{
  "added_at": "2026-10-20",
  "arxiv_id": "2606.31732",
  "arxiv_version_reviewed": "v1",
  "authors": [
    "Yaozhi Zheng",
    "Yilei Jiang",
    "Manyuan Zhang",
    "Yuxuan Wan",
    "Kaituo Feng",
    "Tianshuo Peng",
    "Bo Zhang",
    "Xiangyu Yue"
  ],
  "bibtex_url": "https://awesomerea.org/papers/citations/paper-unicoder.bib",
  "code_url": "https://github.com/JimmyZhengyz/unicoder",
  "date_basis": "arXiv v1 submission date; no separate proceedings or journal publication was verified.",
  "date_basis_zh": "采用 arXiv v1 的提交日期，未核实另行出版的会议论文集或期刊版本。",
  "date_precision": "day",
  "description_en": "UniCoder trains a visual-to-code model for charts, SVGs and webpages. Its reinforcement-learning objective combines rendering checks, image similarity and code-derived visual attributes; low-reward training groups receive reference-code trajectories to improve exploration.",
  "description_zh": "UniCoder 面向图表、SVG 与网页训练视觉到代码模型。强化学习目标结合渲染检查、图像相似度和代码中的视觉属性，并向低奖励训练组加入参考代码轨迹以改善探索。",
  "doi": "10.48550/arXiv.2606.31732",
  "entry_url": "https://awesomerea.org/papers/paper-unicoder/",
  "entry_url_zh": "https://awesomerea.org/zh/papers/paper-unicoder/",
  "evidence_urls": [
    "https://arxiv.org/abs/2606.31732v1",
    "https://arxiv.org/html/2606.31732v1",
    "https://github.com/JimmyZhengyz/unicoder/blob/2844ebd5908d563338580b1bd60f61032bf8fc24/README.md",
    "https://github.com/JimmyZhengyz/unicoder/blob/2844ebd5908d563338580b1bd60f61032bf8fc24/MODEL_RELEASE.md",
    "https://github.com/JimmyZhengyz/unicoder/blob/2844ebd5908d563338580b1bd60f61032bf8fc24/LICENSE",
    "https://github.com/JimmyZhengyz/unicoder/blob/2844ebd5908d563338580b1bd60f61032bf8fc24/THIRD_PARTY_NOTICES.md"
  ],
  "first_arxiv_date": "2026-06-30",
  "id": "paper-unicoder",
  "json_url": "https://awesomerea.org/papers/paper-unicoder/record.json",
  "kind": "research",
  "markdown_url": "https://awesomerea.org/papers/paper-unicoder/index.md",
  "markdown_url_zh": "https://awesomerea.org/zh/papers/paper-unicoder/index.md",
  "metadata_notes": "At 2844ebd, the repository releases ChartMimic/UniSVG evaluation wrappers, not model weights, full training code or datasets. Original repository code is Apache-2.0; adapted benchmarks retain their own terms. The arXiv title includes “UniCoder”; the HTML heading and software citation omit that prefix.",
  "metadata_notes_zh": "仓库 2844ebd 版本发布 ChartMimic／UniSVG 评测封装，未发布模型权重、完整训练代码或数据集。仓库原创代码采用 Apache-2.0，改编基准仍保留原条款。arXiv 标题包含“UniCoder”前缀，HTML 标题及软件引用则省略该前缀。",
  "pdf_url": "https://arxiv.org/pdf/2606.31732v1",
  "project_url": null,
  "publication_date": "2026-06-30",
  "publication_status": "preprint",
  "record_type": "paper",
  "runtime_tested": false,
  "scope_limitations_en": "Experiments concern static visual reconstruction with one 8B backbone, including Design2Code and ScreenBench. Reference code is used during training; successful rendering does not prove interactive behavior. Extending the attribute extractor to new domains requires new schemas/prompts. Interactive interfaces, animation and mobile-app behavior are not evaluated.",
  "scope_limitations_zh": "实验以一个 8B 基座模型研究静态视觉重建，包含 Design2Code 与 ScreenBench。参考代码用于训练，渲染成功不证明交互行为正确；新领域需要调整属性模式与提示词。交互界面、动画及移动应用行为未被评测。",
  "targets": [
    "website"
  ],
  "title": "UniCoder: Unified Visual-to-Code Generation via Symbolic Rewards and Reference-Guided Code Optimization",
  "title_zh": "UniCoder：利用符号奖励与参考代码引导优化的统一视觉到代码生成",
  "url": "https://arxiv.org/abs/2606.31732",
  "venue": "arXiv",
  "verification": "source-reviewed",
  "verified_at": "2026-10-20",
  "why_fits_en": "Explores training signals for recovering code from visual website references.",
  "why_fits_zh": "探索根据网页视觉参考恢复代码的训练信号。",
  "year": 2026
}