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        <title>LLM-Based Explicit Models of Opponents for Multi-Agent Games</title>
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        <namePart type="given">XiaoPeng</namePart>
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            <title>Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)</title>
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            <namePart type="given">Luis</namePart>
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    <abstract>In multi-agent scenarios, the ability to anticipate and respond to opponents is essential, particularly in environments involving adversarial and collaborative interactions. In this paper, we introduce Explicit Models of Opponents (EMO) based on Large Language Models (LLMs), enabling agents to better predict and adapt to diverse, dynamic multi-agent interactions. Unlike traditional methods that often simplify multi-agent interactions using a single opponent model, EMO constructs an individual model for each opponent and aligns these models working in synergy through a bi-level feedback-refinement framework. We test EMO alongside several reasoning methods in multi-player deduction games, where agents must infer hidden information about their opponents. The results show that EMO significantly enhances agents’ decision-making, outperforming traditional single-model approaches. Our findings demonstrate that EMO can be a powerful tool for enhancing LLM-based agents in complex multi-agent systems.</abstract>
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%0 Conference Proceedings
%T LLM-Based Explicit Models of Opponents for Multi-Agent Games
%A Yu, XiaoPeng
%A Zhang, Wanpeng
%A Lu, Zongqing
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F yu-etal-2025-llm
%X In multi-agent scenarios, the ability to anticipate and respond to opponents is essential, particularly in environments involving adversarial and collaborative interactions. In this paper, we introduce Explicit Models of Opponents (EMO) based on Large Language Models (LLMs), enabling agents to better predict and adapt to diverse, dynamic multi-agent interactions. Unlike traditional methods that often simplify multi-agent interactions using a single opponent model, EMO constructs an individual model for each opponent and aligns these models working in synergy through a bi-level feedback-refinement framework. We test EMO alongside several reasoning methods in multi-player deduction games, where agents must infer hidden information about their opponents. The results show that EMO significantly enhances agents’ decision-making, outperforming traditional single-model approaches. Our findings demonstrate that EMO can be a powerful tool for enhancing LLM-based agents in complex multi-agent systems.
%U https://aclanthology.org/2025.naacl-long.41/
%P 892-911

Markdown (Informal)

[LLM-Based Explicit Models of Opponents for Multi-Agent Games](https://aclanthology.org/2025.naacl-long.41/) (Yu et al., NAACL 2025)

• LLM-Based Explicit Models of Opponents for Multi-Agent Games (Yu et al., NAACL 2025)

ACL

• XiaoPeng Yu, Wanpeng Zhang, and Zongqing Lu. 2025. LLM-Based Explicit Models of Opponents for Multi-Agent Games. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 892–911, Albuquerque, New Mexico. Association for Computational Linguistics.