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Figures as Programs: Recursive Generation of Editable Scientific Figures
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arXiv AI
Sep 2
Figures as Programs: Recursive Generation of Editable Scientific Figures
The paper introduces “FigTree”, a multi-agent system that automatically converts a scientific paper into a structured vector figure by recursively constructing SVG programs. It decomposes figures into hierarchical regions, generates each region as a short SVG program, and assembles them, using a render‑critic refinement loop to trace and repair visual defects. Evaluations show that “FigTree” produces high‑quality figures and allows more effective editing than raster‑based methods.
arXiv AI
Jun 26
SciFig: Towards Automating Editable Figure Generation for Scientific Papers
arXiv:2601. 04390v2 Announce Type: replace Abstract: High-quality methodology figures are central to scientific communication, yet they remain difficult and time-consuming to create.
arXiv AI
Jul 17
SciDiagramEdit: Learning to Edit Scientific Diagrams from Paper Revisions
arXiv:2607. 15272v1 Announce Type: cross Abstract: Editing the figures in a research paper is a routine and time-consuming part of everyday research practice: authors relabel components, rearrange panels, and restyle visuals as they revise their manuscripts.
arXiv Computer Vision
Sep 15
VectorHarness: Recovering Editable, Relation-Preserving Structure from Scientific Graphics
arXiv:2609.13294v1 Announce Type: new Abstract: Converting scientific graphics into editable representations remains a challenging problem for image-to-code generation because of their heterogeneous...
arXiv AI
Sep 2
EdiTikZ: Scientific Figure Editing from Revision Trajectories
arXiv:2609.01409v1 Announce Type: new Abstract: Vision-language models (VLMs) have shown strong performance in generating scientific figures from text or images. However, producing publication-ready...
arXiv Machine Learning
Aug 7
GenGA: Editable and Data-Grounded Graphical Abstract Generation for Academic Papers
arXiv:2608. 05478v1 Announce Type: cross Abstract: Graphical Abstracts (GAs) visually summarize the key findings of academic papers, playing a crucial role in facilitating the understanding of research content.