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English(EN) Knowing Whom to Trust is not Enough: The Trust--Agreement Gap in Repeated Multi-Party LLM Negotiation

LLM代理在多方谈判中面临信任-协议差距

作者 PulseAugur 编辑部 · [2 个来源] ·

一篇新研究论文探讨了涉及大型语言模型（LLM）代理的重复多方谈判中信任的挑战。该研究引入了一个框架，代理可以从中形成并利用先前交互的信任估计。虽然具有信任意识的代理可以识别对手，但研究发现了一个“信任-协议差距”，表明准确的信任估计并不能自动带来更好的谈判结果。该论文认为，关于如何评估和纳入信任的明确指导对于提高此类场景下的协议达成率至关重要。 AI

影响 强调了在LLM代理中建立明确的信任机制以实现有效谈判的必要性，这可能会影响未来的多代理系统设计。

排序理由 该集群包含一篇在arXiv上发表的研究论文，详细介绍了一项关于LLM代理的新研究。

在 arXiv cs.AI 阅读 →

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

LLM代理在多方谈判中面临信任-协议差距

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该集群包含一篇在arXiv上发表的研究论文，详细介绍了一项关于LLM代理的新研究。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Amit Sarker, Moumita Choudhury, Saaduddin Mahmud, Shlomo Zilberstein ·

    知晓信任对象并非易事：重复多方LLM谈判中的信任-协议鸿沟

    arXiv:2610.31971v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly participate in negotiations on behalf of individuals and organizations, yet they remain vulnerable to manipulation, particularly in repeated multi-party interactions. Classical multi-ag…

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Shlomo Zilberstein ·

    知道信任谁还不够：重复多方LLM谈判中的信任-协议差距

    Large language model (LLM) agents increasingly participate in negotiations on behalf of individuals and organizations, yet they remain vulnerable to manipulation, particularly in repeated multi-party interactions. Classical multi-agent systems address this challenge through expli…

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