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This paper studies how LLM agents negotiate in a dynamic supply chain bargaining problem, benchmarking nine models from OpenAI, Google, and Alibaba against a Bayesian equilibrium and finding that capability, provider identity, and prompt design shape surplus creation and division.
Announcement of the GLEE Competition at IAB@NeurIPS 2026, where participants build AI agents for bargaining, negotiation, and persuasion in live multi-turn games, with a $6,000 prize pool and optional paper submission.
Study shows LLM agents can model counterparty preferences in negotiation but fail to turn that knowledge into strategic bargaining to improve outcomes, limiting their effectiveness in multi-turn negotiations.