SciFig: Towards Automating Scientific Figure Generation

• URL: http://arxiv.org/abs/2601.04390v1
• Date: Wed, 07 Jan 2026 20:56:58 GMT
• Title: SciFig: Towards Automating Scientific Figure Generation
• Authors: Siyuan Huang, Yutong Gao, Juyang Bai, Yifan Zhou, Zi Yin, Xinxin Liu, Rama Chellappa, Chun Pong Lau, Sayan Nag, Cheng Peng, Shraman Pramanick,
• Abstract summary: SciFig is an end-to-end AI agent system that generates publication-ready pipeline figures directly from research paper texts.<n>We introduce a rubric-based evaluation framework that analyzes 2,219 real scientific figures to extract evaluation rubrics.<n>SciFig demonstrates remarkable performance: achieving 70.1$%$ overall quality on dataset-level evaluation and 66.2$%$ on paper-specific evaluation.
• Score: 41.73701976318102
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Creating high-quality figures and visualizations for scientific papers is a time-consuming task that requires both deep domain knowledge and professional design skills. Despite over 2.5 million scientific papers published annually, the figure generation process remains largely manual. We introduce $\textbf{SciFig}$, an end-to-end AI agent system that generates publication-ready pipeline figures directly from research paper texts. SciFig uses a hierarchical layout generation strategy, which parses research descriptions to identify component relationships, groups related elements into functional modules, and generates inter-module connections to establish visual organization. Furthermore, an iterative chain-of-thought (CoT) feedback mechanism progressively improves layouts through multiple rounds of visual analysis and reasoning. We introduce a rubric-based evaluation framework that analyzes 2,219 real scientific figures to extract evaluation rubrics and automatically generates comprehensive evaluation criteria. SciFig demonstrates remarkable performance: achieving 70.1$\%$ overall quality on dataset-level evaluation and 66.2$\%$ on paper-specific evaluation, and consistently high scores across metrics such as visual clarity, structural organization, and scientific accuracy. SciFig figure generation pipeline and our evaluation benchmark will be open-sourced.

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