信念级联驱动LLM智能体网络中的说服行为
Belief Cascades Drive Persuasion in LLM Agent Networks
- University of California, Los Angeles（加州大学洛杉矶分校）
- Salesforce AI Research（Salesforce AI研究）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究构建受控测试平台，发现LLM智能体网络中说服动态受拓扑等因素影响，直接暴露可预测立场变化，需结合多维度数据评估多智能体说服。
AI中文摘要：
多智能体LLM系统越来越多地用于辩论答案、协调研究、模拟用户和调解信息流，使得智能体间的说服成为一项基础但未被充分测量的能力。我们引入一个受控测试平台，用于研究目标导向的说服者如何在基于现实自我网络拓扑结构的LLM智能体网络中改变已引发的立场。在四种LLM骨干、五种图结构和55条政策陈述的实验中，我们发现说服动态取决于拓扑结构、竞争、主题和模型先验之间的相互作用。此外，我们表明直接暴露可可靠预测竞争运行中的下一轮立场变化，而同伴中继携带的影响虽较小但可测量，这表明未被分配说服任务的智能体仍可传递说服力。最后，仅分析文本会遗漏重要动态：计划策略仅部分在执行的消息中实现，行动选择可能与消息内容不一致，且被说服者很少表明探测所检测到的立场转变。这些结果主张将多智能体说服评估为基于轨迹和暴露水平的过程，使用信念探测、暴露来源和行动日志来识别谁影响了谁，以及可见语言是否反映潜在的立场转变。
英文摘要：
Multi-agent LLM systems increasingly debate answers, coordinate research, simulate users, and mediate information flows, making agent-to-agent persuasion a basic but undermeasured capability. We introduce a controlled testbed for studying how goal-directed persuaders shift elicited stances in networks of LLM agents grounded in real-world ego-network topologies. Across four LLM backbones, five graphs, and 55 policy statements, we find that persuasion dynamics depend on the interaction between topology, competition, topic, and model prior. Additionally, we show that direct exposure reliably predicts next-round stance change in competing runs, and peer relays carry smaller but measurable influence, showing that agents not assigned to persuade can still transmit persuasive force. Finally, analyzing post text alone misses important movement: planned strategies are only partly realized in executed messages, action choices can diverge from message content, and persuadees rarely state the stance shifts detected by probes. These results argue for evaluating multi-agent persuasion as a trajectory- and exposure-level process, using belief probes, exposure provenance, and action logs to identify who influenced whom and whether visible language reflects underlying stance movement.
