SciFig: Towards Automating Scientific Figure Generation
This study addresses the labor-intensive and expertise-dependent process of generating high-quality flowcharts from scientific papers. To overcome this challenge, the authors propose the first end-to-end AI agent system capable of automatically constructing publication-ready diagrams directly from paper text. The approach innovatively integrates hierarchical module layout generation, functional clustering, and connection modeling, augmented by an iterative chain-of-thought (CoT) visual reasoning mechanism and a rule-based automated evaluation framework. Evaluated on both general and paper-specific datasets, the system achieves overall quality scores of 70.1% and 66.2%, respectively, demonstrating strong performance in visual clarity, structural organization, and scientific accuracy.