How can LLMs improve multi-agent cooperation through ToM?
TOMCAT: THEORY-OF-MIND FOR COOPERATIVE AGENTS IN TEAMS VIA MULTIAGENT DIFFUSION POLICIES
February 26, 2025
https://arxiv.org/pdf/2502.18438-
This paper introduces TOMCAT, a new framework for coordinating AI agents in team-based tasks. TOMCAT helps agents understand and predict their teammates' behaviors, even if those teammates have different goals or act suboptimally. It uses a "Theory of Mind" model (ToMnet) to predict teammate actions and motivations and a diffusion model (MADiff) to generate coordinated action plans. A key feature is dynamic replanning, which allows agents to adapt to unexpected teammate actions by generating new plans as needed.
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Relevant to LLM-based multi-agent systems:
- TOMCAT offers a mechanism for LLMs to reason about the intentions and behaviors of other LLMs in a multi-agent environment.
- The ToMnet component could be adapted to leverage the rich contextual understanding of LLMs.
- MADiff provides a way to generate flexible and coordinated action plans for multiple LLMs. The dynamic replanning feature is especially relevant for real-world applications where LLM interactions are unpredictable.
- The concept of agent "profiles" used in the research can be extended to represent the diverse personalities, knowledge bases, and communication styles of different LLMs.
- While the current research relies on training data from simpler agents, future work could explore directly applying TOMCAT to interactions between pre-trained LLMs.
- The authors acknowledge the challenge of unknown teammates, which is a critical aspect of open multi-agent LLM systems where arbitrary LLMs can interact. Their proposed future research on adapting TOMCAT to handle this scenario is of particular interest.