Agentic Scientific Visualization Generation and Caption Semantic Alignment

In the era of data-intensive scientific discovery, visualization serves as a crucial cognitive tool for researchers, while captions that align with visuals are essential for accurately conveying scientific intent. However, current scientific visualization workflows face significant challenges, including high technical barriers and semantic misalignment among user intent, visual output, and textual descriptions. To address these issues, this paper proposes SciVis-AGE, a visual analytics system based on multi-agent collaboration. Its core methodologies comprise an agent-based task decomposition and operator encapsulation approach for automatic visualization generation, and a multi-agent triangular debate mechanism for semantic alignment and caption optimization. The system effectively reduces the technical burden on domain experts and, through iterative debate among Intent Guardian, Visual Verifier, and Annotation Checker agents, ensures precise alignment of generated images and captions with user intent, visual content, and highlighted features, thereby enhancing the rigor and efficiency of scientific communication.