Mind-Map Agent: Enhancing Cooperative Task Planning through Communication Alignment with Large Language Models

ICLR 2026 Conference Submission17451 Authors

Keywords: Embodied Agents, Natural Language Cooperation, Human-Robot Interaction

TL;DR: Mind-Map Memory enhances cooperative task planning in embodied agents by aligning natural language communication with structured reasoning, enabling collaboration with fewer conversations by mitigating confabulation and redundant communication.

Abstract: Embodied agents that collaborate with humans through natural language have become an active area of research, offering flexibility in cooperative planning and execution. Debate-based approaches often depend on repeated consensus procedures, which can increase dialogue frequency and risk over-communication. At the same time, LLMs are prone to hallucination during dialogue processing, sometimes causing confabulation and reducing consistency in long-term strategies. We introduce the Mind-Map Agent, an approach that guides reasoning with explicit cooperative strategies while maintaining structured long-term memory to disentangle dialogue, task state, and planning context. The generated Mind-Maps support coherent long-horizon planning, reduce redundant dialogue, and enhance interpretability in multi-agent interaction. Evaluations on Communicative Watch-and-Help and ThreeDWorld Multi-Agent Transport indicate that the Mind-Map Agent achieves more stable efficiency compared to classical planners and LLM agents across different model scales and environments. Our results suggest that Mind-Map reasoning enables cooperative agents to accomplish tasks with fewer conversations while sustaining effective collaboration.

Primary Area: applications to robotics, autonomy, planning

Submission Number: 17451