Preventing the Collapse of Peer Review Requires Verification-First AI
This addresses the risk of peer review collapse for the scientific community, proposing a verification-first approach to prevent proxy optimization.
The paper tackles the problem of AI-assisted peer review potentially collapsing due to proxy-sovereign evaluation, where reviewers optimize for scores rather than scientific truth, and finds that a phase transition occurs when verification capacity is outpaced by claims, leading to incentive collapse.
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.