研究分布式智能体通信如何导致集体认知错误，提出自适应门控机制优化协作效率。
The Cost of Consensus: Malignant Epistemic Herding and Adaptive Gating in Distributed Multi-Agent Search
- 设计动态通信门控机制，按需选择是否分享信念以减少冗余
- 发现过度沟通会引发错误共识，即使群体快速达成一致
- 适用于带宽受限的侦察、防御等实时协同场景
现实世界中的分布式智能体常在信息不全的情况下协同工作。虽然共享信念有助于任务完成，但通信会消耗带宽、引入延迟，不当通信反而损害集体推理。这一矛盾在分布式传感网络、自主侦察和协同网络安全等带宽受限场景中尤为突出。现有研究关注探索与通信策略，却未考察通信频率与内容如何共同影响集体信念状态。本文提出‘认知对齐’（epistemic alignment）概念——即智能体对环境内部信念的一致性。有效通信使智能体收敛于正确假设；设计不佳则可能导致群体自信地达成错误共识。我们证明该现象无法仅通过协调度量（如Jensen-Shannon散度或共识速率）检测。
原文摘要 · Abstract (English)
Distributed agents in real-world settings frequently must coordinate under uncertainty with only partial observations. Coordination is necessary to share beliefs to aid in task completion, but communication costs bandwidth, introduces latency, and if done poorly, can degrade collective reasoning. This tension is especially acute in bandwidth-constrained deployments such as distributed sensing networks, autonomous reconnaissance, and collaborative cyber defense, where excessive transmission carries direct operational costs. Existing work has focused on multi-agent exploration and communication strategies, but not on how communication frequency and content jointly shape the collective belief state. Central to this challenge is the degree to which agents maintain compatible internal beliefs about the environment, a property we term \textit{epistemic alignment}. When agents share beliefs effectively, they converge on correct hypotheses; when communication is poorly designed, agents may converge confidently on wrong ones. We formalize this distinction and show it is not detectable from coordination metrics alone such as Jensen-Shannon Divergence or rate to consensus.
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