AI RESEARCH
Distilling Bayesian Belief States into Language Models for Auditable Negotiation
arXiv CS.CL
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ArXi:2605.04507v1 Announce Type: new Negotiation agents must infer what their counterpart values, update those beliefs over dialogue turns, and choose actions under uncertainty. End-to-end large language models (LLMs) can imitate negotiation dialogue, but their opponent beliefs are usually implicit and difficult to inspect. We propose BOND (Bayesian Opponent-belief Negotiation Distillation), a framework for auditable negotiation.