VectorDiff: A Manifesto for a Differential, Semantically Rich Vector Animation Format for Scientific and AI-Driven Visualization

The proliferation of dynamic, multidimensional data in computational science, ranging from medical imaging and molecular simulations to artificial intelligence-generated content, has exposed critical limitations of existing visualization and animation formats. Raster video formats lack scalability and semantic depth, while traditional vector animation standards are often long-winded and unsuited to capturing the subtle, incremental changes inherent in scientific processes. We introduce VectorDiff, a novel differential vector animation format designed to address these challenges. VectorDiff utilizes a declarative JSON-based structure that defines a baseScene and a timeline with explicit, time-stamped transformations. By storing only the difference (delta) between states, it achieves unparalleled performance and data precision. This paper introduces the basic principles of the VectorDiff format, its architecture, and its transformation potential in four key areas: diagnostic medical imaging, molecular dynamics, robotic surgery, and AI-generated content. We argue that VectorDiff is not just an alternative format but a necessary paradigm shift toward a more efficient, precise, and semantically aware representation of dynamic scientific phenomena.