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Adaptive Theory of Mind for LLM-based Multi-Agent Coordination

AAAI 2026 · Multi-agent systems and games › LLM agents · approach: LLM multi-agent decision making · 0 citations (OpenAlex)

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Shows that mismatched theory-of-mind (ToM) orders between LLM agents hurt coordination and proposes A-ToM, an agent that maintains ToM-0/1/2 hypothetical partners and uses Follow-the-Leader or Hedge expert-advice learning to align with the partner's ToM order.

Problem

LLM-based agents with explicit higher-order Theory of Mind often fail to coordinate with partners because their ToM reasoning depths are misaligned, leading to insufficient or excessive reasoning about the partner.

Approach

Frame ToM-order alignment as an online expert-advice problem: maintain hypothetical ToM-0/1/2 agents as experts, predict the partner's action from the expert chosen by FTL or Hedge based on past prediction accuracy, and then best-respond to that prediction.

Key results

Surprising findings

Limitations

Experimental setup

Benchmarks: Repeated matrix game (Memory-1, Memory-N) Grid World Navigation Game 1 Grid World Navigation Game 2 Overcooked

Metrics: points completion time

Baselines: ToM-0 ToM-1 ToM-2 Greedy PBT

Builds on: expert advice Follow-the-Leader Hedge theory of mind prompting

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