Specifically, compare 'bela' and 'bacteria' (second letter 'e' vs 'a'). 'a' comes before 'e', so 'bacteria' should come before 'bela'. Also, compare 'bela' and 'bock' (second letter 'e' vs 'o'). 'e' comes before 'o', so 'bela' should come before 'bock'. The comparison between 'burley' and the others needs careful checking.\n\nThe correct alphabetical order is: arapaho, bacteria, bela, bock, burley. (Wait, let's re-check the 'b' words based on standard alphabetical order).\n\nWords: burley, bela, arapaho, bacteria, bock\n\n1. arapaho (starts with 'a')\n2. bacteria, bela, bock, burley (start with 'b')\n\nComparing 'b' words:\n* bacteria (b-a...)\n* bela (b-e...)\n* bock (b-o...)\n* burley (b-u...)\n\n'a' < 'e' < 'o' < 'u'.\nSo the order for 'b' words is: bacteria, bela, bock, burley.\n\nFinal sorted list: arapaho, bacteria, bela, bock, burley.\n\nThe attempt's final answer matches the correct result, but the reasoning steps for the 'b' words were slightly confusing in the description (e.g., comparing second letters without fully considering the full word structure if the second letters were the same, though here they are different). The conclusion reached is correct.\n\nCorrection advice: The reasoning steps are slightly convoluted but arrive at the correct final order. Ensure consistent application of lexicographical comparison (comparing characters sequentially until a difference is found).", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.484", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:14.340893+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.4938, + "error": null, + "event": "model_call", + "event_seq": 485, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: burley bela arapaho bacteria bock\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: burley, bela, arapaho, bacteria, bock.\n\nWe need to sort these words alphabetically. We compare them letter by letter:\n\n1. **bela** (Starts with 'b', second letter 'e')\n2. **bacteria** (Starts with 'b', second letter 'a')\n3. **bock** (Starts with 'b', second letter 'o')\n4. **burley** (Starts with 'b', second letter 'u')\n5. **arapaho** (Starts with 'a')\n\nComparing the first letters: 'a' comes before 'b'.\nSo, **arapaho** is the first word.\n\nNow we compare the words starting with 'b': burley, bela, bacteria, bock.\n\nComparing the second letters:\n* bela: 'e'\n* bacteria: 'a'\n* bock: 'o'\n* burley: 'u'\n\n'a' comes before 'e', 'o', and 'u'. So, **bacteria** comes next.\n\nRemaining words: burley, bela, bock.\n\nComparing the second letters of the remaining words:\n* bela: 'e'\n* bock: 'o'\n* burley: 'u'\n\n'e' comes before 'o' and 'u'. So, **bela** comes next.\n\nRemaining words: burley, bock.\n\nComparing the second letters:\n* bock: 'o'\n* burley: 'u'\n\n'o' comes before 'u'. So, **bock** comes next.\n\nThe last word is **burley**.\n\nThe sorted order is: arapaho, bacteria, bela, bock, burley.\n\nAnswer: arapaho, bacteria, bela, bock, burley\nAnswer: arapaho, bacteria, bela, bock, burley\n\nFeedback:\nThe sorting logic is flawed. When comparing words starting with the same letter, you must compare the entire word sequentially.\n\nCorrection advice: Re-evaluate the comparison between 'bela', 'bacteria', 'bock', and 'burley' by comparing them letter by letter until a difference is found. Specifically, compare 'bela' and 'bacteria' (second letter 'e' vs 'a'). 'a' comes before 'e', so 'bacteria' should come before 'bela'. Also, compare 'bela' and 'bock' (second letter 'e' vs 'o'). 'e' comes before 'o', so 'bela' should come before 'bock'. The comparison between 'burley' and the others needs careful checking.\n\nThe correct alphabetical order is: arapaho, bacteria, bela, bock, burley. (Wait, let's re-check the 'b' words based on standard alphabetical order).\n\nWords: burley, bela, arapaho, bacteria, bock\n\n1. arapaho (starts with 'a')\n2. bacteria, bela, bock, burley (start with 'b')\n\nComparing 'b' words:\n* bacteria (b-a...)\n* bela (b-e...)\n* bock (b-o...)\n* burley (b-u...)\n\n'a' < 'e' < 'o' < 'u'.\nSo the order for 'b' words is: bacteria, bela, bock, burley.\n\nFinal sorted list: arapaho, bacteria, bela, bock, burley.\n\nThe attempt's final answer matches the correct result, but the reasoning steps for the 'b' words were slightly confusing in the description (e.g., comparing second letters without fully considering the full word structure if the second letters were the same, though here they are different). The conclusion reached is correct.\n\nCorrection advice: The reasoning steps are slightly convoluted but arrive at the correct final order. Ensure consistent application of lexicographical comparison (comparing characters sequentially until a difference is found).", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0124", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: arapaho, bacteria, bela, bock, burley", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.485", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:14.838099+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.5954, + "error": null, + "event": "model_call", + "event_seq": 486, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: sweatband murray\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0022", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The given words are: sweatband, murray.\n\nTo sort them alphabetically, we compare them letter by letter:\n\n1. **s**weatband\n2. **m**urray\n\nSince 'm' comes before 's' in the alphabet, \"murray\" comes before \"sweatband\".\n\nThe alphabetical order is: murray, sweatband.\n\nAnswer: murray, sweatband\nAnswer: murray, sweatband", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.486", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:15.437449+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.5789, + "error": null, + "event": "model_call", + "event_seq": 487, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: sweatband murray\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given words are: sweatband, murray.\n\nTo sort them alphabetically, we compare them letter by letter:\n\n1. **s**weatband\n2. **m**urray\n\nSince 'm' comes before 's' in the alphabet, \"murray\" comes before \"sweatband\".\n\nThe alphabetical order is: murray, sweatband.\n\nAnswer: murray, sweatband\nAnswer: murray, sweatband", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0022", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempt is factually correct and follows the instructions perfectly.\n\nCorrection advice: None.", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.487", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:16.025517+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.5112, + "error": null, + "event": "model_call", + "event_seq": 488, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: sweatband murray\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given words are: sweatband, murray.\n\nTo sort them alphabetically, we compare them letter by letter:\n\n1. **s**weatband\n2. **m**urray\n\nSince 'm' comes before 's' in the alphabet, \"murray\" comes before \"sweatband\".\n\nThe alphabetical order is: murray, sweatband.\n\nAnswer: murray, sweatband\nAnswer: murray, sweatband\n\nFeedback:\nThe attempt is factually correct and follows the instructions perfectly.\n\nCorrection advice: None.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0022", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: murray, sweatband", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.488", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:16.542997+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.1763, + "error": null, + "event": "model_call", + "event_seq": 489, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: bituminous ami decadent knickerbocker exeter\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0029", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The given list of words is: bituminous, ami, decadent, knickerbocker, exeter.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **ami**: Starts with 'a'\n2. **bituminous**: Starts with 'b'\n3. **decadent**: Starts with 'd'\n4. **exeter**: Starts with 'e'\n5. **knickerbocker**: Starts with 'k'\n\nComparing the first letters: a, b, d, e, k.\nIn alphabetical order: a, b, d, e, k.\n\n1. **ami** (a)\n2. **bituminous** (b)\n3. **decadent** (d)\n4. **exeter** (e)\n5. **knickerbocker** (k)\n\nThe sorted list is: ami, bituminous, decadent, exeter, knickerbocker.\n\nAnswer: ami, bituminous, decadent, exeter, knickerbocker", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.489", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:17.724572+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.2149, + "error": null, + "event": "model_call", + "event_seq": 490, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: bituminous ami decadent knickerbocker exeter\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: bituminous, ami, decadent, knickerbocker, exeter.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **ami**: Starts with 'a'\n2. **bituminous**: Starts with 'b'\n3. **decadent**: Starts with 'd'\n4. **exeter**: Starts with 'e'\n5. **knickerbocker**: Starts with 'k'\n\nComparing the first letters: a, b, d, e, k.\nIn alphabetical order: a, b, d, e, k.\n\n1. **ami** (a)\n2. **bituminous** (b)\n3. **decadent** (d)\n4. **exeter** (e)\n5. **knickerbocker** (k)\n\nThe sorted list is: ami, bituminous, decadent, exeter, knickerbocker.\n\nAnswer: ami, bituminous, decadent, exeter, knickerbocker", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0029", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempted answer is correct.", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.490", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:17.944747+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.3012, + "error": null, + "event": "model_call", + "event_seq": 491, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: bituminous ami decadent knickerbocker exeter\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: bituminous, ami, decadent, knickerbocker, exeter.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **ami**: Starts with 'a'\n2. **bituminous**: Starts with 'b'\n3. **decadent**: Starts with 'd'\n4. **exeter**: Starts with 'e'\n5. **knickerbocker**: Starts with 'k'\n\nComparing the first letters: a, b, d, e, k.\nIn alphabetical order: a, b, d, e, k.\n\n1. **ami** (a)\n2. **bituminous** (b)\n3. **decadent** (d)\n4. **exeter** (e)\n5. **knickerbocker** (k)\n\nThe sorted list is: ami, bituminous, decadent, exeter, knickerbocker.\n\nAnswer: ami, bituminous, decadent, exeter, knickerbocker\n\nFeedback:\nThe attempted answer is correct.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0029", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: ami, bituminous, decadent, exeter, knickerbocker", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.491", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:18.251035+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.8206, + "error": null, + "event": "model_call", + "event_seq": 492, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: greasy lapidary mark\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0109", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The given list of words is: greasy, lapidary, mark.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **greasy** (starts with 'g')\n2. **lapidary** (starts with 'l')\n3. **mark** (starts with 'm')\n\nComparing the first letters: 'g', 'l', 'm'.\nIn alphabetical order, 'g' comes before 'l', and 'l' comes before 'm'.\n\nTherefore, the alphabetical order is: greasy, lapidary, mark.\n\nAnswer: greasy, lapidary, mark", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.492", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:19.076608+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.5782, + "error": null, + "event": "model_call", + "event_seq": 493, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: greasy lapidary mark\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: greasy, lapidary, mark.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **greasy** (starts with 'g')\n2. **lapidary** (starts with 'l')\n3. **mark** (starts with 'm')\n\nComparing the first letters: 'g', 'l', 'm'.\nIn alphabetical order, 'g' comes before 'l', and 'l' comes before 'm'.\n\nTherefore, the alphabetical order is: greasy, lapidary, mark.\n\nAnswer: greasy, lapidary, mark", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0109", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempted answer is correct.\n\nAnswer: greasy, lapidary, mark", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.493", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:19.658516+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.6274, + "error": null, + "event": "model_call", + "event_seq": 494, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: greasy lapidary mark\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: greasy, lapidary, mark.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **greasy** (starts with 'g')\n2. **lapidary** (starts with 'l')\n3. **mark** (starts with 'm')\n\nComparing the first letters: 'g', 'l', 'm'.\nIn alphabetical order, 'g' comes before 'l', and 'l' comes before 'm'.\n\nTherefore, the alphabetical order is: greasy, lapidary, mark.\n\nAnswer: greasy, lapidary, mark\n\nFeedback:\nThe attempted answer is correct.\n\nAnswer: greasy, lapidary, mark", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0109", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: greasy, lapidary, mark", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.494", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:20.291273+00:00" + }, + { + "cycle": 0, + "duration_sec": 2.6465, + "error": null, + "event": "model_call", + "event_seq": 495, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: thirteenth tinfoil thimble snuff ernie pow celebrity abuilding indiscoverable chigger burgher synchronous yap medlar exorcism newsboy\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0039", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words sorted alphabetically:\n\n1. abuilding\n2. celebrity\n3. burgher\n4. chigger\n5. indiscoverable\n6. medlar\n7. newsboy\n8. exorcism\n9. pow\n10. snuff\n11. synchronous\n12. thirteenth\n13. tinfoil\n14. thimble\n15. ernie\n16. yap\n\nLet's re-sort them carefully:\n\n* **a**building\n* **c**elebrity\n* **b**urgher\n* **c**higger\n* **i**ndiscoverable\n* **m**edlar\n* **n**ewsboy\n* **e**xorcism (comes before pow)\n* **p**ow\n* **s**nuff\n* **s**ynchronous\n* **t**hirteenth\n* **t**infoil\n* **t**himble\n* **e**rnie (comes before thimble)\n* **y**ap\n\nLet's do a strict alphabetical sort:\n\n1. abuilding\n2. celebrity\n3. burgher\n4. chigger\n5. ernie\n6. indiscoverable\n7. medlar\n8. newsboy\n9. exorcism\n10. pow\n11. snuff\n12. synchronous\n13. thirteenth\n14. tinfoil\n15. thimble\n16. yap\n\nWait, let's check the 't' words: thirteenth, tinfoil, thimble.\nthirteenth (thir...)\ntinfoil (tinf...)\nthimble (thim...)\nthimble comes before thirteenth.\ntinfoil comes before thimble.\nthirteenth comes after tinfoil.\n\nCorrected 't' order: tinfoil, thimble, thirteenth.\n\nLet's re-sort the whole list:\n\n1. abuilding\n2. celebrity\n3. burgher\n4. chigger\n5. ernie\n6. indiscoverable\n7. medlar\n8. newsboy\n9. exorcism\n10. pow\n11. snuff\n12. synchronous\n13. tinfoil\n14. thimble\n15. thirteenth\n16. yap\n\nAnswer: abuilding, celebrity, burgher, chigger, ernie, indiscoverable, medlar, newsboy, exorcism, pow, snuff, synchronous, tinfoil, thimble, thirteenth, yap", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.495", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:22.942357+00:00" + }, + { + "cycle": 0, + "duration_sec": 2.6731, + "error": null, + "event": "model_call", + "event_seq": 496, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: thirteenth tinfoil thimble snuff ernie pow celebrity abuilding indiscoverable chigger burgher synchronous yap medlar exorcism newsboy\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. abuilding\n2. celebrity\n3. burgher\n4. chigger\n5. indiscoverable\n6. medlar\n7. newsboy\n8. exorcism\n9. pow\n10. snuff\n11. synchronous\n12. thirteenth\n13. tinfoil\n14. thimble\n15. ernie\n16. yap\n\nLet's re-sort them carefully:\n\n* **a**building\n* **c**elebrity\n* **b**urgher\n* **c**higger\n* **i**ndiscoverable\n* **m**edlar\n* **n**ewsboy\n* **e**xorcism (comes before pow)\n* **p**ow\n* **s**nuff\n* **s**ynchronous\n* **t**hirteenth\n* **t**infoil\n* **t**himble\n* **e**rnie (comes before thimble)\n* **y**ap\n\nLet's do a strict alphabetical sort:\n\n1. abuilding\n2. celebrity\n3. burgher\n4. chigger\n5. ernie\n6. indiscoverable\n7. medlar\n8. newsboy\n9. exorcism\n10. pow\n11. snuff\n12. synchronous\n13. thirteenth\n14. tinfoil\n15. thimble\n16. yap\n\nWait, let's check the 't' words: thirteenth, tinfoil, thimble.\nthirteenth (thir...)\ntinfoil (tinf...)\nthimble (thim...)\nthimble comes before thirteenth.\ntinfoil comes before thimble.\nthirteenth comes after tinfoil.\n\nCorrected 't' order: tinfoil, thimble, thirteenth.\n\nLet's re-sort the whole list:\n\n1. abuilding\n2. celebrity\n3. burgher\n4. chigger\n5. ernie\n6. indiscoverable\n7. medlar\n8. newsboy\n9. exorcism\n10. pow\n11. snuff\n12. synchronous\n13. tinfoil\n14. thimble\n15. thirteenth\n16. yap\n\nAnswer: abuilding, celebrity, burgher, chigger, ernie, indiscoverable, medlar, newsboy, exorcism, pow, snuff, synchronous, tinfoil, thimble, thirteenth, yap", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0039", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The initial sorting attempt contains several errors in the final list and the reasoning steps.\n\n1. **Error in initial sorting/listing:** The attempt lists 16 items, but the original list has 18 words: \"thirteenth, tinfoil, thimble, snuff, ernie, pow, celebrity, abuilding, indiscoverable, chigger, burgher, synchronous, yap, medlar, exorcism, newsboy\". (Count: 16 words listed in the attempt, but the original list has 18 words if we count them all: 1. thirteenth, 2. tinfoil, 3. thimble, 4. snuff, 5. ernie, 6. pow, 7. celebrity, 8. abuilding, 9. indiscoverable, 10. chigger, 11. burgher, 12. synchronous, 13. yap, 14. medlar, 15. exorcism, 16. newsboy. Wait, let's recount the original list provided: thirteenth, tinfoil, thimble, snuff, ernie, pow, celebrity, abuilding, indiscoverable, chigger, burgher, synchronous, yap, medlar, exorcism, newsboy. That is 16 words.)\n * Let's re-examine the original list provided in the prompt: \"thirteenth tinfoil thimble snuff ernie pow celebrity abuilding indiscoverable chigger burgher synchronous yap medlar exorcism newsboy\". (16 words confirmed).\n\n2. **Error in reasoning/final sort:** The attempt's final sort seems to have mixed up the order of 'ernie' and 'exorcism' relative to other words, and the final 't' word ordering logic was applied incorrectly in the final sequence.\n\n**Correction Advice:** Re-sort the entire list strictly alphabetically. Pay close attention to the initial letters and the ordering of words starting with 't' (thirteenth, tinfoil, thimble).\n\nAnswer: abuilding, celebrity, burgher, chigger, ernie, indiscoverable, medlar, newsboy, exorcism, pow, snuff, synchronous, thirteenth, tinfoil, thimble, yap", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.496", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:25.619038+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.8608, + "error": null, + "event": "model_call", + "event_seq": 497, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: thirteenth tinfoil thimble snuff ernie pow celebrity abuilding indiscoverable chigger burgher synchronous yap medlar exorcism newsboy\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. abuilding\n2. celebrity\n3. burgher\n4. chigger\n5. indiscoverable\n6. medlar\n7. newsboy\n8. exorcism\n9. pow\n10. snuff\n11. synchronous\n12. thirteenth\n13. tinfoil\n14. thimble\n15. ernie\n16. yap\n\nLet's re-sort them carefully:\n\n* **a**building\n* **c**elebrity\n* **b**urgher\n* **c**higger\n* **i**ndiscoverable\n* **m**edlar\n* **n**ewsboy\n* **e**xorcism (comes before pow)\n* **p**ow\n* **s**nuff\n* **s**ynchronous\n* **t**hirteenth\n* **t**infoil\n* **t**himble\n* **e**rnie (comes before thimble)\n* **y**ap\n\nLet's do a strict alphabetical sort:\n\n1. abuilding\n2. celebrity\n3. burgher\n4. chigger\n5. ernie\n6. indiscoverable\n7. medlar\n8. newsboy\n9. exorcism\n10. pow\n11. snuff\n12. synchronous\n13. thirteenth\n14. tinfoil\n15. thimble\n16. yap\n\nWait, let's check the 't' words: thirteenth, tinfoil, thimble.\nthirteenth (thir...)\ntinfoil (tinf...)\nthimble (thim...)\nthimble comes before thirteenth.\ntinfoil comes before thimble.\nthirteenth comes after tinfoil.\n\nCorrected 't' order: tinfoil, thimble, thirteenth.\n\nLet's re-sort the whole list:\n\n1. abuilding\n2. celebrity\n3. burgher\n4. chigger\n5. ernie\n6. indiscoverable\n7. medlar\n8. newsboy\n9. exorcism\n10. pow\n11. snuff\n12. synchronous\n13. tinfoil\n14. thimble\n15. thirteenth\n16. yap\n\nAnswer: abuilding, celebrity, burgher, chigger, ernie, indiscoverable, medlar, newsboy, exorcism, pow, snuff, synchronous, tinfoil, thimble, thirteenth, yap\n\nFeedback:\nThe initial sorting attempt contains several errors in the final list and the reasoning steps.\n\n1. **Error in initial sorting/listing:** The attempt lists 16 items, but the original list has 18 words: \"thirteenth, tinfoil, thimble, snuff, ernie, pow, celebrity, abuilding, indiscoverable, chigger, burgher, synchronous, yap, medlar, exorcism, newsboy\". (Count: 16 words listed in the attempt, but the original list has 18 words if we count them all: 1. thirteenth, 2. tinfoil, 3. thimble, 4. snuff, 5. ernie, 6. pow, 7. celebrity, 8. abuilding, 9. indiscoverable, 10. chigger, 11. burgher, 12. synchronous, 13. yap, 14. medlar, 15. exorcism, 16. newsboy. Wait, let's recount the original list provided: thirteenth, tinfoil, thimble, snuff, ernie, pow, celebrity, abuilding, indiscoverable, chigger, burgher, synchronous, yap, medlar, exorcism, newsboy. That is 16 words.)\n * Let's re-examine the original list provided in the prompt: \"thirteenth tinfoil thimble snuff ernie pow celebrity abuilding indiscoverable chigger burgher synchronous yap medlar exorcism newsboy\". (16 words confirmed).\n\n2. **Error in reasoning/final sort:** The attempt's final sort seems to have mixed up the order of 'ernie' and 'exorcism' relative to other words, and the final 't' word ordering logic was applied incorrectly in the final sequence.\n\n**Correction Advice:** Re-sort the entire list strictly alphabetically. Pay close attention to the initial letters and the ordering of words starting with 't' (thirteenth, tinfoil, thimble).\n\nAnswer: abuilding, celebrity, burgher, chigger, ernie, indiscoverable, medlar, newsboy, exorcism, pow, snuff, synchronous, thirteenth, tinfoil, thimble, yap", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0039", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: abuilding, celebrity, burgher, chigger, ernie, indiscoverable, medlar, newsboy, exorcism, pow, snuff, synchronous, thirteenth, tinfoil, thimble, yap", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.497", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:26.485369+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.5916, + "error": null, + "event": "model_call", + "event_seq": 498, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: heterostructure bertrand careful wherewith libra eyelid feign southeastern paste snip\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0122", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words to sort: heterostructure, bertrand, careful, wherewith, libra, eyelid, feign, southeastern, paste, snip.\n\nLet's sort them alphabetically:\n\n1. careful\n2. bertrand\n3. eyelid\n4. feign\n5. heterostructure\n6. libra\n7. paste\n8. southeastern\n9. snip\n10. wherewith\n\nDetailed comparison:\n* **c**areful\n* **b**ertrand\n* **e**yelid\n* **f**eign\n* **h**eterostructure\n* **l**ibra\n* **p**aste\n* **s**outheastern\n* **s**nip (southeastern comes before snip because 'o' comes before 'n') - Wait, let's recheck 's' words.\n * southeastern\n * snip\n * southeastern vs snip: southeastern comes before snip.\n* **w**herewith\n\nLet's re-sort the 's' words:\n* southeastern\n* snip\n\nThe final sorted list is:\n1. careful\n2. bertrand\n3. eyelid\n4. feign\n5. heterostructure\n6. libra\n7. paste\n8. southeastern\n9. snip\n10. wherewith\n\nAnswer: careful, bertrand, eyelid, feign, heterostructure, libra, paste, southeastern, snip, wherewith", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.498", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:28.082191+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.111, + "error": null, + "event": "model_call", + "event_seq": 499, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: heterostructure bertrand careful wherewith libra eyelid feign southeastern paste snip\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words to sort: heterostructure, bertrand, careful, wherewith, libra, eyelid, feign, southeastern, paste, snip.\n\nLet's sort them alphabetically:\n\n1. careful\n2. bertrand\n3. eyelid\n4. feign\n5. heterostructure\n6. libra\n7. paste\n8. southeastern\n9. snip\n10. wherewith\n\nDetailed comparison:\n* **c**areful\n* **b**ertrand\n* **e**yelid\n* **f**eign\n* **h**eterostructure\n* **l**ibra\n* **p**aste\n* **s**outheastern\n* **s**nip (southeastern comes before snip because 'o' comes before 'n') - Wait, let's recheck 's' words.\n * southeastern\n * snip\n * southeastern vs snip: southeastern comes before snip.\n* **w**herewith\n\nLet's re-sort the 's' words:\n* southeastern\n* snip\n\nThe final sorted list is:\n1. careful\n2. bertrand\n3. eyelid\n4. feign\n5. heterostructure\n6. libra\n7. paste\n8. southeastern\n9. snip\n10. wherewith\n\nAnswer: careful, bertrand, eyelid, feign, heterostructure, libra, paste, southeastern, snip, wherewith", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0122", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The sorting logic for the 's' words is flawed. 'southeastern' comes before 'snip' because 'o' comes before 'n'. The attempt correctly identifies this in the detailed comparison but then incorrectly states the final order in the list.\n\nCorrection advice: The final sorted list is correct based on standard alphabetical order. No changes are needed to the final answer, but the reasoning step regarding the 's' words should be confirmed as correct.\n\nAnswer: careful, bertrand, eyelid, feign, heterostructure, libra, paste, southeastern, snip, wherewith", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.499", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:29.197725+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.7635, + "error": null, + "event": "model_call", + "event_seq": 500, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: heterostructure bertrand careful wherewith libra eyelid feign southeastern paste snip\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words to sort: heterostructure, bertrand, careful, wherewith, libra, eyelid, feign, southeastern, paste, snip.\n\nLet's sort them alphabetically:\n\n1. careful\n2. bertrand\n3. eyelid\n4. feign\n5. heterostructure\n6. libra\n7. paste\n8. southeastern\n9. snip\n10. wherewith\n\nDetailed comparison:\n* **c**areful\n* **b**ertrand\n* **e**yelid\n* **f**eign\n* **h**eterostructure\n* **l**ibra\n* **p**aste\n* **s**outheastern\n* **s**nip (southeastern comes before snip because 'o' comes before 'n') - Wait, let's recheck 's' words.\n * southeastern\n * snip\n * southeastern vs snip: southeastern comes before snip.\n* **w**herewith\n\nLet's re-sort the 's' words:\n* southeastern\n* snip\n\nThe final sorted list is:\n1. careful\n2. bertrand\n3. eyelid\n4. feign\n5. heterostructure\n6. libra\n7. paste\n8. southeastern\n9. snip\n10. wherewith\n\nAnswer: careful, bertrand, eyelid, feign, heterostructure, libra, paste, southeastern, snip, wherewith\n\nFeedback:\nThe sorting logic for the 's' words is flawed. 'southeastern' comes before 'snip' because 'o' comes before 'n'. The attempt correctly identifies this in the detailed comparison but then incorrectly states the final order in the list.\n\nCorrection advice: The final sorted list is correct based on standard alphabetical order. No changes are needed to the final answer, but the reasoning step regarding the 's' words should be confirmed as correct.\n\nAnswer: careful, bertrand, eyelid, feign, heterostructure, libra, paste, southeastern, snip, wherewith", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0122", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: careful, bertrand, eyelid, feign, heterostructure, libra, paste, southeastern, snip, wherewith", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.500", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:29.966276+00:00" + }, + { + "cycle": 0, + "duration_sec": 2.9292, + "error": null, + "event": "model_call", + "event_seq": 501, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: murk pvc singlet yeats muslim coachmen clergymen companion gazpacho embark pristine granular hobble fatten allot too niggle chauncey threefold coddington\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0098", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words sorted alphabetically:\n\n1. chauncey\n2. coddington\n3. companion\n4. coachmen\n5. clergymen\n6. embark\n7. fatten\n8. threefold\n9. granular\n10. gazpacho\n11. hobble\n12. murk\n13. muslim\n14. niggle\n15. pristine\n16. pvc\n17. singlet\n18. too\n19. yeats\n\nLet's re-sort them carefully:\n\n* **c**hauncey\n* **c**oddington\n* **c**ompanion\n* **c**oachmen\n* **c**lergymen\n* **e**mbark\n* **f**atten\n* **f**reedom (Wait, 'threefold' comes before 'fatten' if we look at the 't' sound, but alphabetically 'fatten' comes before 'threefold'.)\n* **g**azpacho\n* **g**ranular\n* **h**obble\n* **m**urk\n* **m**uslim\n* **n**iggle\n* **p**ristine\n* **p**vc\n* **s**inglet\n* **t**oo\n* **y**eats\n\nLet's do a strict alphabetical sort:\n\n1. chauncey\n2. coddington\n3. companion\n4. coachmen\n5. clergymen\n6. embark\n7. fatten\n8. threefold\n9. granular\n10. gazpacho\n11. hobble\n12. murk\n13. muslim\n14. niggle\n15. pristine\n16. pvc\n17. singlet\n18. too\n19. yeats\n\n(Checking 'fatten' vs 'threefold': F-A-T-T-E-N vs T-H-R-E-E-F-O-L-D. 'fatten' comes first.)\n(Checking 'granular' vs 'gazpacho': G-R vs G-A. 'gazpacho' comes before 'granular'.)\n\nCorrected final sort:\n\n1. chauncey\n2. coddington\n3. companion\n4. coachmen\n5. clergymen\n6. embark\n7. fatten\n8. threefold\n9. gazpacho\n10. granular\n11. hobble\n12. murk\n13. muslim\n14. niggle\n15. pristine\n16. pvc\n17. singlet\n18. too\n19. yeats\n\nAnswer: chauncey, coddington, companion, coachmen, clergymen, embark, fatten, threefold, gazpacho, granular, hobble, murk, muslim, niggle, pristine, pvc, singlet, too, yeats", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.501", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:32.899654+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.5062, + "error": null, + "event": "model_call", + "event_seq": 502, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: murk pvc singlet yeats muslim coachmen clergymen companion gazpacho embark pristine granular hobble fatten allot too niggle chauncey threefold coddington\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. chauncey\n2. coddington\n3. companion\n4. coachmen\n5. clergymen\n6. embark\n7. fatten\n8. threefold\n9. granular\n10. gazpacho\n11. hobble\n12. murk\n13. muslim\n14. niggle\n15. pristine\n16. pvc\n17. singlet\n18. too\n19. yeats\n\nLet's re-sort them carefully:\n\n* **c**hauncey\n* **c**oddington\n* **c**ompanion\n* **c**oachmen\n* **c**lergymen\n* **e**mbark\n* **f**atten\n* **f**reedom (Wait, 'threefold' comes before 'fatten' if we look at the 't' sound, but alphabetically 'fatten' comes before 'threefold'.)\n* **g**azpacho\n* **g**ranular\n* **h**obble\n* **m**urk\n* **m**uslim\n* **n**iggle\n* **p**ristine\n* **p**vc\n* **s**inglet\n* **t**oo\n* **y**eats\n\nLet's do a strict alphabetical sort:\n\n1. chauncey\n2. coddington\n3. companion\n4. coachmen\n5. clergymen\n6. embark\n7. fatten\n8. threefold\n9. granular\n10. gazpacho\n11. hobble\n12. murk\n13. muslim\n14. niggle\n15. pristine\n16. pvc\n17. singlet\n18. too\n19. yeats\n\n(Checking 'fatten' vs 'threefold': F-A-T-T-E-N vs T-H-R-E-E-F-O-L-D. 'fatten' comes first.)\n(Checking 'granular' vs 'gazpacho': G-R vs G-A. 'gazpacho' comes before 'granular'.)\n\nCorrected final sort:\n\n1. chauncey\n2. coddington\n3. companion\n4. coachmen\n5. clergymen\n6. embark\n7. fatten\n8. threefold\n9. gazpacho\n10. granular\n11. hobble\n12. murk\n13. muslim\n14. niggle\n15. pristine\n16. pvc\n17. singlet\n18. too\n19. yeats\n\nAnswer: chauncey, coddington, companion, coachmen, clergymen, embark, fatten, threefold, gazpacho, granular, hobble, murk, muslim, niggle, pristine, pvc, singlet, too, yeats", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0098", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The initial attempt's sorting logic contained errors, specifically in the manual re-sorting steps, although the final list provided in the \"Corrected final sort\" section appears to be the intended final answer.\n\n1. **Factual/Reasoning Error:** The attempt incorrectly noted a potential issue between 'fatten' and 'threefold' and then corrected it, but the initial reasoning was flawed.\n2. **Arithmetic Error:** None.\n3. **Instruction Following Error:** The attempt provided a preliminary list, then a confusing re-sort attempt, and finally a \"Corrected final sort.\" The final output format is correct (a list followed by the final answer line).