WereWolf-Plus: An Update of Werewolf Game setting Based on DSGBench
This work addresses the problem of incomplete evaluation and poor scalability in benchmarking multi-agent strategic reasoning for researchers in AI and multi-agent systems, representing an incremental update to existing platforms.
The authors tackled the limitations of existing Werewolf game benchmarks for evaluating LLM-based agents by proposing WereWolf-Plus, a platform that offers customizable roles, flexible model assignments, and comprehensive metrics, resulting in improved extensibility and reliability for multi-agent strategic reasoning research.
With the rapid development of LLM-based agents, increasing attention has been given to their social interaction and strategic reasoning capabilities. However, existing Werewolf-based benchmarking platforms suffer from overly simplified game settings, incomplete evaluation metrics, and poor scalability. To address these limitations, we propose WereWolf-Plus, a multi-model, multi-dimensional, and multi-method benchmarking platform for evaluating multi-agent strategic reasoning in the Werewolf game. The platform offers strong extensibility, supporting customizable configurations for roles such as Seer, Witch, Hunter, Guard, and Sheriff, along with flexible model assignment and reasoning enhancement strategies for different roles. In addition, we introduce a comprehensive set of quantitative evaluation metrics for all special roles, werewolves, and the sheriff, and enrich the assessment dimensions for agent reasoning ability, cooperation capacity, and social influence. WereWolf-Plus provides a more flexible and reliable environment for advancing research on inference and strategic interaction within multi-agent communities. Our code is open sourced at https://github.com/MinstrelsyXia/WereWolfPlus.