Seeing is Retraining: How VizGenie Turns Visualization into a Self-Improving AI Loop
Scientific visualization has long been caught in a bind: the more complex the dataset, the more domain-specific the visualization, and the harder it is to automate. From MRI scans to hurricane simulations, modern scientific data is massive, high-dimensional, and notoriously messy. While dashboards and 2D plots have benefitted from LLM-driven automation, 3D volumetric visualization—especially in high-performance computing (HPC) settings—has remained stubbornly manual. VizGenie changes that. Developed at Los Alamos National Laboratory, VizGenie is a hybrid agentic system that doesn’t just automate visualization tasks—it refines itself through them. It blends traditional visualization tools (like VTK) with dynamically generated Python modules and augments this with vision-language models fine-tuned on domain-specific images. The result: a system that can answer questions like “highlight the tissue boundaries” and actually improve its answers over time. ...