arXiv Machine Learning By Aman Behera, Payal Payal

Post-Audit Mirage: Identifiability Limits of Offline Agent Verification

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The paper introduces the concept of a "Post‑Audit Mirage," showing that offline tests can fail to detect whether an agent update will remain safe once deployed. It formalizes this limitation using matched safe and harmful lifecycles that produce identical evidence to a verifier while an isolated scorer observes opposite outcomes, and presents a theorem describing the trade‑off between error and abstention. The authors also propose a benchmark and a tool called IdentifiedRangeMonitor that attempts to recover the missing distinction in certain settings, but notes that monitoring may still be undecided for rare groups, small effects, or missing observations.

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Post-Audit Mirage: Identifiability Limits of Offline Agent Verification

The paper introduces the Post‑Audit Mirage, a framework showing that offline tests may not detect whether an agent update remains safe after deployment. It defines a benchmark that uses matched safe and harmful lifecycles to expose identical verifier‑visible evidence while an isolated scorer records opposite deployment truth, and presents a theorem on the error‑abstention tradeoff using observational equivalence. The IdentifiedRangeMonitor tool applies fresh group‑labelled harm outcomes to decide whether to deploy, hold, or remain uncertain, and experiments demonstrate that monitoring can recover missing distinctions in favorable settings but may stay undecided for rare groups or small effects.

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