Early work on monitorability evaluations
- Source
- metr.org
- Date
Context
A capable may also be harder to supervise. METR's early monitorability experiment asks agents to complete a hidden side task while a separate model watches for departures from the main assignment. Stronger agents were better at completing side tasks unnoticed, while stronger monitors were better at detecting them.
Access to reasoning traces helped detection in some tested settings, but the size of the effect differed by agent, possibly because one model's traces were summarized rather than raw. The small task set and limited optimization of both agents and monitors make this preliminary evidence. It measures supervision under a particular setup, rather than establishing that visible reasoning guarantees detection or that model capability alone determines safety.
- AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
- eval — A repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.
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