\n\nThe final sorted list provided in the attempt is correct based on standard alphabetical ordering.\n\nCorrection Advice: The final sorted list is correct. Remove the intermediate, confusing sorting steps to present a clean solution.\n\nAnswer: chauncey, coddington, companion, coachmen, clergymen, embark, fatten, threefold, gazpacho, granular, hobble, murk, muslim, niggle, pristine, pvc, singlet, too, yeats", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.502", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:34.410147+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.8426, + "error": null, + "event": "model_call", + "event_seq": 503, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: murk pvc singlet yeats muslim coachmen clergymen companion gazpacho embark pristine granular hobble fatten allot too niggle chauncey threefold coddington\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. chauncey\n2. coddington\n3. companion\n4. coachmen\n5. clergymen\n6. embark\n7. fatten\n8. threefold\n9. granular\n10. gazpacho\n11. hobble\n12. murk\n13. muslim\n14. niggle\n15. pristine\n16. pvc\n17. singlet\n18. too\n19. yeats\n\nLet's re-sort them carefully:\n\n* **c**hauncey\n* **c**oddington\n* **c**ompanion\n* **c**oachmen\n* **c**lergymen\n* **e**mbark\n* **f**atten\n* **f**reedom (Wait, 'threefold' comes before 'fatten' if we look at the 't' sound, but alphabetically 'fatten' comes before 'threefold'.)\n* **g**azpacho\n* **g**ranular\n* **h**obble\n* **m**urk\n* **m**uslim\n* **n**iggle\n* **p**ristine\n* **p**vc\n* **s**inglet\n* **t**oo\n* **y**eats\n\nLet's do a strict alphabetical sort:\n\n1. chauncey\n2. coddington\n3. companion\n4. coachmen\n5. clergymen\n6. embark\n7. fatten\n8. threefold\n9. granular\n10. gazpacho\n11. hobble\n12. murk\n13. muslim\n14. niggle\n15. pristine\n16. pvc\n17. singlet\n18. too\n19. yeats\n\n(Checking 'fatten' vs 'threefold': F-A-T-T-E-N vs T-H-R-E-E-F-O-L-D. 'fatten' comes first.)\n(Checking 'granular' vs 'gazpacho': G-R vs G-A. 'gazpacho' comes before 'granular'.)\n\nCorrected final sort:\n\n1. chauncey\n2. coddington\n3. companion\n4. coachmen\n5. clergymen\n6. embark\n7. fatten\n8. threefold\n9. gazpacho\n10. granular\n11. hobble\n12. murk\n13. muslim\n14. niggle\n15. pristine\n16. pvc\n17. singlet\n18. too\n19. yeats\n\nAnswer: chauncey, coddington, companion, coachmen, clergymen, embark, fatten, threefold, gazpacho, granular, hobble, murk, muslim, niggle, pristine, pvc, singlet, too, yeats\n\nFeedback:\nThe initial attempt's sorting logic contained errors, specifically in the manual re-sorting steps, although the final list provided in the \"Corrected final sort\" section appears to be the intended final answer.\n\n1. **Factual/Reasoning Error:** The attempt incorrectly noted a potential issue between 'fatten' and 'threefold' and then corrected it, but the initial reasoning was flawed.\n2. **Arithmetic Error:** None.\n3. **Instruction Following Error:** The attempt provided a preliminary list, then a confusing re-sort attempt, and finally a \"Corrected final sort.\" The final output format is correct (a list followed by the final answer line).\n\nThe final sorted list provided in the attempt is correct based on standard alphabetical ordering.\n\nCorrection Advice: The final sorted list is correct. Remove the intermediate, confusing sorting steps to present a clean solution.\n\nAnswer: chauncey, coddington, companion, coachmen, clergymen, embark, fatten, threefold, gazpacho, granular, hobble, murk, muslim, niggle, pristine, pvc, singlet, too, yeats", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0098", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: chauncey, coddington, companion, coachmen, clergymen, embark, fatten, threefold, gazpacho, granular, hobble, murk, muslim, niggle, pristine, pvc, singlet, too, yeats", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.503", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:35.260323+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.9546, + "error": null, + "event": "model_call", + "event_seq": 504, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: million boone chalcedony sinai satin genteel alternate charity olden\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0113", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words to sort: million, boone, chalcedony, sinai, satin, genteel, alternate, charity, olden\n\n1. **alternate**\n2. **boone**\n3. **chalcedony**\n4. **charity**\n5. **genteel**\n6. **million**\n7. **olden**\n8. **satin**\n9. **sinai**\n\nLet's re-sort them carefully:\n\n* **a**lternate\n* **b**oone\n* **c**halcedony\n* **c**harity (comes after chalcedony because 'h' comes before 'r')\n* **g**enteel\n* **m**illion\n* **o**lden\n* **s**atin\n* **s**inai (satin comes before sinai because 'a' comes before 'i')\n\nLet's re-examine the 'c' words: chalcedony, charity.\nchalcedony (ch-a-l)\ncharity (ch-a-r)\n'l' comes before 'r', so chalcedony comes before charity.\n\nLet's re-examine the 's' words: satin, sinai.\nsatin (s-a-t)\nsinai (s-i-n)\n'a' comes before 'i', so satin comes before sinai.\n\nFinal alphabetical order:\n1. alternate\n2. boone\n3. chalcedony\n4. charity\n5. genteel\n6. million\n7. olden\n8. satin\n9. sinai\n\nAnswer: alternate, boone, chalcedony, charity, genteel, million, olden, satin, sinai", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.504", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:37.221907+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.5989, + "error": null, + "event": "model_call", + "event_seq": 505, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: million boone chalcedony sinai satin genteel alternate charity olden\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words to sort: million, boone, chalcedony, sinai, satin, genteel, alternate, charity, olden\n\n1. **alternate**\n2. **boone**\n3. **chalcedony**\n4. **charity**\n5. **genteel**\n6. **million**\n7. **olden**\n8. **satin**\n9. **sinai**\n\nLet's re-sort them carefully:\n\n* **a**lternate\n* **b**oone\n* **c**halcedony\n* **c**harity (comes after chalcedony because 'h' comes before 'r')\n* **g**enteel\n* **m**illion\n* **o**lden\n* **s**atin\n* **s**inai (satin comes before sinai because 'a' comes before 'i')\n\nLet's re-examine the 'c' words: chalcedony, charity.\nchalcedony (ch-a-l)\ncharity (ch-a-r)\n'l' comes before 'r', so chalcedony comes before charity.\n\nLet's re-examine the 's' words: satin, sinai.\nsatin (s-a-t)\nsinai (s-i-n)\n'a' comes before 'i', so satin comes before sinai.\n\nFinal alphabetical order:\n1. alternate\n2. boone\n3. chalcedony\n4. charity\n5. genteel\n6. million\n7. olden\n8. satin\n9. sinai\n\nAnswer: alternate, boone, chalcedony, charity, genteel, million, olden, satin, sinai", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0113", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempted answer is correct. The sorting logic is sound, and the final order is alphabetically correct.\n\nAnswer: alternate, boone, chalcedony, charity, genteel, million, olden, satin, sinai", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.505", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:37.824759+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.6001, + "error": null, + "event": "model_call", + "event_seq": 506, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: million boone chalcedony sinai satin genteel alternate charity olden\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words to sort: million, boone, chalcedony, sinai, satin, genteel, alternate, charity, olden\n\n1. **alternate**\n2. **boone**\n3. **chalcedony**\n4. **charity**\n5. **genteel**\n6. **million**\n7. **olden**\n8. **satin**\n9. **sinai**\n\nLet's re-sort them carefully:\n\n* **a**lternate\n* **b**oone\n* **c**halcedony\n* **c**harity (comes after chalcedony because 'h' comes before 'r')\n* **g**enteel\n* **m**illion\n* **o**lden\n* **s**atin\n* **s**inai (satin comes before sinai because 'a' comes before 'i')\n\nLet's re-examine the 'c' words: chalcedony, charity.\nchalcedony (ch-a-l)\ncharity (ch-a-r)\n'l' comes before 'r', so chalcedony comes before charity.\n\nLet's re-examine the 's' words: satin, sinai.\nsatin (s-a-t)\nsinai (s-i-n)\n'a' comes before 'i', so satin comes before sinai.\n\nFinal alphabetical order:\n1. alternate\n2. boone\n3. chalcedony\n4. charity\n5. genteel\n6. million\n7. olden\n8. satin\n9. sinai\n\nAnswer: alternate, boone, chalcedony, charity, genteel, million, olden, satin, sinai\n\nFeedback:\nThe attempted answer is correct. The sorting logic is sound, and the final order is alphabetically correct.\n\nAnswer: alternate, boone, chalcedony, charity, genteel, million, olden, satin, sinai", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0113", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: alternate, boone, chalcedony, charity, genteel, million, olden, satin, sinai", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.506", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:38.428814+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.1825, + "error": null, + "event": "model_call", + "event_seq": 507, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: impede appliance pulitzer superior\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0072", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The given list of words is: impede, appliance, pulitzer, superior.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **impede** (starts with 'i')\n2. **appliance** (starts with 'a')\n3. **pulitzer** (starts with 'p')\n4. **superior** (starts with 's')\n\nComparing the first letters: 'a', 'i', 'p', 's'.\nAlphabetical order of the first letters: 'a', 'i', 'p', 's'.\n\n1. **appliance** (starts with 'a')\n2. **impede** (starts with 'i')\n3. **pulitzer** (starts with 'p')\n4. **superior** (starts with 's')\n\nThe sorted list is: appliance, impede, pulitzer, superior.\n\nAnswer: appliance, impede, pulitzer, superior\nAnswer: appliance, impede, pulitzer, superior", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.507", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:39.617364+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.4888, + "error": null, + "event": "model_call", + "event_seq": 508, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: impede appliance pulitzer superior\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: impede, appliance, pulitzer, superior.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **impede** (starts with 'i')\n2. **appliance** (starts with 'a')\n3. **pulitzer** (starts with 'p')\n4. **superior** (starts with 's')\n\nComparing the first letters: 'a', 'i', 'p', 's'.\nAlphabetical order of the first letters: 'a', 'i', 'p', 's'.\n\n1. **appliance** (starts with 'a')\n2. **impede** (starts with 'i')\n3. **pulitzer** (starts with 'p')\n4. **superior** (starts with 's')\n\nThe sorted list is: appliance, impede, pulitzer, superior.\n\nAnswer: appliance, impede, pulitzer, superior\nAnswer: appliance, impede, pulitzer, superior", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0072", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The reasoning is flawed. The comparison of the first letters in the initial attempt was incorrect.\n\nCorrection advice: Re-evaluate the alphabetical order of the starting letters ('i', 'a', 'p', 's') and sort the words based on that correct order.\n\nAnswer: appliance, impede, pulitzer, superior", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.508", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:40.115357+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.4279, + "error": null, + "event": "model_call", + "event_seq": 509, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: impede appliance pulitzer superior\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: impede, appliance, pulitzer, superior.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **impede** (starts with 'i')\n2. **appliance** (starts with 'a')\n3. **pulitzer** (starts with 'p')\n4. **superior** (starts with 's')\n\nComparing the first letters: 'a', 'i', 'p', 's'.\nAlphabetical order of the first letters: 'a', 'i', 'p', 's'.\n\n1. **appliance** (starts with 'a')\n2. **impede** (starts with 'i')\n3. **pulitzer** (starts with 'p')\n4. **superior** (starts with 's')\n\nThe sorted list is: appliance, impede, pulitzer, superior.\n\nAnswer: appliance, impede, pulitzer, superior\nAnswer: appliance, impede, pulitzer, superior\n\nFeedback:\nThe reasoning is flawed. The comparison of the first letters in the initial attempt was incorrect.\n\nCorrection advice: Re-evaluate the alphabetical order of the starting letters ('i', 'a', 'p', 's') and sort the words based on that correct order.\n\nAnswer: appliance, impede, pulitzer, superior", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0072", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The given list of words is: impede, appliance, pulitzer, superior.\n\nTo sort these words alphabetically, we compare them letter by letter based on the first letter of each word:\n- impede starts with 'i'\n- appliance starts with 'a'\n- pulitzer starts with 'p'\n- superior starts with 's'\n\nThe starting letters are: 'i', 'a', 'p', 's'.\n\nSorting these letters alphabetically: 'a', 'i', 'p', 's'.\n\n1. The word starting with 'a' is **appliance**.\n2. The word starting with 'i' is **impede**.\n3. The word starting with 'p' is **pulitzer**.\n4. The word starting with 's' is **superior**.\n\nThe correctly sorted list is: appliance, impede, pulitzer, superior.\n\nAnswer: appliance, impede, pulitzer, superior", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.509", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:41.547557+00:00" + }, + { + "cycle": 0, + "duration_sec": 2.8059, + "error": null, + "event": "model_call", + "event_seq": 510, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: covalent spiderwort horowitz divisive spiritual cheshire affluent gideon quadrature julio peanut epsilon diagnostician grover folklore gothic salient\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0127", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words sorted alphabetically:\n\n1. affluent\n2. cheshire\n3. covalent\n4. diagnostician\n5. divisive\n6. epsilon\n7. folklore\n8. gothic\n9. gideon\n10. grover\n11. horowitz\n12. julio\n13. peanut\n14. quadrature\n15. salient\n16. spiderwort\n17. spiritual\n\nLet's re-sort them carefully:\n\n* **a**ffluent\n* **c**ovalent, **c**heshire\n* **d**iagnostician, **d**ivisive\n* **e**psilon\n* **f**olklore, **g**othic, **g**ideon, **g**rover\n* **h**orowitz\n* **j**ulio\n* **p**eanut\n* **q**uadrature\n* **s**alient, **s**piderwort, **s**piritual\n\n**Detailed Alphabetical Sort:**\n\n1. affluent\n2. cheshire\n3. covalent\n4. diagnostician\n5. divisive\n6. epsilon\n7. folklore\n8. gothic\n9. gideon\n10. grover\n11. horowitz\n12. julio\n13. peanut\n14. quadrature\n15. salient\n16. spiderwort\n17. spiritual\n\nWait, let's check the 'G' words: gothic, gideon, grover.\nGothic (go...)\nGideon (gi...)\nGrover (gr...)\nOrder: gothic, gideon, grover.\n\nLet's check the 'S' words: salient, spiderwort, spiritual.\nSalient (sal...)\nSpiderwort (spi...)\nSpiritual (spi...)\nSalient comes before Spiderwort and Spiritual.\nSpiderwort (spi**d**)\nSpiritual (spi**r**)\nOrder: salient, spiderwort, spiritual.\n\n**Final Sorted List:**\n\n1. affluent\n2. cheshire\n3. covalent\n4. diagnostician\n5. divisive\n6. epsilon\n7. folklore\n8. gothic\n9. gideon\n10. grover\n11. horowitz\n12. julio\n13. peanut\n14. quadrature\n15. salient\n16. spiderwort\n17. spiritual\n\n(Re-checking G words: gothic, gideon, grover. G-o-t, G-i-d, G-r-o. Correct order: gothic, gideon, grover.)\n\nAnswer: affluent, cheshire, covalent, diagnostician, divisive, epsilon, folklore, gothic, gideon, grover, horowitz, julio, peanut, quadrature, salient, spiderwort, spiritual", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.510", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:44.357726+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.8807, + "error": null, + "event": "model_call", + "event_seq": 511, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: covalent spiderwort horowitz divisive spiritual cheshire affluent gideon quadrature julio peanut epsilon diagnostician grover folklore gothic salient\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. affluent\n2. cheshire\n3. covalent\n4. diagnostician\n5. divisive\n6. epsilon\n7. folklore\n8. gothic\n9. gideon\n10. grover\n11. horowitz\n12. julio\n13. peanut\n14. quadrature\n15. salient\n16. spiderwort\n17. spiritual\n\nLet's re-sort them carefully:\n\n* **a**ffluent\n* **c**ovalent, **c**heshire\n* **d**iagnostician, **d**ivisive\n* **e**psilon\n* **f**olklore, **g**othic, **g**ideon, **g**rover\n* **h**orowitz\n* **j**ulio\n* **p**eanut\n* **q**uadrature\n* **s**alient, **s**piderwort, **s**piritual\n\n**Detailed Alphabetical Sort:**\n\n1. affluent\n2. cheshire\n3. covalent\n4. diagnostician\n5. divisive\n6. epsilon\n7. folklore\n8. gothic\n9. gideon\n10. grover\n11. horowitz\n12. julio\n13. peanut\n14. quadrature\n15. salient\n16. spiderwort\n17. spiritual\n\nWait, let's check the 'G' words: gothic, gideon, grover.\nGothic (go...)\nGideon (gi...)\nGrover (gr...)\nOrder: gothic, gideon, grover.\n\nLet's check the 'S' words: salient, spiderwort, spiritual.\nSalient (sal...)\nSpiderwort (spi...)\nSpiritual (spi...)\nSalient comes before Spiderwort and Spiritual.\nSpiderwort (spi**d**)\nSpiritual (spi**r**)\nOrder: salient, spiderwort, spiritual.\n\n**Final Sorted List:**\n\n1. affluent\n2. cheshire\n3. covalent\n4. diagnostician\n5. divisive\n6. epsilon\n7. folklore\n8. gothic\n9. gideon\n10. grover\n11. horowitz\n12. julio\n13. peanut\n14. quadrature\n15. salient\n16. spiderwort\n17. spiritual\n\n(Re-checking G words: gothic, gideon, grover. G-o-t, G-i-d, G-r-o. Correct order: gothic, gideon, grover.)\n\nAnswer: affluent, cheshire, covalent, diagnostician, divisive, epsilon, folklore, gothic, gideon, grover, horowitz, julio, peanut, quadrature, salient, spiderwort, spiritual", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0127", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempted answer contains an error in the final sorted list based on the initial list provided.\n\nCorrection advice: Review the sorting of the words, especially the 'G' words and the 'S' words, against the full list to ensure strict alphabetical order. The final list provided in the attempt seems to have an error in the placement of 'gideon' and 'grover' relative to 'gothic', and potentially in the 'S' words. Re-verify the entire sequence.\n\nAnswer: affluent, cheshire, covalent, diagnostician, divisive, epsilon, folklore, gothic, gideon, grover, horowitz, julio, peanut, quadrature, salient, spiderwort, spiritual", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.511", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:45.245416+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.5899, + "error": null, + "event": "model_call", + "event_seq": 512, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: covalent spiderwort horowitz divisive spiritual cheshire affluent gideon quadrature julio peanut epsilon diagnostician grover folklore gothic salient\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. affluent\n2. cheshire\n3. covalent\n4. diagnostician\n5. divisive\n6. epsilon\n7. folklore\n8. gothic\n9. gideon\n10. grover\n11. horowitz\n12. julio\n13. peanut\n14. quadrature\n15. salient\n16. spiderwort\n17. spiritual\n\nLet's re-sort them carefully:\n\n* **a**ffluent\n* **c**ovalent, **c**heshire\n* **d**iagnostician, **d**ivisive\n* **e**psilon\n* **f**olklore, **g**othic, **g**ideon, **g**rover\n* **h**orowitz\n* **j**ulio\n* **p**eanut\n* **q**uadrature\n* **s**alient, **s**piderwort, **s**piritual\n\n**Detailed Alphabetical Sort:**\n\n1. affluent\n2. cheshire\n3. covalent\n4. diagnostician\n5. divisive\n6. epsilon\n7. folklore\n8. gothic\n9. gideon\n10. grover\n11. horowitz\n12. julio\n13. peanut\n14. quadrature\n15. salient\n16. spiderwort\n17. spiritual\n\nWait, let's check the 'G' words: gothic, gideon, grover.\nGothic (go...)\nGideon (gi...)\nGrover (gr...)\nOrder: gothic, gideon, grover.\n\nLet's check the 'S' words: salient, spiderwort, spiritual.\nSalient (sal...)\nSpiderwort (spi...)\nSpiritual (spi...)\nSalient comes before Spiderwort and Spiritual.\nSpiderwort (spi**d**)\nSpiritual (spi**r**)\nOrder: salient, spiderwort, spiritual.\n\n**Final Sorted List:**\n\n1. affluent\n2. cheshire\n3. covalent\n4. diagnostician\n5. divisive\n6. epsilon\n7. folklore\n8. gothic\n9. gideon\n10. grover\n11. horowitz\n12. julio\n13. peanut\n14. quadrature\n15. salient\n16. spiderwort\n17. spiritual\n\n(Re-checking G words: gothic, gideon, grover. G-o-t, G-i-d, G-r-o. Correct order: gothic, gideon, grover.)\n\nAnswer: affluent, cheshire, covalent, diagnostician, divisive, epsilon, folklore, gothic, gideon, grover, horowitz, julio, peanut, quadrature, salient, spiderwort, spiritual\n\nFeedback:\nThe attempted answer contains an error in the final sorted list based on the initial list provided.\n\nCorrection advice: Review the sorting of the words, especially the 'G' words and the 'S' words, against the full list to ensure strict alphabetical order. The final list provided in the attempt seems to have an error in the placement of 'gideon' and 'grover' relative to 'gothic', and potentially in the 'S' words. Re-verify the entire sequence.\n\nAnswer: affluent, cheshire, covalent, diagnostician, divisive, epsilon, folklore, gothic, gideon, grover, horowitz, julio, peanut, quadrature, salient, spiderwort, spiritual", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0127", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: affluent, cheshire, covalent, diagnostician, divisive, epsilon, folklore, gothic, gideon, grover, horowitz, julio, peanut, quadrature, salient, spiderwort, spiritual", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.512", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:45.839484+00:00" + }, + { + "cycle": 0, + "duration_sec": 2.51, + "error": null, + "event": "model_call", + "event_seq": 513, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: butterscotch veterinarian kochab molybdate extoller amos farrell academia glance predatory beautiful ford fiducial metzler europium circuitous diatom monomer\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0028", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words sorted alphabetically:\n\n1. academia\n2. amos\n3. beautiful\n4. butterscotch\n5. circuitous\n6. diatom\n7. extoller\n8. farrell\n9. fiducial\n10. glance\n11. kochab\n12. ladmoner (Wait, the list has 'monomer'. Let's re-check the list.)\n\nOriginal List: butterscotch veterinarian kochab molybdate extoller amos farrell academia glance predatory beautiful ford fiducial metzler europium circuitous diatom monomer\n\nLet's sort them systematically:\n\n1. academia\n2. amos\n3. beautiful\n4. butterscotch\n5. circuitous\n6. diatom\n7. extoller\n8. farrell\n9. fiducial\n10. glance\n11. kochab\n12. metzler\n13. molybdate\n14. monomer\n15. predatory\n16. europium\n17. veterinarian\n18. ford\n\nLet's re-sort carefully:\n\nA: academia, amos\nB: beautiful, butterscotch\nC: circuitous\nD: diatom\nE: extoller, europium\nF: farrell, fiducial, ford\nG: glance\nK: kochab\nM: molybdate, monomer, metzler\nP: predatory\nV: veterinarian\n\nSorting within groups:\n\nA: academia, amos\nB: beautiful, butterscotch\nC: circuitous\nD: diatom\nE: extoller, europium\nF: farrell, fiducial, ford\nG: glance\nK: kochab\nM: molybdate, monomer, metzler (molybdate, monomer, metzler)\nP: predatory\nV: veterinarian\n\nFinal Alphabetical Order:\n1. academia\n2. amos\n3. beautiful\n4. butterscotch\n5. circuitous\n6. diatom\n7. extoller\n8. europium\n9. farrell\n10. fiducial\n11. ford\n12. glance\n13. kochab\n14. molybdate\n15. monomer\n16. metzler\n17. predatory\n18. veterinarian\n\nAnswer: academia, amos, beautiful, butterscotch, circuitous, diatom, extoller, europium, farrell, fiducial, ford, glance, kochab, molybdate, monomer, metzler, predatory, veterinarian", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.513", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:48.353782+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.1948, + "error": null, + "event": "model_call", + "event_seq": 514, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: butterscotch veterinarian kochab molybdate extoller amos farrell academia glance predatory beautiful ford fiducial metzler europium circuitous diatom monomer\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. academia\n2. amos\n3. beautiful\n4. butterscotch\n5. circuitous\n6. diatom\n7. extoller\n8. farrell\n9. fiducial\n10. glance\n11. kochab\n12. ladmoner (Wait, the list has 'monomer'. Let's re-check the list.)\n\nOriginal List: butterscotch veterinarian kochab molybdate extoller amos farrell academia glance predatory beautiful ford fiducial metzler europium circuitous diatom monomer\n\nLet's sort them systematically:\n\n1. academia\n2. amos\n3. beautiful\n4. butterscotch\n5. circuitous\n6. diatom\n7. extoller\n8. farrell\n9. fiducial\n10. glance\n11. kochab\n12. metzler\n13. molybdate\n14. monomer\n15. predatory\n16. europium\n17. veterinarian\n18. ford\n\nLet's re-sort carefully:\n\nA: academia, amos\nB: beautiful, butterscotch\nC: circuitous\nD: diatom\nE: extoller, europium\nF: farrell, fiducial, ford\nG: glance\nK: kochab\nM: molybdate, monomer, metzler\nP: predatory\nV: veterinarian\n\nSorting within groups:\n\nA: academia, amos\nB: beautiful, butterscotch\nC: circuitous\nD: diatom\nE: extoller, europium\nF: farrell, fiducial, ford\nG: glance\nK: kochab\nM: molybdate, monomer, metzler (molybdate, monomer, metzler)\nP: predatory\nV: veterinarian\n\nFinal Alphabetical Order:\n1. academia\n2. amos\n3. beautiful\n4. butterscotch\n5. circuitous\n6. diatom\n7. extoller\n8. europium\n9. farrell\n10. fiducial\n11. ford\n12. glance\n13. kochab\n14. molybdate\n15. monomer\n16. metzler\n17. predatory\n18. veterinarian\n\nAnswer: academia, amos, beautiful, butterscotch, circuitous, diatom, extoller, europium, farrell, fiducial, ford, glance, kochab, molybdate, monomer, metzler, predatory, veterinarian", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0028", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempted answer has several errors in the sorting process.\n\n1. **Missing/Incorrect Words:** The original list contains \"veterinarian\" and \"molybdate\", which were handled, but the sorting logic needs to be strictly alphabetical.\n2. **Incorrect Grouping/Ordering:** The final list provided in the attempt does not match a strict alphabetical sort of the original words. For example, 'europium' should come before 'farrell' if sorting by the first letter, or the entire list needs to be checked against the correct alphabetical order.\n\nCorrection Advice: Re-sort the entire list strictly alphabetically. Ensure all 18 words from the original list are present and in the correct sequence.", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.514", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:49.552805+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.3391, + "error": null, + "event": "model_call", + "event_seq": 515, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: butterscotch veterinarian kochab molybdate extoller amos farrell academia glance predatory beautiful ford fiducial metzler europium circuitous diatom monomer\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. academia\n2. amos\n3. beautiful\n4. butterscotch\n5. circuitous\n6. diatom\n7. extoller\n8. farrell\n9. fiducial\n10. glance\n11. kochab\n12. ladmoner (Wait, the list has 'monomer'. Let's re-check the list.)\n\nOriginal List: butterscotch veterinarian kochab molybdate extoller amos farrell academia glance predatory beautiful ford fiducial metzler europium circuitous diatom monomer\n\nLet's sort them systematically:\n\n1. academia\n2. amos\n3. beautiful\n4. butterscotch\n5. circuitous\n6. diatom\n7. extoller\n8. farrell\n9. fiducial\n10. glance\n11. kochab\n12. metzler\n13. molybdate\n14. monomer\n15. predatory\n16. europium\n17. veterinarian\n18. ford\n\nLet's re-sort carefully:\n\nA: academia, amos\nB: beautiful, butterscotch\nC: circuitous\nD: diatom\nE: extoller, europium\nF: farrell, fiducial, ford\nG: glance\nK: kochab\nM: molybdate, monomer, metzler\nP: predatory\nV: veterinarian\n\nSorting within groups:\n\nA: academia, amos\nB: beautiful, butterscotch\nC: circuitous\nD: diatom\nE: extoller, europium\nF: farrell, fiducial, ford\nG: glance\nK: kochab\nM: molybdate, monomer, metzler (molybdate, monomer, metzler)\nP: predatory\nV: veterinarian\n\nFinal Alphabetical Order:\n1. academia\n2. amos\n3. beautiful\n4. butterscotch\n5. circuitous\n6. diatom\n7. extoller\n8. europium\n9. farrell\n10. fiducial\n11. ford\n12. glance\n13. kochab\n14. molybdate\n15. monomer\n16. metzler\n17. predatory\n18. veterinarian\n\nAnswer: academia, amos, beautiful, butterscotch, circuitous, diatom, extoller, europium, farrell, fiducial, ford, glance, kochab, molybdate, monomer, metzler, predatory, veterinarian\n\nFeedback:\nThe attempted answer has several errors in the sorting process.\n\n1. **Missing/Incorrect Words:** The original list contains \"veterinarian\" and \"molybdate\", which were handled, but the sorting logic needs to be strictly alphabetical.\n2. **Incorrect Grouping/Ordering:** The final list provided in the attempt does not match a strict alphabetical sort of the original words. For example, 'europium' should come before 'farrell' if sorting by the first letter, or the entire list needs to be checked against the correct alphabetical order.\n\nCorrection Advice: Re-sort the entire list strictly alphabetically. Ensure all 18 words from the original list are present and in the correct sequence.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0028", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The original list of words is: butterscotch, veterinarian, kochab, molybdate, extoller, amos, farrell, academia, glance, predatory, beautiful, ford, fiducial, metzler, europium, circuitous, diatom, monomer.\n\nSorting these words alphabetically:\n\n1. academia\n2. amos\n3. beautiful\n4. butterscotch\n5. circuitous\n6. diatom\n7. extoller\n8. europium\n9. farrell\n10. fiducial\n11. ford\n12. glance\n13. kochab\n14. molybdate\n15. monomer\n16. metzler\n17. predatory\n18. veterinarian\n\nAnswer: academia, amos, beautiful, butterscotch, circuitous, diatom, extoller, europium, farrell, fiducial, ford, glance, kochab, molybdate, monomer, metzler, predatory, veterinarian", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.515", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:50.896135+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.848, + "error": null, + "event": "model_call", + "event_seq": 516, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: plural dose allstate stalin dyad multitudinous powderpuff\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0073", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The given list of words is: plural, dose, allstate, stalin, dyad, multitudinous, powderpuff.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter:**\n * plural (p)\n * dose (d)\n * allstate (a)\n * stalin (s)\n * dyad (d)\n * multitudinous (m)\n * powderpuff (p)\n\n Words starting with 'a': allstate\n Words starting with 'd': dose, dyad\n Words starting with 'm': multitudinous\n Words starting with 'p': plural, powderpuff\n Words starting with 's': stalin\n\n2. **Sort words starting with 'd':**\n * dose\n * dyad\n (dose comes before dyad)\n\n3. **Sort words starting with 'p':**\n * plural\n * powderpuff\n (plural comes before powderpuff)\n\n4. **Assemble the sorted list based on the first letter order (A, D, M, P, S):**\n * allstate (A)\n * dose (D)\n * dyad (D)\n * multitudinous (M)\n * plural (P)\n * powderpuff (P)\n * stalin (S)\n\n5. **Refine the order within groups:**\n * D group: dose, dyad (dose before dyad)\n * P group: plural, powderpuff (plural before powderpuff)\n\nThe final alphabetical order is: allstate, dose, dyad, multitudinous, plural, powderpuff, stalin.\n\nAnswer: allstate, dose, dyad, multitudinous, plural, powderpuff, stalin", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.516", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:52.748558+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.6004, + "error": null, + "event": "model_call", + "event_seq": 517, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: plural dose allstate stalin dyad multitudinous powderpuff\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: plural, dose, allstate, stalin, dyad, multitudinous, powderpuff.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter:**\n * plural (p)\n * dose (d)\n * allstate (a)\n * stalin (s)\n * dyad (d)\n * multitudinous (m)\n * powderpuff (p)\n\n Words starting with 'a': allstate\n Words starting with 'd': dose, dyad\n Words starting with 'm': multitudinous\n Words starting with 'p': plural, powderpuff\n Words starting with 's': stalin\n\n2. **Sort words starting with 'd':**\n * dose\n * dyad\n (dose comes before dyad)\n\n3. **Sort words starting with 'p':**\n * plural\n * powderpuff\n (plural comes before powderpuff)\n\n4. **Assemble the sorted list based on the first letter order (A, D, M, P, S):**\n * allstate (A)\n * dose (D)\n * dyad (D)\n * multitudinous (M)\n * plural (P)\n * powderpuff (P)\n * stalin (S)\n\n5. **Refine the order within groups:**\n * D group: dose, dyad (dose before dyad)\n * P group: plural, powderpuff (plural before powderpuff)\n\nThe final alphabetical order is: allstate, dose, dyad, multitudinous, plural, powderpuff, stalin.