diff --git a/content/ai-metascience/index.md b/content/ai-metascience/index.md
index 1259328306e..828881e0bbb 100644
--- a/content/ai-metascience/index.md
+++ b/content/ai-metascience/index.md
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@@ -485,43 +513,44 @@ Here are the sessions and the talks in each, in the order they run. **Click any
Monday 28 SeptemberAI in the research pipeline
+All Monday recordings on YouTube
Morning
09:40 When AI writes the literature
The problem as it already shows up in the correction record, the pressure producing it, and two routes to a better literature.
-09:40 AI-Generated Content as an Emerging Driver of Journal Retractions Anton Sokolov (Tyche Institute) Abstract
-09:50 When every hypothesis is published: agentic AI and the collapse of selection at production Matt Spick (University of Surrey) Abstract
-10:10 The Conceptual Armament Effect: Armed AI Reaches Above a Human 99th-Percentile Paper Kenji Yamada (independent researcher, Japan) Abstract
-10:30 How should AI-assisted research be documented? Seven guiding questions Maximilian Frank (LMU Munich) Abstract
+09:40 AI-Generated Content as an Emerging Driver of Journal Retractions Anton Sokolov (Tyche Institute) Abstract Watch
+09:50 When every hypothesis is published: agentic AI and the collapse of selection at production Matt Spick (University of Surrey) Abstract Watch
+10:10 The Conceptual Armament Effect: Armed AI Reaches Above a Human 99th-Percentile Paper Kenji Yamada (independent researcher, Japan) Abstract Watch
+10:30 How should AI-assisted research be documented? Seven guiding questions Maximilian Frank (LMU Munich) Abstract Watch
11:10 Evidence synthesis: capability, comparison, assurance
From what frontier models can extract, through a head-to-head test of the tools built on them, to the safeguards that would make an AI-assisted review checkable.
-11:10 Can frontier LLMs code studies for meta-analysis? Shaheed Azaad (University of Münster) Abstract
-11:30 Fast, but incomplete and inaccurate? Performance of AI-powered tools for systematic literature reviews Rene Bekkers (VU Amsterdam) Abstract
-11:50 ReviewAid: confidence-scored, locally runnable LLM screening and extraction Vihaan Sahu (Georgian National University SEU) Abstract
+11:10 Can frontier LLMs code studies for meta-analysis? Shaheed Azaad (University of Münster) Abstract Watch
+11:30 Fast, but incomplete and inaccurate? Performance of AI-powered tools for systematic literature reviews Rene Bekkers (VU Amsterdam) Abstract Watch
+11:50 ReviewAid: confidence-scored, locally runnable LLM screening and extraction Vihaan Sahu (Georgian National University SEU) Abstract Watch
Afternoon
13:15 Evaluating science, and its evaluators
What it takes to make a paper checkable in the first place, what human review actually does, what a computational novelty signal might add, and a tool that checks a paper against what its authors said they would do.
-13:15 Metacheck: automated, validated checks for open science practices Cristian Mesquida (Eindhoven University of Technology) Abstract
-13:35 Gender and disciplinary differences in 39,280 SNSF grant peer review reports Stefan Müller (University College Dublin) Abstract
-13:55 Tracing Scientific Novelty in Transformer Representations: Toward Computational Models of Novelty Signals Anna Kruzenshtern (LUT University) Abstract
-14:15 RegCheck: an LLM-assisted tool for comparing registrations with papers Jamie Cummins (University of Bern) Abstract
+13:15 Metacheck: automated, validated checks for open science practices Cristian Mesquida (Eindhoven University of Technology) Abstract Watch
+13:35 Gender and disciplinary differences in 39,280 SNSF grant peer review reports Stefan Müller (University College Dublin) Abstract Watch
+13:55 Tracing Scientific Novelty in Transformer Representations: Toward Computational Models of Novelty Signals Anna Kruzenshtern (LUT University) Abstract Watch
+14:15 RegCheck: an LLM-assisted tool for comparing registrations with papers Jamie Cummins (University of Bern) Abstract Watch
15:40 Conditions for legitimate adoption
What researchers do with these tools, what open infrastructure and open data would require, who gains or loses when the tools arrive, the rules of use that follow, and what it would take to make research outputs readable by machines in the first place.
15:40 How health researchers actually use GenAI: findings from a global survey Danny Maupin (University of Surrey) Abstract
-15:55 AI and researchers with Specific Learning Disabilities: access gains and new inequities Chelsea Sawyer (University of Manchester) Abstract
-16:15 The FOCUS Framework: Ten Simple Rules for Optimal and Careful Use of Generative AI in Science Mohamed Helmy (University of Saskatchewan) Abstract
-16:35 Machine-readable, not AI-interpretable: standards for FAIR data and code Daniel Lakens (Eindhoven University of Technology) Abstract
+15:55 AI and researchers with Specific Learning Disabilities: access gains and new inequities Chelsea Sawyer (University of Manchester) Abstract Watch
+16:15 The FOCUS Framework: Ten Simple Rules for Optimal and Careful Use of Generative AI in Science Mohamed Helmy (University of Saskatchewan) Abstract Watch
+16:35 Machine-readable, not AI-interpretable: standards for FAIR data and code Daniel Lakens (Eindhoven University of Technology) Abstract Watch
17:10 Closing provocation
@@ -532,20 +561,21 @@ Here are the sessions and the talks in each, in the order they run. **Click any
Tuesday 29 SeptemberReplications and reproductions
+All Tuesday recordings on YouTube
Morning
09:30 Symposium: Can AI Help Us Understand How Findings Move from Animal Studies to Human Studies?
