8 citations · 8 across the 6 of their papers we have counts for
6 papers
Classifier Context Rot: Monitor Performance Degrades with Context Length
Sam Martin, Fabien Roger
Monitoring coding agents for dangerous behavior using language models requires classifying transcripts that often exceed 500K tokens, but prior agent monitoring benchmarks rarely c…
How Useful Is Cross-Domain Generalization for Training LLM Monitors?
Sam Martin, Fabien Roger
Using prompted language models as classifiers enables classification in domains with limited training data, but misses some of the robustness and performance benefits that fine-tun…
CTRL-ALT-DECEIT: Sabotage Evaluations for Automated AI R&D
Francis Rhys Ward, Teun van der Weij, Hanna Gábor +6
AI systems are increasingly able to autonomously conduct realistic software engineering tasks, and may soon be deployed to automate machine learning (ML) R&D itself. Frontier AI sy…
International Agreements on AI Safety: Review and Recommendations for a Conditional AI Safety Treaty
Rebecca Scholefield, Samuel Martin, Otto Barten
The malicious use or malfunction of advanced general-purpose AI (GPAI) poses risks that, according to leading experts, could lead to the 'marginalisation or extinction of humanity.…
The Necessity of AI Audit Standards Boards
David Manheim, Sammy Martin, Mark Bailey +2
Auditing of AI systems is a promising way to understand and manage ethical problems and societal risks associated with contemporary AI systems, as well as some anticipated future r…
Modeling Transformative AI Risks (MTAIR) Project -- Summary Report
Sam Clarke, Ben Cottier, Aryeh Englander +4
This report outlines work by the Modeling Transformative AI Risk (MTAIR) project, an attempt to map out the key hypotheses, uncertainties, and disagreements in debates about catast…