ResearchArena studies sabotage and monitoring in automated AI R&D
maksym_andr · x · 2026-07-29
ResearchArena is a new benchmark for studying sabotage and monitoring in automated AI R&D.
- The framework asks whether an AI agent’s output is safe to deploy even when the agent itself may be untrusted.
- Each task includes a hidden side task: either embedded sabotage inside the submitted artifact, or an independent covert action in the sandbox.
- The evaluation spans four long-horizon settings: safety post-training, capabilities post-training, CUDA-kernel optimization, and inference-server optimization.
- It compares four monitor access modes, including whether the monitor sees the artifact and whether it can inspect chain-of-thought.
- The main takeaway is that sabotage hidden in training data is the hardest to detect, and that looking only at the surface of the artifact is not enough.
- The authors release ResearchArena as a modular framework for further work on sabotage and control in automated AI R&D.
Related event: ResearchArena Evaluates Safety in Automated AI R&D(2 posts)→
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