\n\nAnswer: allstate, dose, dyad, multitudinous, plural, powderpuff, stalin", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0073", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempted answer is factually correct and follows the instructions well. The sorting logic is sound.\n\nAnswer: allstate, dose, dyad, multitudinous, plural, powderpuff, stalin", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.517", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:53.353341+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.4812, + "error": null, + "event": "model_call", + "event_seq": 518, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: plural dose allstate stalin dyad multitudinous powderpuff\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: plural, dose, allstate, stalin, dyad, multitudinous, powderpuff.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter:**\n * plural (p)\n * dose (d)\n * allstate (a)\n * stalin (s)\n * dyad (d)\n * multitudinous (m)\n * powderpuff (p)\n\n Words starting with 'a': allstate\n Words starting with 'd': dose, dyad\n Words starting with 'm': multitudinous\n Words starting with 'p': plural, powderpuff\n Words starting with 's': stalin\n\n2. **Sort words starting with 'd':**\n * dose\n * dyad\n (dose comes before dyad)\n\n3. **Sort words starting with 'p':**\n * plural\n * powderpuff\n (plural comes before powderpuff)\n\n4. **Assemble the sorted list based on the first letter order (A, D, M, P, S):**\n * allstate (A)\n * dose (D)\n * dyad (D)\n * multitudinous (M)\n * plural (P)\n * powderpuff (P)\n * stalin (S)\n\n5. **Refine the order within groups:**\n * D group: dose, dyad (dose before dyad)\n * P group: plural, powderpuff (plural before powderpuff)\n\nThe final alphabetical order is: allstate, dose, dyad, multitudinous, plural, powderpuff, stalin.\n\nAnswer: allstate, dose, dyad, multitudinous, plural, powderpuff, stalin\n\nFeedback:\nThe attempted answer is factually correct and follows the instructions well. The sorting logic is sound.\n\nAnswer: allstate, dose, dyad, multitudinous, plural, powderpuff, stalin", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0073", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: allstate, dose, dyad, multitudinous, plural, powderpuff, stalin", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.518", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:53.838725+00:00" + }, + { + "cycle": 0, + "duration_sec": 2.3675, + "error": null, + "event": "model_call", + "event_seq": 519, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: bruckner admiration technion bandgap heron postprocessor maidenhair fungus allusion gambit accessory proscenium cruise\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0084", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words sorted alphabetically:\n\n1. admiration\n2. accessory\n3. allusion\n4. bandgap\n5. bruckner\n6. cruise\n7. fungus\n8. gambit\n9. heron\n10. maidenhair\n11. postprocessor\n12. proscenium\n13. technion\n\nLet's re-sort them carefully:\n\n* **a**dmiration\n* **a**ccessory\n* **a**llusion\n* **b**andgap\n* **b**ruckner\n* **c**ruise\n* **f**ungus\n* **g**ambit\n* **h**eron\n* **m**aidenhair\n* **p**ostprocessor\n* **p**roscenium\n* **t**echnion\n\nComparing 'admiration' and 'accessory': 'admiration' comes before 'accessory'.\nComparing 'accessory' and 'allusion': 'accessory' comes before 'allusion'. (Wait, 'a'llusion vs 'a'ccessory. 'll' vs 'cc'. 'll' comes before 'cc' in standard alphabetical order if we look at the third letter, but let's compare them directly: a-l-l-u-s-i-o-n vs a-c-c-e-s-s-o-r. 'c' comes before 'l', so 'accessory' comes before 'allusion'.)\n\nLet's re-sort the 'a' words:\n1. admiration\n2. accessory\n3. allusion\n\nLet's re-sort the rest:\n* bandgap\n* bruckner\n* cruise\n* fungus\n* gambit\n* heron\n* maidenhair\n* postprocessor\n* proscenium\n* technion\n\nFinal sorted list:\n1. admiration\n2. accessory\n3. allusion\n4. bandgap\n5. bruckner\n6. cruise\n7. fungus\n8. gambit\n9. heron\n10. maidenhair\n11. postprocessor\n12. proscenium\n13. technion\n\nAnswer: admiration, accessory, allusion, bandgap, bruckner, cruise, fungus, gambit, heron, maidenhair, postprocessor, proscenium, technion", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.519", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:56.210728+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.6391, + "error": null, + "event": "model_call", + "event_seq": 520, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: bruckner admiration technion bandgap heron postprocessor maidenhair fungus allusion gambit accessory proscenium cruise\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. admiration\n2. accessory\n3. allusion\n4. bandgap\n5. bruckner\n6. cruise\n7. fungus\n8. gambit\n9. heron\n10. maidenhair\n11. postprocessor\n12. proscenium\n13. technion\n\nLet's re-sort them carefully:\n\n* **a**dmiration\n* **a**ccessory\n* **a**llusion\n* **b**andgap\n* **b**ruckner\n* **c**ruise\n* **f**ungus\n* **g**ambit\n* **h**eron\n* **m**aidenhair\n* **p**ostprocessor\n* **p**roscenium\n* **t**echnion\n\nComparing 'admiration' and 'accessory': 'admiration' comes before 'accessory'.\nComparing 'accessory' and 'allusion': 'accessory' comes before 'allusion'. (Wait, 'a'llusion vs 'a'ccessory. 'll' vs 'cc'. 'll' comes before 'cc' in standard alphabetical order if we look at the third letter, but let's compare them directly: a-l-l-u-s-i-o-n vs a-c-c-e-s-s-o-r. 'c' comes before 'l', so 'accessory' comes before 'allusion'.)\n\nLet's re-sort the 'a' words:\n1. admiration\n2. accessory\n3. allusion\n\nLet's re-sort the rest:\n* bandgap\n* bruckner\n* cruise\n* fungus\n* gambit\n* heron\n* maidenhair\n* postprocessor\n* proscenium\n* technion\n\nFinal sorted list:\n1. admiration\n2. accessory\n3. allusion\n4. bandgap\n5. bruckner\n6. cruise\n7. fungus\n8. gambit\n9. heron\n10. maidenhair\n11. postprocessor\n12. proscenium\n13. technion\n\nAnswer: admiration, accessory, allusion, bandgap, bruckner, cruise, fungus, gambit, heron, maidenhair, postprocessor, proscenium, technion", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0084", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The sorting logic contains an error in the comparison of the 'a' words.\n\nCorrection advice: Re-evaluate the alphabetical order of \"admiration\", \"accessory\", and \"allusion\". \"accessory\" should come before \"allusion\" because 'c' comes before 'l'.\n\nAnswer: admiration, accessory, allusion, bandgap, bruckner, cruise, fungus, gambit, heron, maidenhair, postprocessor, proscenium, technion", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.520", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:56.854934+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.7424, + "error": null, + "event": "model_call", + "event_seq": 521, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: bruckner admiration technion bandgap heron postprocessor maidenhair fungus allusion gambit accessory proscenium cruise\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. admiration\n2. accessory\n3. allusion\n4. bandgap\n5. bruckner\n6. cruise\n7. fungus\n8. gambit\n9. heron\n10. maidenhair\n11. postprocessor\n12. proscenium\n13. technion\n\nLet's re-sort them carefully:\n\n* **a**dmiration\n* **a**ccessory\n* **a**llusion\n* **b**andgap\n* **b**ruckner\n* **c**ruise\n* **f**ungus\n* **g**ambit\n* **h**eron\n* **m**aidenhair\n* **p**ostprocessor\n* **p**roscenium\n* **t**echnion\n\nComparing 'admiration' and 'accessory': 'admiration' comes before 'accessory'.\nComparing 'accessory' and 'allusion': 'accessory' comes before 'allusion'. (Wait, 'a'llusion vs 'a'ccessory. 'll' vs 'cc'. 'll' comes before 'cc' in standard alphabetical order if we look at the third letter, but let's compare them directly: a-l-l-u-s-i-o-n vs a-c-c-e-s-s-o-r. 'c' comes before 'l', so 'accessory' comes before 'allusion'.)\n\nLet's re-sort the 'a' words:\n1. admiration\n2. accessory\n3. allusion\n\nLet's re-sort the rest:\n* bandgap\n* bruckner\n* cruise\n* fungus\n* gambit\n* heron\n* maidenhair\n* postprocessor\n* proscenium\n* technion\n\nFinal sorted list:\n1. admiration\n2. accessory\n3. allusion\n4. bandgap\n5. bruckner\n6. cruise\n7. fungus\n8. gambit\n9. heron\n10. maidenhair\n11. postprocessor\n12. proscenium\n13. technion\n\nAnswer: admiration, accessory, allusion, bandgap, bruckner, cruise, fungus, gambit, heron, maidenhair, postprocessor, proscenium, technion\n\nFeedback:\nThe sorting logic contains an error in the comparison of the 'a' words.\n\nCorrection advice: Re-evaluate the alphabetical order of \"admiration\", \"accessory\", and \"allusion\". \"accessory\" should come before \"allusion\" because 'c' comes before 'l'.\n\nAnswer: admiration, accessory, allusion, bandgap, bruckner, cruise, fungus, gambit, heron, maidenhair, postprocessor, proscenium, technion", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0084", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: admiration, accessory, allusion, bandgap, bruckner, cruise, fungus, gambit, heron, maidenhair, postprocessor, proscenium, technion", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.521", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:52:57.612091+00:00" + }, + { + "cycle": 0, + "duration_sec": 4.2032, + "error": null, + "event": "model_call", + "event_seq": 522, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: comet pincer snippet manley injun spokesperson hydrate heusen cocksure\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0065", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The given list of words is: comet, pincer, snippet, manley, injun, spokesperson, hydrate, heusen, cocksure.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter:**\n * comet (c)\n * pincer (p)\n * snippet (s)\n * manley (m)\n * injun (i)\n * spokesperson (s)\n * hydrate (h)\n * heusen (h)\n * cocksure (c)\n\n Words starting with 'c': comet, cocksure\n Words starting with 'h': hydrate, heusen\n Words starting with 'i': injun\n Words starting with 'm': manley\n Words starting with 'p': pincer\n Words starting with 's': snippet, spokesperson\n\n2. **Sort words starting with 'c':**\n * comet\n * cocksure\n (comet comes before cocksure)\n\n3. **Sort words starting with 'h':**\n * hydrate\n * heusen\n(hydrate comes before heusen)\n\n4. **Sort words starting with 'i':**\n * injun\n\n5. **Sort words starting with 'm':**\n * manley\n\n6. **Sort words starting with 'p':**\n * pincer\n\n7. **Sort words starting with 's':**\n * snippet\n * spokesperson\n(snippet comes before spokesperson)\n\n8. **Combine the sorted groups:**\n * comet\n * cocksure\n * hydrate\n * heusen\n * injun\n * manley\n * pincer\n * snippet\n * spokesperson\n\nLet's re-check the full alphabetical order:\nC: comet, cocksure\nH: hydrate, heusen\nI: injun\nM: manley\nP: pincer\nS: snippet, spokesperson\n\nComparing C words: comet vs cocksure. 'o' comes before 'o' (wait, 'm' vs 'o'). comet vs cocksure. 'o' vs 'o'. 'm' vs 'c'. 'm' comes before 'c' in the second letter? No, 'o' vs 'o'.\ncomet (co...)\ncocksure (co...)\ncomet (co**m**et)\ncocksure (co**c**ksure)\n'c' comes before 'm'. So, cocksure comes before comet.\nCorrect C order: cocksure, comet.\n\nComparing H words: hydrate vs heusen. 'y' vs 'e'. 'e' comes before 'y'.\nCorrect H order: heusen, hydrate.\n\nComparing S words: snippet vs spokesperson. 'i' vs 'p'. 'i' comes before 'p'.\nCorrect S order: snippet, spokesperson.\n\nFinal sorted list:\n1. cocksure\n2. comet\n3. heusen\n4. hydrate\n5. injun\n6. manley\n7. pincer\n8. snippet\n9. spokesperson\n\nLet's re-verify the full list alphabetically:\ncomet (c o m e t)\npincer (p i n c e r)\nsnippet (s n i p p e t)\nmanley (m a n l e y)\ninjun (i n j u n)\nspokesperson (s p o k e s p e r o n)\nhydrate (h y d r a t e)\nheusen (h e u s e n)\ncocksure (c o c k s u r e)\n\nAlphabetical order based on first letter:\nC: cocksure, comet\nH: heusen, hydrate\nI: injun\nM: manley\nP: pincer\nS: snippet, spokesperson\n\nFinal sequence: cocksure, comet, heusen, hydrate, injun, manley, pincer, snippet, spokesperson.\n\nAnswer: cocksure, comet, heusen, hydrate, injun, manley, pincer, snippet, spokesperson", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.522", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:01.827411+00:00" + }, + { + "cycle": 0, + "duration_sec": 2.4674, + "error": null, + "event": "model_call", + "event_seq": 523, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: comet pincer snippet manley injun spokesperson hydrate heusen cocksure\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: comet, pincer, snippet, manley, injun, spokesperson, hydrate, heusen, cocksure.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter:**\n * comet (c)\n * pincer (p)\n * snippet (s)\n * manley (m)\n * injun (i)\n * spokesperson (s)\n * hydrate (h)\n * heusen (h)\n * cocksure (c)\n\n Words starting with 'c': comet, cocksure\n Words starting with 'h': hydrate, heusen\n Words starting with 'i': injun\n Words starting with 'm': manley\n Words starting with 'p': pincer\n Words starting with 's': snippet, spokesperson\n\n2. **Sort words starting with 'c':**\n * comet\n * cocksure\n (comet comes before cocksure)\n\n3. **Sort words starting with 'h':**\n * hydrate\n * heusen\n(hydrate comes before heusen)\n\n4. **Sort words starting with 'i':**\n * injun\n\n5. **Sort words starting with 'm':**\n * manley\n\n6. **Sort words starting with 'p':**\n * pincer\n\n7. **Sort words starting with 's':**\n * snippet\n * spokesperson\n(snippet comes before spokesperson)\n\n8. **Combine the sorted groups:**\n * comet\n * cocksure\n * hydrate\n * heusen\n * injun\n * manley\n * pincer\n * snippet\n * spokesperson\n\nLet's re-check the full alphabetical order:\nC: comet, cocksure\nH: hydrate, heusen\nI: injun\nM: manley\nP: pincer\nS: snippet, spokesperson\n\nComparing C words: comet vs cocksure. 'o' comes before 'o' (wait, 'm' vs 'o'). comet vs cocksure. 'o' vs 'o'. 'm' vs 'c'. 'm' comes before 'c' in the second letter? No, 'o' vs 'o'.\ncomet (co...)\ncocksure (co...)\ncomet (co**m**et)\ncocksure (co**c**ksure)\n'c' comes before 'm'. So, cocksure comes before comet.\nCorrect C order: cocksure, comet.\n\nComparing H words: hydrate vs heusen. 'y' vs 'e'. 'e' comes before 'y'.\nCorrect H order: heusen, hydrate.\n\nComparing S words: snippet vs spokesperson. 'i' vs 'p'. 'i' comes before 'p'.\nCorrect S order: snippet, spokesperson.\n\nFinal sorted list:\n1. cocksure\n2. comet\n3. heusen\n4. hydrate\n5. injun\n6. manley\n7. pincer\n8. snippet\n9. spokesperson\n\nLet's re-verify the full list alphabetically:\ncomet (c o m e t)\npincer (p i n c e r)\nsnippet (s n i p p e t)\nmanley (m a n l e y)\ninjun (i n j u n)\nspokesperson (s p o k e s p e r o n)\nhydrate (h y d r a t e)\nheusen (h e u s e n)\ncocksure (c o c k s u r e)\n\nAlphabetical order based on first letter:\nC: cocksure, comet\nH: heusen, hydrate\nI: injun\nM: manley\nP: pincer\nS: snippet, spokesperson\n\nFinal sequence: cocksure, comet, heusen, hydrate, injun, manley, pincer, snippet, spokesperson.\n\nAnswer: cocksure, comet, heusen, hydrate, injun, manley, pincer, snippet, spokesperson", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0065", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The initial sorting logic in the attempt contained errors in comparing words starting with the same letter, specifically for 'C' and 'H'.\n\n1. **Error in C sorting:** The attempt concluded `comet` before `cocksure`. Comparing `comet` and `cocksure`: 'c' vs 'c' (tie). Second letter: 'o' vs 'o' (tie). Third letter: 'm' vs 'c'. Since 'c' comes before 'm', `cocksure` should precede `comet`.\n2. **Error in H sorting:** The attempt concluded `hydrate` before `heusen`. Comparing `hydrate` and `heusen`: 'h' vs 'h' (tie). Second letter: 'y' vs 'e'. Since 'e' comes before 'y', `heusen` should precede `hydrate`.\n\nThe final derived list in the attempt was: `cocksure, comet, heusen, hydrate, injun, manley, pincer, snippet, spokesperson`.\n\nLet's re-verify the correct order based on standard alphabetical rules:\nC: cocksure, comet (c-o-c vs c-o-m) -> cocksure, comet\nH: heusen, hydrate (h-e vs h-y) -> heusen, hydrate\nI: injun\nM: manley\nP: pincer\nS: snippet, spokesperson (s-n vs s-p) -> snippet, spokesperson\n\nCorrect sequence: cocksure, comet, heusen, hydrate, injun, manley, pincer, snippet, spokesperson.\n\nThe attempt's final derived list matches the correct sequence, despite the intermediate reasoning errors noted in steps 2 and 3 of the attempt's internal process.\n\nAnswer: cocksure, comet, heusen, hydrate, injun, manley, pincer, snippet, spokesperson", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.523", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:04.303083+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.5392, + "error": null, + "event": "model_call", + "event_seq": 524, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: comet pincer snippet manley injun spokesperson hydrate heusen cocksure\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: comet, pincer, snippet, manley, injun, spokesperson, hydrate, heusen, cocksure.\n\nHere is the alphabetical sorting process:\n\n1. **Compare the first letter:**\n * comet (c)\n * pincer (p)\n * snippet (s)\n * manley (m)\n * injun (i)\n * spokesperson (s)\n * hydrate (h)\n * heusen (h)\n * cocksure (c)\n\n Words starting with 'c': comet, cocksure\n Words starting with 'h': hydrate, heusen\n Words starting with 'i': injun\n Words starting with 'm': manley\n Words starting with 'p': pincer\n Words starting with 's': snippet, spokesperson\n\n2. **Sort words starting with 'c':**\n * comet\n * cocksure\n (comet comes before cocksure)\n\n3. **Sort words starting with 'h':**\n * hydrate\n * heusen\n(hydrate comes before heusen)\n\n4. **Sort words starting with 'i':**\n * injun\n\n5. **Sort words starting with 'm':**\n * manley\n\n6. **Sort words starting with 'p':**\n * pincer\n\n7. **Sort words starting with 's':**\n * snippet\n * spokesperson\n(snippet comes before spokesperson)\n\n8. **Combine the sorted groups:**\n * comet\n * cocksure\n * hydrate\n * heusen\n * injun\n * manley\n * pincer\n * snippet\n * spokesperson\n\nLet's re-check the full alphabetical order:\nC: comet, cocksure\nH: hydrate, heusen\nI: injun\nM: manley\nP: pincer\nS: snippet, spokesperson\n\nComparing C words: comet vs cocksure. 'o' comes before 'o' (wait, 'm' vs 'o'). comet vs cocksure. 'o' vs 'o'. 'm' vs 'c'. 'm' comes before 'c' in the second letter? No, 'o' vs 'o'.\ncomet (co...)\ncocksure (co...)\ncomet (co**m**et)\ncocksure (co**c**ksure)\n'c' comes before 'm'. So, cocksure comes before comet.\nCorrect C order: cocksure, comet.\n\nComparing H words: hydrate vs heusen. 'y' vs 'e'. 'e' comes before 'y'.\nCorrect H order: heusen, hydrate.\n\nComparing S words: snippet vs spokesperson. 'i' vs 'p'. 'i' comes before 'p'.\nCorrect S order: snippet, spokesperson.\n\nFinal sorted list:\n1. cocksure\n2. comet\n3. heusen\n4. hydrate\n5. injun\n6. manley\n7. pincer\n8. snippet\n9. spokesperson\n\nLet's re-verify the full list alphabetically:\ncomet (c o m e t)\npincer (p i n c e r)\nsnippet (s n i p p e t)\nmanley (m a n l e y)\ninjun (i n j u n)\nspokesperson (s p o k e s p e r o n)\nhydrate (h y d r a t e)\nheusen (h e u s e n)\ncocksure (c o c k s u r e)\n\nAlphabetical order based on first letter:\nC: cocksure, comet\nH: heusen, hydrate\nI: injun\nM: manley\nP: pincer\nS: snippet, spokesperson\n\nFinal sequence: cocksure, comet, heusen, hydrate, injun, manley, pincer, snippet, spokesperson.\n\nAnswer: cocksure, comet, heusen, hydrate, injun, manley, pincer, snippet, spokesperson\n\nFeedback:\nThe initial sorting logic in the attempt contained errors in comparing words starting with the same letter, specifically for 'C' and 'H'.\n\n1. **Error in C sorting:** The attempt concluded `comet` before `cocksure`. Comparing `comet` and `cocksure`: 'c' vs 'c' (tie). Second letter: 'o' vs 'o' (tie). Third letter: 'm' vs 'c'. Since 'c' comes before 'm', `cocksure` should precede `comet`.\n2. **Error in H sorting:** The attempt concluded `hydrate` before `heusen`. Comparing `hydrate` and `heusen`: 'h' vs 'h' (tie). Second letter: 'y' vs 'e'. Since 'e' comes before 'y', `heusen` should precede `hydrate`.\n\nThe final derived list in the attempt was: `cocksure, comet, heusen, hydrate, injun, manley, pincer, snippet, spokesperson`.\n\nLet's re-verify the correct order based on standard alphabetical rules:\nC: cocksure, comet (c-o-c vs c-o-m) -> cocksure, comet\nH: heusen, hydrate (h-e vs h-y) -> heusen, hydrate\nI: injun\nM: manley\nP: pincer\nS: snippet, spokesperson (s-n vs s-p) -> snippet, spokesperson\n\nCorrect sequence: cocksure, comet, heusen, hydrate, injun, manley, pincer, snippet, spokesperson.\n\nThe attempt's final derived list matches the correct sequence, despite the intermediate reasoning errors noted in steps 2 and 3 of the attempt's internal process.\n\nAnswer: cocksure, comet, heusen, hydrate, injun, manley, pincer, snippet, spokesperson", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0065", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: cocksure, comet, heusen, hydrate, injun, manley, pincer, snippet, spokesperson", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.524", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:04.846687+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.915, + "error": null, + "event": "model_call", + "event_seq": 525, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: slurp raytheon gloucester\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0061", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The given list of words is: slurp, raytheon, gloucester.\n\nTo sort these words alphabetically, we compare them letter by letter from the beginning.\n\n1. **s**lurp\n2. **r**aytheon\n3. **g**looucester\n\nComparing the first letters: G comes before R, and R comes before S.\nSo, 'gloucester' comes first.\n\nNow we compare 'slurp' and 'raytheon'.\n's' comes before 'r' is false. 'r' comes before 's'.\nSo, 'raytheon' comes before 'slurp'.\n\nThe alphabetical order is:\n1. gloucester\n2. raytheon\n3. slurp\n\nAnswer: gloucester, raytheon, slurp\n\nAnswer: gloucester, raytheon, slurp", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.525", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:05.766405+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.8155, + "error": null, + "event": "model_call", + "event_seq": 526, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: slurp raytheon gloucester\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: slurp, raytheon, gloucester.\n\nTo sort these words alphabetically, we compare them letter by letter from the beginning.\n\n1. **s**lurp\n2. **r**aytheon\n3. **g**looucester\n\nComparing the first letters: G comes before R, and R comes before S.\nSo, 'gloucester' comes first.\n\nNow we compare 'slurp' and 'raytheon'.\n's' comes before 'r' is false. 'r' comes before 's'.\nSo, 'raytheon' comes before 'slurp'.\n\nThe alphabetical order is:\n1. gloucester\n2. raytheon\n3. slurp\n\nAnswer: gloucester, raytheon, slurp\n\nAnswer: gloucester, raytheon, slurp", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0061", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempt incorrectly omits the word \"gloucester\" from the initial list provided in the task description (\"slurp raytheon gloucester\"). It also incorrectly compares the first letters in the final comparison step.\n\nCorrection advice:\n1. Include all four words in the initial sorting process.\n2. Correct the comparison logic for 'slurp' and 'raytheon' (R comes before S).", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.526", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:06.586864+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.2713, + "error": null, + "event": "model_call", + "event_seq": 527, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: slurp raytheon gloucester\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: slurp, raytheon, gloucester.\n\nTo sort these words alphabetically, we compare them letter by letter from the beginning.\n\n1. **s**lurp\n2. **r**aytheon\n3. **g**looucester\n\nComparing the first letters: G comes before R, and R comes before S.\nSo, 'gloucester' comes first.\n\nNow we compare 'slurp' and 'raytheon'.\n's' comes before 'r' is false. 'r' comes before 's'.\nSo, 'raytheon' comes before 'slurp'.\n\nThe alphabetical order is:\n1. gloucester\n2. raytheon\n3. slurp\n\nAnswer: gloucester, raytheon, slurp\n\nAnswer: gloucester, raytheon, slurp\n\nFeedback:\nThe attempt incorrectly omits the word \"gloucester\" from the initial list provided in the task description (\"slurp raytheon gloucester\"). It also incorrectly compares the first letters in the final comparison step.\n\nCorrection advice:\n1. Include all four words in the initial sorting process.\n2. Correct the comparison logic for 'slurp' and 'raytheon' (R comes before S).", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0061", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The given list of words is: slurp, raytheon, gloucester.\n\nTo sort these words alphabetically, we compare them letter by letter from the beginning.\n\nThe words are:\n1. slurp (starts with s)\n2. raytheon (starts with r)\n3. gloucester (starts with g)\n\nComparing the first letters: G comes before R, and R comes before S.\nThe order of the first letters is G, R, S.\n\nTherefore, the alphabetical order of the words is:\n1. gloucester (G)\n2. raytheon (R)\n3. slurp (S)\n\nAnswer: gloucester, raytheon, slurp", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.527", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:07.866304+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.9413, + "error": null, + "event": "model_call", + "event_seq": 528, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: tiresome sweepstake introvert carport firewood\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0017", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The given list of words is: tiresome, sweepstake, introvert, carport, firewood.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **carport** (Starts with 'c')\n2. **firewood** (Starts with 'f')\n3. **introvert** (Starts with 'i')\n4. **sweepstake** (Starts with 's')\n5. **tiresome** (Starts with 't')\n\nLet's verify the order:\nC comes before F, I, S, T.\nF comes before I, S, T.\nI comes before S, T.\nS comes before T.\n\nThe alphabetical order is: carport, firewood, introvert, sweepstake, tiresome.\n\nAnswer: carport, firewood, introvert, sweepstake, tiresome", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.528", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:08.814004+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.2331, + "error": null, + "event": "model_call", + "event_seq": 529, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: tiresome sweepstake introvert carport firewood\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: tiresome, sweepstake, introvert, carport, firewood.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **carport** (Starts with 'c')\n2. **firewood** (Starts with 'f')\n3. **introvert** (Starts with 'i')\n4. **sweepstake** (Starts with 's')\n5. **tiresome** (Starts with 't')\n\nLet's verify the order:\nC comes before F, I, S, T.\nF comes before I, S, T.\nI comes before S, T.\nS comes before T.\n\nThe alphabetical order is: carport, firewood, introvert, sweepstake, tiresome.\n\nAnswer: carport, firewood, introvert, sweepstake, tiresome", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0017", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempted answer is correct.\n\nCorrection advice: None.", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.529", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:09.051908+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.3091, + "error": null, + "event": "model_call", + "event_seq": 530, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: tiresome sweepstake introvert carport firewood\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: tiresome, sweepstake, introvert, carport, firewood.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **carport** (Starts with 'c')\n2. **firewood** (Starts with 'f')\n3. **introvert** (Starts with 'i')\n4. **sweepstake** (Starts with 's')\n5. **tiresome** (Starts with 't')\n\nLet's verify the order:\nC comes before F, I, S, T.\nF comes before I, S, T.\nI comes before S, T.\nS comes before T.\n\nThe alphabetical order is: carport, firewood, introvert, sweepstake, tiresome.\n\nAnswer: carport, firewood, introvert, sweepstake, tiresome\n\nFeedback:\nThe attempted answer is correct.\n\nCorrection advice: None.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0017", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: carport, firewood, introvert, sweepstake, tiresome", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.530", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:09.366851+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.6439, + "error": null, + "event": "model_call", + "event_seq": 531, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: intercept tamp geopolitic loci newsmen umber credit atmospheric lunge siren swart chess\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0002", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words to sort: intercept, tamp, geopolitic, loci, newsmen, umber, credit, atmospheric, lunge, siren, swart, chess.\n\nLet's sort them alphabetically:\n\n1. atmospheric\n2. chess\n3. credit\n4. geopolitic\n5. intercept\n6. loci\n7. lunge\n8. newsmen\n9. siren\n10. swart\n11. tamp\n12. umber\n\nLet's re-check the sorting carefully:\n\n* **a**tmospheric\n* **c**hess\n* **c**redit\n* **g**eopolitic\n* **i**ntercept\n* **l**oci\n* **l**unge\n* **n**ewsmens\n* **s**iren\n* **s**wart\n* **t**amp\n* **u**mber\n\nComparing 'loci' and 'lunge': 'loci' comes before 'lunge'.\n\nComparing 'siren' and 'swart': 'siren' comes before 'swart'.\n\nThe correct alphabetical order is:\n1. atmospheric\n2. chess\n3. credit\n4. geopolitic\n5. intercept\n6. loci\n7. lunge\n8. newsmen\n9. siren\n10. swart\n11. tamp\n12. umber\n\nAnswer: atmospheric, chess, credit, geopolitic, intercept, loci, lunge, newsmen, siren, swart, tamp, umber", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.531", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:11.020429+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.4034, + "error": null, + "event": "model_call", + "event_seq": 532, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: intercept tamp geopolitic loci newsmen umber credit atmospheric lunge siren swart chess\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words to sort: intercept, tamp, geopolitic, loci, newsmen, umber, credit, atmospheric, lunge, siren, swart, chess.\n\nLet's sort them alphabetically:\n\n1. atmospheric\n2. chess\n3. credit\n4. geopolitic\n5. intercept\n6. loci\n7. lunge\n8. newsmen\n9. siren\n10. swart\n11. tamp\n12. umber\n\nLet's re-check the sorting carefully:\n\n* **a**tmospheric\n* **c**hess\n* **c**redit\n* **g**eopolitic\n* **i**ntercept\n* **l**oci\n* **l**unge\n* **n**ewsmens\n* **s**iren\n* **s**wart\n* **t**amp\n* **u**mber\n\nComparing 'loci' and 'lunge': 'loci' comes before 'lunge'.\n\nComparing 'siren' and 'swart': 'siren' comes before 'swart'.\n\nThe correct alphabetical order is:\n1. atmospheric\n2. chess\n3. credit\n4. geopolitic\n5. intercept\n6. loci\n7. lunge\n8. newsmen\n9. siren\n10. swart\n11. tamp\n12. umber\n\nAnswer: atmospheric, chess, credit, geopolitic, intercept, loci, lunge, newsmen, siren, swart, tamp, umber", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0002", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The sorting is mostly correct, but there is an error in the initial list provided in the attempt compared to the original list.\n\nCorrection advice: Ensure all words from the original list are present and correctly sorted. The attempt correctly sorts the words it lists, but the initial list provided in the attempt is missing one word from the original prompt (it lists 12 words, but the original prompt lists 12 words, let's re-verify the original list).\n\nOriginal list: intercept, tamp, geopolitic, loci, newsmen, umber, credit, atmospheric, lunge, siren, swart, chess (12 words)\nAttempt list: intercept, tamp, geopolitic, loci, newsmen, umber, credit, atmospheric, lunge, siren, swart, chess (12 words)\n\nThe sorting logic seems sound based on standard alphabetical order.\n\nAnswer: atmospheric, chess, credit, geopolitic, intercept, loci, lunge, newsmen, siren, swart, tamp, umber", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.532", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:12.432276+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.5387, + "error": null, + "event": "model_call", + "event_seq": 533, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: intercept tamp geopolitic loci newsmen umber credit atmospheric lunge siren swart chess\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words to sort: intercept, tamp, geopolitic, loci, newsmen, umber, credit, atmospheric, lunge, siren, swart, chess.\n\nLet's sort them alphabetically:\n\n1. atmospheric\n2. chess\n3. credit\n4. geopolitic\n5. intercept\n6. loci\n7. lunge\n8. newsmen\n9. siren\n10. swart\n11. tamp\n12. umber\n\nLet's re-check the sorting carefully:\n\n* **a**tmospheric\n* **c**hess\n* **c**redit\n* **g**eopolitic\n* **i**ntercept\n* **l**oci\n* **l**unge\n* **n**ewsmens\n* **s**iren\n* **s**wart\n* **t**amp\n* **u**mber\n\nComparing 'loci' and 'lunge': 'loci' comes before 'lunge'.\n\nComparing 'siren' and 'swart': 'siren' comes before 'swart'.