LLM-Coordination: Evaluating and Analyzing Multi-agent Coordination Abilities in Large Language Models
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Large Language Models (LLMs) have demonstrated emergent common-sense reasoning and Theory of Mind (ToM) capabilities, making them promising candidates for developing coordination agents.This study introduces the LLM-Coordination Benchmark, a novel benchmark for analyzing LLMs in the context of Pure Coordination Settings, where agents must cooperate to maximize gains.Our benchmark evaluates LLMs through two distinct tasks.The first is Agentic Coordination, where LLMs act as proactive participants in four pure coordination games.The second is Coordination Question Answering (CoordQA), which tests LLMs on 198 multiplechoice questions across these games to evaluate three key abilities: Environment Comprehension, ToM Reasoning, and Joint Planning.Results from Agentic Coordination experiments reveal that LLM-Agents excel in multi-agent coordination settings where decision-making primarily relies on environmental variables but face challenges in scenarios requiring active consideration of partners' beliefs and intentions.The CoordQA experiments further highlight significant room for improvement in LLMs' Theory of Mind reasoning and joint planning capabilities.Zero-Shot Coordination (ZSC) experiments in the Agentic Coordination setting demonstrate that LLM agents, unlike RL methods, exhibit robustness to unseen partners.These findings indicate the potential of LLMs as Agents in pure coordination setups and underscore areas for improvement.
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This signals a deeper agent interaction dynamic challenge: the collapse of 'theory of mind', where agents fail to accurately model other agents' informational needs.
“This signals a deeper agent interaction dynamic challenge: the collapse of 'theory of mind' [48], where agents fail to accurately model other agents' informational needs.”
From Why Do Multi-Agent LLM Systems Fail? (2025) · cites this paper as [48]
The Discussion (section 5; page unavailable) says LLM agents struggle with planning when advanced theory-of-mind reasoning is required in Hanabi. Appendix D.2 identifies assessing what information a partner needs as a theory-of-mind question. The paper does not establish a general collapse of theory of mind or show that agents consistently fail to model partners’ informational needs; it notes that GPT-4-turbo performs reasonably well. (LLM, conf=0.82)
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- Authors
- Agashe, S., Fan, Y., Reyna, A., & Wang, X. E.
- Affiliations
- University of California, Santa Cruz (US)
- Venue
- Findings of the Association for Computational Linguistics: NAACL 2025
- Published
- 2025
- Pages
- 8038-8057
- OpenAlex
- W4411119815
- Cited by
- 32
- References
- 0
- Citations by year
- 2026: 13 · 2025: 14 · 2024: 5
- FWCI
- 25.60
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: NAACL (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
- Social Sciences › Decision Sciences › Management Science and Operations Research › Game Theory and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- large language models, multi-agent coordination, pure coordination games, theory of mind, collaborative planning, emergent capabilities
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