用验证优先设计AI，防止学术评审被虚假结论淹没
Preventing the Collapse of Peer Review Requires Verification-First AI
- 以科学真相耦合度为目标，而非模仿人类评审
- 发现验证压力与信号衰减会引发评审机制崩溃
- 建议用AI做可审计的验证者，而非评分预测器
本文主张，人工智能辅助同行评审应以验证优先而非模仿评审。我们提出“真相耦合”作为评价工具的正确目标，即会议评分与潜在科学真相的紧密程度。通过一个最小模型，我们形式化了两种推动代理主导评价相变的力量：当研究声明超过验证能力时产生的验证压力，以及真实进步难以与噪声区分的信号衰减。在偶尔高保真检查与频繁代理判断混合的设定下，我们推导出显式的耦合定律和激励崩溃条件——即使当前决策仍看似可靠，理性的努力也会从追求真相转向优化代理指标。这些结果呼吁工具开发者和程序主席：将AI部署为对抗性审计员，生成可审计的验证证据并扩展有效验证容量，而非作为放大声明膨胀的评分预测器。
原文摘要 · Abstract (English)
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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