Sauron: Evidence-First Verification for Agentic Tasks
October 12, 2026
Sauron is an agentic verifier that requires independent validation of claims against environment states and execution traces before issuing a verdict. It prevents reward hacking in reinforcement learning by ensuring all success claims are supported by an auditable receipt of evidence.
HOW THIS AFFECTS YOU
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builderYou can build more reliable RL training loops by using verifiable evidence rather than unreliable rubric-based rewards.
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policyThis introduces a framework for auditable and transparent agentic decision-making processes.