mlsanityData to 14 Oct 2026

Preference Estimation via Opponent Modeling in Multi-Agent Negotiation

arXiv 2604.15687 ↗First posted 17 Apr 2026cs.CLcited by 0

On the map: LLM agents and tool use → Social simulation and multi-agent debate

Abstract (arXiv)

Automated negotiation in complex, multi-party and multi-issue settings critically depends on accurate opponent modeling. However, conventional numerical-only approaches fail to capture the qualitative information embedded in natural language interactions, resulting in unstable and incomplete preference estimation. Although Large Language Models (LLMs) enable rich semantic understanding of utterances, it remains challenging to quantitatively incorporate such information into a consistent opponent modeling. To tackle this issue, we propose a novel preference estimation method integrating natural language information into a structured Bayesian opponent modeling framework. Our approach leverages LLMs to extract qualitative cues from utterances and converts them into probabilistic formats for dynamic belief tracking. Experimental results on a multi-party benchmark demonstrate that our framework improves the full agreement rate and preference estimation accuracy by integrating probabilistic reasoning with natural language understanding.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 6 of its 15 references that are in the map. First posted on arXiv 17 Apr 2026.

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6 of 15 references.

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Same direction, newest first

  1. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate30 May

Other references · 5

  1. 2023Evaluating Large Language Models in Theory of Mind Taskscited by 222
  2. 2023Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedbackcited by 207
  3. 2016Opponent Modeling in Deep Reinforcement Learningcited by 97
  4. 2023Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiationcited by 96
  5. 2024NegotiationToM: A Benchmark for Stress-testing Machine Theory of Mind on Negotiation Surroundingcited by 51

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  5. 2026Predicting Decisions of AI Agents from Limited Interaction through Text-Tabular Modeling
  6. 2025The Price of Thought: A Multilingual Analysis of Reasoning, Performance, and Cost of Negotiation in Large Language Models
  7. 2025Strategic Tradeoffs Between Humans and AI in Multi-Agent Bargaining
  8. 2024AgreeMate: Teaching LLMs to Haggle