用大模型打造可语音交互的狼人杀游戏，提升用户沉浸感
Verbal Werewolf: Engage Users with Verbalized Agentic Werewolf Game Framework
- 基于大模型构建双通道系统：实时推理+语音合成
- 无需外部模块即可近实时运行，显著提升交互体验
- 适合社交娱乐、人机协作研究者关注
社交推理类游戏（如狼人杀）在后疫情时代需求上升，亟需能支持人类与AI协同的智能框架。传统狼人杀依赖口语交流，非常适合利用大语言模型（LLM）的推理与对话能力。已有研究显示LLM在狼人杀中表现优于人类，但依赖外部模块导致延迟高，仅限学术使用。本文提出「Verbal Werewolf」——一个基于LLM的狼人杀系统，优化两条并行路径：由先进大模型驱动的游戏逻辑，以及微调后的文本转语音（TTS）模块，将文字输出转化为自然语音。系统无需外部决策模块，实现近实时运行，借助DeepSeek V3等现代大模型的增强推理能力，带来更拟人化、更具参与感的游戏体验，相比现有纯文本框架显著提升用户参与度。
原文摘要 · Abstract (English)
The growing popularity of social deduction games has created an increasing need for intelligent frameworks where humans can collaborate with AI agents, particularly in post-pandemic contexts with heightened psychological and social pressures. Social deduction games like Werewolf, traditionally played through verbal communication, present an ideal application for Large Language Models (LLMs) given their advanced reasoning and conversational capabilities. Prior studies have shown that LLMs can outperform humans in Werewolf games, but their reliance on external modules introduces latency that left their contribution in academic domain only, and omit such game should be user-facing. We propose \textbf{Verbal Werewolf}, a novel LLM-based Werewolf game system that optimizes two parallel pipelines: gameplay powered by state-of-the-art LLMs and a fine-tuned Text-to-Speech (TTS) module that brings text output to life. Our system operates in near real-time without external decision-making modules, leveraging the enhanced reasoning capabilities of modern LLMs like DeepSeek V3 to create a more engaging and anthropomorphic gaming experience that significantly improves user engagement compared to existing text-only frameworks.
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