RESEARCH · RESEARCH · #2559
Trust–Agreement Gap in Repeated Multi‑Party LLM Negotiation (arXiv:2610.31971v1)
This arXiv preprint introduces a repeated, multi‑party, multi‑issue negotiation framework where recurring LLM agents maintain pairwise trust estimates while one participant covertly attempts to block agreement. The study finds a "trust–agreement gap": agents can identify the adversary within a few games, and explicit trust can raise agreement rates by up to 23 percentage points, but accurate trust estimates only improve outcomes when agents are explicitly guided to use them in decision-making.
KEY POINTS
- This arXiv preprint introduces a repeated, multi‑party, multi‑issue negotiation framework where recurring LLM agents maintain pairwise trust estimates while one participant covertly attempts to block agreement.
- The study finds a "trust–agreement gap": agents can identify the adversary within a few games, and explicit trust can raise agreement rates by up to 23 percentage points, but accurate trust estimates only improve outcomes when agents are explicitly guided to use them in decision-making.
- The result matters because it shows that forming trust estimates alone is insufficient — system designers must explicitly integrate and guide the use of trust in LLM decision processes to improve repeated multi‑party negotiation outcomes.
WHY IT MATTERS
The result matters because it shows that forming trust estimates alone is insufficient — system designers must explicitly integrate and guide the use of trust in LLM decision processes to improve repeated multi‑party negotiation outcomes.