理解多智能体大语言模型系统中的通信失败

arXiv cs.AI 论文
multi-agent-systems llm-reasoning communication-failures taxonomy consensus failure-analysis

摘要

提出一个基于语料库的多智能体LLM系统通信失败分类体系，基于3211条失败轨迹分析发现失败主要源于恢复/校正与共识/决策问题，而非首轮错误，并建议加强分歧处理与少数信号保留。

arXiv:2610.32060v1 Announce Type: new Abstract: Multi-agent LLM systems aim to improve reasoning through debate, evidence sharing, and mutual correction, but interaction can also introduce failures that final accuracy alone cannot explain. Agents may converge too early, ignore dissent, suppress correct minority answers, or preserve a wrong framing across rounds. We present a corpus-grounded taxonomy of communication failures, built from 3,211 failed multi-round trajectories across medical QA, coding, finance, multi-hop reasoning, ethics, and embodied text tasks. Using this taxonomy, we analyze failure distributions across datasets and system designs, round-level error dynamics, recovery hardness, majority transitions, minority-signal suppression, and self-reported confidence. We find that failures are dominated by recovery/correction and consensus/decision problems rather than isolated first-round mistakes, with many emerging after agents first observe peer responses. Based on these findings, we recommend stronger disagreement handling, evidence verification, minority-signal preservation, and recovery checks before finalization.
查看原文

相似文章

97.2% 的安全拦截机制在多跳智能体交接后不再发挥拦截作用（arXiv 新论文）。你会传递哪些信息，以确保“必须”始终是“必须”？

Reddit r/AI_Agents

一篇新的 arXiv 论文（《当“必须”变成“也许”》，Sun 等人著）发现，在经过多跳智能体交接后，97.2% 的上游安全阻断约束会因压缩、计划同化或责任归属推延而失去约束力——即使权限保持不变——这表明，交接产物本身需要采用明确保留约束的设计。