arXiv:2606. 26614v1 Announce Type: cross Abstract: Large language model (LLM) agents enable natural language interaction for scientific visualization (SciVis).
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2604. 27996v3 Announce Type: replace Abstract: This paper examines how large language model (LLM) agents perform on scientific visualization (SciVis) tasks that require generating visualization workflows from natural-language instructions.
arXiv:2606. 05525v1 Announce Type: new Abstract: Recent advances in agentic visualization have enabled the translation of natural language into executable scientific visualization (SciVis) workflows.
arXiv:2603. 29139v2 Announce Type: replace Abstract: Recent advances in large language models (LLMs) have enabled agentic systems to translate natural-language intent into executable scientific visualization (SciVis) tasks.
OSWorld-Science is a benchmark and evaluation environment for computer-using agents that use visual language models (VLMs) to perform scientific software tasks. It includes 12 VLMs and 146 high-quality tasks across domains such as molecular drawing, pathology image analysis, statistical computing, and physical simulation, with artifact-based evaluation and a harness that logs interactions and supports model comparison. The benchmark was developed through expert proposals and iterative human–AI co‑design, and results show that current VLMs still struggle with key scientific questions, offering insights into factors like language, reasoning, and context length.
arXiv:2606. 00370v1 Announce Type: cross Abstract: Diverse genomics data, scientific questions, and analysis tasks typically demand highly specialized visualizations.
arXiv:2609.24094v1 Announce Type: cross Abstract: Visual analytics (VA) enables sensemaking through interactive visualization, but effective analysis often requires experts to translate high-level in...