arXiv Computer Vision
SciGen-Verifier: A Multimodal Reasoner for Explainable Verification in Scientific Image Generation
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
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
Aug 26
Scientific Image Synthesis: Benchmarking, Methodologies, and Downstream Utility
The paper presents a systematic study of scientific image synthesis, comparing pixel‑based generation and programmatic approaches. It introduces ImgCoder, a logic‑driven framework that follows an "understand‑plan‑code" workflow to enhance structural precision, and SciGenBench, a benchmark that evaluates images for information utility and logical validity. The authors find that pixel‑based models exhibit systematic failure modes and that fine‑tuning large multimodal models on rigorously verified synthetic images consistently improves downstream reasoning performance.
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.
arXiv Computer Vision
Aug 31
Scientific Graphics Program Synthesis via Dual Self-Consistency Reinforcement Learning
arXiv:2604.06079v2 Announce Type: replace Abstract: Graphics Program Synthesis is pivotal for interpreting and editing visual data, effectively facilitating the reverse-engineering of static visuals...
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
Jul 21
ExpertVerse: A General-Purpose Benchmark for Expert-Level Reasoning in Knowledge-Intensive Visual Synthesis
Recent advances in multimodal generative models have enabled instruction-based image generation to move beyond semantic manipulation to knowledge-driven visual reasoning. However, these methods focus on explicit commonsense reasoning, shallow causal understanding, and direct knowledge recall, failing at knowledge-intensive generation.