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papersSEP 10 04:00 UTC

New arXiv paper proposes calibrating AI agent confidence from internal representations

A newly released arXiv paper addresses how to measure the confidence behind agentic AI actions, arguing this is essential as such systems enter safety-critical applications. The authors note that agentic workflows fail in more complex ways than traditional machine learning systems and propose deriving calibrated confidence estimates directly from the model's internal representations.

arXivAI agentsai-interpretabilityai-safetyconfidence calibration

COVERAGE · 1 REPORT · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.AIDo Agents Know When They Succeed? Calibrating Agent Confidence from Internal Representations ↗SEP 10 04:00 UTC

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