Benchmarking Multimodal Large Language Models for Scientific Visualization Literacy

Benchmark reveals uneven scientific visualization literacy across six MLLMs

A new arXiv preprint benchmarks six multimodal LLMs (three closed-source, three open-source) on a standardized 49-item scientific visualization literacy test spanning 18 visualizations, 8 techniques, and 11 task types, comparing results against 485 human participants. Gemini emerges as the strongest model, exceeding the human mean, while open-source models fall below the human baseline. Performance is highly uneven: models handle scientific illustration and spatial tasks well but struggle with texture-based visualizations, flow-direction interpretation, and quantitative estimation. The authors argue SciVis literacy should be treated as a necessary evaluation dimension for multimodal AI systems.