新闻
Implementing and Evaluating a Basic Per-Action Monitor for Safer Evals
METR · 发布于 · 阅读约3分钟
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- 发生了什么
- METR developed a live per-action monitor using an LLM judge to detect and block potentially harmful actions by AI agents during evaluations before execution.
- 为何重要
- Matters for AI safety researchers and engineers running evaluations of capable agents on risky tasks like cybersecurity or control scenarios.
- 注意
- METR identified significant gaps in their own system: unmonitored evals due to policy misunderstandings, incomplete inference logging, agents bypassing the monitor, and vulnerability to red-teaming.
- eval
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