Francis Rhys Ward

TL;DR: This document provides an overview of publicly available evidence regarding the monitoring practices of internally deployed agents within frontier AI companies (focusing on OAI, Anthropic, and GDM).[1] This is intended as a reference for factual claims about monitoring practices. In general, there is limited public evidence about GDM monitoring...

Thanks to Johannes Treutlein, Jan Betley, Lennie Wells, Joey Yudelson, Arun Jose, Anna Marešová, Asvin Gothandaraman, and Clément Dumas for discussions and feedback. Edit (9th Oct 2026): I've shared the codebase along with some checkpoints. Edit (14th Oct 2026): We found and fixed two bugs after publication; the numbers hardly...

Summary As models become more powerful, one proposed strategy for reducing the threat from misaligned AIs is to make deals with them: offer compensation in exchange for revealing misalignment or behaving in an aligned way. We tested this empirically with model organisms of misalignment, as a proof of concept and...

TL;DR: We run astra on the task-suite from Think Fast. * GPT-6 Astra is close to saturation on our suite, meaning that giving a confident estimate of time horizons (TH) is more challenging. * Our rough estimate is that Astra’s 50% TH is in [8mins, 1 hour] and probably around...

This is the fifth post in the sequence Implications of Continual Learning for LLM Agents. Summary While writing our continual learning sequence, we sent a survey to a number of AI safety researchers with questions about continual learning. This post summarizes the results of that survey. We asked whether respondents...

This is the fourth post in the sequence Implications of Continual Learning for LLM Agents. Summary Continual learning is a capability that largely doesn’t exist yet in LLMs. We first want to acknowledge that this may make it difficult to identify tractable angles of attack for making CL safer: it...

This is the third post in our sequence Implications of Continual Learning for LLM Agents. Summary We argue that continual learning (CL) has two major potential safety implications: it may enable changes to LLM goals and values after deployment, and it eliminates the last-mover advantage held by current safety interventions....