\n\nThe correct alphabetical order is:\n1. atmospheric\n2. chess\n3. credit\n4. geopolitic\n5. intercept\n6. loci\n7. lunge\n8. newsmen\n9. siren\n10. swart\n11. tamp\n12. umber\n\nAnswer: atmospheric, chess, credit, geopolitic, intercept, loci, lunge, newsmen, siren, swart, tamp, umber\n\nFeedback:\nThe sorting is mostly correct, but there is an error in the initial list provided in the attempt compared to the original list.\n\nCorrection advice: Ensure all words from the original list are present and correctly sorted. The attempt correctly sorts the words it lists, but the initial list provided in the attempt is missing one word from the original prompt (it lists 12 words, but the original prompt lists 12 words, let's re-verify the original list).\n\nOriginal list: intercept, tamp, geopolitic, loci, newsmen, umber, credit, atmospheric, lunge, siren, swart, chess (12 words)\nAttempt list: intercept, tamp, geopolitic, loci, newsmen, umber, credit, atmospheric, lunge, siren, swart, chess (12 words)\n\nThe sorting logic seems sound based on standard alphabetical order.\n\nAnswer: atmospheric, chess, credit, geopolitic, intercept, loci, lunge, newsmen, siren, swart, tamp, umber", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0002", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: atmospheric, chess, credit, geopolitic, intercept, loci, lunge, newsmen, siren, swart, tamp, umber", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.533", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:12.975677+00:00" + }, + { + "cycle": 0, + "duration_sec": 2.2374, + "error": null, + "event": "model_call", + "event_seq": 534, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: gloriana milord rendezvous schnabel allegoric loge lollipop salle collate prolix mast euphony\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0062", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words to sort alphabetically:\ngloriana, milord, rendezvous, schnabel, allegoric, loge, lollipop, salle, collate, prolix, mast, euphony\n\nLet's sort them letter by letter:\n\n1. **allegoric** (A)\n2. **collate** (C)\n3. **gloriana** (G)\n4. **loge** (L) - *Comparing loge and lollipop, 'loge' comes before 'lollipop'*\n5. **lollipop** (L)\n6. **milord** (M)\n7. **mast** (M) - *Comparing milord and mast, 'mast' comes before 'milord'*\n8. **prolix** (P)\n9. **rendezvous** (R)\n10. **salle** (S)\n11. **schnabel** (S) - *Comparing salle and schnabel, 'salle' comes before 'schnabel'*\n12. **euphony** (E) - *Wait, 'euphony' starts with E, so it should come before 'gloriana' (G).*\n\nLet's re-sort based on the first letter:\n\nE: euphony\nA: allegoric\nC: collate\nG: gloriana\nL: loge, lollipop\nM: mast, milord\nP: prolix\nR: rendezvous\nS: salle, schnabel\n\n**Sorting within groups:**\n\n* **L:** loge, lollipop (loge comes before lollipop)\n* **M:** mast, milord (mast comes before milord)\n* **S:** salle, schnabel (salle comes before schnabel)\n\n**Final Alphabetical Order:**\n\n1. euphony\n2. allegoric\n3. collate\n4. gloriana\n5. loge\n6. lollipop\n7. mast\n8. milord\n9. prolix\n10. rendezvous\n11. salle\n12. schnabel\n\nAnswer: euphony, allegoric, collate, gloriana, loge, lollipop, mast, milord, prolix, rendezvous, salle, schnabel", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.534", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:15.217928+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.4249, + "error": null, + "event": "model_call", + "event_seq": 535, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: gloriana milord rendezvous schnabel allegoric loge lollipop salle collate prolix mast euphony\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words to sort alphabetically:\ngloriana, milord, rendezvous, schnabel, allegoric, loge, lollipop, salle, collate, prolix, mast, euphony\n\nLet's sort them letter by letter:\n\n1. **allegoric** (A)\n2. **collate** (C)\n3. **gloriana** (G)\n4. **loge** (L) - *Comparing loge and lollipop, 'loge' comes before 'lollipop'*\n5. **lollipop** (L)\n6. **milord** (M)\n7. **mast** (M) - *Comparing milord and mast, 'mast' comes before 'milord'*\n8. **prolix** (P)\n9. **rendezvous** (R)\n10. **salle** (S)\n11. **schnabel** (S) - *Comparing salle and schnabel, 'salle' comes before 'schnabel'*\n12. **euphony** (E) - *Wait, 'euphony' starts with E, so it should come before 'gloriana' (G).*\n\nLet's re-sort based on the first letter:\n\nE: euphony\nA: allegoric\nC: collate\nG: gloriana\nL: loge, lollipop\nM: mast, milord\nP: prolix\nR: rendezvous\nS: salle, schnabel\n\n**Sorting within groups:**\n\n* **L:** loge, lollipop (loge comes before lollipop)\n* **M:** mast, milord (mast comes before milord)\n* **S:** salle, schnabel (salle comes before schnabel)\n\n**Final Alphabetical Order:**\n\n1. euphony\n2. allegoric\n3. collate\n4. gloriana\n5. loge\n6. lollipop\n7. mast\n8. milord\n9. prolix\n10. rendezvous\n11. salle\n12. schnabel\n\nAnswer: euphony, allegoric, collate, gloriana, loge, lollipop, mast, milord, prolix, rendezvous, salle, schnabel", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0062", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempt contains a factual error in the initial sorting step and a logical error in the final ordering based on the first letter.\n\nCorrection advice: Re-evaluate the initial sorting based strictly on the first letter of each word. Ensure all words are correctly grouped and then sorted alphabetically within those groups.", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.535", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:15.647473+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.4681, + "error": null, + "event": "model_call", + "event_seq": 536, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: gloriana milord rendezvous schnabel allegoric loge lollipop salle collate prolix mast euphony\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words to sort alphabetically:\ngloriana, milord, rendezvous, schnabel, allegoric, loge, lollipop, salle, collate, prolix, mast, euphony\n\nLet's sort them letter by letter:\n\n1. **allegoric** (A)\n2. **collate** (C)\n3. **gloriana** (G)\n4. **loge** (L) - *Comparing loge and lollipop, 'loge' comes before 'lollipop'*\n5. **lollipop** (L)\n6. **milord** (M)\n7. **mast** (M) - *Comparing milord and mast, 'mast' comes before 'milord'*\n8. **prolix** (P)\n9. **rendezvous** (R)\n10. **salle** (S)\n11. **schnabel** (S) - *Comparing salle and schnabel, 'salle' comes before 'schnabel'*\n12. **euphony** (E) - *Wait, 'euphony' starts with E, so it should come before 'gloriana' (G).*\n\nLet's re-sort based on the first letter:\n\nE: euphony\nA: allegoric\nC: collate\nG: gloriana\nL: loge, lollipop\nM: mast, milord\nP: prolix\nR: rendezvous\nS: salle, schnabel\n\n**Sorting within groups:**\n\n* **L:** loge, lollipop (loge comes before lollipop)\n* **M:** mast, milord (mast comes before milord)\n* **S:** salle, schnabel (salle comes before schnabel)\n\n**Final Alphabetical Order:**\n\n1. euphony\n2. allegoric\n3. collate\n4. gloriana\n5. loge\n6. lollipop\n7. mast\n8. milord\n9. prolix\n10. rendezvous\n11. salle\n12. schnabel\n\nAnswer: euphony, allegoric, collate, gloriana, loge, lollipop, mast, milord, prolix, rendezvous, salle, schnabel\n\nFeedback:\nThe attempt contains a factual error in the initial sorting step and a logical error in the final ordering based on the first letter.\n\nCorrection advice: Re-evaluate the initial sorting based strictly on the first letter of each word. Ensure all words are correctly grouped and then sorted alphabetically within those groups.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0062", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "E: euphony\nA: allegoric\nC: collate\nG: gloriana\nL: loge, lollipop\nM: mast, milord\nP: prolix\nR: rendezvous\nS: salle, schnabel\n\nSorting within groups:\nL: loge, lollipop (loge comes before lollipop)\nM: mast, milord (mast comes before milord)\nS: salle, schnabel (salle comes before schnabel)\n\nFinal Alphabetical Order:\n1. euphony (E)\n2. allegoric (A)\n3. collate (C)\n4. gloriana (G)\n5. loge (L)\n6. lollipop (L)\n7. mast (M)\n8. milord (M)\n9. prolix (P)\n10. rendezvous (R)\n11. salle (S)\n12. schnabel (S)\n\nAnswer: euphony, allegoric, collate, gloriana, loge, lollipop, mast, milord, prolix, rendezvous, salle, schnabel", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.536", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:17.120420+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.8119, + "error": null, + "event": "model_call", + "event_seq": 537, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: syndrome therefrom\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0046", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The words to be sorted are: syndrome, therefrom.\n\nTo sort them alphabetically, we compare them letter by letter:\n\n1. **syndrome**: Starts with 's', followed by 'y', 'n', 'd', 'r', 'o', 'm', 'e'.\n2. **therefrom**: Starts with 't', followed by 'h', 'e', 'r', 'e', 'f', 'r', 'o', 'm'.\n\nComparing the first letters: 's' comes before 't' in the alphabet.\n\nTherefore, \"syndrome\" comes before \"therefrom\".\n\nThe alphabetical order is: syndrome, therefrom.\n\nAnswer: syndrome, therefrom", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.537", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:17.937277+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.4715, + "error": null, + "event": "model_call", + "event_seq": 538, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: syndrome therefrom\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe words to be sorted are: syndrome, therefrom.\n\nTo sort them alphabetically, we compare them letter by letter:\n\n1. **syndrome**: Starts with 's', followed by 'y', 'n', 'd', 'r', 'o', 'm', 'e'.\n2. **therefrom**: Starts with 't', followed by 'h', 'e', 'r', 'e', 'f', 'r', 'o', 'm'.\n\nComparing the first letters: 's' comes before 't' in the alphabet.\n\nTherefore, \"syndrome\" comes before \"therefrom\".\n\nThe alphabetical order is: syndrome, therefrom.\n\nAnswer: syndrome, therefrom", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0046", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempt is factually correct and follows the instructions perfectly.\n\nCorrection advice: None.", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.538", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:18.413870+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.4214, + "error": null, + "event": "model_call", + "event_seq": 539, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: syndrome therefrom\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe words to be sorted are: syndrome, therefrom.\n\nTo sort them alphabetically, we compare them letter by letter:\n\n1. **syndrome**: Starts with 's', followed by 'y', 'n', 'd', 'r', 'o', 'm', 'e'.\n2. **therefrom**: Starts with 't', followed by 'h', 'e', 'r', 'e', 'f', 'r', 'o', 'm'.\n\nComparing the first letters: 's' comes before 't' in the alphabet.\n\nTherefore, \"syndrome\" comes before \"therefrom\".\n\nThe alphabetical order is: syndrome, therefrom.\n\nAnswer: syndrome, therefrom\n\nFeedback:\nThe attempt is factually correct and follows the instructions perfectly.\n\nCorrection advice: None.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0046", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: syndrome, therefrom", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.539", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:18.840374+00:00" + }, + { + "cycle": 0, + "duration_sec": 4.0756, + "error": null, + "event": "model_call", + "event_seq": 540, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: saul fresco teen chopstick footwork smalley anaplasmosis palisade orthography storey bumble pilate clue ingot fiesta\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0054", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words sorted alphabetically:\n\n1. anaplasmosis\n2. bumble\n3. chopstick\n4. clue\n5. fresco\n6. footwork\n7. fiesta\n8. ingot\n9. orthography\n10. palisade\n11. pilate\n12. saul\n13. smalley\n14. storey\n15. teen\n\nLet's re-sort them carefully:\n\n* anaplasmosis (A)\n* bumble (B)\n* chopstick (C)\n* clue (C) - clue comes before chopstick\n* fresco (F)\n* footwork (F) - footwork comes after fresco\n* fiesta (F) - fiesta comes after footwork\n* ingot (I)\n* orthography (O)\n* palisade (P)\n* pilate (P) - pilate comes after palisade\n* saul (S)\n* smalley (S) - saul comes before smalley\n* storey (S) - storey comes after smalley\n* teen (T)\n\nLet's do a strict alphabetical sort:\n\n1. anaplasmosis\n2. bumble\n3. chopstick\n4. clue\n5. fresco\n6. footwork\n7. fiesta\n8. ingot\n9. orthography\n10. palisade\n11. pilate\n12. saul\n13. smalley\n14. storey\n15. teen\n\nWait, let's re-check the 'S' words: saul, smalley, storey.\nS-a-u-l\nS-m-a-l-l-e-y\nS-t-o-r-e-y\n'a' comes before 'm' and 't'. So, saul comes first.\n'm' comes before 't'. So, smalley comes before storey.\nOrder for S words: saul, smalley, storey.\n\nLet's re-check the 'F' words: fresco, footwork, fiesta.\nF-r-e-s-c-o\nF-o-o-t-w-o-r-k\nF-i-e-s-t-a\nOrder: fresco, footwork, fiesta. (e < o < i is false. e < i < o is false. e < o < i is false. e < i < o is false. e < i < o is false. e < i < o is false. e < i < o is false. e < i < o is false. e < i < o is false. e < i < o is false. e < i < o is false.)\nF-r-e...\nF-o-o...\nF-i-e...\n'e' comes before 'i' and 'o'. So, fresco is first.\nComparing footwork and fiesta: footwork (o) vs fiesta (i). 'i' comes before 'o'. So, fiesta comes before footwork.\nOrder for F words: fresco, fiesta, footwork.\n\nLet's re-check the 'P' words: palisade, pilate.\nP-a-l-i-s-a-d-e\nP-i-l-a-t-e\n'a' comes before 'i'. So, palisade comes before pilate.\nOrder for P words: palisade, pilate.\n\nFinal sorted list:\n1. anaplasmosis\n2. bumble\n3. chopstick\n4. clue\n5. fresco\n6. fiesta\n7. footwork\n8. ingot\n9. orthography\n10. palisade\n11. pilate\n12. saul\n13. smalley\n14. storey\n15. teen\n\nAnswer: anaplasmosis, bumble, chopstick, clue, fresco, fiesta, footwork, ingot, orthography, palisade, pilate, saul, smalley, storey, teen", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.540", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:22.920829+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.7483, + "error": null, + "event": "model_call", + "event_seq": 541, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: saul fresco teen chopstick footwork smalley anaplasmosis palisade orthography storey bumble pilate clue ingot fiesta\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. anaplasmosis\n2. bumble\n3. chopstick\n4. clue\n5. fresco\n6. footwork\n7. fiesta\n8. ingot\n9. orthography\n10. palisade\n11. pilate\n12. saul\n13. smalley\n14. storey\n15. teen\n\nLet's re-sort them carefully:\n\n* anaplasmosis (A)\n* bumble (B)\n* chopstick (C)\n* clue (C) - clue comes before chopstick\n* fresco (F)\n* footwork (F) - footwork comes after fresco\n* fiesta (F) - fiesta comes after footwork\n* ingot (I)\n* orthography (O)\n* palisade (P)\n* pilate (P) - pilate comes after palisade\n* saul (S)\n* smalley (S) - saul comes before smalley\n* storey (S) - storey comes after smalley\n* teen (T)\n\nLet's do a strict alphabetical sort:\n\n1. anaplasmosis\n2. bumble\n3. chopstick\n4. clue\n5. fresco\n6. footwork\n7. fiesta\n8. ingot\n9. orthography\n10. palisade\n11. pilate\n12. saul\n13. smalley\n14. storey\n15. teen\n\nWait, let's re-check the 'S' words: saul, smalley, storey.\nS-a-u-l\nS-m-a-l-l-e-y\nS-t-o-r-e-y\n'a' comes before 'm' and 't'. So, saul comes first.\n'm' comes before 't'. So, smalley comes before storey.\nOrder for S words: saul, smalley, storey.\n\nLet's re-check the 'F' words: fresco, footwork, fiesta.\nF-r-e-s-c-o\nF-o-o-t-w-o-r-k\nF-i-e-s-t-a\nOrder: fresco, footwork, fiesta. (e < o < i is false. e < i < o is false. e < o < i is false. e < i < o is false. e < i < o is false. e < i < o is false. e < i < o is false. e < i < o is false. e < i < o is false. e < i < o is false. e < i < o is false.)\nF-r-e...\nF-o-o...\nF-i-e...\n'e' comes before 'i' and 'o'. So, fresco is first.\nComparing footwork and fiesta: footwork (o) vs fiesta (i). 'i' comes before 'o'. So, fiesta comes before footwork.\nOrder for F words: fresco, fiesta, footwork.\n\nLet's re-check the 'P' words: palisade, pilate.\nP-a-l-i-s-a-d-e\nP-i-l-a-t-e\n'a' comes before 'i'. So, palisade comes before pilate.\nOrder for P words: palisade, pilate.\n\nFinal sorted list:\n1. anaplasmosis\n2. bumble\n3. chopstick\n4. clue\n5. fresco\n6. fiesta\n7. footwork\n8. ingot\n9. orthography\n10. palisade\n11. pilate\n12. saul\n13. smalley\n14. storey\n15. teen\n\nAnswer: anaplasmosis, bumble, chopstick, clue, fresco, fiesta, footwork, ingot, orthography, palisade, pilate, saul, smalley, storey, teen", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0054", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The initial attempt's final list matches the final derived list, but the reasoning steps provided within the attempt are overly complicated and contain errors in the intermediate comparisons (e.g., the analysis of 'F' words). The final sorted list itself appears correct based on the final derived logic.\n\nCorrection advice: Simplify the reasoning steps. The final sorted list is correct, but the intermediate comparisons should be clearer and more direct.\n\nAnswer: anaplasmosis, bumble, chopstick, clue, fresco, fiesta, footwork, ingot, orthography, palisade, pilate, saul, smalley, storey, teen", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.541", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:23.674296+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.7036, + "error": null, + "event": "model_call", + "event_seq": 542, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: saul fresco teen chopstick footwork smalley anaplasmosis palisade orthography storey bumble pilate clue ingot fiesta\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. anaplasmosis\n2. bumble\n3. chopstick\n4. clue\n5. fresco\n6. footwork\n7. fiesta\n8. ingot\n9. orthography\n10. palisade\n11. pilate\n12. saul\n13. smalley\n14. storey\n15. teen\n\nLet's re-sort them carefully:\n\n* anaplasmosis (A)\n* bumble (B)\n* chopstick (C)\n* clue (C) - clue comes before chopstick\n* fresco (F)\n* footwork (F) - footwork comes after fresco\n* fiesta (F) - fiesta comes after footwork\n* ingot (I)\n* orthography (O)\n* palisade (P)\n* pilate (P) - pilate comes after palisade\n* saul (S)\n* smalley (S) - saul comes before smalley\n* storey (S) - storey comes after smalley\n* teen (T)\n\nLet's do a strict alphabetical sort:\n\n1. anaplasmosis\n2. bumble\n3. chopstick\n4. clue\n5. fresco\n6. footwork\n7. fiesta\n8. ingot\n9. orthography\n10. palisade\n11. pilate\n12. saul\n13. smalley\n14. storey\n15. teen\n\nWait, let's re-check the 'S' words: saul, smalley, storey.\nS-a-u-l\nS-m-a-l-l-e-y\nS-t-o-r-e-y\n'a' comes before 'm' and 't'. So, saul comes first.\n'm' comes before 't'. So, smalley comes before storey.\nOrder for S words: saul, smalley, storey.\n\nLet's re-check the 'F' words: fresco, footwork, fiesta.\nF-r-e-s-c-o\nF-o-o-t-w-o-r-k\nF-i-e-s-t-a\nOrder: fresco, footwork, fiesta. (e < o < i is false. e < i < o is false. e < o < i is false. e < i < o is false. e < i < o is false. e < i < o is false. e < i < o is false. e < i < o is false. e < i < o is false. e < i < o is false. e < i < o is false.)\nF-r-e...\nF-o-o...\nF-i-e...\n'e' comes before 'i' and 'o'. So, fresco is first.\nComparing footwork and fiesta: footwork (o) vs fiesta (i). 'i' comes before 'o'. So, fiesta comes before footwork.\nOrder for F words: fresco, fiesta, footwork.\n\nLet's re-check the 'P' words: palisade, pilate.\nP-a-l-i-s-a-d-e\nP-i-l-a-t-e\n'a' comes before 'i'. So, palisade comes before pilate.\nOrder for P words: palisade, pilate.\n\nFinal sorted list:\n1. anaplasmosis\n2. bumble\n3. chopstick\n4. clue\n5. fresco\n6. fiesta\n7. footwork\n8. ingot\n9. orthography\n10. palisade\n11. pilate\n12. saul\n13. smalley\n14. storey\n15. teen\n\nAnswer: anaplasmosis, bumble, chopstick, clue, fresco, fiesta, footwork, ingot, orthography, palisade, pilate, saul, smalley, storey, teen\n\nFeedback:\nThe initial attempt's final list matches the final derived list, but the reasoning steps provided within the attempt are overly complicated and contain errors in the intermediate comparisons (e.g., the analysis of 'F' words). The final sorted list itself appears correct based on the final derived logic.\n\nCorrection advice: Simplify the reasoning steps. The final sorted list is correct, but the intermediate comparisons should be clearer and more direct.\n\nAnswer: anaplasmosis, bumble, chopstick, clue, fresco, fiesta, footwork, ingot, orthography, palisade, pilate, saul, smalley, storey, teen", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0054", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: anaplasmosis, bumble, chopstick, clue, fresco, fiesta, footwork, ingot, orthography, palisade, pilate, saul, smalley, storey, teen", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.542", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:24.383085+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.3139, + "error": null, + "event": "model_call", + "event_seq": 543, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: livre substitution ix frisian panoramic buckley\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0096", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The given list of words is: livre, substitution, ix, frisian, panoramic, buckley.\n\nWe need to sort these words alphabetically. We will compare them letter by letter.\n\n1. **buckley** (Starts with 'b')\n2. **frisian** (Starts with 'f')\n3. **ix** (Starts with 'i')\n4. **livre** (Starts with 'l')\n5. **panoramic** (Starts with 'p')\n6. **substitution** (Starts with 's')\n\nLet's re-sort based on the first letter:\nB: buckley\nF: frisian\nI: ix\nL: livre\nP: panoramic\nS: substitution\n\nAlphabetical order of the first letters: B, F, I, L, P, S.\n\nThe sorted list is:\n1. buckley\n2. frisian\n3. ix\n4. livre\n5. panoramic\n6. substitution\n\nAnswer: buckley, frisian, ix, livre, panoramic, substitution\n\nAnswer: buckley, frisian, ix, livre, panoramic, substitution", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.543", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:25.703486+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.496, + "error": null, + "event": "model_call", + "event_seq": 544, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: livre substitution ix frisian panoramic buckley\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: livre, substitution, ix, frisian, panoramic, buckley.\n\nWe need to sort these words alphabetically. We will compare them letter by letter.\n\n1. **buckley** (Starts with 'b')\n2. **frisian** (Starts with 'f')\n3. **ix** (Starts with 'i')\n4. **livre** (Starts with 'l')\n5. **panoramic** (Starts with 'p')\n6. **substitution** (Starts with 's')\n\nLet's re-sort based on the first letter:\nB: buckley\nF: frisian\nI: ix\nL: livre\nP: panoramic\nS: substitution\n\nAlphabetical order of the first letters: B, F, I, L, P, S.\n\nThe sorted list is:\n1. buckley\n2. frisian\n3. ix\n4. livre\n5. panoramic\n6. substitution\n\nAnswer: buckley, frisian, ix, livre, panoramic, substitution\n\nAnswer: buckley, frisian, ix, livre, panoramic, substitution", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0096", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempt is factually correct and follows the instructions perfectly. The sorting logic is sound.\n\nCorrection advice: None.", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.544", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:26.204504+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.4449, + "error": null, + "event": "model_call", + "event_seq": 545, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: livre substitution ix frisian panoramic buckley\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: livre, substitution, ix, frisian, panoramic, buckley.\n\nWe need to sort these words alphabetically. We will compare them letter by letter.\n\n1. **buckley** (Starts with 'b')\n2. **frisian** (Starts with 'f')\n3. **ix** (Starts with 'i')\n4. **livre** (Starts with 'l')\n5. **panoramic** (Starts with 'p')\n6. **substitution** (Starts with 's')\n\nLet's re-sort based on the first letter:\nB: buckley\nF: frisian\nI: ix\nL: livre\nP: panoramic\nS: substitution\n\nAlphabetical order of the first letters: B, F, I, L, P, S.\n\nThe sorted list is:\n1. buckley\n2. frisian\n3. ix\n4. livre\n5. panoramic\n6. substitution\n\nAnswer: buckley, frisian, ix, livre, panoramic, substitution\n\nAnswer: buckley, frisian, ix, livre, panoramic, substitution\n\nFeedback:\nThe attempt is factually correct and follows the instructions perfectly. The sorting logic is sound.\n\nCorrection advice: None.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0096", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: buckley, frisian, ix, livre, panoramic, substitution", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.545", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:26.654497+00:00" + }, + { + "cycle": 0, + "duration_sec": 3.5092, + "error": null, + "event": "model_call", + "event_seq": 546, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: plebiscite colombo inviolate quagmire garry satanic magnanimity aeneas notoriety nevada foothold glycerine lucre tennyson type scription pompey softball spleenwort fox\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0008", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words sorted alphabetically:\n\n1. aeneas\n2. foothold\n3. fox\n4. glycerine\n5. garry\n6. inflaton (Assuming 'inviolate' was intended, but using the provided word 'inviolate') - Let's stick to the provided list: inviolate\n7. lucre\n8. magnanimity\n9. nevada\n10. pompey\n11. plebiscite\n12. quagmire\n13. satanic\n14. scription (Assuming 'scription' is a typo for 'scriptio' or similar, but sorting as written)\n15. softball\n16. spleenwort\n17. tennyson\n18. type\n\nLet's re-sort carefully:\n\n* aeneas\n* foothold\n* fox\n* glycerine\n* garry\n* inviolate\n* lucre\n* magnanimity\n* nevada\n* pompey\n* plebiscite\n* quagmire\n* satanic\n* scription\n* softball\n* spleenwort\n* tennyson\n* type\n\n**Alphabetical Order Check:**\naeneas (A)\nfoothold (F)\nfox (F) - fox comes before foothold\nglycerine (G)\ngarry (G) - garry comes after glycerine\ninviolate (I)\nlucre (L)\nmagnanimity (M)\nnevada (N)\npompey (P)\nplebiscite (P) - pompey comes before plebiscite\nquagmire (Q)\nsatanic (S)\nscription (S) - satanic comes before scription\nsoftball (S) - scription comes before softball\nspleenwort (S) - softball comes before spleenwort\ntennyson (T)\ntype (T) - tennyson comes before type\n\n**Refined Sorting:**\n\n1. aeneas\n2. fox\n3. foothold\n4. glycerine\n5. garry\n6. inviolate\n7. lucre\n8. magnanimity\n9. nevada\n10. pompey\n11. plebiscite\n12. quagmire\n13. satanic\n14. scription\n15. softball\n16. spleenwort\n17. tennyson\n18. type\n\n(Note on P words: pompey, plebiscite. 'pompey' comes before 'plebiscite'.)\n(Note on S words: satanic, scription, softball, spleenwort. s-a, s-c, s-o, s-p. satanic, scription, softball, spleenwort.)\n\nFinal check on S words:\nsatanic\nscription\nsoftball\nspleenwort\n(satanic, scription, softball, spleenwort) - This order is correct based on the second letter (a, c, o, p).\n\nAnswer: aeneas, fox, foothold, glycerine, garry, inviolate, lucre, magnanimity, nevada, pompey, plebiscite, quagmire, satanic, scription, softball, spleenwort, tennyson, type\n\nAnswer: aeneas, fox, foothold, glycerine, garry, inviolate, lucre, magnanimity, nevada, pompey, plebiscite, quagmire, satanic, scription, softball, spleenwort, tennyson, type", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.546", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:30.170179+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.4963, + "error": null, + "event": "model_call", + "event_seq": 547, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: plebiscite colombo inviolate quagmire garry satanic magnanimity aeneas notoriety nevada foothold glycerine lucre tennyson type scription pompey softball spleenwort fox\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. aeneas\n2. foothold\n3. fox\n4. glycerine\n5. garry\n6. inflaton (Assuming 'inviolate' was intended, but using the provided word 'inviolate') - Let's stick to the provided list: inviolate\n7. lucre\n8. magnanimity\n9. nevada\n10. pompey\n11. plebiscite\n12. quagmire\n13. satanic\n14. scription (Assuming 'scription' is a typo for 'scriptio' or similar, but sorting as written)\n15. softball\n16. spleenwort\n17. tennyson\n18. type\n\nLet's re-sort carefully:\n\n* aeneas\n* foothold\n* fox\n* glycerine\n* garry\n* inviolate\n* lucre\n* magnanimity\n* nevada\n* pompey\n* plebiscite\n* quagmire\n* satanic\n* scription\n* softball\n* spleenwort\n* tennyson\n* type\n\n**Alphabetical Order Check:**\naeneas (A)\nfoothold (F)\nfox (F) - fox comes before foothold\nglycerine (G)\ngarry (G) - garry comes after glycerine\ninviolate (I)\nlucre (L)\nmagnanimity (M)\nnevada (N)\npompey (P)\nplebiscite (P) - pompey comes before plebiscite\nquagmire (Q)\nsatanic (S)\nscription (S) - satanic comes before scription\nsoftball (S) - scription comes before softball\nspleenwort (S) - softball comes before spleenwort\ntennyson (T)\ntype (T) - tennyson comes before type\n\n**Refined Sorting:**\n\n1. aeneas\n2. fox\n3. foothold\n4. glycerine\n5. garry\n6. inviolate\n7. lucre\n8. magnanimity\n9. nevada\n10. pompey\n11. plebiscite\n12. quagmire\n13. satanic\n14. scription\n15. softball\n16. spleenwort\n17. tennyson\n18. type\n\n(Note on P words: pompey, plebiscite. 'pompey' comes before 'plebiscite'.)\n(Note on S words: satanic, scription, softball, spleenwort. s-a, s-c, s-o, s-p. satanic, scription, softball, spleenwort.)\n\nFinal check on S words:\nsatanic\nscription\nsoftball\nspleenwort\n(satanic, scription, softball, spleenwort) - This order is correct based on the second letter (a, c, o, p).\n\nAnswer: aeneas, fox, foothold, glycerine, garry, inviolate, lucre, magnanimity, nevada, pompey, plebiscite, quagmire, satanic, scription, softball, spleenwort, tennyson, type\n\nAnswer: aeneas, fox, foothold, glycerine, garry, inviolate, lucre, magnanimity, nevada, pompey, plebiscite, quagmire, satanic, scription, softball, spleenwort, tennyson, type", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0008", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempt contains several errors in the initial sorting and reasoning, although the final refined sorting appears mostly correct based on the provided list.\n\n1. **Error in Initial Sorting/Reasoning:** The attempt incorrectly places 'fox' before 'foothold' in the initial list check, and the subsequent manual re-sort seems to have corrected this, but the reasoning steps are messy.\n2. **Error in Word List Handling:** The attempt mentions assumptions about typos ('inviolate' vs 'inviolate', 'scription' vs 'scriptio'), which is unnecessary for a strict sorting task unless explicitly instructed to correct typos. The sorting must be done on the words *as provided*.\n3. **Error in Final List Construction:** The final list provided in the attempt is slightly different from the final refined sorting shown, and the final answer provided is a comma-separated list, not the required format.\n\n**Correction Advice:**\nFocus only on the strict alphabetical sorting of the exact words provided in the original list. Ensure consistent comparison rules (case-insensitive, standard dictionary order).\n\nAnswer: aeneas, foothold, fox, glycerine, garry, inviolate, lucre, magnanimity, nevada, pompey, plebiscite, quagmire, satanic, scription, softball, spleenwort, tennyson, type", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.547", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:31.672616+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.845, + "error": null, + "event": "model_call", + "event_seq": 548, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: plebiscite colombo inviolate quagmire garry satanic magnanimity aeneas notoriety nevada foothold glycerine lucre tennyson type scription pompey softball spleenwort fox\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. aeneas\n2. foothold\n3. fox\n4. glycerine\n5. garry\n6. inflaton (Assuming 'inviolate' was intended, but using the provided word 'inviolate') - Let's stick to the provided list: inviolate\n7. lucre\n8. magnanimity\n9. nevada\n10. pompey\n11. plebiscite\n12. quagmire\n13. satanic\n14. scription (Assuming 'scription' is a typo for 'scriptio' or similar, but sorting as written)\n15. softball\n16. spleenwort\n17. tennyson\n18. type\n\nLet's re-sort carefully:\n\n* aeneas\n* foothold\n* fox\n* glycerine\n* garry\n* inviolate\n* lucre\n* magnanimity\n* nevada\n* pompey\n* plebiscite\n* quagmire\n* satanic\n* scription\n* softball\n* spleenwort\n* tennyson\n* type\n\n**Alphabetical Order Check:**\naeneas (A)\nfoothold (F)\nfox (F) - fox comes before foothold\nglycerine (G)\ngarry (G) - garry comes after glycerine\ninviolate (I)\nlucre (L)\nmagnanimity (M)\nnevada (N)\npompey (P)\nplebiscite (P) - pompey comes before plebiscite\nquagmire (Q)\nsatanic (S)\nscription (S) - satanic comes before scription\nsoftball (S) - scription comes before softball\nspleenwort (S) - softball comes before spleenwort\ntennyson (T)\ntype (T) - tennyson comes before type\n\n**Refined Sorting:**\n\n1. aeneas\n2. fox\n3. foothold\n4. glycerine\n5. garry\n6. inviolate\n7. lucre\n8. magnanimity\n9. nevada\n10. pompey\n11. plebiscite\n12. quagmire\n13. satanic\n14. scription\n15. softball\n16. spleenwort\n17. tennyson\n18. type\n\n(Note on P words: pompey, plebiscite. 'pompey' comes before 'plebiscite'.)