Hugging Face Trending Papers
Figures as Programs: Recursive Generation of Editable Scientific Figures
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arXiv AI
2d ago
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 AI
2d ago
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.
arXiv Machine Learning
Aug 28
Chart2SVG: Editable SVG Generation from Raster Chart Images
Chart2SVG is a multimodal large language model that transforms static raster chart images into editable SVGs enriched with semantic structure. By embedding chart‑specific semantic tokens into a vision‑language framework and training on the Beagle+ dataset of 33K distilled chart samples, the model captures both geometric primitives and their functional roles. The resulting SVGs are visually accurate and structurally consistent, and the accompanying Chart Structure Graph (CSG) exposes visual dependencies for interactive exploration, chart repurposing, and layout reuse.