arXiv Machine Learning
Jul 21
SciForma: Structure-Faithful Generation of Scientific Diagrams
arXiv:2607. 18091v1 Announce Type: cross Abstract: Structural fidelity is essential to scientific methodology diagrams.
SciForma: Structure-Faithful Generation of Scientific Diagrams
Structural fidelity is essential to scientific methodology diagrams. To communicate research logic, these diagrams must faithfully render components, directional relations, and textual annotations.
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Hugging Face Trending Papers
6d ago
Towards Physics-Faithful Generation of Scientific Diagrams
Text-to-image generation has reached photorealistic quality, yet state-of-the-art systems remain unreliable at producing scientific diagrams, whose value depends not on appearance but on physical faithfulness: correct force directions, valid coordinate systems, consistent thermodynamic states, and equations matching the depicted scenario. Trained on web imagery with physically shallow captions, generic models produce diagrams that look plausible but are physically wrong, harmful in education and scientific communication.
arXiv Machine Learning
Jun 30
Can AI Draw Science? A Benchmark for Evaluating Scientific Figure Generation by Text-to-Image and Multimodal Models
arXiv:2606. 28406v1 Announce Type: new Abstract: Text-to-image and multimodal generative models are increasingly used to produce scientific figures such as mechanism diagrams, experimental-design schematics, conceptual frameworks, and graphical abstracts.
Hugging Face Trending Papers
Jun 29
SciIR: A Large-scale Training Dataset and Benchmark for Scientific Image Reasoning Generation
While Text-to-Image (T2I) models have shown remarkable success in generating photorealistic visual content, they still struggle with the rigorous semantic alignment and logical reasoning required for scientific imagery. Inspired by Peirce's Semiotic Triad, we introduce Scientific Image Reasoning (SciIR), a comprehensive resource for training and evaluation of scientific image generation.
Hugging Face Trending Papers
Jun 23
S1-Omni-Image: A Unified Model for Scientific Image Understanding, Generation, and Editing
We present S1-Omni-Image, an open-weight unified multimodal model for scientific image understanding, generation, and editing. Unlike general-purpose image generation models, scientific image tasks require not only high-fidelity synthesis, but also robust understanding of scientific semantics, structural relations, domain knowledge, and task intent.
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
Jun 12
SciR: A Controllable Benchmark for Scientific Reasoning in LLMs
arXiv:2606. 13020v1 Announce Type: new Abstract: Three paradigmatic forms of inference recur across scientific reasoning: deduction, induction, and causal abduction.
arXiv AI
Jun 30
Unlocking the Visual Record of Materials Science: A Large-Scale Multimodal Dataset from Scientific Literature
arXiv:2606. 29667v1 Announce Type: cross Abstract: The materials science literature encodes decades of experimental knowledge in figures, yet this visual record remains locked away and inaccessible to AI at scale.
arXiv Machine Learning
Aug 11
Math-Vision Diagrams: A Comprehensive Benchmark for Evaluating LLM Mathematical Diagram Generation Capabilities
arXiv:2608. 08964v1 Announce Type: new Abstract: The generation of mathematically precise diagrams from tex- tual prompts has emerged as a critical yet underexplored capability of Large Language Models (LLMs).
arXiv Machine Learning
Jul 31
GVR-Coder: A Visual-Feedback Framework for Structured SVG Generation in Complex Document and Meeting Scenarios
arXiv:2607. 28073v1 Announce Type: new Abstract: In demanding professional environments and meeting review scenarios, lengthy text often imposes a high cognitive load.
arXiv AI
1d ago
VFIG: Vectorizing Complex Figures in SVG with Vision-Language Models
arXiv:2603. 24575v2 Announce Type: replace-cross Abstract: Scalable Vector Graphics (SVG) are essential for technical illustration and digital design, offering resolution independence and semantic editability.