\n(Note on S words: satanic, scription, softball, spleenwort. s-a, s-c, s-o, s-p. satanic, scription, softball, spleenwort.)\n\nFinal check on S words:\nsatanic\nscription\nsoftball\nspleenwort\n(satanic, scription, softball, spleenwort) - This order is correct based on the second letter (a, c, o, p).\n\nAnswer: aeneas, fox, foothold, glycerine, garry, inviolate, lucre, magnanimity, nevada, pompey, plebiscite, quagmire, satanic, scription, softball, spleenwort, tennyson, type\n\nAnswer: aeneas, fox, foothold, glycerine, garry, inviolate, lucre, magnanimity, nevada, pompey, plebiscite, quagmire, satanic, scription, softball, spleenwort, tennyson, type\n\nFeedback:\nThe attempt contains several errors in the initial sorting and reasoning, although the final refined sorting appears mostly correct based on the provided list.\n\n1. **Error in Initial Sorting/Reasoning:** The attempt incorrectly places 'fox' before 'foothold' in the initial list check, and the subsequent manual re-sort seems to have corrected this, but the reasoning steps are messy.\n2. **Error in Word List Handling:** The attempt mentions assumptions about typos ('inviolate' vs 'inviolate', 'scription' vs 'scriptio'), which is unnecessary for a strict sorting task unless explicitly instructed to correct typos. The sorting must be done on the words *as provided*.\n3. **Error in Final List Construction:** The final list provided in the attempt is slightly different from the final refined sorting shown, and the final answer provided is a comma-separated list, not the required format.\n\n**Correction Advice:**\nFocus only on the strict alphabetical sorting of the exact words provided in the original list. Ensure consistent comparison rules (case-insensitive, standard dictionary order).\n\nAnswer: aeneas, foothold, fox, glycerine, garry, inviolate, lucre, magnanimity, nevada, pompey, plebiscite, quagmire, satanic, scription, softball, spleenwort, tennyson, type", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0008", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: aeneas, foothold, fox, glycerine, garry, inviolate, lucre, magnanimity, nevada, pompey, plebiscite, quagmire, satanic, scription, softball, spleenwort, tennyson, type", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.548", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:32.522945+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.605, + "error": null, + "event": "model_call", + "event_seq": 549, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: catechism daddy\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0115", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The given list of words is: catechism, daddy.\n\nTo sort them alphabetically, we compare them letter by letter:\n\n1. **catechism**: Starts with 'c'\n2. **daddy**: Starts with 'd'\n\nSince 'c' comes before 'd' in the alphabet, \"catechism\" comes before \"daddy\".\n\nThe alphabetical order is: catechism, daddy.\n\nAnswer: catechism, daddy", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.549", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:33.137460+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.6367, + "error": null, + "event": "model_call", + "event_seq": 550, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: catechism daddy\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: catechism, daddy.\n\nTo sort them alphabetically, we compare them letter by letter:\n\n1. **catechism**: Starts with 'c'\n2. **daddy**: Starts with 'd'\n\nSince 'c' comes before 'd' in the alphabet, \"catechism\" comes before \"daddy\".\n\nThe alphabetical order is: catechism, daddy.\n\nAnswer: catechism, daddy", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0115", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempt is factually correct and follows the instructions perfectly.\n\nCorrection advice: None.", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.550", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:33.785253+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.5758, + "error": null, + "event": "model_call", + "event_seq": 551, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: catechism daddy\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: catechism, daddy.\n\nTo sort them alphabetically, we compare them letter by letter:\n\n1. **catechism**: Starts with 'c'\n2. **daddy**: Starts with 'd'\n\nSince 'c' comes before 'd' in the alphabet, \"catechism\" comes before \"daddy\".\n\nThe alphabetical order is: catechism, daddy.\n\nAnswer: catechism, daddy\n\nFeedback:\nThe attempt is factually correct and follows the instructions perfectly.\n\nCorrection advice: None.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0115", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: catechism, daddy", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.551", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:34.368469+00:00" + }, + { + "cycle": 0, + "duration_sec": 2.7635, + "error": null, + "event": "model_call", + "event_seq": 552, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: holocene muriel r&d pathetic talon mare staircase bradshaw aperture sigh\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0033", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The given list of words is: holocene, muriel, r&d, pathetic, talon, mare, staircase, bradshaw, aperture, sigh.\n\nTo sort them alphabetically, we compare them letter by letter:\n\n1. **aperture** (starts with 'a')\n2. **bradshaw** (starts with 'b')\n3. **holocene** (starts with 'h')\n4. **mare** (starts with 'm')\n5. **muriel** (starts with 'm', comes after 'mare' because 'u' comes after 'a')\n6. **pathetic** (starts with 'p')\n7. **r&d** (starts with 'r')\n8. **sigh** (starts with 's')\n9. **staircase** (starts with 's', comes after 'sigh' because 't' comes after 'i')\n10. **talon** (starts with 't', comes after 'staircase' because 'a' comes before 's' in the second letter, wait, let's recheck 'staircase' and 'talon'. 'st' vs 'ta'. 's' comes before 't', so 'staircase' comes before 'talon'.)\n\nLet's re-sort systematically:\n\nWords: holocene, muriel, r&d, pathetic, talon, mare, staircase, bradshaw, aperture, sigh\n\n1. **a**perture\n2. **b**radshaw\n3. **h**olocene\n4. **m**are\n5. **m**uriel (mare vs muriel: mare ends, muriel continues. 'mare' comes before 'muriel')\n6. **p**athetic\n7. **r**&d\n8. **s**igh\n9. **s**taircase\n10. **t**alon\n\nComparing 'sigh' and 'staircase':\nsigh (s-i)\nstaircase (s-t)\n'i' comes before 't', so 'sigh' comes before 'staircase'.\n\nComparing 'talon' and 'staircase':\ntalon (t-a)\nstaircase (s-t)\n's' comes before 't', so 'staircase' comes before 'talon'.\n\nFinal sorted order:\n1. aperture\n2. bradshaw\n3. holocene\n4. mare\n5. muriel\n6. pathetic\n7. r&d\n8. sigh\n9. staircase\n10. talon\n\nAnswer: aperture, bradshaw, holocene, mare, muriel, pathetic, r&d, sigh, staircase, talon", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.552", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:37.137230+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.0999, + "error": null, + "event": "model_call", + "event_seq": 553, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: holocene muriel r&d pathetic talon mare staircase bradshaw aperture sigh\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: holocene, muriel, r&d, pathetic, talon, mare, staircase, bradshaw, aperture, sigh.\n\nTo sort them alphabetically, we compare them letter by letter:\n\n1. **aperture** (starts with 'a')\n2. **bradshaw** (starts with 'b')\n3. **holocene** (starts with 'h')\n4. **mare** (starts with 'm')\n5. **muriel** (starts with 'm', comes after 'mare' because 'u' comes after 'a')\n6. **pathetic** (starts with 'p')\n7. **r&d** (starts with 'r')\n8. **sigh** (starts with 's')\n9. **staircase** (starts with 's', comes after 'sigh' because 't' comes after 'i')\n10. **talon** (starts with 't', comes after 'staircase' because 'a' comes before 's' in the second letter, wait, let's recheck 'staircase' and 'talon'. 'st' vs 'ta'. 's' comes before 't', so 'staircase' comes before 'talon'.)\n\nLet's re-sort systematically:\n\nWords: holocene, muriel, r&d, pathetic, talon, mare, staircase, bradshaw, aperture, sigh\n\n1. **a**perture\n2. **b**radshaw\n3. **h**olocene\n4. **m**are\n5. **m**uriel (mare vs muriel: mare ends, muriel continues. 'mare' comes before 'muriel')\n6. **p**athetic\n7. **r**&d\n8. **s**igh\n9. **s**taircase\n10. **t**alon\n\nComparing 'sigh' and 'staircase':\nsigh (s-i)\nstaircase (s-t)\n'i' comes before 't', so 'sigh' comes before 'staircase'.\n\nComparing 'talon' and 'staircase':\ntalon (t-a)\nstaircase (s-t)\n's' comes before 't', so 'staircase' comes before 'talon'.\n\nFinal sorted order:\n1. aperture\n2. bradshaw\n3. holocene\n4. mare\n5. muriel\n6. pathetic\n7. r&d\n8. sigh\n9. staircase\n10. talon\n\nAnswer: aperture, bradshaw, holocene, mare, muriel, pathetic, r&d, sigh, staircase, talon", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0033", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The sorting logic contains errors, particularly in the comparison steps and the final ordering of 'sigh', 'staircase', and 'talon'.\n\nCorrection advice: Review the alphabetical comparison for words starting with the same letter (like 'm' words, or 's' words) and ensure the comparison proceeds correctly through the letters. Specifically, re-evaluate the relative order of 'sigh', 'staircase', and 'talon'.\n\nAnswer: aperture, bradshaw, holocene, mare, muriel, pathetic, r&d, sigh, staircase, talon", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.553", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:38.244403+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.2766, + "error": null, + "event": "model_call", + "event_seq": 554, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: holocene muriel r&d pathetic talon mare staircase bradshaw aperture sigh\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: holocene, muriel, r&d, pathetic, talon, mare, staircase, bradshaw, aperture, sigh.\n\nTo sort them alphabetically, we compare them letter by letter:\n\n1. **aperture** (starts with 'a')\n2. **bradshaw** (starts with 'b')\n3. **holocene** (starts with 'h')\n4. **mare** (starts with 'm')\n5. **muriel** (starts with 'm', comes after 'mare' because 'u' comes after 'a')\n6. **pathetic** (starts with 'p')\n7. **r&d** (starts with 'r')\n8. **sigh** (starts with 's')\n9. **staircase** (starts with 's', comes after 'sigh' because 't' comes after 'i')\n10. **talon** (starts with 't', comes after 'staircase' because 'a' comes before 's' in the second letter, wait, let's recheck 'staircase' and 'talon'. 'st' vs 'ta'. 's' comes before 't', so 'staircase' comes before 'talon'.)\n\nLet's re-sort systematically:\n\nWords: holocene, muriel, r&d, pathetic, talon, mare, staircase, bradshaw, aperture, sigh\n\n1. **a**perture\n2. **b**radshaw\n3. **h**olocene\n4. **m**are\n5. **m**uriel (mare vs muriel: mare ends, muriel continues. 'mare' comes before 'muriel')\n6. **p**athetic\n7. **r**&d\n8. **s**igh\n9. **s**taircase\n10. **t**alon\n\nComparing 'sigh' and 'staircase':\nsigh (s-i)\nstaircase (s-t)\n'i' comes before 't', so 'sigh' comes before 'staircase'.\n\nComparing 'talon' and 'staircase':\ntalon (t-a)\nstaircase (s-t)\n's' comes before 't', so 'staircase' comes before 'talon'.\n\nFinal sorted order:\n1. aperture\n2. bradshaw\n3. holocene\n4. mare\n5. muriel\n6. pathetic\n7. r&d\n8. sigh\n9. staircase\n10. talon\n\nAnswer: aperture, bradshaw, holocene, mare, muriel, pathetic, r&d, sigh, staircase, talon\n\nFeedback:\nThe sorting logic contains errors, particularly in the comparison steps and the final ordering of 'sigh', 'staircase', and 'talon'.\n\nCorrection advice: Review the alphabetical comparison for words starting with the same letter (like 'm' words, or 's' words) and ensure the comparison proceeds correctly through the letters. Specifically, re-evaluate the relative order of 'sigh', 'staircase', and 'talon'.\n\nAnswer: aperture, bradshaw, holocene, mare, muriel, pathetic, r&d, sigh, staircase, talon", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0033", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: aperture, bradshaw, holocene, mare, muriel, pathetic, r&d, sigh, staircase, talon", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.554", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:38.527939+00:00" + }, + { + "cycle": 0, + "duration_sec": 3.2066, + "error": null, + "event": "model_call", + "event_seq": 555, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: matte game aldebaran inkling fiberboard pulsate lakeside shape mcgee watchworks entrepreneurial boxwood peruse backyard cabbage polyhedra lightface rowley rae invincible\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0057", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words sorted alphabetically:\n\n1. aldebaran\n2. backyard\n3. cabbage\n4. entrepreneurial\n5. fiberboard\n6. game\n7. inkling\n8. invincible\n9. lake\n10. lightface\n11. matte\n12. mcgee\n13. peruse\n14. pulsate\n15. polyhedra\n16. rae\n17. rowley\n18. shape\n19. watchworks\n20. boxwood\n\nLet's re-sort them carefully:\n\n* **a**: aldebaran, backyard\n* **b**: cabbage, boxwood\n* **e**: entrepreneurial\n* **f**: fiberboard\n* **g**: game\n* **i**: inkling, invincible\n* **l**: lakeside, lightface\n* **m**: matte, mcgee\n* **p**: pulsate, polyhedra, peruse\n* **r**: rae, rowley\n* **s**: shape\n* **w**: watchworks\n\nLet's do a full alphabetical sort:\n\n1. aldebaran\n2. backyard\n3. boxwood\n4. cabbage\n5. entrepreneurial\n6. fiberboard\n7. game\n8. inkling\n9. invincible\n10. lakeside (Assuming 'lakeside' is the intended word, as 'lakes' is not in the list, but 'lakeside' is often implied or a common word. Checking the list again: \"lakeside\" is not in the list. The list contains \"lakeside\" if we assume \"lakeside\" is present. Let's re-examine the provided list: matte game aldebaran inkling fiberboard pulsate lakeside shape mcgee watchworks entrepreneurial boxwood peruse backyard cabbage polyhedra lightface rowley rae invincible)\n * *Correction: The word is \"lakeside\".*\n11. lightface\n12. matte\n13. mcgee\n14. peruse\n15. pulsate\n16. polyhedra\n17. rae\n18. rowley\n19. shape\n20. watchworks\n\nLet's re-sort based on the exact list provided:\nmatte, game, aldebaran, inkling, fiberboard, pulsate, lakeside, shape, mcgee, watchworks, entrepreneurial, boxwood, peruse, backyard, cabbage, polyhedra, lightface, rowley, rae, invincible\n\n1. aldebaran\n2. backyard\n3. cabbage\n4. boxwood\n5. entrepreneurial\n6. fiberboard\n7. game\n8. inkling\n9. invincible\n10. lakeside\n11. lightface\n12. matte\n13. mcgee\n14. peruse\n15. pulsate\n16. polyhedra\n17. rae\n18. rowley\n19. shape\n20. watchworks\n\nAnswer: aldebaran, backyard, cabbage, boxwood, entrepreneurial, fiberboard, game, inkling, invincible, lakeside, lightface, matte, mcgee, peruse, pulsate, polyhedra, rae, rowley, shape, watchworks", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.555", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:41.740047+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.9616, + "error": null, + "event": "model_call", + "event_seq": 556, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: matte game aldebaran inkling fiberboard pulsate lakeside shape mcgee watchworks entrepreneurial boxwood peruse backyard cabbage polyhedra lightface rowley rae invincible\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. aldebaran\n2. backyard\n3. cabbage\n4. entrepreneurial\n5. fiberboard\n6. game\n7. inkling\n8. invincible\n9. lake\n10. lightface\n11. matte\n12. mcgee\n13. peruse\n14. pulsate\n15. polyhedra\n16. rae\n17. rowley\n18. shape\n19. watchworks\n20. boxwood\n\nLet's re-sort them carefully:\n\n* **a**: aldebaran, backyard\n* **b**: cabbage, boxwood\n* **e**: entrepreneurial\n* **f**: fiberboard\n* **g**: game\n* **i**: inkling, invincible\n* **l**: lakeside, lightface\n* **m**: matte, mcgee\n* **p**: pulsate, polyhedra, peruse\n* **r**: rae, rowley\n* **s**: shape\n* **w**: watchworks\n\nLet's do a full alphabetical sort:\n\n1. aldebaran\n2. backyard\n3. boxwood\n4. cabbage\n5. entrepreneurial\n6. fiberboard\n7. game\n8. inkling\n9. invincible\n10. lakeside (Assuming 'lakeside' is the intended word, as 'lakes' is not in the list, but 'lakeside' is often implied or a common word. Checking the list again: \"lakeside\" is not in the list. The list contains \"lakeside\" if we assume \"lakeside\" is present. Let's re-examine the provided list: matte game aldebaran inkling fiberboard pulsate lakeside shape mcgee watchworks entrepreneurial boxwood peruse backyard cabbage polyhedra lightface rowley rae invincible)\n * *Correction: The word is \"lakeside\".*\n11. lightface\n12. matte\n13. mcgee\n14. peruse\n15. pulsate\n16. polyhedra\n17. rae\n18. rowley\n19. shape\n20. watchworks\n\nLet's re-sort based on the exact list provided:\nmatte, game, aldebaran, inkling, fiberboard, pulsate, lakeside, shape, mcgee, watchworks, entrepreneurial, boxwood, peruse, backyard, cabbage, polyhedra, lightface, rowley, rae, invincible\n\n1. aldebaran\n2. backyard\n3. cabbage\n4. boxwood\n5. entrepreneurial\n6. fiberboard\n7. game\n8. inkling\n9. invincible\n10. lakeside\n11. lightface\n12. matte\n13. mcgee\n14. peruse\n15. pulsate\n16. polyhedra\n17. rae\n18. rowley\n19. shape\n20. watchworks\n\nAnswer: aldebaran, backyard, cabbage, boxwood, entrepreneurial, fiberboard, game, inkling, invincible, lakeside, lightface, matte, mcgee, peruse, pulsate, polyhedra, rae, rowley, shape, watchworks", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0057", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The initial attempt's manual sorting steps contained errors and inconsistencies, particularly in handling words starting with the same letter and in the final list construction. The final derived answer is mostly correct but the intermediate reasoning was flawed.\n\nCorrection advice: Review the initial manual sorting steps for errors. Ensure all words from the original list are accounted for and sorted strictly alphabetically. The final list provided in the attempt is correct based on the provided list.", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.556", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:42.710643+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.8152, + "error": null, + "event": "model_call", + "event_seq": 557, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: matte game aldebaran inkling fiberboard pulsate lakeside shape mcgee watchworks entrepreneurial boxwood peruse backyard cabbage polyhedra lightface rowley rae invincible\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. aldebaran\n2. backyard\n3. cabbage\n4. entrepreneurial\n5. fiberboard\n6. game\n7. inkling\n8. invincible\n9. lake\n10. lightface\n11. matte\n12. mcgee\n13. peruse\n14. pulsate\n15. polyhedra\n16. rae\n17. rowley\n18. shape\n19. watchworks\n20. boxwood\n\nLet's re-sort them carefully:\n\n* **a**: aldebaran, backyard\n* **b**: cabbage, boxwood\n* **e**: entrepreneurial\n* **f**: fiberboard\n* **g**: game\n* **i**: inkling, invincible\n* **l**: lakeside, lightface\n* **m**: matte, mcgee\n* **p**: pulsate, polyhedra, peruse\n* **r**: rae, rowley\n* **s**: shape\n* **w**: watchworks\n\nLet's do a full alphabetical sort:\n\n1. aldebaran\n2. backyard\n3. boxwood\n4. cabbage\n5. entrepreneurial\n6. fiberboard\n7. game\n8. inkling\n9. invincible\n10. lakeside (Assuming 'lakeside' is the intended word, as 'lakes' is not in the list, but 'lakeside' is often implied or a common word. Checking the list again: \"lakeside\" is not in the list. The list contains \"lakeside\" if we assume \"lakeside\" is present. Let's re-examine the provided list: matte game aldebaran inkling fiberboard pulsate lakeside shape mcgee watchworks entrepreneurial boxwood peruse backyard cabbage polyhedra lightface rowley rae invincible)\n * *Correction: The word is \"lakeside\".*\n11. lightface\n12. matte\n13. mcgee\n14. peruse\n15. pulsate\n16. polyhedra\n17. rae\n18. rowley\n19. shape\n20. watchworks\n\nLet's re-sort based on the exact list provided:\nmatte, game, aldebaran, inkling, fiberboard, pulsate, lakeside, shape, mcgee, watchworks, entrepreneurial, boxwood, peruse, backyard, cabbage, polyhedra, lightface, rowley, rae, invincible\n\n1. aldebaran\n2. backyard\n3. cabbage\n4. boxwood\n5. entrepreneurial\n6. fiberboard\n7. game\n8. inkling\n9. invincible\n10. lakeside\n11. lightface\n12. matte\n13. mcgee\n14. peruse\n15. pulsate\n16. polyhedra\n17. rae\n18. rowley\n19. shape\n20. watchworks\n\nAnswer: aldebaran, backyard, cabbage, boxwood, entrepreneurial, fiberboard, game, inkling, invincible, lakeside, lightface, matte, mcgee, peruse, pulsate, polyhedra, rae, rowley, shape, watchworks\n\nFeedback:\nThe initial attempt's manual sorting steps contained errors and inconsistencies, particularly in handling words starting with the same letter and in the final list construction. The final derived answer is mostly correct but the intermediate reasoning was flawed.\n\nCorrection advice: Review the initial manual sorting steps for errors. Ensure all words from the original list are accounted for and sorted strictly alphabetically. The final list provided in the attempt is correct based on the provided list.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0057", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: aldebaran, backyard, cabbage, boxwood, entrepreneurial, fiberboard, game, inkling, invincible, lakeside, lightface, matte, mcgee, peruse, pulsate, polyhedra, rae, rowley, shape, watchworks", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.557", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:43.535511+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.8153, + "error": null, + "event": "model_call", + "event_seq": 558, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: blutwurst horizon uranyl arraign portentous faber rancho peste glacier convenient inconspicuous\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0105", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words to sort: blutwurst, horizon, uranyl, arraign, portentous, faber, rancho, peste, glacier, convenient, inconspicuous.\n\nLet's sort them alphabetically:\n\n1. arraign\n2. blutwurst\n3. convenient\n4. faber\n5. glacier\n6. horizon\n7. inconspicuous\n8. portentous\n9. peste\n10. rancho\n11. uranyl\n\nLet's re-check the sorting carefully, especially for words starting with similar letters:\n\n* **a**rraign\n* **b**lutwurst\n* **c**onvenient\n* **f**aber\n* **g**lacier\n* **h**orizon\n* **i**nconspicuous\n* **p**ortentous\n* **p**este (Peste comes before Portentous because 'e' comes before 'o')\n* **r**ancho\n* **u**ranyl\n\nLet's re-sort the 'P' words:\n* portentous\n* peste\n'peste' comes before 'portentous'.\n\nCorrected order:\n1. arraign\n2. blutwurst\n3. convenient\n4. faber\n5. glacier\n6. horizon\n7. inconspicuous\n8. peste\n9. portentous\n10. rancho\n11. uranyl\n\nAnswer: arraign, blutwurst, convenient, faber, glacier, horizon, inconspicuous, peste, portentous, rancho, uranyl\n\nAnswer: arraign, blutwurst, convenient, faber, glacier, horizon, inconspicuous, peste, portentous, rancho, uranyl", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.558", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:45.357732+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.8388, + "error": null, + "event": "model_call", + "event_seq": 559, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: blutwurst horizon uranyl arraign portentous faber rancho peste glacier convenient inconspicuous\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words to sort: blutwurst, horizon, uranyl, arraign, portentous, faber, rancho, peste, glacier, convenient, inconspicuous.\n\nLet's sort them alphabetically:\n\n1. arraign\n2. blutwurst\n3. convenient\n4. faber\n5. glacier\n6. horizon\n7. inconspicuous\n8. portentous\n9. peste\n10. rancho\n11. uranyl\n\nLet's re-check the sorting carefully, especially for words starting with similar letters:\n\n* **a**rraign\n* **b**lutwurst\n* **c**onvenient\n* **f**aber\n* **g**lacier\n* **h**orizon\n* **i**nconspicuous\n* **p**ortentous\n* **p**este (Peste comes before Portentous because 'e' comes before 'o')\n* **r**ancho\n* **u**ranyl\n\nLet's re-sort the 'P' words:\n* portentous\n* peste\n'peste' comes before 'portentous'.\n\nCorrected order:\n1. arraign\n2. blutwurst\n3. convenient\n4. faber\n5. glacier\n6. horizon\n7. inconspicuous\n8. peste\n9. portentous\n10. rancho\n11. uranyl\n\nAnswer: arraign, blutwurst, convenient, faber, glacier, horizon, inconspicuous, peste, portentous, rancho, uranyl\n\nAnswer: arraign, blutwurst, convenient, faber, glacier, horizon, inconspicuous, peste, portentous, rancho, uranyl", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0105", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The sorting logic for the 'P' words is incorrect. 'peste' should come before 'portentous' because 'e' comes before 'o' in the second letter, but the provided list has 'portentous' before 'peste' in the initial attempt's re-check, and the final answer reflects the correct order.\n\nCorrection advice: The final sorted list is correct based on standard alphabetical order. No changes are needed to the final answer.", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.559", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:46.201917+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.5327, + "error": null, + "event": "model_call", + "event_seq": 560, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: blutwurst horizon uranyl arraign portentous faber rancho peste glacier convenient inconspicuous\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words to sort: blutwurst, horizon, uranyl, arraign, portentous, faber, rancho, peste, glacier, convenient, inconspicuous.\n\nLet's sort them alphabetically:\n\n1. arraign\n2. blutwurst\n3. convenient\n4. faber\n5. glacier\n6. horizon\n7. inconspicuous\n8. portentous\n9. peste\n10. rancho\n11. uranyl\n\nLet's re-check the sorting carefully, especially for words starting with similar letters:\n\n* **a**rraign\n* **b**lutwurst\n* **c**onvenient\n* **f**aber\n* **g**lacier\n* **h**orizon\n* **i**nconspicuous\n* **p**ortentous\n* **p**este (Peste comes before Portentous because 'e' comes before 'o')\n* **r**ancho\n* **u**ranyl\n\nLet's re-sort the 'P' words:\n* portentous\n* peste\n'peste' comes before 'portentous'.\n\nCorrected order:\n1. arraign\n2. blutwurst\n3. convenient\n4. faber\n5. glacier\n6. horizon\n7. inconspicuous\n8. peste\n9. portentous\n10. rancho\n11. uranyl\n\nAnswer: arraign, blutwurst, convenient, faber, glacier, horizon, inconspicuous, peste, portentous, rancho, uranyl\n\nAnswer: arraign, blutwurst, convenient, faber, glacier, horizon, inconspicuous, peste, portentous, rancho, uranyl\n\nFeedback:\nThe sorting logic for the 'P' words is incorrect. 'peste' should come before 'portentous' because 'e' comes before 'o' in the second letter, but the provided list has 'portentous' before 'peste' in the initial attempt's re-check, and the final answer reflects the correct order.\n\nCorrection advice: The final sorted list is correct based on standard alphabetical order. No changes are needed to the final answer.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0105", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: arraign, blutwurst, convenient, faber, glacier, horizon, inconspicuous, peste, portentous, rancho, uranyl", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.560", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:46.740011+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.6013, + "error": null, + "event": "model_call", + "event_seq": 561, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: cornerstone green brindle prowl nonce ballard scrappy credulous des pitney difluoride toward predilect nostalgic rape mixup horseplay jew curio\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0067", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words sorted alphabetically:\n\n1. ballard\n2. brindle\n3. cornerstone\n4. credulous\n5. curio\n6. des\n7. difluoride\n8. green\n9. horseplay\n10. jew\n11. mixup\n12. nonce\n13. nostalgic\n14. pitney\n15. predilect\n16. prowl\n17. rape\n18. scrappy\n19. toward\n\nLet's re-sort them carefully:\n\nOriginal list: cornerstone, green, brindle, prowl, nonce, ballard, scrappy, credulous, des, pitney, difluoride, toward, predilect, nostalgic, rape, mixup, horseplay, jew, curio\n\nSorted list:\n1. ballard\n2. brindle\n3. cornerstone\n4. credulous\n5. curio\n6. des\n7. difluoride\n8. green\n9. horseplay\n10. jew\n11. mixup\n12. nonce\n13. nostalgic\n14. pitney\n15. predilect\n16. prowl\n17. rape\n18. scrappy\n19. toward\n\nAnswer: ballard, brindle, cornerstone, credulous, curio, des, difluoride, green, horseplay, jew, mixup, nonce, nostalgic, pitney, predilect, prowl, rape, scrappy, toward", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.561", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:48.346804+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.8535, + "error": null, + "event": "model_call", + "event_seq": 562, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: cornerstone green brindle prowl nonce ballard scrappy credulous des pitney difluoride toward predilect nostalgic rape mixup horseplay jew curio\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. ballard\n2. brindle\n3. cornerstone\n4. credulous\n5. curio\n6. des\n7. difluoride\n8. green\n9. horseplay\n10. jew\n11. mixup\n12. nonce\n13. nostalgic\n14. pitney\n15. predilect\n16. prowl\n17. rape\n18. scrappy\n19. toward\n\nLet's re-sort them carefully:\n\nOriginal list: cornerstone, green, brindle, prowl, nonce, ballard, scrappy, credulous, des, pitney, difluoride, toward, predilect, nostalgic, rape, mixup, horseplay, jew, curio\n\nSorted list:\n1. ballard\n2. brindle\n3. cornerstone\n4. credulous\n5. curio\n6. des\n7. difluoride\n8. green\n9. horseplay\n10. jew\n11. mixup\n12. nonce\n13. nostalgic\n14. pitney\n15. predilect\n16. prowl\n17. rape\n18. scrappy\n19. toward\n\nAnswer: ballard, brindle, cornerstone, credulous, curio, des, difluoride, green, horseplay, jew, mixup, nonce, nostalgic, pitney, predilect, prowl, rape, scrappy, toward", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0067", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempted answer has an error in the initial sorting and the final list presentation. The initial sorting provided in the attempt is incorrect, and the final answer format is slightly inconsistent with the request (though the final list itself seems to be the intended sorted list).\n\nCorrection advice: Review the alphabetical order of all words carefully. Ensure that words starting with the same letter are sorted by the subsequent letters.\n\nAnswer: ballard, brindle, cornerstone, credulous, curio, des, difluoride, green, horseplay, jew, mixup, nonce, nostalgic, pitney, predilect, prowl, rape, scrappy, toward", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.562", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:49.211076+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.664, + "error": null, + "event": "model_call", + "event_seq": 563, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: cornerstone green brindle prowl nonce ballard scrappy credulous des pitney difluoride toward predilect nostalgic rape mixup horseplay jew curio\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. ballard\n2. brindle\n3. cornerstone\n4. credulous\n5. curio\n6. des\n7. difluoride\n8. green\n9. horseplay\n10. jew\n11. mixup\n12. nonce\n13. nostalgic\n14. pitney\n15. predilect\n16. prowl\n17. rape\n18. scrappy\n19. toward\n\nLet's re-sort them carefully:\n\nOriginal list: cornerstone, green, brindle, prowl, nonce, ballard, scrappy, credulous, des, pitney, difluoride, toward, predilect, nostalgic, rape, mixup, horseplay, jew, curio\n\nSorted list:\n1. ballard\n2. brindle\n3. cornerstone\n4. credulous\n5. curio\n6. des\n7. difluoride\n8. green\n9. horseplay\n10. jew\n11. mixup\n12. nonce\n13. nostalgic\n14. pitney\n15. predilect\n16. prowl\n17. rape\n18. scrappy\n19. toward\n\nAnswer: ballard, brindle, cornerstone, credulous, curio, des, difluoride, green, horseplay, jew, mixup, nonce, nostalgic, pitney, predilect, prowl, rape, scrappy, toward\n\nFeedback:\nThe attempted answer has an error in the initial sorting and the final list presentation. The initial sorting provided in the attempt is incorrect, and the final answer format is slightly inconsistent with the request (though the final list itself seems to be the intended sorted list).\n\nCorrection advice: Review the alphabetical order of all words carefully. Ensure that words starting with the same letter are sorted by the subsequent letters.\n\nAnswer: ballard, brindle, cornerstone, credulous, curio, des, difluoride, green, horseplay, jew, mixup, nonce, nostalgic, pitney, predilect, prowl, rape, scrappy, toward", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0067", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: ballard, brindle, cornerstone, credulous, curio, des, difluoride, difluoride, green, horseplay, jew, mixup, nonce, nostalgic, pitney, predilect, prowl, rape, scrappy, toward", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.563", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:49.880540+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.8281, + "error": null, + "event": "model_call", + "event_seq": 564, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: oakland seaborg jacobi membrane trapezoidal allis marmot toggle anthology\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0066", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words to sort alphabetically:\noakland, seaborg, jacobi, membrane, trapezoidal, allis, marmot, toggle, anthology\n\nLet's sort them letter by letter:\n\n1. **allis** (A)\n2. **anthology** (A) - 'allis' comes before 'anthology' because 'l' comes before 'n'.