Preventing the Collapse of Peer Review Requires Verification-First AI

• URL: http://arxiv.org/abs/2601.16909v1
• Date: Fri, 23 Jan 2026 17:17:32 GMT
• Title: Preventing the Collapse of Peer Review Requires Verification-First AI
• Authors: Lei You, Lele Cao, Iryna Gurevych,
• Abstract summary: We propose truth-coupling, i.e. how tightly venue scores track latent scientific truth.<n>We formalize two forces that drive a phase transition toward proxy-sovereign evaluation.
• Score: 49.995126139461085
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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