AI RESEARCH
VESTA: Visual Exploration with Statistical Tool Agents
arXiv CS.AI
•
ArXi:2606.00384v1 Announce Type: new Fitting quantitative models to data is a central step in scientific workflows, yet it remains one of the least automated. Recent agent-based systems leverage language and vision-language models (VLMs) to iteratively propose and refine statistical models, but these systems struggle on challenging modeling tasks. To address these limitations, we