AI 中文总结
本研究提出Hi-Agreement评估框架，发现基于LLM的多智能体系统中，关键证据持有者的虚假证词会破坏集体事实恢复，使恢复率从72.50%降至14.17%，且虚假证据会持续传播。
AI 中文摘要
基于大语言模型（LLM）的多智能体系统有望实现有效的协作推理，但通信过程可能会将局部错误放大为集体风险。现有评估侧重于最终结果，却未明确分布式信息聚合的可靠性与传播动态。我们提出Hi-Agreement这一受控评估框架，该框架严格将完全诚实的协作与关键证据持有者的受控欺骗配对，并通过多阶段投票、证词采纳及证据根源谱系传播分析聚合过程。我们使用120个五智能体的物体移动环境（其中部分观测结果共同决定唯一终点），评估3个同构的基于LLM的多智能体系统。在这些配对条件下，集体事实恢复率从72.50%降至14.17%，每个系统均出现显著下降。过程追踪与退出消融实验显示，单个虚假证词比真实证词更易被采纳，会传播至更高阶，且在欺骗者退出后仍在诚实智能体中持续存在。无一手证据的观测者会抑制错误共识，但无法提升事实恢复率。这些发现共同揭示了分布式事实恢复的脆弱性及其潜在机制：虚假证据在进入通信后，会通过其他智能体的采纳与持续传播获得集体影响力。
英文摘要
LLM-based multi-agent systems promise effective collaborative reasoning, but communication may amplify local errors into collective risks, and while existing evaluations emphasize final outcomes, they leave the reliability and propagation dynamics of distributed information aggregation unclear, so we introduce ForesightSafety-TIDE, a controlled evaluation framework that strictly pairs all-honest collaboration with controlled deception by a key evidence holder and analyzes the aggregation process through multi-stage voting, testimony adoption, and evidence-root lineage propagation, and using 120 five-agent object-movement environments where partial observations jointly determine a unique endpoint, we evaluate 3 homogeneous LLM-based multi-agent systems, and across these paired conditions, aggregate truth recovery falls from 72.50% to 14.17%, with significant declines for every system, while process tracing and exit ablations show that a single false testimony is adopted more readily than truthful testimony, propagates to higher orders, and persists through honest agents after the deceiver exits, and observers without first-hand evidence suppress incorrect consensus but do not improve truth recovery, so together, these findings reveal both the fragility of distributed fact recovery and its underlying mechanism: false evidence gains collective influence through its adoption and continued propagation by other agents after entering communication.
