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Revac: A Social Deduction Reasoning Agent

arXiv 2604.19523 ↗First posted 21 Apr 2026cs.AIcited by 0

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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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 10 references that are in the map. First posted on arXiv 21 Apr 2026.

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References · 7

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
  3. 2023Theory of Mind for Multi-Agent Collaboration via Large Language Modelscited by 135
  4. 2023Playing the Werewolf game with artificial intelligence for language understandingcited by 16
  5. 2025Training Language Models for Social Deduction with Multi-Agent Reinforcement Learningcited by 16
  6. 2021RLupus: Cooperation through emergent communication in The Werewolf social deduction gamecited by 11
  7. 2024Enhancing Dialogue Generation in Werewolf Game Through Situation Analysis and Persuasion Strategiescited by 3

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