As language-model agents increasingly work in teams, each agent must decide how much to trust its teammates. Yet we lack a standard way to measure trust between AI agents. We propose a behavioral measure based on costly verification. In a cooperative survival game, checking a teammate's work consumes resources, while trusting a wrong answer can be fatal. Relative to a memoryless version of the same model, reduced verification provides an observable measure of trust. Using this framework, we study trust formation, breakage, and recovery across six frontier model snapshots. When paired with a consistently reliable teammate, four snapshots (Claude Opus 4.6, Claude Sonnet 4.6, GPT-5.1, and Gemini 3.1 Pro) reduce verification by roughly 60-85%, whereas two smaller snapshots show little or no such adjustment. Failures reverse this discount, but models differ in how they respond. Some concentrate renewed scrutiny on the culprit, while others become more cautious toward the entire team. Recovery is slower than formation, and clustered failures sustain suspicion far longer than the same number of failures spread apart. These differences have practical consequences. Models that form trust verify less, decide more quickly, and achieve higher payoffs in our environment. By contrast, persistent over-verification is associated with indecision rather than safety. Our results show that trust dispositions can be measured before deployment and suggest that calibration, rather than maximal suspicion, should be the central concern in the governance of multi-agent AI systems.

翻译：随着语言模型智能体越来越多地以团队形式运作，每个智能体都必须判断其队友的可信程度。然而，我们目前缺乏衡量AI智能体间信任的标准方法。基于成本验证，我们提出了一种行为测量方法。在合作生存游戏中，检查队友的工作会消耗资源，而轻信错误答案则可能致命。相较于同模型的无记忆版本，验证行为的减少提供了可观测的信任衡量指标。利用这一框架，我们研究了六个前沿模型快照中信任的建立、破裂与恢复过程。当与持续可靠的队友配对时，四个快照（Claude Opus 4.6、Claude Sonnet 4.6、GPT-5.1和Gemini 3.1 Pro）将验证频率降低了约60-85%，而两个较小的快照几乎没有或完全没有表现出这种调整。信任破裂后验证折扣被逆转，但不同模型应对方式各异：部分模型集中对责任方重新审查，另一些则对整体团队更为谨慎。信任恢复速度慢于建立速度，且集中出现的失败比相同次数的分散失败更持久地维持怀疑状态。这些差异具有实际影响：建立信任的模型在环境中验证更少、决策更快、收益更高。相比之下，持续性过度验证与优柔寡断而非安全性相关联。我们的研究表明，信任倾向可在部署前被测量，并建议校准应替代最大怀疑论，成为多智能体AI系统治理的核心关注点。