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    <abstract>High-quality schematic diagrams, which provide a conceptual overview of the research, play a crucial role in summarizing and clarifying a study’s core ideas. However, creating these diagrams is time-consuming for authors and remains challenging for current AI systems, as it requires both a deep understanding of the paper’s content and a strong sense of visual design. To address this, we introduce SCISKETCH, an open-source framework that supports two automated workflows for schematic diagram generation using foundation models, shown in Figure 1. 1) In the graphic-code-based workflow, SCISKETCH follows a two-stage pipeline: it first produces a layout plan expressed in a graphical code language with a self-refinement and self-verification mechanism. It then integrates empirical images and symbolic icons to create a visually coherent, informative diagram. 2) In the image-based workflow, SCISKETCH directly synthesizes the diagram image through image generation with a self-refinement mechanism. Through both automatic and human evaluations, we show that SCISKETCH outperforms several state-of-the-art foundation models, including GPT-4o, and Gemini-2.5-Pro, in generating schematic diagrams for scientific papers. We make SCISKETCH fully open-sourced, providing researchers with an accessible, extensible tool for high-quality schematic diagram generation in scientific fields.</abstract>
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%0 Conference Proceedings
%T SciSketch: An Open-source Framework for Automated Schematic Diagram Generation in Scientific Papers
%A Wang, Zihang
%A Zhao, Yilun
%A Zhang, Kaiyan
%A Zhao, Chen
%A Patwardhan, Manasi
%A Cohan, Arman
%Y Habernal, Ivan
%Y Schulam, Peter
%Y Tiedemann, Jörg
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-334-0
%F wang-etal-2025-scisketch
%X High-quality schematic diagrams, which provide a conceptual overview of the research, play a crucial role in summarizing and clarifying a study’s core ideas. However, creating these diagrams is time-consuming for authors and remains challenging for current AI systems, as it requires both a deep understanding of the paper’s content and a strong sense of visual design. To address this, we introduce SCISKETCH, an open-source framework that supports two automated workflows for schematic diagram generation using foundation models, shown in Figure 1. 1) In the graphic-code-based workflow, SCISKETCH follows a two-stage pipeline: it first produces a layout plan expressed in a graphical code language with a self-refinement and self-verification mechanism. It then integrates empirical images and symbolic icons to create a visually coherent, informative diagram. 2) In the image-based workflow, SCISKETCH directly synthesizes the diagram image through image generation with a self-refinement mechanism. Through both automatic and human evaluations, we show that SCISKETCH outperforms several state-of-the-art foundation models, including GPT-4o, and Gemini-2.5-Pro, in generating schematic diagrams for scientific papers. We make SCISKETCH fully open-sourced, providing researchers with an accessible, extensible tool for high-quality schematic diagram generation in scientific fields.
%U https://aclanthology.org/2025.emnlp-demos.28/
%P 403-417

Markdown (Informal)

[SciSketch: An Open-source Framework for Automated Schematic Diagram Generation in Scientific Papers](https://aclanthology.org/2025.emnlp-demos.28/) (Wang et al., EMNLP 2025)

• SciSketch: An Open-source Framework for Automated Schematic Diagram Generation in Scientific Papers (Wang et al., EMNLP 2025)

ACL

• Zihang Wang, Yilun Zhao, Kaiyan Zhang, Chen Zhao, Manasi Patwardhan, and Arman Cohan. 2025. SciSketch: An Open-source Framework for Automated Schematic Diagram Generation in Scientific Papers. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 403–417, Suzhou, China. Association for Computational Linguistics.