Enhancing Reasoning Capabilities of Large Language Models through an External Thinker Module: A Case Study in the Game of Werewolf

This paper presents an innovative framework that integrates Large Language Models (LLMs) with an external Thinker module to enhance the reasoning capabilities of LLM-based agents. The framework forms a reasoning hierarchy where LLMs handle intuitive System-1 tasks, while the Thinker focuses on cognitive System-2 tasks that require complex logical analysis and domain-specific knowledge.