MIND: Multi-agent inference for negotiation dialogue in travel planning
On the map: LLM agents and tool use → Social simulation and multi-agent debate
Abstract (arXiv)
While Multi-Agent Debate (MAD) research has advanced, its efficacy in coordinating complex stakeholder interests such as travel planning remains largely unexplored. To bridge this gap, we propose MIND (Multi-agent Inference for Negotiation Dialogue), a framework designed to simulate realistic consensus-building among travelers with heterogeneous preferences. Grounded in the Theory of Mind (ToM), MIND introduces a Strategic Appraisal phase that infers opponent willingness (w) from linguistic nuances with 90.2% accuracy. Experimental results demonstrate that MIND outperforms traditional MAD frameworks, achieving a 20.5% improvement in High-w Hit and a 30.7% increase in Debate Hit-Rate, effectively prioritizing high-stakes constraints. Furthermore, qualitative evaluations via LLM-as-a-Judge confirm that MIND surpasses baselines in Rationality (68.8%) and Fluency (72.4%), securing an overall win rate of 68.3%. These findings validate that MIND effectively models human negotiation dynamics to derive persuasive consensus.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 9 of its 15 references that are in the map. First posted on arXiv 23 Mar 2026.
Builds on
9 of 15 references.
Closest ancestors · 3
Same direction, newest first
- 2025Voting or Consensus? Decision-Making in Multi-Agent Debate26 Feb
- 2024Debate-to-Write: A Persona-Driven Multi-Agent Framework for Diverse Argument Generation28 Jun
- 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate30 May
Other references · 6
- 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,739
- 2024TravelPlanner: A Benchmark for Real-World Planning with Language Agentscited by 385
- 2017Deal or No Deal? End-to-End Learning for Negotiation Dialoguescited by 202
- 2025TripTailor: A Real-World Benchmark for Personalized Travel Planningcited by 22
- 2025TripCraft: A Benchmark for Spatio-Temporally Fine Grained Travel Planningcited by 21
- 2025Decompose-ToM: Enhancing Theory of Mind Reasoning in Large Language Models through Simulation and Task Decompositioncited by 17
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2024NegotiationToM: A Benchmark for Stress-testing Machine Theory of Mind on Negotiation Surrounding
- 2026Theory of Mind and Persuasion Beyond Conversation: Assessing the Capacity of LLMs to Induce Belief States via Planning and Action
- 2025Do Large Language Models Have a Planning Theory of Mind? Evidence from MindGames: a Multi-Step Persuasion Task
- 2025Effects of Theory of Mind and Prosocial Beliefs on Steering Human-Aligned Behaviors of LLMs in Ultimatum Games
- 2023Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs
- 2026Mind the Gap: Theory-of-Mind-Grounded Friction for Epistemic Alignment
- 2026MA2P: A Meta-Cognitive Autonomous Intelligent Agents Framework for Complex Persuasion
- 2026Multi-Agent Debate Strategies: Survey, Taxonomy, and Challenges