\n3. **jacobi** (J)\n4. **marmot** (M)\n5. **membrane** (M) - 'marmot' comes before 'membrane' because 'a' comes before 'e'.\n6. **oakland** (O)\n7. **seaborg** (S)\n8. **trapezoidal** (T)\n9. **toggle** (T) - 'trapezoidal' comes before 'toggle' because 'r' comes before 'o'.\n\nLet's re-check the 'M' words:\nmarmot\nmembrane\n'marmot' vs 'membrane': m-a-r vs m-e-m. 'a' comes before 'e', so marmot comes before membrane.\n\nLet's re-check the 'T' words:\ntrapezoidal\ntoggle\n'trapezoidal' vs 'toggle': t-r-a vs t-o-g. 'r' comes before 'o', so trapezoidal comes before toggle.\n\nFinal sorted order:\n1. allis\n2. anthology\n3. jacobi\n4. marmot\n5. membrane\n6. oakland\n7. seaborg\n8. trapezoidal\n9. toggle\n\nAnswer: allis, anthology, jacobi, marmot, membrane, oakland, seaborg, trapezoidal, toggle", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.564", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:51.714240+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.5166, + "error": null, + "event": "model_call", + "event_seq": 565, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: oakland seaborg jacobi membrane trapezoidal allis marmot toggle anthology\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words to sort alphabetically:\noakland, seaborg, jacobi, membrane, trapezoidal, allis, marmot, toggle, anthology\n\nLet's sort them letter by letter:\n\n1. **allis** (A)\n2. **anthology** (A) - 'allis' comes before 'anthology' because 'l' comes before 'n'.\n3. **jacobi** (J)\n4. **marmot** (M)\n5. **membrane** (M) - 'marmot' comes before 'membrane' because 'a' comes before 'e'.\n6. **oakland** (O)\n7. **seaborg** (S)\n8. **trapezoidal** (T)\n9. **toggle** (T) - 'trapezoidal' comes before 'toggle' because 'r' comes before 'o'.\n\nLet's re-check the 'M' words:\nmarmot\nmembrane\n'marmot' vs 'membrane': m-a-r vs m-e-m. 'a' comes before 'e', so marmot comes before membrane.\n\nLet's re-check the 'T' words:\ntrapezoidal\ntoggle\n'trapezoidal' vs 'toggle': t-r-a vs t-o-g. 'r' comes before 'o', so trapezoidal comes before toggle.\n\nFinal sorted order:\n1. allis\n2. anthology\n3. jacobi\n4. marmot\n5. membrane\n6. oakland\n7. seaborg\n8. trapezoidal\n9. toggle\n\nAnswer: allis, anthology, jacobi, marmot, membrane, oakland, seaborg, trapezoidal, toggle", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0066", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The sorting logic for 'allis' and 'anthology' is incorrect. 'anthology' should come before 'allis' because 'n' comes before 'l' in the second position (A-N vs A-L).\n\nCorrection advice: Re-evaluate the comparison between 'allis' and 'anthology'. 'anthology' should precede 'allis'.", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.565", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:52.242587+00:00" + }, + { + "cycle": 0, + "duration_sec": 2.8273, + "error": null, + "event": "model_call", + "event_seq": 566, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: oakland seaborg jacobi membrane trapezoidal allis marmot toggle anthology\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words to sort alphabetically:\noakland, seaborg, jacobi, membrane, trapezoidal, allis, marmot, toggle, anthology\n\nLet's sort them letter by letter:\n\n1. **allis** (A)\n2. **anthology** (A) - 'allis' comes before 'anthology' because 'l' comes before 'n'.\n3. **jacobi** (J)\n4. **marmot** (M)\n5. **membrane** (M) - 'marmot' comes before 'membrane' because 'a' comes before 'e'.\n6. **oakland** (O)\n7. **seaborg** (S)\n8. **trapezoidal** (T)\n9. **toggle** (T) - 'trapezoidal' comes before 'toggle' because 'r' comes before 'o'.\n\nLet's re-check the 'M' words:\nmarmot\nmembrane\n'marmot' vs 'membrane': m-a-r vs m-e-m. 'a' comes before 'e', so marmot comes before membrane.\n\nLet's re-check the 'T' words:\ntrapezoidal\ntoggle\n'trapezoidal' vs 'toggle': t-r-a vs t-o-g. 'r' comes before 'o', so trapezoidal comes before toggle.\n\nFinal sorted order:\n1. allis\n2. anthology\n3. jacobi\n4. marmot\n5. membrane\n6. oakland\n7. seaborg\n8. trapezoidal\n9. toggle\n\nAnswer: allis, anthology, jacobi, marmot, membrane, oakland, seaborg, trapezoidal, toggle\n\nFeedback:\nThe sorting logic for 'allis' and 'anthology' is incorrect. 'anthology' should come before 'allis' because 'n' comes before 'l' in the second position (A-N vs A-L).\n\nCorrection advice: Re-evaluate the comparison between 'allis' and 'anthology'. 'anthology' should precede 'allis'.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0066", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words to sort alphabetically:\noakland, seaborg, jacobi, membrane, trapezoidal, allis, marmot, toggle, anthology\n\nSorting based on the feedback:\n1. **anthology** (A-n)\n2. **allis** (A-l) - 'n' comes before 'l' is incorrect. Let's re-examine the comparison:\n anthology: A, n, t, h, o, l, o, g, y\n allis: A, l, l, i, s\n\n Comparing the second letter: 'n' vs 'l'. Since 'l' comes before 'n' in the alphabet, 'allis' should come before 'anthology'.\n\n Let's check the original feedback: \"anthology should come before allis because 'n' comes before 'l' in the second position (A-N vs A-L).\" This statement is factually incorrect based on standard alphabetical order (L comes before N).\n\n Standard alphabetical order check:\n A-l-l-i-s (allis)\n A-n-t-h-o-l-o-g-y (anthology)\n 'l' comes before 'n'. Therefore, 'allis' comes before 'anthology'.\n\n Let's proceed with the standard alphabetical order:\n\n A words:\n allis (A-l)\n anthology (A-n)\n 'allis' comes before 'anthology'.\n\n J words:\n jacobi\n\n M words:\n marmot (M-a)\n membrane (M-e)\n 'marmot' comes before 'membrane' ('a' before 'e').\n\n O words:\n oakland\n\n S words:\n seaborg\n\n T words:\n trapezoidal (T-r)\n toggle (T-o)\n 'trapezoidal' comes before 'toggle' ('r' before 'o').\n\n Corrected order based on standard alphabetical rules:\n 1. allis\n 2. anthology\n 3. jacobi\n 4. marmot\n 5. membrane\n 6. oakland\n 7. seaborg\n 8. trapezoidal\n 9. toggle\n\nAnswer: allis, anthology, jacobi, marmot, membrane, oakland, seaborg, trapezoidal, toggle", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.566", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:55.075405+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.2073, + "error": null, + "event": "model_call", + "event_seq": 567, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: testate orchestra permafrost leasehold shiva\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0027", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The given list of words is: testate, orchestra, permafrost, leasehold, shiva.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **leasehold** (Starts with 'l')\n2. ** orchestra** (Starts with 'o')\n3. **permafrost** (Starts with 'p')\n4. **shiva** (Starts with 's')\n5. **testate** (Starts with 't')\n\nLet's re-sort based on the first letter: L, O, P, S, T.\n\n1. **leasehold** (L)\n2. **orchestra** (O)\n3. **permafrost** (P)\n4. **shiva** (S)\n5. **testate** (T)\n\nThe alphabetical order is: leasehold, orchestra, permafrost, shiva, testate.\n\nAnswer: leasehold, orchestra, permafrost, shiva, testate\nAnswer: leasehold, orchestra, permafrost, shiva, testate", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.567", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:56.288373+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.1369, + "error": null, + "event": "model_call", + "event_seq": 568, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: testate orchestra permafrost leasehold shiva\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: testate, orchestra, permafrost, leasehold, shiva.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **leasehold** (Starts with 'l')\n2. ** orchestra** (Starts with 'o')\n3. **permafrost** (Starts with 'p')\n4. **shiva** (Starts with 's')\n5. **testate** (Starts with 't')\n\nLet's re-sort based on the first letter: L, O, P, S, T.\n\n1. **leasehold** (L)\n2. **orchestra** (O)\n3. **permafrost** (P)\n4. **shiva** (S)\n5. **testate** (T)\n\nThe alphabetical order is: leasehold, orchestra, permafrost, shiva, testate.\n\nAnswer: leasehold, orchestra, permafrost, shiva, testate\nAnswer: leasehold, orchestra, permafrost, shiva, testate", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0027", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempt has a factual error in the initial list provided and a minor formatting/logic error in the sorting process.\n\nCorrection advice:\n1. The initial list in the attempt is missing the word \"shiva\" from the initial list provided in the prompt (\"testate orchestra permafrost leasehold shiva\"). Ensure all words are included.\n2. The sorting logic is correct based on the words present, but the initial step of listing the words is incomplete/incorrectly transcribed.\n\nAnswer: leasehold, orchestra, permafrost, shiva, testate", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.568", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:57.432597+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.6428, + "error": null, + "event": "model_call", + "event_seq": 569, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: testate orchestra permafrost leasehold shiva\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: testate, orchestra, permafrost, leasehold, shiva.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **leasehold** (Starts with 'l')\n2. ** orchestra** (Starts with 'o')\n3. **permafrost** (Starts with 'p')\n4. **shiva** (Starts with 's')\n5. **testate** (Starts with 't')\n\nLet's re-sort based on the first letter: L, O, P, S, T.\n\n1. **leasehold** (L)\n2. **orchestra** (O)\n3. **permafrost** (P)\n4. **shiva** (S)\n5. **testate** (T)\n\nThe alphabetical order is: leasehold, orchestra, permafrost, shiva, testate.\n\nAnswer: leasehold, orchestra, permafrost, shiva, testate\nAnswer: leasehold, orchestra, permafrost, shiva, testate\n\nFeedback:\nThe attempt has a factual error in the initial list provided and a minor formatting/logic error in the sorting process.\n\nCorrection advice:\n1. The initial list in the attempt is missing the word \"shiva\" from the initial list provided in the prompt (\"testate orchestra permafrost leasehold shiva\"). Ensure all words are included.\n2. The sorting logic is correct based on the words present, but the initial step of listing the words is incomplete/incorrectly transcribed.\n\nAnswer: leasehold, orchestra, permafrost, shiva, testate", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0027", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: leasehold, orchestra, permafrost, shiva, testate", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.569", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:53:58.083049+00:00" + }, + { + "cycle": 0, + "duration_sec": 3.4111, + "error": null, + "event": "model_call", + "event_seq": 570, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: corpulent diagnose code scalp bombproof fluorine blythe swipe honeybee damn pore maharaja cytolysis solicit\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0092", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words sorted alphabetically:\n\n1. bombproof\n2. bpythe (Assuming \"blythe\" is a typo for \"bpythe\" or sorting based on the provided spelling) - If we strictly use \"blythe\":\n3. corpulent\n4. code\n5. cytolysis\n6. damn\n7. diagnose\n8. fluorine\n9. honeybee\n10. maharaja\n11. pore\n12. solicit\n13. scalp\n14. swipe\n\nLet's re-sort carefully using the exact words provided: corpulent, diagnose, code, scalp, bombproof, fluorine, blythe, swipe, honeybee, damn, pore, maharaja, cytolysis, solicit.\n\n1. bombproof\n2. bpythe (blythe)\n3. code\n4. corpulent\n5. cytolysis\n6. damn\n7. diagnose\n8. fluorine\n9. honeybee\n10. maharaja\n11. pore\n12. solicit\n13. scalp\n14. swipe\n\nSorting \"blythe\" (b-l-y-t-h-e) relative to others:\n* bombproof (b-o-m-b-p-r-o-o-f)\n* blythe (b-l-y-t-h-e)\n* code (c-o-d-e)\n* corpulent (c-o-r-p-u-l-e-n-t)\n* cytolysis (c-y-t-o-l-y-s-i-s)\n* damn (d-a-m-n)\n* diagnose (d-i-a-g-n-o-s-e)\n* fluorine (f-l-u-o-r-i-n-e)\n* honeybee (h-o-n-e-y-b-e-e)\n* maharaja (m-a-h-a-r-a-j-a)\n* pore (p-o-r-e)\n* solicit (s-o-l-i-c-i-t)\n* scalp (s-c-a-l-p)\n* swipe (s-w-i-p-e)\n\nComparing 'bombproof' and 'blythe': 'b' is the same. 'o' vs 'l'. 'l' comes before 'o'.\nWait, 'bombproof' starts with 'b', 'blythe' starts with 'b'.\nbombproof: b-o-m...\nblythe: b-l-y...\n'l' comes before 'o'. So, blythe comes before bombproof.\n\nCorrected order for 'b' words:\n1. blythe\n2. bombproof\n\nFinal Alphabetical Order:\n1. blythe\n2. bombproof\n3. code\n4. corpulent\n5. cytolysis\n6. damn\n7. diagnose\n8. fluorine\n9. honeybee\n10. maharaja\n11. pore\n12. solicit\n13. scalp\n14. swipe\n\nAnswer: blythe, bombproof, code, corpulent, cytolysis, damn, diagnose, fluorine, honeybee, maharaja, pore, solicit, scalp, swipe", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.570", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:54:01.504463+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.7497, + "error": null, + "event": "model_call", + "event_seq": 571, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: corpulent diagnose code scalp bombproof fluorine blythe swipe honeybee damn pore maharaja cytolysis solicit\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. bombproof\n2. bpythe (Assuming \"blythe\" is a typo for \"bpythe\" or sorting based on the provided spelling) - If we strictly use \"blythe\":\n3. corpulent\n4. code\n5. cytolysis\n6. damn\n7. diagnose\n8. fluorine\n9. honeybee\n10. maharaja\n11. pore\n12. solicit\n13. scalp\n14. swipe\n\nLet's re-sort carefully using the exact words provided: corpulent, diagnose, code, scalp, bombproof, fluorine, blythe, swipe, honeybee, damn, pore, maharaja, cytolysis, solicit.\n\n1. bombproof\n2. bpythe (blythe)\n3. code\n4. corpulent\n5. cytolysis\n6. damn\n7. diagnose\n8. fluorine\n9. honeybee\n10. maharaja\n11. pore\n12. solicit\n13. scalp\n14. swipe\n\nSorting \"blythe\" (b-l-y-t-h-e) relative to others:\n* bombproof (b-o-m-b-p-r-o-o-f)\n* blythe (b-l-y-t-h-e)\n* code (c-o-d-e)\n* corpulent (c-o-r-p-u-l-e-n-t)\n* cytolysis (c-y-t-o-l-y-s-i-s)\n* damn (d-a-m-n)\n* diagnose (d-i-a-g-n-o-s-e)\n* fluorine (f-l-u-o-r-i-n-e)\n* honeybee (h-o-n-e-y-b-e-e)\n* maharaja (m-a-h-a-r-a-j-a)\n* pore (p-o-r-e)\n* solicit (s-o-l-i-c-i-t)\n* scalp (s-c-a-l-p)\n* swipe (s-w-i-p-e)\n\nComparing 'bombproof' and 'blythe': 'b' is the same. 'o' vs 'l'. 'l' comes before 'o'.\nWait, 'bombproof' starts with 'b', 'blythe' starts with 'b'.\nbombproof: b-o-m...\nblythe: b-l-y...\n'l' comes before 'o'. So, blythe comes before bombproof.\n\nCorrected order for 'b' words:\n1. blythe\n2. bombproof\n\nFinal Alphabetical Order:\n1. blythe\n2. bombproof\n3. code\n4. corpulent\n5. cytolysis\n6. damn\n7. diagnose\n8. fluorine\n9. honeybee\n10. maharaja\n11. pore\n12. solicit\n13. scalp\n14. swipe\n\nAnswer: blythe, bombproof, code, corpulent, cytolysis, damn, diagnose, fluorine, honeybee, maharaja, pore, solicit, scalp, swipe", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0092", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempt contains errors in the initial sorting and the final derived list.\n\n1. **Instruction Following/Factual Error (Sorting Logic):** The attempt incorrectly compares 'bombproof' and 'blythe'. 'blythe' (b-l...) should come before 'bombproof' (b-o...) because 'l' precedes 'o'. The attempt's initial ordering was flawed, although the final derived list seems to have corrected this specific pair.\n2. **Arithmetic/Factual Error (Word List):** The attempt incorrectly lists \"bpythe\" instead of the provided word \"blythe\" in its initial analysis, although it acknowledges the issue later.\n3. **Instruction Following Error (Output Format):** The attempt provides a detailed step-by-step reasoning but fails to present the final answer in the required format (a single line answer).\n\n**Correction Advice:**\n1. Ensure strict alphabetical comparison for all words.\n2. Verify the relative order of 'blythe' and 'bombproof' (blythe comes before bombproof).\n3. Present the final sorted list concisely as requested by the original task instructions.", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.571", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:54:03.264922+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.3313, + "error": null, + "event": "model_call", + "event_seq": 572, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: corpulent diagnose code scalp bombproof fluorine blythe swipe honeybee damn pore maharaja cytolysis solicit\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. bombproof\n2. bpythe (Assuming \"blythe\" is a typo for \"bpythe\" or sorting based on the provided spelling) - If we strictly use \"blythe\":\n3. corpulent\n4. code\n5. cytolysis\n6. damn\n7. diagnose\n8. fluorine\n9. honeybee\n10. maharaja\n11. pore\n12. solicit\n13. scalp\n14. swipe\n\nLet's re-sort carefully using the exact words provided: corpulent, diagnose, code, scalp, bombproof, fluorine, blythe, swipe, honeybee, damn, pore, maharaja, cytolysis, solicit.\n\n1. bombproof\n2. bpythe (blythe)\n3. code\n4. corpulent\n5. cytolysis\n6. damn\n7. diagnose\n8. fluorine\n9. honeybee\n10. maharaja\n11. pore\n12. solicit\n13. scalp\n14. swipe\n\nSorting \"blythe\" (b-l-y-t-h-e) relative to others:\n* bombproof (b-o-m-b-p-r-o-o-f)\n* blythe (b-l-y-t-h-e)\n* code (c-o-d-e)\n* corpulent (c-o-r-p-u-l-e-n-t)\n* cytolysis (c-y-t-o-l-y-s-i-s)\n* damn (d-a-m-n)\n* diagnose (d-i-a-g-n-o-s-e)\n* fluorine (f-l-u-o-r-i-n-e)\n* honeybee (h-o-n-e-y-b-e-e)\n* maharaja (m-a-h-a-r-a-j-a)\n* pore (p-o-r-e)\n* solicit (s-o-l-i-c-i-t)\n* scalp (s-c-a-l-p)\n* swipe (s-w-i-p-e)\n\nComparing 'bombproof' and 'blythe': 'b' is the same. 'o' vs 'l'. 'l' comes before 'o'.\nWait, 'bombproof' starts with 'b', 'blythe' starts with 'b'.\nbombproof: b-o-m...\nblythe: b-l-y...\n'l' comes before 'o'. So, blythe comes before bombproof.\n\nCorrected order for 'b' words:\n1. blythe\n2. bombproof\n\nFinal Alphabetical Order:\n1. blythe\n2. bombproof\n3. code\n4. corpulent\n5. cytolysis\n6. damn\n7. diagnose\n8. fluorine\n9. honeybee\n10. maharaja\n11. pore\n12. solicit\n13. scalp\n14. swipe\n\nAnswer: blythe, bombproof, code, corpulent, cytolysis, damn, diagnose, fluorine, honeybee, maharaja, pore, solicit, scalp, swipe\n\nFeedback:\nThe attempt contains errors in the initial sorting and the final derived list.\n\n1. **Instruction Following/Factual Error (Sorting Logic):** The attempt incorrectly compares 'bombproof' and 'blythe'. 'blythe' (b-l...) should come before 'bombproof' (b-o...) because 'l' precedes 'o'. The attempt's initial ordering was flawed, although the final derived list seems to have corrected this specific pair.\n2. **Arithmetic/Factual Error (Word List):** The attempt incorrectly lists \"bpythe\" instead of the provided word \"blythe\" in its initial analysis, although it acknowledges the issue later.\n3. **Instruction Following Error (Output Format):** The attempt provides a detailed step-by-step reasoning but fails to present the final answer in the required format (a single line answer).\n\n**Correction Advice:**\n1. Ensure strict alphabetical comparison for all words.\n2. Verify the relative order of 'blythe' and 'bombproof' (blythe comes before bombproof).\n3. Present the final sorted list concisely as requested by the original task instructions.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0092", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: blythe, bombproof, code, corpulent, cytolysis, damn, diagnose, fluorine, honeybee, maharaja, pore, solicit, scalp, swipe", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.572", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:54:03.601707+00:00" + }, + { + "cycle": 0, + "duration_sec": 3.9345, + "error": null, + "event": "model_call", + "event_seq": 573, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: zinc coriolanus leather easternmost highwaymen scavenge musky cutlet synaptic creak heaven paula astigmat muscular godson boyish\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0116", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words sorted alphabetically:\n\n1. astigmat\n2. boyish\n3. creak\n4. coriolanus\n5. cutlet\n6. easternmost\n7. godson\n8. heaven\n9. highwaymen\n10. leather\n11. musky\n12. muscular\n13. paula\n14. scavenge\n15. synaptic\n16. zinc\n\nLet's re-sort them carefully:\n\n* **a**stigmat\n* **b**oyish\n* **c**reak, **c**oriolanus, **c**utlet (creak, coriolanus, cutlet)\n* **e**asternmost\n* **g**odson\n* **h**aven, **h**ighwaymen\n* **l**eather\n* **m**usky, **m**uscular\n* **p**aula\n* **s**cavenge, **s**ynaptic\n* **z**inc\n\nDetailed sorting:\n\n1. astigmat\n2. boyish\n3. creak\n4. coriolanus\n5. cutlet\n6. easternmost\n7. godson\n8. heaven\n9. highwaymen\n10. leather\n11. musky\n12. muscular\n13. paula\n14. scavenge\n15. synaptic\n16. zinc\n\n(Comparing 'heaven' and 'highwaymen': H-e vs H-i. 'heaven' comes before 'highwaymen'.)\n(Comparing 'musky' and 'muscular': 'musky' comes before 'muscular'.)\n\nFinal sorted list:\nastigmat, boyish, creak, coriolanus, cutlet, easternmost, godson, heaven, highwaymen, leather, musky, muscular, paula, scavenge, synaptic, zinc\n\nAnswer: astigmat, boyish, creak, coriolanus, cutlet, easternmost, godson, heaven, highwaymen, leather, musky, muscular, paula, scavenge, synaptic, zinc", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.573", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:54:07.542029+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.7534, + "error": null, + "event": "model_call", + "event_seq": 574, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: zinc coriolanus leather easternmost highwaymen scavenge musky cutlet synaptic creak heaven paula astigmat muscular godson boyish\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. astigmat\n2. boyish\n3. creak\n4. coriolanus\n5. cutlet\n6. easternmost\n7. godson\n8. heaven\n9. highwaymen\n10. leather\n11. musky\n12. muscular\n13. paula\n14. scavenge\n15. synaptic\n16. zinc\n\nLet's re-sort them carefully:\n\n* **a**stigmat\n* **b**oyish\n* **c**reak, **c**oriolanus, **c**utlet (creak, coriolanus, cutlet)\n* **e**asternmost\n* **g**odson\n* **h**aven, **h**ighwaymen\n* **l**eather\n* **m**usky, **m**uscular\n* **p**aula\n* **s**cavenge, **s**ynaptic\n* **z**inc\n\nDetailed sorting:\n\n1. astigmat\n2. boyish\n3. creak\n4. coriolanus\n5. cutlet\n6. easternmost\n7. godson\n8. heaven\n9. highwaymen\n10. leather\n11. musky\n12. muscular\n13. paula\n14. scavenge\n15. synaptic\n16. zinc\n\n(Comparing 'heaven' and 'highwaymen': H-e vs H-i. 'heaven' comes before 'highwaymen'.)\n(Comparing 'musky' and 'muscular': 'musky' comes before 'muscular'.)\n\nFinal sorted list:\nastigmat, boyish, creak, coriolanus, cutlet, easternmost, godson, heaven, highwaymen, leather, musky, muscular, paula, scavenge, synaptic, zinc\n\nAnswer: astigmat, boyish, creak, coriolanus, cutlet, easternmost, godson, heaven, highwaymen, leather, musky, muscular, paula, scavenge, synaptic, zinc", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0116", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The sorting logic has a minor error in the initial grouping and the final comparison between 'heaven' and 'highwaymen'.\n\nCorrection advice: Review the alphabetical order for words starting with the same letter (e.g., 'c' words, 'h' words, 'm' words) and ensure strict alphabetical comparison for tie-breakers. Specifically, check the order of 'heaven' and 'highwaymen'.\n\nAnswer: astigmat, boyish, creak, coriolanus, cutlet, easternmost, godson, heaven, highwaymen, leather, musky, muscular, paula, scavenge, synaptic, zinc", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.574", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:54:08.301987+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.3634, + "error": null, + "event": "model_call", + "event_seq": 575, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: zinc coriolanus leather easternmost highwaymen scavenge musky cutlet synaptic creak heaven paula astigmat muscular godson boyish\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. astigmat\n2. boyish\n3. creak\n4. coriolanus\n5. cutlet\n6. easternmost\n7. godson\n8. heaven\n9. highwaymen\n10. leather\n11. musky\n12. muscular\n13. paula\n14. scavenge\n15. synaptic\n16. zinc\n\nLet's re-sort them carefully:\n\n* **a**stigmat\n* **b**oyish\n* **c**reak, **c**oriolanus, **c**utlet (creak, coriolanus, cutlet)\n* **e**asternmost\n* **g**odson\n* **h**aven, **h**ighwaymen\n* **l**eather\n* **m**usky, **m**uscular\n* **p**aula\n* **s**cavenge, **s**ynaptic\n* **z**inc\n\nDetailed sorting:\n\n1. astigmat\n2. boyish\n3. creak\n4. coriolanus\n5. cutlet\n6. easternmost\n7. godson\n8. heaven\n9. highwaymen\n10. leather\n11. musky\n12. muscular\n13. paula\n14. scavenge\n15. synaptic\n16. zinc\n\n(Comparing 'heaven' and 'highwaymen': H-e vs H-i. 'heaven' comes before 'highwaymen'.)\n(Comparing 'musky' and 'muscular': 'musky' comes before 'muscular'.)\n\nFinal sorted list:\nastigmat, boyish, creak, coriolanus, cutlet, easternmost, godson, heaven, highwaymen, leather, musky, muscular, paula, scavenge, synaptic, zinc\n\nAnswer: astigmat, boyish, creak, coriolanus, cutlet, easternmost, godson, heaven, highwaymen, leather, musky, muscular, paula, scavenge, synaptic, zinc\n\nFeedback:\nThe sorting logic has a minor error in the initial grouping and the final comparison between 'heaven' and 'highwaymen'.\n\nCorrection advice: Review the alphabetical order for words starting with the same letter (e.g., 'c' words, 'h' words, 'm' words) and ensure strict alphabetical comparison for tie-breakers. Specifically, check the order of 'heaven' and 'highwaymen'.\n\nAnswer: astigmat, boyish, creak, coriolanus, cutlet, easternmost, godson, heaven, highwaymen, leather, musky, muscular, paula, scavenge, synaptic, zinc", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0116", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: astigmat, boyish, creak, coriolanus, cutlet, easternmost, godson, heaven, highwaymen, leather, musky, muscular, paula, scavenge, synaptic, zinc", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.575", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:54:08.671841+00:00" + }, + { + "cycle": 0, + "duration_sec": 4.0299, + "error": null, + "event": "model_call", + "event_seq": 576, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: statutory feed spavin hecatomb pestle plume figural pasty giveth incense undulate middle blackstone tel obstinacy toothpaste hunt sinkhole\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0060", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words sorted alphabetically:\n\n1. blackstone\n2. feed\n3. figural\n4. giveth\n5. hecatomb\n6. hunt\n7. incense\n8. middle\n9. obstinacy\n10. pasty\n11. pestle\n12. plume\n13. sinkhole\n14. statutory\n15. tel\n16. toothpaste\n17. undulate\n\nLet's re-sort them carefully:\n\n* **b**lackstone\n* **f**eed\n* **f**igural\n* **g**iveth\n* **h**ecatomb\n* **h**unt\n* **i**ncense\n* **m**iddle\n* **o**bstinacy\n* **p**asty\n* **p**estle\n* **p**lume\n* **s**inkhole\n* **s**tatutory\n* **t**el\n* **t**oothpaste (toothpaste)\n* **u**ndulate\n\nComparing words starting with 'f': feed, figural. (feed comes before figural)\nComparing words starting with 'p': pasty, pestle, plume. (plume, pasty, pestle) - Wait, P-L, P-A, P-E. Plume, Pasty, Pestle.\nP-l-u-m-e\nP-a-s-t-y\nP-e-s-t-l-e\nAlphabetical order for P words: plume, pasty, pestle.\n\nLet's re-sort the full list:\nblackstone (B)\nfeed (F)\nfigural (F)\ngiveth (G)\nhecatomb (H)\nhunt (H)\nincense (I)\nmiddle (M)\nobstinacy (O)\npasty (P)\npestle (P)\nplume (P)\nsinkhole (S)\nstatutory (S)\ntel (T)\ntoothpaste (T)\nundulate (U)\n\n**Detailed Alphabetical Sort:**\n\n1. blackstone\n2. feed\n3. figural\n4. giveth\n5. hecatomb\n6. hunt\n7. incense\n8. middle\n9. obstinacy\n10. pasty\n11. pestle\n12. plume\n13. sinkhole\n14. statutory\n15. tel\n16. toothpaste\n17. undulate\n\n(Checking P words again: pasty, pestle, plume. P-a, P-e, P-l. A comes before E, E comes before L. So: pasty, pestle, plume. Wait, L comes before E in the alphabet? No. A, E, L. A < E < L. So: pasty, pestle, plume is incorrect. P-a, P-e, P-l. A < E < L. So: pasty, pestle, plume is wrong. Let's check the letters: P-a-s-t-y, P-e-s-t-l-e, P-l-u-m-e. A comes before E, E comes before L. So: pasty, pestle, plume is correct based on the second letter.)\n\nP-a-sty\nP-e-stle\nP-l-ume\nA < E < L. So: pasty, pestle, plume. This is correct.\n\nFinal check of the full list order:\nblackstone, feed, figural, giveth, hecatomb, hunt, incense, middle, obstinacy, pasty, pestle, plume, sinkhole, statutory, tel, toothpaste, undulate.\n\nAnswer: blackstone, feed, figural, giveth, hecatomb, hunt, incense, middle, obstinacy, pasty, pestle, plume, sinkhole, statutory, tel, toothpaste, undulate", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.576", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:54:12.708381+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.572, + "error": null, + "event": "model_call", + "event_seq": 577, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: statutory feed spavin hecatomb pestle plume figural pasty giveth incense undulate middle blackstone tel obstinacy toothpaste hunt sinkhole\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. blackstone\n2. feed\n3. figural\n4. giveth\n5. hecatomb\n6. hunt\n7. incense\n8. middle\n9. obstinacy\n10. pasty\n11. pestle\n12. plume\n13. sinkhole\n14. statutory\n15. tel\n16. toothpaste\n17. undulate\n\nLet's re-sort them carefully:\n\n* **b**lackstone\n* **f**eed\n* **f**igural\n* **g**iveth\n* **h**ecatomb\n* **h**unt\n* **i**ncense\n* **m**iddle\n* **o**bstinacy\n* **p**asty\n* **p**estle\n* **p**lume\n* **s**inkhole\n* **s**tatutory\n* **t**el\n* **t**oothpaste (toothpaste)\n* **u**ndulate\n\nComparing words starting with 'f': feed, figural. (feed comes before figural)\nComparing words starting with 'p': pasty, pestle, plume. (plume, pasty, pestle) - Wait, P-L, P-A, P-E. Plume, Pasty, Pestle.\nP-l-u-m-e\nP-a-s-t-y\nP-e-s-t-l-e\nAlphabetical order for P words: plume, pasty, pestle.\n\nLet's re-sort the full list:\nblackstone (B)\nfeed (F)\nfigural (F)\ngiveth (G)\nhecatomb (H)\nhunt (H)\nincense (I)\nmiddle (M)\nobstinacy (O)\npasty (P)\npestle (P)\nplume (P)\nsinkhole (S)\nstatutory (S)\ntel (T)\ntoothpaste (T)\nundulate (U)\n\n**Detailed Alphabetical Sort:**\n\n1. blackstone\n2. feed\n3. figural\n4. giveth\n5. hecatomb\n6. hunt\n7. incense\n8. middle\n9. obstinacy\n10. pasty\n11. pestle\n12. plume\n13. sinkhole\n14. statutory\n15. tel\n16. toothpaste\n17. undulate\n\n(Checking P words again: pasty, pestle, plume. P-a, P-e, P-l. A comes before E, E comes before L. So: pasty, pestle, plume. Wait, L comes before E in the alphabet? No. A, E, L. A < E < L. So: pasty, pestle, plume is incorrect. P-a, P-e, P-l. A < E < L. So: pasty, pestle, plume is wrong. Let's check the letters: P-a-s-t-y, P-e-s-t-l-e, P-l-u-m-e. A comes before E, E comes before L. So: pasty, pestle, plume is correct based on the second letter.)\n\nP-a-sty\nP-e-stle\nP-l-ume\nA < E < L. So: pasty, pestle, plume. This is correct.\n\nFinal check of the full list order:\nblackstone, feed, figural, giveth, hecatomb, hunt, incense, middle, obstinacy, pasty, pestle, plume, sinkhole, statutory, tel, toothpaste, undulate.\n\nAnswer: blackstone, feed, figural, giveth, hecatomb, hunt, incense, middle, obstinacy, pasty, pestle, plume, sinkhole, statutory, tel, toothpaste, undulate", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0060", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Correction advice: The initial attempt's manual sorting logic for the 'P' words was confusing and contained an internal contradiction/doubt, although the final derived order seems correct based on the standard alphabetical rule (P-a, P-e, P-l). The final list provided in the attempt is correct based on the words given. No factual, reasoning, arithmetic, or instruction-following errors were found in the final output.", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.577", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:54:13.291358+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.6358, + "error": null, + "event": "model_call", + "event_seq": 578, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: statutory feed spavin hecatomb pestle plume figural pasty giveth incense undulate middle blackstone tel obstinacy toothpaste hunt sinkhole\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. blackstone\n2. feed\n3. figural\n4. giveth\n5. hecatomb\n6. hunt\n7. incense\n8. middle\n9. obstinacy\n10. pasty\n11. pestle\n12. plume\n13. sinkhole\n14. statutory\n15. tel\n16. toothpaste\n17. undulate\n\nLet's re-sort them carefully:\n\n* **b**lackstone\n* **f**eed\n* **f**igural\n* **g**iveth\n* **h**ecatomb\n* **h**unt\n* **i**ncense\n* **m**iddle\n* **o**bstinacy\n* **p**asty\n* **p**estle\n* **p**lume\n* **s**inkhole\n* **s**tatutory\n* **t**el\n* **t**oothpaste (toothpaste)\n* **u**ndulate\n\nComparing words starting with 'f': feed, figural. (feed comes before figural)\nComparing words starting with 'p': pasty, pestle, plume. (plume, pasty, pestle) - Wait, P-L, P-A, P-E. Plume, Pasty, Pestle.\nP-l-u-m-e\nP-a-s-t-y\nP-e-s-t-l-e\nAlphabetical order for P words: plume, pasty, pestle.