用多智能体系统生成3-5分钟中文脱口秀，兼顾笑点、节奏与表演性。
OpenMic: A Multi-Agent-Based Stand-Up Comedy Generation System
- 多智能体协同迭代规划，分工优化笑点、节奏与舞台表现
- 引入RAG检索增强生成，解决数据与任务不匹配问题
- 微调专用段子模型，强化上下文呼应和长线笑点设计
中文脱口秀生成不仅需要文本创作，还需文化语境下的幽默感、精准节奏把控及舞台表演提示，并依赖隐含的多步推理。然而，现有中文幽默数据集多用于幽默理解与评估，难以支撑长篇脱口秀生成，直接监督存在任务错位。为此，我们提出OpenMic，一个基于AutoGen的端到端多智能体系统，可将用户提供的生活话题转化为3-5分钟的中文脱口秀表演，并生成带配音的喜剧视频。OpenMic通过多轮迭代式规划，协调多个专业智能体共同优化幽默性、时间控制与可表演性。为缓解数据-任务错配，系统采用检索增强生成（RAG）实现内容扎根与创意扩展，并微调专用JokeWriter模型，更好掌握脱口秀特有的“铺垫-笑点”结构与长程回调机制。
原文摘要 · Abstract (English)
Chinese stand-up comedy generation goes beyond plain text generation, requiring culturally grounded humor, precise timing, stage-performance cues, and implicit multi-step reasoning. Moreover, commonly used Chinese humor datasets are often better suited for humor understanding and evaluation than for long-form stand-up generation, making direct supervision misaligned with the target task. To address these challenges, we present OpenMic, an end-to-end multi-agent system built on AutoGen that transforms a user-provided life topic into a 3-5 minute Chinese stand-up performance and further produces a narrated comedy video. OpenMic orchestrates multiple specialized agents in a multi-round iterative loop-planning to jointly optimize humor, timing, and performability. To mitigate the dataset-task mismatch, we augment generation with retrieval-augmented generation (RAG) for material grounding and idea expansion, and we fine-tune a dedicated JokeWriter to better internalize stand-up-specific setup-punchline structures and long-range callbacks.
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