{
  "annotation_id": "ffce1e2a-083e-42df-bf50-fb9f488be343",
  "date_created": "2026-02-17T05:53:15.967000Z",
  "date_modified": "2026-02-17T05:53:15.967000Z",
  "file_hash": "a848447029694275993286339a6e9a1096cd55b6832ebe4f68a48ba40d2d5783",
  "private": false,
  "record": {
    "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.",
    "arxiv_id": "2601.08288",
    "authors": [
      "Yuyang Wu",
      "Hanzhong Cao",
      "Jianhao Chen",
      "Yufei Li"
    ],
    "categories": [
      "cs.AI"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "OpenMic: A Multi-Agent-Based Stand-Up Comedy Generation System",
    "url": "https://arxiv.org/abs/2601.08288",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "df4be65a-b662-4ebe-ba3b-efaeea98a14c",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}