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LLM agents struggle with trust in multi-party negotiations

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the challenges of trust in repeated multi-party negotiations involving Large Language Model (LLM) agents. The study introduces a framework where agents can form and use trust estimates from past interactions. While trust-aware agents can identify adversaries, the research found a "trust--agreement gap," indicating that accurate trust estimates do not automatically lead to better negotiation outcomes. The paper suggests that explicitly guiding agents to use trust in their decision-making can improve agreement rates by up to 23 percentage points, highlighting the critical role of how trust is assessed and applied. AI

IMPACT Highlights the need for explicit trust mechanisms in LLM agents for improved negotiation outcomes.

RANK_REASON Research paper published on arXiv detailing findings about LLM agent behavior. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM agents struggle with trust in multi-party negotiations

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Research paper published on arXiv detailing findings about LLM agent behavior. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

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

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

    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…

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