Revac: A Social Deduction Reasoning Agent

arXiv cs.AI / 4/22/2026

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Key Points

  • Revac-8 is an AI reasoning agent designed for social deduction games like Mafia, focusing on inference under uncertainty and dealing with deceptive, incomplete information rather than perfect-information search.
  • The agent’s performance in the MindGames Arena “Social Deduction” track earned it first place, reflecting strong real-game strategic decision-making.
  • Revac-8 evolved from a simple two-stage reasoning setup into a multi-module system that combines memory-based player profiling, social-graph analysis of accusations/defenses, and adaptive tone selection for communication.
  • The work emphasizes that structured memory and communication adaptability are key to excelling in high-stakes, human-interaction-driven environments.
  • The contribution is framed as both a system design and an evaluation study, demonstrating how modular reasoning and social modeling can improve outcomes in deception-heavy settings.

Abstract

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.