arXiv cs.MA· Amit Sarker, Moumita Choudhury, Saaduddin Mahmud, Shlomo Zilberstein·· 12 小时前AI 评分34
LLM 智能体在重复多方谈判中的信任—协议差距
Knowing Whom to Trust is not Enough: The Trust--Agreement Gap in Repeated Multi-Party LLM Negotiation
AI 导读
研究提出重复多方多议题 LLM 谈判框架，让智能体在每轮博弈后维护成对信任估计并带入后续谈判。结果显示存在“信任—协议差距”：信任感知型智能体能在几轮内识别出暗中阻挠协议的对手，但准确信任估计并不总能带来更好的协议。只有当智能体被明确引导在决策中使用信任时，协议率才最多提升 23 个百分点。
正文 · 原文
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-agent systems address this challenge through explicit trust and reputation models, whereas existing work on LLM negotiation largely treats trust as an emergent phenomenon. We investigate whether LLM agents can form useful trust estimates from prior interactions and leverage them to improve future negotiation outcomes. We introduce a repeated, multi-party, multi-issue negotiation framework in which recurring agents participate in a sequence of negotiation games while one participant covertly attempts to prevent agreement. After each game, agents assess their counterparts and maintain pairwise trust estimates that carry over to subsequent negotiations. We compare trust-aware and trust-agnostic agents and analyze how explicit trust influences negotiation outcomes. Our results reveal a \emph{trust--agreement gap}: trust-aware agents can identify the adversary within a few games, but accurate trust estimates do not consistently translate into better agreements. Trust increases agreement rates by up to 23 percentage points, but only when agents are explicitly guided to use it in their decisions. Explicit trust can therefore improve repeated LLM negotiations, but its value depends critically on how trust is assessed and incorporated into agents' reasoning.
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA) |
| Cite as: | arXiv:2610.31971 [cs.AI] |
| (or arXiv:2610.31971v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.31971 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Amit Sarker [view email]
[v1]
Thu, 22 Oct 2026 22:24:10 UTC (698 KB)
来源：arXiv cs.MA · arxiv.org