# OpenMic: A Multi-Agent-Based Stand-Up Comedy Generation System

arXiv: https://arxiv.org/abs/2601.08288 · HTML: https://mlsanity.com/paper/2601.08288/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 9 references that are in the map.
- First posted on arXiv 13 Jan 2026.

## Builds on (most cited first, 6 of 6)

- [HoLLMwood: Unleashing the Creativity of Large Language Models in Screenwriting via Role Playing](https://mlsanity.com/paper/2406.11683/) (2024)
- [AI Humor Generation: Cognitive, Social and Creative Skills for Effective Humor](https://mlsanity.com/paper/2502.07981/) (2025)
- [Can Language Models Make Fun? A Case Study in Chinese Comical Crosstalk](https://mlsanity.com/paper/2207.00735/) (2022)
- [Who's Laughing Now? An Overview of Computational Humour Generation and Explanation](https://mlsanity.com/paper/2509.21175/) (2025)
- [CFunModel: A "Funny" Language Model Capable of Chinese Humor Generation and Processing](https://mlsanity.com/paper/2503.20417/) (2025)
- [Assessing the Capabilities of LLMs in Humor:A Multi-dimensional Analysis of Oogiri Generation and Evaluation](https://mlsanity.com/paper/2511.09133/) (2025)

## Abstract (arXiv)

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.