LiveFigure: Generating Editable Scientific Illustration with VLM Agents

Scientific illustration figures are essential for depicting research works' conceptual designs, methodology, and experimental workflows, playing a pivotal role in communicating complex academic insights. However, creating high-quality scientific illustrations remains a labor-intensive task for human scientists. While recent generative image models have advanced prompt-based editing, the synthesis of fully editable figures remains a fundamental challenge. Valid editability involves structured transformations of graphical elements, scales, attributes, and text, rather than simple pixel-level changes. Existing models generate raster outputs that do not support manual correction or layout adjustment, limiting their utility in scientific publishing, where editable vector figures are typically required for submission. To address this challenge, we introduce LiveFigure, an agentic framework driven by VLM agents that imitates the multi-step drawing workflow of human researchers. It first plans figure blueprints by drawing inspiration from high-quality references in previous works, then generates executable scripts that produce figures via the PowerPoint interface based on skills and experience, and finally refines the outputs with targeted visual diagnostics, producing fully vectorized, editable figures that meet publication standards. Extensive experiments demonstrate that LiveFigure generates inherently editable figures that are both visually clear and aesthetically appealing, achieving 80% publication-readiness within just 17 manual edits—far surpassing the 24% rate of the strongest baseline, NanoBanna. Human preference studies further validate this advantage, with LiveFigure securing a 60% win rate against NanoBanna. Our code is available at https://anonymous.4open.science/r/LiveFigure.