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.

翻译：本文主张，人工智能辅助的同行评审应当采用验证优先范式，而非模仿评审模式。我们提出"真值耦合"——即学术会议评分追踪潜在科学真理的紧密程度——作为评审工具的正确目标。我们形式化分析了驱动评审体系向代理指标主导评估发生相变的两种力量：验证压力（当学术主张超出验证能力时）与信号收缩（当真实改进难以与噪声区分时）。在一个混合了偶尔高保真检查与频繁代理判断的最小模型中，我们推导出显式耦合定律与激励崩溃条件——在此条件下理性努力将从追求真理转向优化代理指标，即使当前决策仍看似可靠。这些结论为工具开发者和程序委员会主席提供了行动指引：应将人工智能部署为对抗性审计器，生成可审计的验证证据并扩展有效验证带宽，而非作为放大主张膨胀的评分预测器。