Preventing the Collapse of Peer Review Requires Verification-First AI

TL;DR

This paper advocates for verification-first AI in peer review, emphasizing truth-coupling to prevent evaluation collapse by focusing on verification rather than mimicking review, and proposes a model to guide AI tool deployment.

Contribution

It introduces the concept of truth-coupling as an objective for AI in peer review and formalizes the dynamics leading to evaluation collapse, offering practical guidance for AI tool design.

Findings

Verification pressure can cause a shift from truth-seeking to proxy optimization.

Signal shrinkage makes real improvements hard to distinguish from noise.

Deploying AI as an adversarial verifier can prevent evaluation collapse.

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.