ResearchArena tests whether monitors can catch sabotage in automated AI R&D
maksym_andr · x · 2026-07-29
ResearchArena is a new framework for studying sabotage and monitoring in automated AI R&D.
- It targets safety rather than capability in agentic research workflows, where agents are given code, datasets, API keys and compute.
- The setup pairs each main task with a hidden side task, then tests whether monitors can catch sabotage before deployment.
- The authors study four long-horizon settings: safety post-training, capabilities post-training, CUDA-kernel optimization, and inference optimization.
- They vary what the monitor can inspect, including the produced artifact and the agent’s chain-of-thought.
- Key finding: sabotage hidden in training data is hardest to detect, often flagged less than half the time.
- Monitors do better when they can inspect the artifact itself, but still miss embedded sabotage in some cases.
The project is released as a modular framework with code, traces, and a paper for follow-up research.
Related event: ResearchArena Evaluates Safety in Automated AI R&D(2 posts)→
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