English(EN) Multi-agent discussion gains less when dissent is withheld

新模型解释了大型语言模型智能体如何达成错误共识

作者 PulseAugur 编辑部 · [2 个来源] · 2026-09-29 18:00

一个新模型解释了大型语言模型（LLM）的多智能体系统有时会因为智能体压制异议而达成错误共识。该模型识别出一个关键的压制率，低于该比率时，讨论可以提高准确性。在大型语言模型和 HiddenBench、MedEInst 等基准测试上的实证测试证实，指示智能体避免压制异议可以增加讨论带来的收益。 AI

影响为提高多智能体大型语言模型系统的可靠性提供了理论框架和实证证据。

排序理由该集群包含一篇研究论文，详细介绍了多智能体大型语言模型行为的新模型。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

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arXiv cs.CL TIER_1 English(EN) · Chand Sahil Mansuri, Xin Wang, Mengying Li, Bryan Acton, Rory Eckardt, Dhaval Patel, Sadamori Kojaku · 2026-10-01 04:00

当压制异议时，多智能体讨论的收益会降低

arXiv:2609.38324v1 Announce Type: cross Abstract: Multi-agent systems of LLMs add discussion to majority voting and are therefore expected to be more capable. However, empirical reports conflict on whether discussion improves accuracy or leads to an incorrect consensus. Here, we …
arXiv cs.MA (Multiagent) TIER_1 English(EN) · Sadamori Kojaku · 2026-09-29 18:00

当压制异议时，多智能体讨论的收益会降低

Multi-agent systems of LLMs add discussion to majority voting and are therefore expected to be more capable. However, empirical reports conflict on whether discussion improves accuracy or leads to an incorrect consensus. Here, we introduce a parsimonious model that explains when …