@inproceedings{lin-etal-2026-autofigure,
    title = "{A}uto{F}igure-Edit: Generating Editable Scientific Illustrations via Reference-Guided Styling",
    author = "Lin, Zhen  and
      Xie, Qiujie  and
      Zhu, Minjun  and
      Li, Shichen  and
      Sun, QiYao  and
      Gu, Enhao  and
      Ding, Yiran  and
      Sun, Ke  and
      Guo, Fang  and
      Lu, Panzhong  and
      Ning, Zhiyuan  and
      Weng, Yixuan  and
      Zhang, Yue",
    editor = "Durrett, Greg  and
      Jian, Ping",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-demo.6/",
    pages = "57--67",
    ISBN = "979-8-89176-392-0",
    abstract = "High-quality scientific illustrations are essential for communicating complex scientific and technical concepts, yet existing automated systems remain limited in editability, stylistic controllability, and efficiency. We present AutoFigure-Edit, an end-to-end system that generates fully editable scientific illustrations from long-form scientific text while enabling flexible style adaptation through user-provided reference images. By combining long-context understanding, reference-guided styling, and native SVG editing, it enables efficient creation and refinement of high-quality scientific illustrations. To facilitate further progress in this field, we release the video at https://youtu.be/10IH8SyJjAQ, the full codebase at https://github.com/ResearAI/AutoFigure-Edit and provide a live demo for easy access and interactive use at https://autofigure.cc/."
}