arXiv:2607. 18514v1 Announce Type: cross Abstract: Visual diagrams, figures, and tables are central to scientific papers, and convey information beyond what is captured in text.
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arXiv:2608. 14075v1 Announce Type: new Abstract: Scientific figures and tables encode essential experimental evidence, yet remain difficult for digital libraries and multimodal AI systems to retrieve and interpret.
VIALS is a new visual question‑answering benchmark comprising 161 interpretation tasks based on real scientific artifacts such as gel blots, microscopy images, plasmid maps, flow cytometry plots, and molecular structures. The benchmark focuses on the types of images encountered in everyday biotech workflows rather than polished figures from publications. Current state‑of‑the‑art vision‑language models struggle to interpret these domain‑specific images, whereas scientists with relevant expertise find the tasks straightforward.
The ICDAR2026 Competition on Multimodal Reasoning over Documents in Multiple Domains introduced a new Visual Question Answering benchmark that tests reasoning over documents from eight distinct domains such as business reports, scientific papers, and engineering drawings. Twenty valid submissions from eight teams were evaluated, featuring approaches ranging from zero‑shot vision‑language models to multi‑agent ensembles and fine‑tuned multimodal systems. Results indicate that the most effective systems employ structured evidence extraction, retrieval, verification, and orchestration across multiple components rather than single‑pass prompting.
arXiv:2609.15696v1 Announce Type: cross Abstract: Generative AI (GenAI) tools are increasingly woven into how blind and low-vision (BLV) people communicate, not only with digital information, but wit...
arXiv:2505. 16057v2 Announce Type: replace-cross Abstract: AI-Generated (AIG) content has become increasingly widespread by recent advances in generative models and the easy-to-use tools that have significantly lowered the technical barriers for producing highly realistic audio, images, and videos through simple natural language prompts.
The paper investigates how large language models (LLMs) interpret ambiguous or incomplete text prompts for visualization authoring and introduces visual prompts as a complementary modality to improve precision. An empirical study informs the design of VisPilot, a system that allows users to create visualizations using text, sketches, and direct manipulation. A controlled user study and expert evaluation show that multimodal prompts help users convey spatial constraints, local references, and design preferences while maintaining task efficiency comparable to text-only prompting.