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[2601.16909] Preventing the Collapse of Peer Review Requires Verification-First AI
Summary
The paper discusses the need for a verification-first approach in AI-assisted peer review to prevent the collapse of the review process, emphasizing the importance of truth-coupling in evaluating scientific claims.
Why It Matters
As AI technologies increasingly influence academic peer review, ensuring the integrity of scientific evaluation is crucial. This paper highlights the risks of relying on AI for score prediction without adequate verification, advocating for systems that prioritize truth and accountability.
Key Takeaways
- AI-assisted peer review should focus on verification rather than mimicking human review.
- Truth-coupling is essential for maintaining the integrity of scientific evaluations.
- The paper identifies verification pressure and signal shrinkage as key challenges in peer review.
- It proposes deploying AI as an adversarial auditor to enhance verification processes.
- The findings urge tool builders to create systems that prioritize truth-seeking over proxy optimization.
Computer Science > Artificial Intelligence arXiv:2601.16909 (cs) [Submitted on 23 Jan 2026 (v1), last revised 12 Feb 2026 (this version, v2)] Title:Preventing the Collapse of Peer Review Requires Verification-First AI Authors:Lei You, Lele Cao, Iryna Gurevych View a PDF of the paper titled Preventing the Collapse of Peer Review Requires Verification-First AI, by Lei You and 2 other authors View PDF 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. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:260...
