MIND:MULTI-AGENT INFERENCE FOR NEGOTIATION DIALOGUE IN TRAVEL PLANNING

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%. By effectively preventing the "tyranny of the average'' through strategic deliberation, MIND models human-like negotiation dynamics to derive persuasive and equitable consensus.