Four talks, mostly by early-career researchers, on tracing the biomedical evidence trail across publications, trial registries and regulatory documents. Chaired by Benjamin Ineichen.
-09:30 Miriam Kober, Tim Chopard, Vera Bernhard & Simona Doneva (University of Bern) Abstract
+09:30 Miriam Kober, Tim Chopard, Vera Bernhard & Simona Doneva (University of Bern) Abstract Kober Chopard Doneva Ineichen
11:15 Reproducing the record
Replication and reproduction as work actually done, with agents in the loop and with the paperwork that makes a result reusable, followed out to whether anyone downstream notices.
-11:15 Thirty agent-assisted replications, published as nanopublications: the honest debrief Anne Fouilloux (Lifewatch ERIC, Spain), Jean Iaquinta (Vitenhub AS, Norway) Abstract
-11:35 A local, open-weights pipeline for semi-automated replication checklists Johannes Breuer (CAIS Bochum & University of Duisburg-Essen), Mario Haim & Philipp Knöpfle (LMU Munich), Xinyue Zhao (CAIS Bochum) Abstract
-11:55 Do failed replications make their way into public knowledge? Youyou Wu (University College London) Abstract
+11:15 Thirty agent-assisted replications, published as nanopublications: the honest debrief Anne Fouilloux (Lifewatch ERIC, Spain), Jean Iaquinta (Vitenhub AS, Norway) Abstract Watch
+11:35 A local, open-weights pipeline for semi-automated replication checklists Johannes Breuer (CAIS Bochum & University of Duisburg-Essen), Mario Haim & Philipp Knöpfle (LMU Munich), Xinyue Zhao (CAIS Bochum) Abstract Watch
+11:55 Do failed replications make their way into public knowledge? Youyou Wu (University College London) Abstract Watch
Afternoon
@@ -558,23 +588,24 @@ Here are the sessions and the talks in each, in the order they run. **Click any
14:40 Predicting replicability
Three talks on forecasting which findings will hold, and what such forecasts are for.
-14:40 The Predicting Replicability Challenge: three rounds of automated credibility assessment Andrew Tyner (Center for Open Science) Abstract
-15:00 What Are Automated Replicability Predictions For? Lukas Röseler (University of Münster) Abstract
-15:20 Multi-agent AI measures of methodological complexity help forecast study reproducibility Daniele Fanelli (Heriot-Watt & LSE), Yuan Yingfang (Northumbria & Heriot-Watt) Abstract
+14:40 The Predicting Replicability Challenge: three rounds of automated credibility assessment Andrew Tyner (Center for Open Science) Abstract Watch
+15:00 What Are Automated Replicability Predictions For? Lukas Röseler (University of Münster) Abstract Watch
+15:20 Multi-agent AI measures of methodological complexity help forecast study reproducibility Daniele Fanelli (Heriot-Watt & LSE), Yuan Yingfang (Northumbria & Heriot-Watt) Abstract Watch
16:05 Machines that build and test the replication record
First the benchmarks that measure what these agents can actually do, then the two databases being built on that basis with humans kept in the loop, and last the question the day has been circling: can an agent run the checks at human quality?
-16:05 ReplicatorBench: an end-to-end benchmark for AI agents in research replication Bang Nguyen (University of Notre Dame), Dominik Soós (Old Dominion University) Abstract
-16:25 Can AI reviewers catch the errors that matter? A 100-error planted benchmark Paul Litvak (Robyn Dawes Institute) Abstract
-16:45 Keeping humans in the loop: using LLMs to enable replication database validation at scale Hamidreza Behbood (University of Münster), Rohan Tondlekar (FORRT) Abstract
-16:55 The Metascience Observatory: an AI-built replication database and experiments with AI for forensic metascience Dan Elton (The Metascience Observatory) Abstract
-17:15 Can an agentic LLM run computational reproducibility checks at human quality? Yoel Inbar (University of British Columbia) Abstract
+16:05 ReplicatorBench: an end-to-end benchmark for AI agents in research replication Bang Nguyen (University of Notre Dame), Dominik Soós (Old Dominion University) Abstract Watch
+16:25 Can AI reviewers catch the errors that matter? A 100-error planted benchmark Paul Litvak (Robyn Dawes Institute) Abstract Watch
+16:45 Keeping humans in the loop: using LLMs to enable replication database validation at scale Hamidreza Behbood (University of Münster), Rohan Tondlekar (FORRT) Abstract Watch
+16:55 The Metascience Observatory: an AI-built replication database and experiments with AI for forensic metascience Dan Elton (The Metascience Observatory) Abstract Watch
+17:15 Can an agentic LLM run computational reproducibility checks at human quality? Yoel Inbar (University of British Columbia) Abstract Watch
Wednesday 30 SeptemberWorkshops, and analyst variability
+All Wednesday recordings on YouTube
Morning
@@ -712,7 +743,7 @@ Everything runs on Google Meet. We will email joining details to everyone who ha
Live captions are on in every session. If anything else would help you take part, tell us on the registration form, and we will do what we can.
-Where speakers opt in, their talks are published afterwards on the [FORRT YouTube channel](https://youtube.com/@forrtproject9995).
+Recordings of the talks whose speakers opted in are on the [FORRT YouTube channel](https://youtube.com/@forrtproject9995), with one playlist per day: [Monday](https://www.youtube.com/playlist?list=PLS3VdaYKcrew), [Tuesday](https://www.youtube.com/playlist?list=PLSItTTcc91ac) and [Wednesday](https://www.youtube.com/playlist?list=PLZYxy57rykQU). The **Watch** links in the programme above open each talk.
## Code of conduct