Evidence-First Agentic Verification

Key points

• Long-horizon, semi-verifiable agentic tasks often rely on rubric-based evaluation, but the evidence needed to assess these tasks can be spread across the environment state and the execution trace.1
• A judge may overlook this evidence or accept unsupported claims, including an agent's own reports of success.1
• When rubric verdicts serve as reinforcement-learning rewards, such errors can reinforce undesirable behavior.1
• On a paired fault-injection suite covering 13 families, several of which simulate reward hacking, Sauron detects 97% of injected failures while flagging only 2% of clean controls, outperforming baselines even when they are given the full trace.1

Sources (2)

• [1]Evidence-First Agentic Verification
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 04:58 AM

  We introduce Sauron, an evidence-first agentic verifier that investigates both state and trace and relies on a claim only after independently validating it against its source.

  Long-horizon, semi-verifiable agentic tasks often rely on rubric-based evaluation, but the evidence needed to assess these tasks can be spread across the environment state and the execution trace.

• [2]Evidence-First Agentic Verification
  Hugging Face Daily Papers · Oct 13, 12:00 AM · same content

Extractive summary: sentences quoted from the sources.