研究大模型代理网络中的说服机制，发现互动拓扑与竞争关系决定说服效果。
Belief Cascades Drive Persuasion in LLM Agent Networks
- 构建基于真实社交拓扑的多代理测试平台，模拟目标导向的说服过程。
- 直接接触可预测立场变化，非说服角色也通过同伴传递影响力。
- 语言表达未必反映真实立场转变，需结合行为日志与信念探针分析。
多代理大模型系统日益用于辩论、协作研究、模拟用户和信息传递，使代理间的说服能力成为基础但尚未充分衡量的能力。本文引入一个受控测试平台，研究目标导向的说服者如何在基于现实世界自指网络拓扑的代理网络中改变被说服者的立场。在四种大模型主干、五种图结构和55条政策声明下，我们发现说服动态取决于拓扑、竞争、话题和模型先验之间的相互作用。此外，我们发现直接暴露能可靠预测下一轮立场变化，同伴传递虽影响较小但仍可测量，表明未被分配说服任务的代理仍可传播说服力。最后，仅分析文本会遗漏关键信息：计划策略仅部分体现在执行消息中，行动选择常与话语内容偏离，且被说服者很少明确承认探测到的立场变化。这些结果主张将多代理说服评估为轨迹级和暴露级的过程，需结合信念探针、暴露溯源和行动日志，识别谁影响了谁，以及可见语言是否反映潜在立场移动。
原文摘要 · Abstract (English)
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.
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