Revac: A Social Deduction Reasoning Agent
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Abstract (arXiv)
Social deduction games such as Mafia present a unique AI challenge: players must reason under uncertainty, interpret incomplete and intentionally misleading information, evaluate human-like communication, and make strategic elimination decisions. Unlike deterministic board games, success in Mafia depends not on perfect information or brute-force search, but on inference, memory, and adaptability in the presence of deception. This work presents the design and evaluation of Revac-8, an AI agent developed for the Social Deduction track of the MindGames Arena competition, where it achieved first place. The final agent evolved from a simple two-stage reasoning system into a multi-module architecture that integrates memory-based player profiling, social-graph analysis of accusations and defenses, and dynamic tone selection for communication. These results highlight the importance of structured memory and adaptive communication for achieving strong performance in high-stakes social environments.
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References · 7
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
- 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
- 2023Theory of Mind for Multi-Agent Collaboration via Large Language Modelscited by 135
- 2023Playing the Werewolf game with artificial intelligence for language understandingcited by 16
- 2025Training Language Models for Social Deduction with Multi-Agent Reinforcement Learningcited by 16
- 2021RLupus: Cooperation through emergent communication in The Werewolf social deduction gamecited by 11
- 2024Enhancing Dialogue Generation in Werewolf Game Through Situation Analysis and Persuasion Strategiescited by 3
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