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arXiv AI By 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

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The paper examines how large language model (LLM) agents can use trust estimates in repeated multi‑party, multi‑issue negotiations where one participant covertly tries to block agreement. By introducing a framework where agents maintain pairwise trust scores across successive games, the study compares trust‑aware and trust‑agnostic agents. Results show a trust–agreement gap: while trust‑aware agents quickly spot the adversary and trust can raise agreement rates by up to 23 percentage points, this benefit only materializes when agents are explicitly guided to incorporate trust into their decision‑making.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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