用视觉语言模型自动修复图表设计错误，提升可读性与准确性。
De-rendering, Reasoning, and Repairing Charts with Vision-Language Models
- 从图像重建图表结构，结合视觉与语言推理识别问题
- 在1000张图表上生成10452条建议，覆盖10类设计缺陷
- 适合数据可视化作者、教育者及需要提升图表质量的用户
数据可视化在科学传播、新闻报道和日常决策中至关重要，但常因设计错误导致误解。传统规则工具无法理解上下文，通用大模型缺乏可视化原则训练，反馈不可靠。本文提出一种融合图表反渲染、自动化分析与迭代优化的框架：系统从图像重建图表结构，利用视觉-语言模型识别设计缺陷，并基于可视化研究原则提出具体修改建议。用户可选择性应用改进并重新渲染，形成反馈循环，既提升图表质量，也促进可视化素养培养。在Chart2Code基准的1000张图表上，系统生成10452条建议，聚类为10个清晰类别（如坐标轴格式、颜色可访问性、图例一致性）。结果表明，基于LLM的推荐系统能提供结构化、原则驱动的反馈，为智能可视化创作工具开辟新路径。
原文摘要 · Abstract (English)
Data visualizations are central to scientific communication, journalism, and everyday decision-making, yet they are frequently prone to errors that can distort interpretation or mislead audiences. Rule-based visualization linters can flag violations, but they miss context and do not suggest meaningful design changes. Directly querying general-purpose LLMs about visualization quality is unreliable: lacking training to follow visualization design principles, they often produce inconsistent or incorrect feedback. In this work, we introduce a framework that combines chart de-rendering, automated analysis, and iterative improvement to deliver actionable, interpretable feedback on visualization design. Our system reconstructs the structure of a chart from an image, identifies design flaws using vision-language reasoning, and proposes concrete modifications supported by established principles in visualization research. Users can selectively apply these improvements and re-render updated figures, creating a feedback loop that promotes both higher-quality visualizations and the development of visualization literacy. In our evaluation on 1,000 charts from the Chart2Code benchmark, the system generated 10,452 design recommendations, which clustered into 10 coherent categories (e.g., axis formatting, color accessibility, legend consistency). These results highlight the promise of LLM-driven recommendation systems for delivering structured, principle-based feedback on visualization design, opening the door to more intelligent and accessible authoring tools.
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