\n\nLet's re-sort the full list:\nblackstone (B)\nfeed (F)\nfigural (F)\ngiveth (G)\nhecatomb (H)\nhunt (H)\nincense (I)\nmiddle (M)\nobstinacy (O)\npasty (P)\npestle (P)\nplume (P)\nsinkhole (S)\nstatutory (S)\ntel (T)\ntoothpaste (T)\nundulate (U)\n\n**Detailed Alphabetical Sort:**\n\n1. blackstone\n2. feed\n3. figural\n4. giveth\n5. hecatomb\n6. hunt\n7. incense\n8. middle\n9. obstinacy\n10. pasty\n11. pestle\n12. plume\n13. sinkhole\n14. statutory\n15. tel\n16. toothpaste\n17. undulate\n\n(Checking P words again: pasty, pestle, plume. P-a, P-e, P-l. A comes before E, E comes before L. So: pasty, pestle, plume. Wait, L comes before E in the alphabet? No. A, E, L. A < E < L. So: pasty, pestle, plume is incorrect. P-a, P-e, P-l. A < E < L. So: pasty, pestle, plume is wrong. Let's check the letters: P-a-s-t-y, P-e-s-t-l-e, P-l-u-m-e. A comes before E, E comes before L. So: pasty, pestle, plume is correct based on the second letter.)\n\nP-a-sty\nP-e-stle\nP-l-ume\nA < E < L. So: pasty, pestle, plume. This is correct.\n\nFinal check of the full list order:\nblackstone, feed, figural, giveth, hecatomb, hunt, incense, middle, obstinacy, pasty, pestle, plume, sinkhole, statutory, tel, toothpaste, undulate.\n\nAnswer: blackstone, feed, figural, giveth, hecatomb, hunt, incense, middle, obstinacy, pasty, pestle, plume, sinkhole, statutory, tel, toothpaste, undulate\n\nFeedback:\nCorrection advice: The initial attempt's manual sorting logic for the 'P' words was confusing and contained an internal contradiction/doubt, although the final derived order seems correct based on the standard alphabetical rule (P-a, P-e, P-l). The final list provided in the attempt is correct based on the words given. No factual, reasoning, arithmetic, or instruction-following errors were found in the final output.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0060", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: blackstone, feed, figural, giveth, hecatomb, hunt, incense, middle, obstinacy, pasty, pestle, plume, sinkhole, statutory, tel, toothpaste, undulate", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.578", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:54:13.933413+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.7009, + "error": null, + "event": "model_call", + "event_seq": 579, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: thereupon wireman crag scrawny electrolytic wife cutover dickson clytemnestra took diocletian lipton inhuman stalk workplace marginal\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0031", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words sorted alphabetically:\n\n1. clytemnestra\n2. crag\n3. cutover\n4. dickson\n5. diocletian\n6. electrolytic\n7. inhuman\n8. lipton\n9. marginal\n10. stalk\n11. thereupon\n12. workplace\n13. wireman\n14. wife\n15. took\n16. scrawny\n\nLet's re-sort them carefully:\n\n* clytemnestra\n* crag\n* cutover\n* dickson\n* diocletian\n* electrolytic\n* inhuman\n* lipton\n* marginal\n* stalk\n* thereupon\n* workplace\n* wireman\n* wife\n* took\n* scrawny\n\nComparing the initial list: thereupon, wireman, crag, scrawny, electrolytic, wife, cutover, dickson, clytemnestra, took, diocletian, lipton, inhuman, stalk, workplace, marginal.\n\nAlphabetical order:\n1. clytemnestra\n2. crag\n3. cutover\n4. dickson\n5. diocletian\n6. electrolytic\n7. inhuman\n8. lipton\n9. marginal\n10. stalk\n11. thereupon\n12. workplace\n13. wireman\n14. wife\n15. took\n16. scrawny\n\nAnswer: clytemnestra, crag, cutover, dickson, diocletian, electrolytic, inhuman, lipton, marginal, stalk, thereupon, workplace, wireman, wife, took, scrawny", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.579", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:54:15.640930+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.6807, + "error": null, + "event": "model_call", + "event_seq": 580, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: thereupon wireman crag scrawny electrolytic wife cutover dickson clytemnestra took diocletian lipton inhuman stalk workplace marginal\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. clytemnestra\n2. crag\n3. cutover\n4. dickson\n5. diocletian\n6. electrolytic\n7. inhuman\n8. lipton\n9. marginal\n10. stalk\n11. thereupon\n12. workplace\n13. wireman\n14. wife\n15. took\n16. scrawny\n\nLet's re-sort them carefully:\n\n* clytemnestra\n* crag\n* cutover\n* dickson\n* diocletian\n* electrolytic\n* inhuman\n* lipton\n* marginal\n* stalk\n* thereupon\n* workplace\n* wireman\n* wife\n* took\n* scrawny\n\nComparing the initial list: thereupon, wireman, crag, scrawny, electrolytic, wife, cutover, dickson, clytemnestra, took, diocletian, lipton, inhuman, stalk, workplace, marginal.\n\nAlphabetical order:\n1. clytemnestra\n2. crag\n3. cutover\n4. dickson\n5. diocletian\n6. electrolytic\n7. inhuman\n8. lipton\n9. marginal\n10. stalk\n11. thereupon\n12. workplace\n13. wireman\n14. wife\n15. took\n16. scrawny\n\nAnswer: clytemnestra, crag, cutover, dickson, diocletian, electrolytic, inhuman, lipton, marginal, stalk, thereupon, workplace, wireman, wife, took, scrawny", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0031", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempted answer is correct. The sorting of the words is accurate alphabetically.", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.580", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:54:16.331117+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.4142, + "error": null, + "event": "model_call", + "event_seq": 581, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: thereupon wireman crag scrawny electrolytic wife cutover dickson clytemnestra took diocletian lipton inhuman stalk workplace marginal\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. clytemnestra\n2. crag\n3. cutover\n4. dickson\n5. diocletian\n6. electrolytic\n7. inhuman\n8. lipton\n9. marginal\n10. stalk\n11. thereupon\n12. workplace\n13. wireman\n14. wife\n15. took\n16. scrawny\n\nLet's re-sort them carefully:\n\n* clytemnestra\n* crag\n* cutover\n* dickson\n* diocletian\n* electrolytic\n* inhuman\n* lipton\n* marginal\n* stalk\n* thereupon\n* workplace\n* wireman\n* wife\n* took\n* scrawny\n\nComparing the initial list: thereupon, wireman, crag, scrawny, electrolytic, wife, cutover, dickson, clytemnestra, took, diocletian, lipton, inhuman, stalk, workplace, marginal.\n\nAlphabetical order:\n1. clytemnestra\n2. crag\n3. cutover\n4. dickson\n5. diocletian\n6. electrolytic\n7. inhuman\n8. lipton\n9. marginal\n10. stalk\n11. thereupon\n12. workplace\n13. wireman\n14. wife\n15. took\n16. scrawny\n\nAnswer: clytemnestra, crag, cutover, dickson, diocletian, electrolytic, inhuman, lipton, marginal, stalk, thereupon, workplace, wireman, wife, took, scrawny\n\nFeedback:\nThe attempted answer is correct. The sorting of the words is accurate alphabetically.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0031", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: clytemnestra, crag, cutover, dickson, diocletian, electrolytic, inhuman, lipton, marginal, stalk, thereupon, workplace, wireman, wife, took, scrawny", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.581", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:54:16.753829+00:00" + }, + { + "cycle": 0, + "duration_sec": 3.0775, + "error": null, + "event": "model_call", + "event_seq": 582, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: emission upon labour whim fairfax ride crepe prig accomplice az doff clatter circumcircle tea wheelbase pleura incantation choral viaduct lorry\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0055", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words sorted alphabetically:\n\n1. accomplice\n2. choral\n3. crepe\n4. circumcircle\n5. clatter\n6. doff\n7. emission\n8. fairfax\n9. incantation\n10. labour\n11. lorry\n12. pleura\n13. prig\n14. ride\n15. tea\n16. viaduct\n17. wheelbase\n\nLet's re-sort them carefully:\n\n* accomplice (A)\n* choral (C)\n* crepe (C)\n* circumcircle (C)\n* clatter (C)\n* doff (D)\n* emission (E)\n* fairfax (F)\n* incantation (I)\n* labour (L)\n* lorry (L)\n* pleura (P)\n* prig (P)\n* ride (R)\n* tea (T)\n* viaduct (V)\n* wheelbase (W)\n\n**Detailed Alphabetical Sorting:**\n\n1. accomplice\n2. choral\n3. crepe\n4. circumcircle\n5. clatter\n6. doff\n7. emission\n8. fairfax\n9. incantation\n10. labour\n11. lorry\n12. pleura\n13. prig\n14. ride\n15. tea\n16. viaduct\n17. wheelbase\n\n(Checking C words: choral, crepe, circumcircle, clatter. C-h comes before C-l, C-r, C-i. Wait, 'ch' comes before 'l', 'r', 'i'. Let's re-sort the C words: choral, clatter, crepe, circumcircle.)\n\n**Corrected Sorting:**\n\n1. accomplice (A)\n2. choral (C)\n3. clatter (C)\n4. crepe (C)\n5. circumcircle (C)\n6. doff (D)\n7. emission (E)\n8. fairfax (F)\n9. incantation (I)\n10. labour (L)\n11. lorry (L)\n12. pleura (P)\n13. prig (P)\n14. ride (R)\n15. tea (T)\n16. viaduct (V)\n17. wheelbase (W)\n\n(Checking C words again: Ch, Cl, Cr, Ci. Ch < Cl < Cr < Ci. So: choral, clatter, crepe, circumcircle.)\n\nFinal list order:\naccomplice, choral, clatter, crepe, circumcircle, doff, emission, fairfax, incantation, labour, lorry, pleura, prig, ride, tea, viaduct, wheelbase\n\nAnswer: accomplice, choral, clatter, crepe, circumcircle, doff, emission, fairfax, incantation, labour, lorry, pleura, prig, ride, tea, viaduct, wheelbase", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.582", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:54:19.841496+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.4478, + "error": null, + "event": "model_call", + "event_seq": 583, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: emission upon labour whim fairfax ride crepe prig accomplice az doff clatter circumcircle tea wheelbase pleura incantation choral viaduct lorry\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. accomplice\n2. choral\n3. crepe\n4. circumcircle\n5. clatter\n6. doff\n7. emission\n8. fairfax\n9. incantation\n10. labour\n11. lorry\n12. pleura\n13. prig\n14. ride\n15. tea\n16. viaduct\n17. wheelbase\n\nLet's re-sort them carefully:\n\n* accomplice (A)\n* choral (C)\n* crepe (C)\n* circumcircle (C)\n* clatter (C)\n* doff (D)\n* emission (E)\n* fairfax (F)\n* incantation (I)\n* labour (L)\n* lorry (L)\n* pleura (P)\n* prig (P)\n* ride (R)\n* tea (T)\n* viaduct (V)\n* wheelbase (W)\n\n**Detailed Alphabetical Sorting:**\n\n1. accomplice\n2. choral\n3. crepe\n4. circumcircle\n5. clatter\n6. doff\n7. emission\n8. fairfax\n9. incantation\n10. labour\n11. lorry\n12. pleura\n13. prig\n14. ride\n15. tea\n16. viaduct\n17. wheelbase\n\n(Checking C words: choral, crepe, circumcircle, clatter. C-h comes before C-l, C-r, C-i. Wait, 'ch' comes before 'l', 'r', 'i'. Let's re-sort the C words: choral, clatter, crepe, circumcircle.)\n\n**Corrected Sorting:**\n\n1. accomplice (A)\n2. choral (C)\n3. clatter (C)\n4. crepe (C)\n5. circumcircle (C)\n6. doff (D)\n7. emission (E)\n8. fairfax (F)\n9. incantation (I)\n10. labour (L)\n11. lorry (L)\n12. pleura (P)\n13. prig (P)\n14. ride (R)\n15. tea (T)\n16. viaduct (V)\n17. wheelbase (W)\n\n(Checking C words again: Ch, Cl, Cr, Ci. Ch < Cl < Cr < Ci. So: choral, clatter, crepe, circumcircle.)\n\nFinal list order:\naccomplice, choral, clatter, crepe, circumcircle, doff, emission, fairfax, incantation, labour, lorry, pleura, prig, ride, tea, viaduct, wheelbase\n\nAnswer: accomplice, choral, clatter, crepe, circumcircle, doff, emission, fairfax, incantation, labour, lorry, pleura, prig, ride, tea, viaduct, wheelbase", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0055", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The initial sorting attempt had an error in the relative order of the 'C' words. The detailed re-sorting correctly identifies the alphabetical order for the 'C' words.\n\nCorrection advice: The final list provided in the attempt is correct based on the detailed re-sorting.", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.583", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:54:20.298895+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.818, + "error": null, + "event": "model_call", + "event_seq": 584, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: emission upon labour whim fairfax ride crepe prig accomplice az doff clatter circumcircle tea wheelbase pleura incantation choral viaduct lorry\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words sorted alphabetically:\n\n1. accomplice\n2. choral\n3. crepe\n4. circumcircle\n5. clatter\n6. doff\n7. emission\n8. fairfax\n9. incantation\n10. labour\n11. lorry\n12. pleura\n13. prig\n14. ride\n15. tea\n16. viaduct\n17. wheelbase\n\nLet's re-sort them carefully:\n\n* accomplice (A)\n* choral (C)\n* crepe (C)\n* circumcircle (C)\n* clatter (C)\n* doff (D)\n* emission (E)\n* fairfax (F)\n* incantation (I)\n* labour (L)\n* lorry (L)\n* pleura (P)\n* prig (P)\n* ride (R)\n* tea (T)\n* viaduct (V)\n* wheelbase (W)\n\n**Detailed Alphabetical Sorting:**\n\n1. accomplice\n2. choral\n3. crepe\n4. circumcircle\n5. clatter\n6. doff\n7. emission\n8. fairfax\n9. incantation\n10. labour\n11. lorry\n12. pleura\n13. prig\n14. ride\n15. tea\n16. viaduct\n17. wheelbase\n\n(Checking C words: choral, crepe, circumcircle, clatter. C-h comes before C-l, C-r, C-i. Wait, 'ch' comes before 'l', 'r', 'i'. Let's re-sort the C words: choral, clatter, crepe, circumcircle.)\n\n**Corrected Sorting:**\n\n1. accomplice (A)\n2. choral (C)\n3. clatter (C)\n4. crepe (C)\n5. circumcircle (C)\n6. doff (D)\n7. emission (E)\n8. fairfax (F)\n9. incantation (I)\n10. labour (L)\n11. lorry (L)\n12. pleura (P)\n13. prig (P)\n14. ride (R)\n15. tea (T)\n16. viaduct (V)\n17. wheelbase (W)\n\n(Checking C words again: Ch, Cl, Cr, Ci. Ch < Cl < Cr < Ci. So: choral, clatter, crepe, circumcircle.)\n\nFinal list order:\naccomplice, choral, clatter, crepe, circumcircle, doff, emission, fairfax, incantation, labour, lorry, pleura, prig, ride, tea, viaduct, wheelbase\n\nAnswer: accomplice, choral, clatter, crepe, circumcircle, doff, emission, fairfax, incantation, labour, lorry, pleura, prig, ride, tea, viaduct, wheelbase\n\nFeedback:\nThe initial sorting attempt had an error in the relative order of the 'C' words. The detailed re-sorting correctly identifies the alphabetical order for the 'C' words.\n\nCorrection advice: The final list provided in the attempt is correct based on the detailed re-sorting.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0055", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: accomplice, choral, clatter, crepe, circumcircle, doff, emission, fairfax, incantation, labour, lorry, pleura, prig, ride, tea, viaduct, wheelbase", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.584", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:54:21.133312+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.5778, + "error": null, + "event": "model_call", + "event_seq": 585, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: sound consternate tariff blest proximity quizzes xerxes buxton\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0023", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The given list of words is: sound, consternate, tariff, blest, proximity, quizzes, xerxes, buxton.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **blest** (b)\n2. **buxton** (b) - 'l' comes before 'u'\n3. **consternate** (c)\n4. **proximity** (p)\n5. **quizzes** (q)\n6. **sound** (s)\n7. **tariff** (t)\n8. **xerxes** (x)\n\nLet's re-sort carefully:\n\n* **b**: blest, buxton (blest comes before buxton)\n* **c**: consternate\n* **p**: proximity\n* **q**: quizzes\n* **s**: sound\n* **t**: tariff\n* **x**: xerxes\n\nComparing the 'b' words:\n* blest\n* buxton\n'l' comes before 'u', so **blest** comes before **buxton**.\n\nThe sorted order is:\n1. blest\n2. buxton\n3. consternate\n4. proximity\n5. quizzes\n6. sound\n7. tariff\n8. xerxes\n\nAnswer: blest, buxton, consternate, proximity, quizzes, sound, tariff, xerxes\n\nAnswer: blest, buxton, consternate, proximity, quizzes, sound, tariff, xerxes", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.585", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:54:22.718422+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.6307, + "error": null, + "event": "model_call", + "event_seq": 586, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: sound consternate tariff blest proximity quizzes xerxes buxton\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: sound, consternate, tariff, blest, proximity, quizzes, xerxes, buxton.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **blest** (b)\n2. **buxton** (b) - 'l' comes before 'u'\n3. **consternate** (c)\n4. **proximity** (p)\n5. **quizzes** (q)\n6. **sound** (s)\n7. **tariff** (t)\n8. **xerxes** (x)\n\nLet's re-sort carefully:\n\n* **b**: blest, buxton (blest comes before buxton)\n* **c**: consternate\n* **p**: proximity\n* **q**: quizzes\n* **s**: sound\n* **t**: tariff\n* **x**: xerxes\n\nComparing the 'b' words:\n* blest\n* buxton\n'l' comes before 'u', so **blest** comes before **buxton**.\n\nThe sorted order is:\n1. blest\n2. buxton\n3. consternate\n4. proximity\n5. quizzes\n6. sound\n7. tariff\n8. xerxes\n\nAnswer: blest, buxton, consternate, proximity, quizzes, sound, tariff, xerxes\n\nAnswer: blest, buxton, consternate, proximity, quizzes, sound, tariff, xerxes", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0023", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempted answer is correct.\n\nAnswer: blest, buxton, consternate, proximity, quizzes, sound, tariff, xerxes", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.586", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:54:23.357253+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.6768, + "error": null, + "event": "model_call", + "event_seq": 587, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: sound consternate tariff blest proximity quizzes xerxes buxton\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nThe given list of words is: sound, consternate, tariff, blest, proximity, quizzes, xerxes, buxton.\n\nTo sort these words alphabetically, we compare them letter by letter:\n\n1. **blest** (b)\n2. **buxton** (b) - 'l' comes before 'u'\n3. **consternate** (c)\n4. **proximity** (p)\n5. **quizzes** (q)\n6. **sound** (s)\n7. **tariff** (t)\n8. **xerxes** (x)\n\nLet's re-sort carefully:\n\n* **b**: blest, buxton (blest comes before buxton)\n* **c**: consternate\n* **p**: proximity\n* **q**: quizzes\n* **s**: sound\n* **t**: tariff\n* **x**: xerxes\n\nComparing the 'b' words:\n* blest\n* buxton\n'l' comes before 'u', so **blest** comes before **buxton**.\n\nThe sorted order is:\n1. blest\n2. buxton\n3. consternate\n4. proximity\n5. quizzes\n6. sound\n7. tariff\n8. xerxes\n\nAnswer: blest, buxton, consternate, proximity, quizzes, sound, tariff, xerxes\n\nAnswer: blest, buxton, consternate, proximity, quizzes, sound, tariff, xerxes\n\nFeedback:\nThe attempted answer is correct.\n\nAnswer: blest, buxton, consternate, proximity, quizzes, sound, tariff, xerxes", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0023", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: blest, buxton, consternate, proximity, quizzes, sound, tariff, xerxes", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.587", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:54:24.043237+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.3203, + "error": null, + "event": "model_call", + "event_seq": 588, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: deadwood macedonia foxtail block hexadecimal guaranty rubaiyat victoria custodian\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0056", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words to sort: deadwood, macedonia, foxtail, block, hexadecimal, guaranty, rubaiyat, victoria, custodian.\n\nWe will sort them alphabetically:\n\n1. **block**\n2. **custodian**\n3. **deadwood**\n4. **foxtail**\n5. **guaranty**\n6. **hexadecimal**\n7. **macedonia**\n8. **rubaiyat**\n9. **victoria**\n\nLet's re-check the sorting carefully:\n\n* **b**lock\n* **c**ustodian\n* **d**eadwood\n* **f**oxtail\n* **g**uaranty\n* **h**exadecimal\n* **m**acedonia\n* **r**ubaiyat\n* **v**ictoria\n\nThe order seems correct based on the first letter.\n\nAnswer: block, custodian, deadwood, foxtail, guaranty, hexadecimal, macedonia, rubaiyat, victoria\n\nAnswer: block, custodian, deadwood, foxtail, guaranty, hexadecimal, macedonia, rubaiyat, victoria", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.588", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:54:25.372872+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.3273, + "error": null, + "event": "model_call", + "event_seq": 589, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: deadwood macedonia foxtail block hexadecimal guaranty rubaiyat victoria custodian\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words to sort: deadwood, macedonia, foxtail, block, hexadecimal, guaranty, rubaiyat, victoria, custodian.\n\nWe will sort them alphabetically:\n\n1. **block**\n2. **custodian**\n3. **deadwood**\n4. **foxtail**\n5. **guaranty**\n6. **hexadecimal**\n7. **macedonia**\n8. **rubaiyat**\n9. **victoria**\n\nLet's re-check the sorting carefully:\n\n* **b**lock\n* **c**ustodian\n* **d**eadwood\n* **f**oxtail\n* **g**uaranty\n* **h**exadecimal\n* **m**acedonia\n* **r**ubaiyat\n* **v**ictoria\n\nThe order seems correct based on the first letter.\n\nAnswer: block, custodian, deadwood, foxtail, guaranty, hexadecimal, macedonia, rubaiyat, victoria\n\nAnswer: block, custodian, deadwood, foxtail, guaranty, hexadecimal, macedonia, rubaiyat, victoria", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0056", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The sorting is correct.\n\nAnswer: block, custodian, deadwood, foxtail, guaranty, hexadecimal, macedonia, rubaiyat, victoria", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.589", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:54:25.707954+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.7113, + "error": null, + "event": "model_call", + "event_seq": 590, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: deadwood macedonia foxtail block hexadecimal guaranty rubaiyat victoria custodian\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words to sort: deadwood, macedonia, foxtail, block, hexadecimal, guaranty, rubaiyat, victoria, custodian.\n\nWe will sort them alphabetically:\n\n1. **block**\n2. **custodian**\n3. **deadwood**\n4. **foxtail**\n5. **guaranty**\n6. **hexadecimal**\n7. **macedonia**\n8. **rubaiyat**\n9. **victoria**\n\nLet's re-check the sorting carefully:\n\n* **b**lock\n* **c**ustodian\n* **d**eadwood\n* **f**oxtail\n* **g**uaranty\n* **h**exadecimal\n* **m**acedonia\n* **r**ubaiyat\n* **v**ictoria\n\nThe order seems correct based on the first letter.\n\nAnswer: block, custodian, deadwood, foxtail, guaranty, hexadecimal, macedonia, rubaiyat, victoria\n\nAnswer: block, custodian, deadwood, foxtail, guaranty, hexadecimal, macedonia, rubaiyat, victoria\n\nFeedback:\nThe sorting is correct.\n\nAnswer: block, custodian, deadwood, foxtail, guaranty, hexadecimal, macedonia, rubaiyat, victoria", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0056", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: block, custodian, deadwood, foxtail, guaranty, hexadecimal, macedonia, rubaiyat, victoria", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.590", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:54:26.426941+00:00" + }, + { + "cycle": 0, + "duration_sec": 7.1142, + "error": null, + "event": "model_call", + "event_seq": 591, + "inputs": { + "audio": null, + "image": null, + "prompt": "Sort the following words alphabetically: List: berniece bremsstrahlung hoe avalanche showroom goff dactylic befriend hurry housekeep lanka gilbertson posterior flick bong hereafter metazoan\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0104", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Here are the words to sort alphabetically:\nberniece, bremsstrahlung, hoe, avalanche, showroom, goff, dactylic, befriend, hurry, housekeep, lanka, gilbertson, posterior, flick, bong, hereafter, metazoan\n\nLet's sort them:\n\n1. **ache** (Wait, \"ache\" is not in the list. Let's re-examine the list.)\n\nWords: berniece, bremsstrahlung, hoe, avalanche, showroom, goff, dactylic, befriend, hurry, housekeep, lanka, gilbertson, posterior, flick, bong, hereafter, metazoan\n\nSorting by the first letter:\nA: avalanche\nB: berniece, bremsstrahlung, befriend\nD: dactylic\nF: flick\nG: goff, gilbertson\nH: hoe, hurry, housekeep\nL: lanka\nM: metazoan\nP: posterior\nS: showroom\nH: hereafter (already listed under H, but we need to check order)\n\nLet's sort systematically:\n\n1. **ache** (None)\n2. **avalanche**\n3. **befriend**\n4. **berniece**\n5. **bremsstrahlung**\n6. **bong**\n7. **dactylic**\n8. **flick**\n9. **goff**\n10. **gilbertson**\n11. **hereafter**\n12. **hoe**\n13. **hurry**\n14. **housekeep**\n15. **lanka**\n16. **metazoan**\n17. **posterior**\n18. **showroom**\n\nLet's refine the sorting, especially for words starting with the same letter:\n\n**B words:** befriend, berniece, bremsstrahlung (befriend, berniece, bremsstrahlung)\n**H words:** hereafter, hoe, hurry, housekeep (hereafter, hoe, hurry, housekeep)\n**G words:** goff, gilbertson (goff, gilbertson)\n\n**Alphabetical Order:**\n\n1. avalanche (A)\n2. befriend (B)\n3. berniece (B)\n4. bremsstrahlung (B)\n5. bong (B - Wait, 'bong' starts with B, it should come before 'berniece' if we sort strictly by the second letter, but alphabetically, B-o-n-g comes before B-e-r-n-i-e-c-e. Let's re-sort the B words: befriend, berniece, bremsstrahlung, bong)\n * befriend\n * berniece\n * bremsstrahlung\n * bong (B-o-n-g) -> This is incorrect. B-e-f... comes before B-o-n...\n\nCorrect B order: befriend, berniece, bremsstrahlung, bong (Wait, B-o-n-g vs B-e-f-r-i-e-n-d. 'befriend' comes first.)\nB words: befriend, berniece, bremsstrahlung, bong.\nb**e**friend\nb**e**rniece\nb**r**emsstrahlung\nb**o**ng\nOrder: befriend, berniece, bremsstrahlung, bong (No, 'bremsstrahlung' starts with 'br', 'bong' starts with 'bo'. 'bremsstrahlung' comes before 'bong'.)\nCorrect B order: befriend, berniece, bremsstrahlung, bong. (Let's check: befriend, berniece, bremsstrahlung, bong. B-e, B-e, B-r, B-o. 'bremsstrahlung' (br) comes before 'bong' (bo) if we compare the third letter. Wait, 'bo' comes before 'br'. So bong comes before bremsstrahlung.)\nB words sorted: befriend, berniece, bong, bremsstrahlung.\n\nLet's use a standard alphabetical sort:\n\n1. avalanche\n2. befriend\n3. berniece\n4. bong\n5. bremsstrahlung\n6. dactylic\n7. flick\n8. gilbertson\n9. hereafter\n10. hoe\n11. hurry\n12. housekeep\n13. lanka\n14. metazoan\n15. posterior\n16. showroom\n17. goff (G)\n\nRe-sorting G words: goff, gilbertson. (g-o vs g-i. 'gilbertson' comes before 'goff'.)\nCorrect G order: gilbertson, goff.\n\nFinal check of the full list:\navalanche\nbefriend\nberniece\nbong\nbremsstrahlung\ndactylic\nflick\ngilbertson\nhereafter\nhoe\nhurry\nhousekeep\nlanka\nmetazoan\nposterior\nshowroom\ngoff\n\nLet's re-sort the B words one last time:\nbefriend (bef)\nberniece (ber)\nbremsstrahlung (bre)\nbong (bon)\nOrder: befriend, berniece, bremsstrahlung, bong. (b-e, b-e, b-r, b-o. 'bremsstrahlung' (br) comes before 'bong' (bo) if we compare the third letter. Wait, 'bo' comes before 'br'. So bong comes before bremsstrahlung.)\nB words: befriend, berniece, bong, bremsstrahlung.\n\nFinal Sorted List:\n1. avalanche\n2. befriend\n3. berniece\n4. bong\n5. bremsstrahlung\n6. dactylic\n7. flick\n8. gilbertson\n9. hereafter\n10. hoe\n11. hurry\n12. housekeep\n13. lanka\n14. metazoan\n15. posterior\n16. showroom\n17. goff\n\nWait, I missed 'goff' and 'gilbertson' placement.\nG words: gilbertson, goff. (g-i vs g-o. 'gilbertson' comes first.)\n\nLet's re-insert G words:\n... flick, gilbertson, goff, hereafter ...\n\nFinal attempt at full sort:\n1. avalanche\n2. befriend\n3. berniece\n4. bong\n5. bremsstrahlung\n6. dactylic\n7. flick\n8. gilbertson\n9. goff\n10. hereafter\n11. hoe\n12. hurry\n13. housekeep\n14. lanka\n15. metazoan\n16. posterior\n17. showroom\n\n(Checking G words again: gilbertson, goff. g-i vs g-o. 'gilbertson' comes before 'goff'.)\n\nAnswer: avalanche, befriend, berniece, bong, bremsstrahlung, dactylic, flick, gilbertson, goff, hereafter, hoe, hurry, housekeep, lanka, metazoan, posterior, showroom\n\nAnswer: avalanche, befriend, berniece, bong, bremsstrahlung, dactylic, flick, gilbertson, goff, hereafter, hoe, hurry, housekeep, lanka, metazoan,", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.591", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:54:33.554323+00:00" + }, + { + "cycle": 0, + "duration_sec": 1.7425, + "error": null, + "event": "model_call", + "event_seq": 592, + "inputs": { + "audio": null, + "image": null, + "prompt": "Check the attempted answer for factual, reasoning, arithmetic, and instruction-following errors. Give concise correction advice only.\n\nOriginal task:\nSort the following words alphabetically: List: berniece bremsstrahlung hoe avalanche showroom goff dactylic befriend hurry housekeep lanka gilbertson posterior flick bong hereafter metazoan\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words to sort alphabetically:\nberniece, bremsstrahlung, hoe, avalanche, showroom, goff, dactylic, befriend, hurry, housekeep, lanka, gilbertson, posterior, flick, bong, hereafter, metazoan\n\nLet's sort them:\n\n1. **ache** (Wait, \"ache\" is not in the list. Let's re-examine the list.)\n\nWords: berniece, bremsstrahlung, hoe, avalanche, showroom, goff, dactylic, befriend, hurry, housekeep, lanka, gilbertson, posterior, flick, bong, hereafter, metazoan\n\nSorting by the first letter:\nA: avalanche\nB: berniece, bremsstrahlung, befriend\nD: dactylic\nF: flick\nG: goff, gilbertson\nH: hoe, hurry, housekeep\nL: lanka\nM: metazoan\nP: posterior\nS: showroom\nH: hereafter (already listed under H, but we need to check order)\n\nLet's sort systematically:\n\n1. **ache** (None)\n2. **avalanche**\n3. **befriend**\n4. **berniece**\n5. **bremsstrahlung**\n6. **bong**\n7. **dactylic**\n8. **flick**\n9. **goff**\n10. **gilbertson**\n11. **hereafter**\n12. **hoe**\n13. **hurry**\n14. **housekeep**\n15. **lanka**\n16. **metazoan**\n17. **posterior**\n18. **showroom**\n\nLet's refine the sorting, especially for words starting with the same letter:\n\n**B words:** befriend, berniece, bremsstrahlung (befriend, berniece, bremsstrahlung)\n**H words:** hereafter, hoe, hurry, housekeep (hereafter, hoe, hurry, housekeep)\n**G words:** goff, gilbertson (goff, gilbertson)\n\n**Alphabetical Order:**\n\n1. avalanche (A)\n2. befriend (B)\n3. berniece (B)\n4. bremsstrahlung (B)\n5. bong (B - Wait, 'bong' starts with B, it should come before 'berniece' if we sort strictly by the second letter, but alphabetically, B-o-n-g comes before B-e-r-n-i-e-c-e. Let's re-sort the B words: befriend, berniece, bremsstrahlung, bong)\n * befriend\n * berniece\n * bremsstrahlung\n * bong (B-o-n-g) -> This is incorrect. B-e-f... comes before B-o-n...\n\nCorrect B order: befriend, berniece, bremsstrahlung, bong (Wait, B-o-n-g vs B-e-f-r-i-e-n-d. 'befriend' comes first.)\nB words: befriend, berniece, bremsstrahlung, bong.\nb**e**friend\nb**e**rniece\nb**r**emsstrahlung\nb**o**ng\nOrder: befriend, berniece, bremsstrahlung, bong (No, 'bremsstrahlung' starts with 'br', 'bong' starts with 'bo'. 'bremsstrahlung' comes before 'bong'.)\nCorrect B order: befriend, berniece, bremsstrahlung, bong. (Let's check: befriend, berniece, bremsstrahlung, bong. B-e, B-e, B-r, B-o. 'bremsstrahlung' (br) comes before 'bong' (bo) if we compare the third letter. Wait, 'bo' comes before 'br'. So bong comes before bremsstrahlung.)\nB words sorted: befriend, berniece, bong, bremsstrahlung.\n\nLet's use a standard alphabetical sort:\n\n1. avalanche\n2. befriend\n3. berniece\n4. bong\n5. bremsstrahlung\n6. dactylic\n7. flick\n8. gilbertson\n9. hereafter\n10. hoe\n11. hurry\n12. housekeep\n13. lanka\n14. metazoan\n15. posterior\n16. showroom\n17. goff (G)\n\nRe-sorting G words: goff, gilbertson. (g-o vs g-i. 'gilbertson' comes before 'goff'.)\nCorrect G order: gilbertson, goff.\n\nFinal check of the full list:\navalanche\nbefriend\nberniece\nbong\nbremsstrahlung\ndactylic\nflick\ngilbertson\nhereafter\nhoe\nhurry\nhousekeep\nlanka\nmetazoan\nposterior\nshowroom\ngoff\n\nLet's re-sort the B words one last time:\nbefriend (bef)\nberniece (ber)\nbremsstrahlung (bre)\nbong (bon)\nOrder: befriend, berniece, bremsstrahlung, bong. (b-e, b-e, b-r, b-o. 'bremsstrahlung' (br) comes before 'bong' (bo) if we compare the third letter. Wait, 'bo' comes before 'br'. So bong comes before bremsstrahlung.)\nB words: befriend, berniece, bong, bremsstrahlung.\n\nFinal Sorted List:\n1. avalanche\n2. befriend\n3. berniece\n4. bong\n5. bremsstrahlung\n6. dactylic\n7. flick\n8. gilbertson\n9. hereafter\n10. hoe\n11. hurry\n12. housekeep\n13. lanka\n14. metazoan\n15. posterior\n16. showroom\n17. goff\n\nWait, I missed 'goff' and 'gilbertson' placement.\nG words: gilbertson, goff. (g-i vs g-o. 'gilbertson' comes first.)\n\nLet's re-insert G words:\n... flick, gilbertson, goff, hereafter ...\n\nFinal attempt at full sort:\n1. avalanche\n2. befriend\n3. berniece\n4. bong\n5. bremsstrahlung\n6. dactylic\n7. flick\n8. gilbertson\n9. goff\n10. hereafter\n11. hoe\n12. hurry\n13. housekeep\n14. lanka\n15. metazoan\n16. posterior\n17. showroom\n\n(Checking G words again: gilbertson, goff. g-i vs g-o. 'gilbertson' comes before 'goff'.)\n\nAnswer: avalanche, befriend, berniece, bong, bremsstrahlung, dactylic, flick, gilbertson, goff, hereafter, hoe, hurry, housekeep, lanka, metazoan, posterior, showroom\n\nAnswer: avalanche, befriend, berniece, bong, bremsstrahlung, dactylic, flick, gilbertson, goff, hereafter, hoe, hurry, housekeep, lanka, metazoan,", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0104", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "The attempt contains several errors in the sorting logic, particularly in handling words starting with the same letter and in the overall sequence.