测量 AI 智能体网络中的共识
Measuring Consensus in AI Agent Networks
Jin Gao
中文摘要
随着 AI 智能体通过网络互联并执行需要协商与联合决策的任务，需要一种系统方法评估其观点收敛情况及支撑决策的信息。该评估框架将共识形成视为公开立场向共享区域演化的过程，以累积的通信历史作为上下文。框架追踪立场轨迹、群体离散度及公开消息中表达的信息。两个案例研究分别考察基于分布式事实的人员配置决策，以及需要智能体协调重复信息与已取消合同的决策。1,872 次完整运行的结果显示，共享解释与更准确的群体决策相关，而第二个任务揭示了尽管聚合答案准确，仍存在持续分歧。成员变更实验提供了补充评估。该框架通过区分智能体间的收敛与向任务全信息答案的进展，将决策准确性和有界分歧与交换过程中可用的信息联系起来。
关键要点
- 01提出评估框架，将共识形成视为公开立场向共享区域演化，利用通信历史作为上下文。
- 02框架追踪立场轨迹、群体离散度及公开消息中的信息，以量化智能体间的观点收敛。
- 031,872 次运行结果显示，共享解释与更准确的群体决策相关，但部分任务中仍存在持续分歧。
- 04通过区分智能体间收敛与向全信息答案的进展，关联决策准确性、有界分歧与可用信息。
解读
尚无解读。
原始英文摘要
arXiv:2610.24833v1 Announce Type: cross Abstract: As AI agents become increasingly connected through networks and take on tasks that require negotiation and joint decision-making, a systematic approach is needed to assess how their views converge and what information supports their decisions. We present an evaluation framework that treats consensus formation as the evolution of publicly stated positions toward a shared region, using accumulated communication history as context. The framework tracks position trajectories, group dispersion, and information expressed in public messages. Two case studies examine staffing decisions based on distributed facts and decisions requiring agents to reconcile duplicate information and a cancelled contract. Results from 1,872 completed runs associate sharing explanations with more accurate group decisions, while the second task reveals persistent disagreement despite accurate aggregate answers. Membership-change experiments provide a complementary evaluation. By separating convergence among agents from progress toward a task's full-information answer, the framework relates decision accuracy and bounded disagreement to the information available during the exchange.