Abstract
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.
Abstract (translated)
中文脱口秀的生成超越了单纯的文字生成，需要具备文化根基的幽默感、精准的节奏把握、舞台表演提示以及隐含的多步骤推理能力。此外，常用的中文幽默数据集往往更适合于理解和评估幽默，而非长期形式的脱口秀创作，这使得直接监督方法与目标任务不匹配。为了解决这些挑战，我们提出了OpenMic系统，这是一个基于AutoGen构建的端到端多代理系统，它可以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，并进一步制作成有声笑料视频。 OpenMic通过在多个回合迭代规划中协调多个专业化的代理来共同优化幽默感、节奏和可执行性。为了缓解数据集与任务之间的不匹配问题，我们在生成过程中引入了检索增强生成（RAG）技术，用于材料的定位以及观点扩展，并且训练了一个专门的JokeWriter以更好地内化脱口秀特有的铺垫-笑点结构及长程回扣技巧。 这种综合方法不仅提高了中文脱口秀内容的质量和吸引力，还确保了系统输出具有高度的文化相关性和娱乐性。
URL
https://arxiv.org/abs/2601.08288
