Bootstrapped Monitoring: Leveraging Transparent Reasoning to Oversee Stronger AI Agents

Abstract:Trusted monitoring is a cornerstone of AI control. However, as frontier models grow more capable, the increasing capabilities gap between trusted and untrusted models may render trusted models unreliable monitors. We introduce \emph{bootstrapped monitoring}, a protocol that addresses this by inserting a stronger, intermediate untrusted model with transparent chain-of-thought reasoning into the oversight chain. The untrusted monitor ($U_m$) evaluates the agent's actions, while a weaker trusted model ($T$) oversees $U_m$'s reasoning to detect collusion. We evaluate bootstrapped monitoring on multi-turn software engineering tasks (BashArena) across multiple agents and monitors. Bootstrapped monitoring substantially improves catch rates over trusted-only monitoring, even when the untrusted monitor actively colludes with the agent, provided we have access to its raw chain-of-thought. Our results suggest that bootstrapped monitoring can extend the useful lifetime of trusted models in control as AI capabilities advance.

Machine Learning (cs.LG)

arXiv:2606.11998 [cs.LG]

(or arXiv:2606.11998v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.11998 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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