Position: Preventing the Collapse of Peer Review Requires Verification-First AI

Lei You, Lele Cao, Iryna Gurevych

Track: Track 1: Original Research/Position/Education/Attention Track

TL;DR: Peer Review Requires Verification-First AI

Abstract: This paper argues that AI-assisted peer review should be verification-first rather than review-mimicking. We propose truth-coupling, i.e. how tightly venue scores track latent scientific truth, as the right objective for review tools. We formalize two forces that drive a phase transition toward proxy-sovereign evaluation: verification pressure, when claims outpace verification capacity, and signal shrinkage, when real improvements become hard to separate from noise. In a minimal model that mixes occasional high-fidelity checks with frequent proxy judgment, we derive an explicit coupling law and an incentive-collapse condition under which rational effort shifts from truth-seeking to proxy optimization, even when current decisions still appear reliable. These results motivate actions for tool builders and program chairs: deploy AI as an adversarial auditor that generates auditable verification artifacts and expands effective verification bandwidth, rather than as a score predictor that amplifies claim inflation.

Keywords: peer review, verification, scientific incentives, auditability, generative AI, Goodhart's law

Submission Number: 318