\n\n1. **Factual/Instruction Following Error:** The attempt incorrectly identifies and lists \"ache\" as a word not in the list, which is irrelevant to the task.\n2. **Reasoning/Arithmetic Error (Sorting Logic):** The systematic sorting steps are flawed, especially in determining the relative order of words starting with 'B' (e.g., `bong` vs. `bremsstrahlung`) and 'G' (e.g., `goff` vs. `gilbertson`). The final derived list is not strictly alphabetical.\n\n**Correction Advice:** Re-sort the entire list strictly alphabetically, paying close attention to the order of words starting with the same letter (e.g., comparing the second letter for words starting with 'B', or the second letter for words starting with 'G').\n\nAnswer: avalanche, befriend, berniece, bong, bremsstrahlung, dactylic, flick, gilbertson, goff, hereafter, hoe, hurry, housekeep, lanka, metazoan, posterior, showroom", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.592", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:54:35.305519+00:00" + }, + { + "cycle": 0, + "duration_sec": 0.6401, + "error": null, + "event": "model_call", + "event_seq": 593, + "inputs": { + "audio": null, + "image": null, + "prompt": "Produce a corrected final answer to the original task using the feedback. Do not discuss the revision process.\n\nOriginal task:\nSort the following words alphabetically: List: berniece bremsstrahlung hoe avalanche showroom goff dactylic befriend hurry housekeep lanka gilbertson posterior flick bong hereafter metazoan\n\nSolve the problem. Put the final answer on its own last line as 'Answer: '.\n\nAttempt:\nHere are the words to sort alphabetically:\nberniece, bremsstrahlung, hoe, avalanche, showroom, goff, dactylic, befriend, hurry, housekeep, lanka, gilbertson, posterior, flick, bong, hereafter, metazoan\n\nLet's sort them:\n\n1. **ache** (Wait, \"ache\" is not in the list. Let's re-examine the list.)\n\nWords: berniece, bremsstrahlung, hoe, avalanche, showroom, goff, dactylic, befriend, hurry, housekeep, lanka, gilbertson, posterior, flick, bong, hereafter, metazoan\n\nSorting by the first letter:\nA: avalanche\nB: berniece, bremsstrahlung, befriend\nD: dactylic\nF: flick\nG: goff, gilbertson\nH: hoe, hurry, housekeep\nL: lanka\nM: metazoan\nP: posterior\nS: showroom\nH: hereafter (already listed under H, but we need to check order)\n\nLet's sort systematically:\n\n1. **ache** (None)\n2. **avalanche**\n3. **befriend**\n4. **berniece**\n5. **bremsstrahlung**\n6. **bong**\n7. **dactylic**\n8. **flick**\n9. **goff**\n10. **gilbertson**\n11. **hereafter**\n12. **hoe**\n13. **hurry**\n14. **housekeep**\n15. **lanka**\n16. **metazoan**\n17. **posterior**\n18. **showroom**\n\nLet's refine the sorting, especially for words starting with the same letter:\n\n**B words:** befriend, berniece, bremsstrahlung (befriend, berniece, bremsstrahlung)\n**H words:** hereafter, hoe, hurry, housekeep (hereafter, hoe, hurry, housekeep)\n**G words:** goff, gilbertson (goff, gilbertson)\n\n**Alphabetical Order:**\n\n1. avalanche (A)\n2. befriend (B)\n3. berniece (B)\n4. bremsstrahlung (B)\n5. bong (B - Wait, 'bong' starts with B, it should come before 'berniece' if we sort strictly by the second letter, but alphabetically, B-o-n-g comes before B-e-r-n-i-e-c-e. Let's re-sort the B words: befriend, berniece, bremsstrahlung, bong)\n * befriend\n * berniece\n * bremsstrahlung\n * bong (B-o-n-g) -> This is incorrect. B-e-f... comes before B-o-n...\n\nCorrect B order: befriend, berniece, bremsstrahlung, bong (Wait, B-o-n-g vs B-e-f-r-i-e-n-d. 'befriend' comes first.)\nB words: befriend, berniece, bremsstrahlung, bong.\nb**e**friend\nb**e**rniece\nb**r**emsstrahlung\nb**o**ng\nOrder: befriend, berniece, bremsstrahlung, bong (No, 'bremsstrahlung' starts with 'br', 'bong' starts with 'bo'. 'bremsstrahlung' comes before 'bong'.)\nCorrect B order: befriend, berniece, bremsstrahlung, bong. (Let's check: befriend, berniece, bremsstrahlung, bong. B-e, B-e, B-r, B-o. 'bremsstrahlung' (br) comes before 'bong' (bo) if we compare the third letter. Wait, 'bo' comes before 'br'. So bong comes before bremsstrahlung.)\nB words sorted: befriend, berniece, bong, bremsstrahlung.\n\nLet's use a standard alphabetical sort:\n\n1. avalanche\n2. befriend\n3. berniece\n4. bong\n5. bremsstrahlung\n6. dactylic\n7. flick\n8. gilbertson\n9. hereafter\n10. hoe\n11. hurry\n12. housekeep\n13. lanka\n14. metazoan\n15. posterior\n16. showroom\n17. goff (G)\n\nRe-sorting G words: goff, gilbertson. (g-o vs g-i. 'gilbertson' comes before 'goff'.)\nCorrect G order: gilbertson, goff.\n\nFinal check of the full list:\navalanche\nbefriend\nberniece\nbong\nbremsstrahlung\ndactylic\nflick\ngilbertson\nhereafter\nhoe\nhurry\nhousekeep\nlanka\nmetazoan\nposterior\nshowroom\ngoff\n\nLet's re-sort the B words one last time:\nbefriend (bef)\nberniece (ber)\nbremsstrahlung (bre)\nbong (bon)\nOrder: befriend, berniece, bremsstrahlung, bong. (b-e, b-e, b-r, b-o. 'bremsstrahlung' (br) comes before 'bong' (bo) if we compare the third letter. Wait, 'bo' comes before 'br'. So bong comes before bremsstrahlung.)\nB words: befriend, berniece, bong, bremsstrahlung.\n\nFinal Sorted List:\n1. avalanche\n2. befriend\n3. berniece\n4. bong\n5. bremsstrahlung\n6. dactylic\n7. flick\n8. gilbertson\n9. hereafter\n10. hoe\n11. hurry\n12. housekeep\n13. lanka\n14. metazoan\n15. posterior\n16. showroom\n17. goff\n\nWait, I missed 'goff' and 'gilbertson' placement.\nG words: gilbertson, goff. (g-i vs g-o. 'gilbertson' comes first.)\n\nLet's re-insert G words:\n... flick, gilbertson, goff, hereafter ...\n\nFinal attempt at full sort:\n1. avalanche\n2. befriend\n3. berniece\n4. bong\n5. bremsstrahlung\n6. dactylic\n7. flick\n8. gilbertson\n9. goff\n10. hereafter\n11. hoe\n12. hurry\n13. housekeep\n14. lanka\n15. metazoan\n16. posterior\n17. showroom\n\n(Checking G words again: gilbertson, goff. g-i vs g-o. 'gilbertson' comes before 'goff'.)\n\nAnswer: avalanche, befriend, berniece, bong, bremsstrahlung, dactylic, flick, gilbertson, goff, hereafter, hoe, hurry, housekeep, lanka, metazoan, posterior, showroom\n\nAnswer: avalanche, befriend, berniece, bong, bremsstrahlung, dactylic, flick, gilbertson, goff, hereafter, hoe, hurry, housekeep, lanka, metazoan,\n\nFeedback:\nThe attempt contains several errors in the sorting logic, particularly in handling words starting with the same letter and in the overall sequence.\n\n1. **Factual/Instruction Following Error:** The attempt incorrectly identifies and lists \"ache\" as a word not in the list, which is irrelevant to the task.\n2. **Reasoning/Arithmetic Error (Sorting Logic):** The systematic sorting steps are flawed, especially in determining the relative order of words starting with 'B' (e.g., `bong` vs. `bremsstrahlung`) and 'G' (e.g., `goff` vs. `gilbertson`). The final derived list is not strictly alphabetical.\n\n**Correction Advice:** Re-sort the entire list strictly alphabetically, paying close attention to the order of words starting with the same letter (e.g., comparing the second letter for words starting with 'B', or the second letter for words starting with 'G').\n\nAnswer: avalanche, befriend, berniece, bong, bremsstrahlung, dactylic, flick, gilbertson, goff, hereafter, hoe, hurry, housekeep, lanka, metazoan, posterior, showroom", + "video": null + }, + "metadata": { + "candidate": "expanded_budget_self_refine", + "case_id": "bbh_word_sorting-0104", + "generation_kwargs": { + "max_tokens": 1536 + } + }, + "operation": "declarative_pipeline", + "output": "Answer: avalanche, befriend, berniece, bong, bremsstrahlung, dactylic, flick, gilbertson, goff, hereafter, hoe, hurry, housekeep, lanka, metazoan, posterior, showroom", + "role": "target_model", + "schema_version": 6, + "span_id": "m5.model_call.593", + "stage": "M5", + "trace_id": "ca3acc21-281d-483a-90b0-36a73a258cc1", + "ts": "2026-10-11T01:54:35.951820+00:00" + } + ] + } + }, + "summary": { + "backend": "jax_local", + "baseline_accuracy": 0.4062, + "cycles": 1, + "dataset": "bbh_word_sorting", + "fix": { + "attempted": [ + { + "breaks": 2, + "effect": 0.046875, + "fixed": false, + "name": "expanded_budget_self_refine", + "repairs": 5, + "tier": "L2" + } + ], + "recommendation": "{'recommend_tier': 'L3a', 'reason': \"the candidate selected on EXPLORE did not validate on untouched CONFIRM; no candidate within L2 validated; the next intervention space is L3a (internals, read-only: attention/logits guide the scaffold) (note: 'expanded_budget_self_refine' is INCONCLUSIVE, not refuted — 5 fixed / 2 broken, effect +0.047, e=0.8 < 20; re-validating it on more or cleaner pairs (baseline_repeats>1 to drop sampling-unstable cases, or a larger confirm split) is a cheaper next step than escalating the tier)\", 'promising': {'candidate': 'expanded_budget_self_refine', 'n_fixed': 5, 'n_broken': 2, 'effect': 0.046875, 'e_value': 0.7619047619047632}}" + }, + "fix_tier": "L2", + "modality": "llm", + "model": "gemma-4-e2b", + "n_cases": 128, + "n_verified": 0, + "spec": "gemma-4-e2b-it", + "stopped_by": "max_cycles" + } + }, + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(JSON({'baseline': baseline, 'summary': summary, 'stages': stages}))\n", + "for source in sorted(run_dir.rglob('*.py')):\n", + " print('\\n---', source.relative_to(run_dir), '---')\n", + " print(source.read_text())" + ] + } + ], + "metadata": { + "accelerator": "TPU", + "colab": { + "gpuType": "V6E1", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Settings\n", - "\n", - "Usually nothing to change." - ] - }, - { - "cell_type": "code", - "metadata": {}, - "execution_count": null, - "outputs": [], - "source": [ - "import os\n", - "MODEL = os.environ.get('EVALRX_MODEL', 'gemma-4-e2b') # a model with a JAX spec in evalrx/specs.py, e.g. 'gemma-4-e4b'\n", - "DATASET = os.environ.get('EVALRX_DATASET', 'bbh_word_sorting') # or 'cruxeval_output'\n", - "RUN_TAG = 'launch'\n", - "\n", - "# Where the prebuilt image comes from. '' = the public copy on Google Drive.\n", - "# Or a folder of your own: Drive (/content/drive/MyDrive/...), gs://bucket/prefix, or a local path.\n", - "# Its newest image is used; if it has none, one is built and saved there (needs GitHub and PyPI).\n", - "EVALRX_STORE = os.environ.get('EVALRX_STORE', '')\n", - "\n", - "# Optional: a specific image tar (a path, a Drive file link, gs:// or https:// URL).\n", - "EVALRX_IMAGE = os.environ.get('EVALRX_IMAGE', '')\n", - "# Optional: a weights tar (tools/colab/build_weights.sh), for runtimes that cannot read the model's\n", - "# registered source. '' = use one from EVALRX_STORE if it has one, else the registered source.\n", - "EVALRX_WEIGHTS = os.environ.get('EVALRX_WEIGHTS', '')\n", - "EVALRX_IMAGE_SHA256 = EVALRX_WEIGHTS_SHA256 = '' # '' = read .sha256 next to the tar\n", - "\n", - "# Building an image only: where the EvalRX source comes from.\n", - "EVALRX_REF = os.environ.get('EVALRX_REF', 'ruinan') # branch, tag or commit\n", - "EVALRX_GIT_URL = os.environ.get('EVALRX_GIT_URL', 'https://github.com/evalvitals/evalrx.git')\n", - "EVALRX_BUNDLE = os.environ.get('EVALRX_BUNDLE', '') # or an offline bundle (tools/colab/build_bundle.sh)\n", - "EVALRX_REBUILD = os.environ.get('EVALRX_REBUILD', '0') == '1' # build a new image even if the store has one\n", - "EVALRX_BUILD_WEIGHTS = os.environ.get('EVALRX_BUILD_WEIGHTS', '0') == '1' # also pack MODEL's weights into the store" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Find the image\n", - "\n", - "The public image, or the newest one in `EVALRX_STORE`. If `EVALRX_STORE` is empty, the notebook builds an image there." - ] - }, - { - "cell_type": "code", - "metadata": {}, - "execution_count": null, - "outputs": [], - "source": [ - "import hashlib, html, http.client, json, re, shutil, subprocess, sys, tarfile, tempfile, time, urllib.parse, urllib.request\n", - "from pathlib import Path\n", - "\n", - "CONTENT = Path('/content')\n", - "ENV, REPO = CONTENT / 'evalrx-env', CONTENT / 'evalrx'\n", - "DEMO_ROOT = CONTENT / 'evalrx_examples'\n", - "WEIGHTS_NAME = f'evalrx-colab-weights-tpu-{MODEL}.tar'\n", - "# The public copy of the image (Google Drive, shared with anyone who has the link) and its SHA-256.\n", - "PUBLIC_IMAGE = ('https://drive.google.com/file/d/1dkVwb02WnR15NBfDy20m5lOeT_oENMdV', # commit e6e8ec4\n", - " '1a2048c8fc8a6e7effe659ffda4c86a065439d557ada54fc79bb918ddeb36f21')\n", - "# The Colab kernel exports settings for its own Python (PYTHONPATH, UV_*, PIP_*);\n", - "# they must not reach the image's Python, which every later cell runs.\n", - "for key in [k for k in os.environ if k.startswith(('UV_', 'PIP_')) or k in ('PYTHONPATH', 'PYTHONHOME')]:\n", - " del os.environ[key]\n", - "if any(p.startswith('/content/drive/') for p in (EVALRX_STORE, EVALRX_IMAGE, EVALRX_WEIGHTS)) \\\n", - " and not Path('/content/drive/MyDrive').is_dir():\n", - " from google.colab import drive\n", - " drive.mount('/content/drive')\n", - "\n", - "\n", - "def run_visible(command):\n", - " with subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True) as process:\n", - " for line in process.stdout:\n", - " print(line, end='', flush=True)\n", - " if process.wait():\n", - " raise subprocess.CalledProcessError(process.returncode, command)\n", - "\n", - "class Download:\n", - " \"\"\"An HTTP(S) download as a stream. A connection that stalls for a minute or drops early is\n", - " reopened with a Range request at the current byte, so a long download survives both.\"\"\"\n", - "\n", - " def __init__(self, open_at):\n", - " self.open_at, self.pos, self.retries = open_at, 0, 0\n", - " self.response = open_at(0)\n", - " self.size = int(self.response.headers.get('Content-Length') or 0)\n", - "\n", - " def read(self, n=-1):\n", - " parts, want = [], n if n >= 0 else 1 << 62\n", - " while want > 0:\n", - " try:\n", - " chunk = self.response.read1(min(want, 1 << 20))\n", - " except (OSError, http.client.HTTPException) as exc: # socket.timeout is an OSError\n", - " self.reopen(exc)\n", - " continue\n", - " if not chunk:\n", - " if self.size and self.pos < self.size:\n", - " self.reopen('connection closed early')\n", - " continue\n", - " break\n", - " parts.append(chunk)\n", - " self.pos += len(chunk)\n", - " want -= len(chunk)\n", - " return b''.join(parts)\n", - "\n", - " def reopen(self, reason):\n", - " self.retries += 1\n", - " if self.retries > 20:\n", - " raise RuntimeError(f'download failed at byte {self.pos}: {reason}')\n", - " print(f' connection lost at {self.pos / 1e9:.2f} GB ({reason}); resuming', flush=True)\n", - " self.response.close()\n", - " time.sleep(min(60, 2 ** self.retries))\n", - " self.response = self.open_at(self.pos)\n", - " if self.pos and self.response.status != 206:\n", - " raise RuntimeError(f'the server cannot resume a download (HTTP {self.response.status})')\n", - "\n", - " def close(self):\n", - " self.response.close()\n", - "\n", - " def __enter__(self):\n", - " return self\n", - "\n", - " def __exit__(self, *exc):\n", - " self.close()\n", - "\n", - "\n", - "def http_open(url, offset=0):\n", - " headers = {'Range': f'bytes={offset}-'} if offset else {}\n", - " return urllib.request.urlopen(urllib.request.Request(url, headers=headers), timeout=60)\n", - "\n", - "\n", - "def open_drive_file(file_id, offset=0):\n", - " \"\"\"A Google Drive file shared with anyone who has the link. For a large file Drive first answers\n", - " with a virus-scan notice; the notice's form carries the parameters of the actual download.\"\"\"\n", - " response = http_open(f'https://drive.usercontent.google.com/download?id={file_id}&export=download&confirm=t', offset)\n", - " if response.headers.get_content_type() != 'text/html':\n", - " return response\n", - " page = response.read().decode('utf-8', 'replace')\n", - " action = re.search(r']*action=\"([^\"]+)\"', page)\n", - " fields = dict(re.findall(r']*name=\"([^\"]+)\"[^>]*value=\"([^\"]*)\"', page))\n", - " if not action or 'id' not in fields:\n", - " raise RuntimeError(f'Google Drive did not serve file {file_id}: it must be shared with \"Anyone with the link\", '\n", - " 'and Drive refuses a file for a while after too many downloads in a day (\"quota exceeded\")')\n", - " query = urllib.parse.urlencode({k: html.unescape(v) for k, v in fields.items()})\n", - " return http_open(html.unescape(action.group(1)) + '?' + query, offset)\n", - "\n", - "\n", - "def open_source(src):\n", - " \"\"\"A binary stream of a local or Drive path, a Google Drive file link, an https:// URL, or a gs:// object.\"\"\"\n", - " drive_file = re.search(r'drive\\.google\\.com/(?:file/d/|open\\?id=|uc\\?(?:.*&)?id=)([\\w-]+)', src)\n", - " if drive_file:\n", - " return Download(lambda offset: open_drive_file(drive_file.group(1), offset))\n", - " if src.startswith(('http://', 'https://')):\n", - " return Download(lambda offset: http_open(src, offset))\n", - " if src.startswith('gs://'):\n", - " tool = shutil.which('gcloud') or shutil.which('gsutil')\n", - " if tool:\n", - " cmd = [tool, 'storage', 'cat', src] if tool.endswith('gcloud') else [tool, 'cat', src]\n", - " return subprocess.Popen(cmd, stdout=subprocess.PIPE).stdout\n", - " # No Cloud SDK (e.g. Colab TPU runtimes): the kernel's GCS client, with your\n", - " # credentials (google.colab.auth.authenticate_user()), else anonymous.\n", - " from google.cloud import storage\n", - " bucket, _, name = src[len('gs://'):].partition('/')\n", - " try:\n", - " blob = storage.Client(project=os.environ.get('GOOGLE_CLOUD_PROJECT', '_')).bucket(bucket).blob(name)\n", - " blob.reload()\n", - " except Exception:\n", - " blob = storage.Client.create_anonymous_client().bucket(bucket).blob(name)\n", - " return blob.open('rb')\n", - " return open(src, 'rb')\n", - "\n", - "\n", - "def with_gcs(op):\n", - " \"\"\"op(client) with your credentials (google.colab.auth.authenticate_user()), else anonymously.\"\"\"\n", - " from google.cloud import storage\n", - " try:\n", - " return op(storage.Client(project=os.environ.get('GOOGLE_CLOUD_PROJECT', '_')))\n", - " except Exception:\n", - " return op(storage.Client.create_anonymous_client())\n", - "\n", - "\n", - "def store_files(store):\n", - " \"\"\"{file name: modification time} of the store's top level.\"\"\"\n", - " if store.startswith('gs://'):\n", - " bucket, _, prefix = store[len('gs://'):].partition('/')\n", - " prefix = prefix.strip('/') + '/' if prefix.strip('/') else ''\n", - " return with_gcs(lambda client: {b.name[len(prefix):]: b.updated.timestamp()\n", - " for b in client.list_blobs(bucket, prefix=prefix, delimiter='/')})\n", - " folder = Path(store)\n", - " return {p.name: p.stat().st_mtime for p in folder.iterdir() if p.is_file()} if folder.is_dir() else {}\n", - "\n", - "\n", - "def save_to_store(files, store):\n", - " if store.startswith('gs://'):\n", - " bucket, _, prefix = store[len('gs://'):].partition('/')\n", - " from google.cloud import storage\n", - " client = storage.Client(project=os.environ.get('GOOGLE_CLOUD_PROJECT', '_'))\n", - " for p in files:\n", - " print('uploading', p.name, flush=True)\n", - " client.bucket(bucket).blob(f\"{prefix.strip('/')}/{p.name}\".lstrip('/')).upload_from_filename(str(p))\n", - " else:\n", - " Path(store).mkdir(parents=True, exist_ok=True)\n", - " for p in files:\n", - " if p.parent != Path(store):\n", - " print('copying', p.name, 'to', store, flush=True)\n", - " shutil.copy(p, Path(store) / p.name)\n", - "\n", - "\n", - "store = EVALRX_STORE.rstrip('/')\n", - "found = store_files(store) if store else {}\n", - "images = sorted((t, n) for n, t in found.items()\n", - " if n.startswith('evalrx-colab-tpu-image-') and n.endswith('.tar') and n + '.sha256' in found)\n", - "if not EVALRX_IMAGE and images and not EVALRX_REBUILD:\n", - " EVALRX_IMAGE = f'{store}/{images[-1][1]}'\n", - "if not EVALRX_WEIGHTS and WEIGHTS_NAME in found and WEIGHTS_NAME + '.sha256' in found:\n", - " EVALRX_WEIGHTS = f'{store}/{WEIGHTS_NAME}'\n", - "if not store and not EVALRX_IMAGE:\n", - " EVALRX_IMAGE, EVALRX_IMAGE_SHA256 = PUBLIC_IMAGE\n", - "\n", - "if EVALRX_IMAGE:\n", - " print('Prebuilt image, nothing to install:', EVALRX_IMAGE)\n", - "else:\n", - " # Building an image: the only step that downloads packages. It needs GitHub (or EVALRX_BUNDLE) and PyPI.\n", - " assert not store.startswith(('http://', 'https://')), \\\n", - " 'EVALRX_STORE has no image and is not writable: set EVALRX_STORE to a Drive, gs:// or local folder'\n", - " print(f'No image in {store}: installing the environment and building the image (about 15 minutes)')\n", - " assert not ENV.exists(), 'install on a fresh runtime: /content/evalrx-env already exists (Runtime > Disconnect and delete runtime)'\n", - " local = not store.startswith(('gs://', '/content/drive/'))\n", - " work = Path(store) if local else CONTENT / 'evalrx-image-out'\n", - " if EVALRX_BUNDLE:\n", - " with open_source(EVALRX_BUNDLE) as stream, tarfile.open(fileobj=stream, mode='r|') as archive:\n", - " unpack = Path(tempfile.mkdtemp(dir=CONTENT))\n", - " archive.extractall(unpack, **({'filter': 'data'} if hasattr(tarfile, 'data_filter') else {}))\n", - " (unpack / 'evalrx').rename(REPO)\n", - " else:\n", - " assert shutil.which('git'), 'git is not available: set EVALRX_BUNDLE to an offline bundle, or build the image on public Colab'\n", - " run_visible(['git', 'init', '-q', str(REPO)])\n", - " run_visible(['git', '-C', str(REPO), 'fetch', '-q', '--depth', '1', EVALRX_GIT_URL, EVALRX_REF])\n", - " run_visible(['git', '-C', str(REPO), 'checkout', '-q', 'FETCH_HEAD'])\n", - " run_visible(['bash', str(REPO / 'tools/colab/bootstrap.sh'), 'tpu'])\n", - " run_visible(['bash', str(REPO / 'tools/colab/build_image.sh'), 'tpu', str(work)])\n", - " if EVALRX_BUILD_WEIGHTS and not EVALRX_WEIGHTS: # it does not depend on the EvalRX revision\n", - " run_visible(['bash', str(REPO / 'tools/colab/build_weights.sh'), 'tpu', str(work), MODEL])\n", - " EVALRX_WEIGHTS = f'{store}/{WEIGHTS_NAME}'\n", - " built = sorted(p for p in work.iterdir() if p.is_file() and p.suffix in ('.tar', '.sha256')\n", - " and (p.name.startswith('evalrx-colab-tpu-image-') or p.name.startswith(WEIGHTS_NAME)))\n", - " save_to_store(built, store)\n", - " image_tar = max((p for p in built if p.name.startswith('evalrx-colab-tpu-image-') and p.suffix == '.tar'),\n", - " key=lambda p: p.stat().st_mtime)\n", - " EVALRX_IMAGE = f'{store}/{image_tar.name}'\n", - " # This runtime already holds the environment the image was packed from: section 3 keeps it.\n", - " (ENV / '.image-sha256').write_text(Path(str(image_tar) + '.sha256').read_text().split()[0] + '\\n')\n", - " for p in built:\n", - " if p.suffix == '.sha256':\n", - " print(p.read_text().strip())\n", - "print('Weights:', EVALRX_WEIGHTS or f'{MODEL} loads from its registered source')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Restore the environment\n", - "\n", - "Unpacks the image (and the weights tar, if one is given) to `/content`, checking each SHA-256." - ] - }, - { - "cell_type": "code", - "metadata": {}, - "execution_count": null, - "outputs": [], - "source": [ - "def expected_sha256(src, given):\n", - " if given:\n", - " return given.strip().split()[0]\n", - " try:\n", - " with open_source(src + '.sha256') as f:\n", - " return f.read().decode().split()[0]\n", - " except Exception as exc:\n", - " raise RuntimeError(f'no checksum for {src}: put {Path(src).name}.sha256 next to it, '\n", - " 'or set the *_SHA256 variable in section 1') from exc\n", - "\n", - "\n", - "def restore(src, sha256, staging):\n", - " \"\"\"Stream src through SHA-256 into `tar -x` in staging; raise if the checksum differs.\"\"\"\n", - " shutil.rmtree(staging, ignore_errors=True)\n", - " staging.mkdir(parents=True)\n", - " digest, done, start, step = hashlib.sha256(), 0, time.time(), 1 << 30\n", - " tar = subprocess.Popen(['tar', '-x', '-C', str(staging)], stdin=subprocess.PIPE)\n", - " with open_source(src) as stream:\n", - " while True:\n", - " chunk = stream.read(8 << 20)\n", - " if not chunk:\n", - " break\n", - " digest.update(chunk)\n", - " tar.stdin.write(chunk)\n", - " if (done + len(chunk)) // step > done // step:\n", - " print(f' {(done + len(chunk)) / 1e9:.0f} GB, {time.time() - start:.0f} s', flush=True)\n", - " done += len(chunk)\n", - " tar.stdin.close()\n", - " if tar.wait():\n", - " raise RuntimeError(f'tar failed on {src}')\n", - " if digest.hexdigest() != sha256:\n", - " shutil.rmtree(staging)\n", - " raise RuntimeError(f'checksum mismatch for {src}: got {digest.hexdigest()}, expected {sha256}. Copy it again.')\n", - " print(f' verified sha256 {sha256[:16]}..., {done / 1e9:.1f} GB in {time.time() - start:.0f} s')\n", - "\n", - "\n", - "image_sha = expected_sha256(EVALRX_IMAGE, EVALRX_IMAGE_SHA256)\n", - "marker = ENV / '.image-sha256'\n", - "if marker.exists() and marker.read_text().strip() == image_sha:\n", - " print('image already restored:', image_sha[:16])\n", - "else:\n", - " print('restoring', EVALRX_IMAGE)\n", - " staging = CONTENT / '.evalrx-image-staging'\n", - " restore(EVALRX_IMAGE, image_sha, staging)\n", - " for name in ('evalrx', 'evalrx-env'): # fixed paths: the environment refers to them\n", - " shutil.rmtree(CONTENT / name, ignore_errors=True)\n", - " (staging / name).rename(CONTENT / name)\n", - " shutil.rmtree(staging)\n", - " marker.write_text(image_sha + '\\n')\n", - "\n", - "model_path = ''\n", - "if EVALRX_WEIGHTS:\n", - " weights_sha = expected_sha256(EVALRX_WEIGHTS, EVALRX_WEIGHTS_SHA256)\n", - " weights_root = CONTENT / 'evalrx-weights'\n", - " wmarker = weights_root / '.sha256'\n", - " if not (wmarker.exists() and wmarker.read_text().strip() == weights_sha):\n", - " print('restoring', EVALRX_WEIGHTS)\n", - " shutil.rmtree(weights_root, ignore_errors=True)\n", - " restore(EVALRX_WEIGHTS, weights_sha, weights_root)\n", - " wmarker.write_text(weights_sha + '\\n')\n", - " (model_dir,) = [p for p in weights_root.iterdir() if p.is_dir()]\n", - " model_path = str(model_dir)\n", - "\n", - "# Datasets frozen into the image; agy reads GEMINI_API_KEY when its provider is \"gemini\".\n", - "shutil.copytree(REPO / '.bundle/data', DEMO_ROOT / 'data', dirs_exist_ok=True)\n", - "settings = Path.home() / '.gemini/antigravity-cli/settings.json'\n", - "config = json.loads(settings.read_text()) if settings.exists() else {}\n", - "settings.parent.mkdir(parents=True, exist_ok=True)\n", - "settings.write_text(json.dumps({**config, 'modelProvider': 'gemini'}, indent=2))\n", - "\n", - "os.environ.update(EVALRX_ENV=str(ENV), EVALRX_DEMO_ROOT=str(DEMO_ROOT), EVALRX_DATASET=DATASET,\n", - " EVALRX_RUN_TAG=RUN_TAG, EVALRX_MODEL=MODEL, EVALRX_MODEL_PATH=model_path,\n", - " PATH=f\"{ENV / 'bin'}:{os.environ['PATH']}\",\n", - " OMP_NUM_THREADS='2', PYTHONUNBUFFERED='1', TF_CPP_MIN_LOG_LEVEL='2',\n", - " MALLOC_ARENA_MAX='2', MALLOC_TRIM_THRESHOLD_='0')\n", - "os.chdir(REPO)\n", - "image = json.loads((ENV / '.image').read_text())\n", - "print('Image:', json.dumps(image, indent=1))\n", - "print('Model:', MODEL, 'from', model_path or 'the source registered in evalrx/specs.py (gs://gemma-data for Gemma)')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Check the TPU and agy" - ] - }, - { - "cell_type": "code", - "metadata": {}, - "execution_count": null, - "outputs": [], - "source": [ - "run_visible([str(ENV / 'bin/python'), '-c',\n", - " \"import jax; assert jax.default_backend() == 'tpu', jax.default_backend(); \"\n", - " \"print([(str(d), d.device_kind) for d in jax.devices()]); print('jax', jax.__version__)\"])\n", - "print('Source revision:', image['commit'])\n", - "print('Runtime:', os.environ.get('COLAB_RELEASE_TAG', 'non-Colab environment'))\n", - "if not os.environ.get('GEMINI_API_KEY'):\n", - " try:\n", - " from google.colab import userdata\n", - " os.environ['GEMINI_API_KEY'] = userdata.get('GEMINI_API_KEY')\n", - " except Exception:\n", - " import getpass\n", - " os.environ['GEMINI_API_KEY'] = getpass.getpass('Gemini API key: ')\n", - "assert os.environ['GEMINI_API_KEY']\n", - "agent_dir = DEMO_ROOT / 'agent_check'\n", - "run_visible([str(ENV / 'bin/python'), '-c', f\"\"\"\n", - "from pathlib import Path\n", - "from evalrx.agent_runtime.providers.antigravity import AntigravityAgent\n", - "agent_dir = Path({str(agent_dir)!r})\n", - "agent = AntigravityAgent(binary_path={str(ENV / 'bin/agy')!r}, model='gemini-3.8-flash-low',\n", - " timeout_sec=180, extra_args=['--output-format', 'stream-json'])\n", - "result = agent.run('Create agent_check.py that prints AGENT_READY. Run it with Python and report its output. Work only in this directory.', agent_dir)\n", - "print('Agent:', result.provider_name)\n", - "print(result.raw_output)\n", - "assert not result.error, result.error\n", - "assert (agent_dir / 'agent_check.py').exists()\n", - "print((agent_dir / 'agent_check.py').read_text())\n", - "\"\"\"])\n", - "run_visible([str(ENV / 'bin/python'), str(agent_dir / 'agent_check.py')])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Run EvalRX\n", - "\n", - "Baseline, M1–M4 analysis, and M5 repair. About an hour on a TPU v5e." - ] - }, - { - "cell_type": "code", - "metadata": {}, - "execution_count": null, - "outputs": [], - "source": [ - "%%bash\n", - "set -euo pipefail\n", - "\"$EVALRX_ENV/bin/evalrx\" run \\\n", - " --modality llm --model \"$EVALRX_MODEL\" --dataset \"$EVALRX_DATASET\" \\\n", - " --backend jax_local --device tpu --dtype bfloat16 \\\n", - " --limit 128 --download-limit 128 --seed 0 \\\n", - " --max-new-tokens 512 --temperature 0 --analyzer-max-cases 8 \\\n", - " --judge-provider agy --judge-model gemini-3.8-flash-low \\\n", - " --fix-tier L2 --fix-repair-rounds 2 --fix-validation-cases 64 \\\n", - " --allow-codegen --explore --m2-codegen --skip-surgery \\\n", - " --data-dir \"$EVALRX_DEMO_ROOT/data\" --run-dir \"$EVALRX_DEMO_ROOT/runs\" \\\n", - " --run-tag \"$EVALRX_RUN_TAG\" \\\n", - " ${EVALRX_MODEL_PATH:+--model-path \"$EVALRX_MODEL_PATH\"}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Result\n", - "\n", - "A repair counts only if it passes the independent CONFIRM split." - ] - }, - { - "cell_type": "code", - "metadata": {}, - "execution_count": null, - "outputs": [], - "source": [ - "import html\n", - "from IPython.display import display, HTML, Markdown, Image, JSON\n", - "\n", - "def table(rows, columns=None):\n", - " \"\"\"An HTML table from a list of dicts (no pandas: the kernel's packages are not used).\"\"\"\n", - " columns = columns or list(dict.fromkeys(k for row in rows for k in row))\n", - " head = ''.join(f'{html.escape(str(c))}' for c in columns)\n", - " body = ''.join('' + ''.join(f'{html.escape(str(row.get(c, \"\")))}'\n", - " for c in columns) + '' for row in rows)\n", - " display(HTML(f'{head}{body}
'))\n", - "\n", - "run_dir = DEMO_ROOT / f'runs/{MODEL}/{DATASET}.{RUN_TAG}'\n", - "read_json = lambda p: json.loads(p.read_text())\n", - "baseline = read_json(run_dir / 'baseline.json')\n", - "summary = read_json(run_dir / 'summary.json')\n", - "stages = {stage: read_json(run_dir / 'logs' / stage / 'log.json')\n", - " for stage in ['M1', 'M2', 'M3', 'M4', 'M5']\n", - " if (run_dir / 'logs' / stage / 'log.json').exists()}\n", - "fix = (stages.get('M5', {}).get('fix') or [{}])[-1]\n", - "attempts = fix.get('attempted', [])\n", - "print(f\"Baseline: {baseline['n_pass']}/{baseline['n']} ({baseline['accuracy']:.1%})\")\n", - "print(f\"Verified hypotheses: {summary['n_verified']}\")\n", - "print('Frozen candidate:', fix.get('selected_on_explore') or 'none')\n", - "if attempts:\n", - " final = attempts[0]\n", - " table([{\n", - " 'split': 'CONFIRM', 'n': final['n_pairs'],\n", - " 'baseline_correct': final['n_baseline_correct'],\n", - " 'candidate_correct': final['n_candidate_correct'],\n", - " 'repaired_cases': final['n_fixed'], 'regressed_cases': final['n_broken'],\n", - " 'e_value': final['e_value'], 'required_e_value': final['e_threshold'],\n", - " 'verdict': final['verdict'], 'validated_fix': final['fixed'],\n", - " }])\n", - " display(JSON(final['payload']))\n", - " print('Accuracy repair validated.' if final['fixed'] else 'No statistically confirmed accuracy repair.')\n", - "else:\n", - " print('No candidate selected for CONFIRM; no validated accuracy repair.')\n", - "if fix.get('selection_attempted'):\n", - " display(Markdown('### EXPLORE candidates (selection only)'))\n", - " table(fix['selection_attempted'])\n", - "for image_file in sorted((run_dir / 'logs/M2/artifacts').glob('*.png')):\n", - " display(Image(filename=str(image_file)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 7. Every answer" - ] - }, - { - "cell_type": "code", - "metadata": {}, - "execution_count": null, - "outputs": [], - "source": [ - "manifest = read_json(DEMO_ROOT / f'data/{DATASET}/manifest.json')\n", - "questions = {row['id']: row['prompt'] for row in manifest}\n", - "table([{**row, 'question': questions[row['id']]} for row in baseline['cases']],\n", - " ['id', 'question', 'expected', 'observed', 'label'])\n", - "if attempts:\n", - " before = {row['id']: row for row in baseline['cases']}\n", - " table([{\n", - " 'id': cid, 'expected': before[cid]['expected'],\n", - " 'baseline': before[cid]['observed'], 'candidate': answer,\n", - " 'repaired': cid in final['fixed_cases'], 'regressed': cid in final['broken_cases'],\n", - " } for cid, answer in final['outputs'].items()])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 8. Agent and stage records" - ] - }, - { - "cell_type": "code", - "metadata": {}, - "execution_count": null, - "outputs": [], - "source": [ - "display(JSON({'baseline': baseline, 'summary': summary, 'stages': stages}))\n", - "for source in sorted(run_dir.rglob('*.py')):\n", - " print('\\n---', source.relative_to(run_dir), '---')\n", - " print(source.read_text())" - ] - } - ], - "nbformat": 4, - "nbformat_minor": 5, - "metadata": { - "accelerator": "TPU", - "colab": { - "name": "evalrx_tpu.ipynb", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "name": "python" - } - } -} + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file