SciDiagramEdit: Learning to Edit Scientific Diagrams from Paper Revisions
For researchers and practitioners who need to automate figure editing in scientific papers, this work provides a benchmark and learning framework, though it is incremental as it adapts existing agentic learning to a new domain.
The paper introduces SciDiagramEdit, a benchmark and framework for editing scientific figures via natural-language instructions, using before/after figure pairs from arXiv revisions. The method achieves progressive improvement in edit accuracy on a held-out validation set.
Editing the figures in a research paper is a routine and time-consuming part of everyday research practice: authors relabel components, rearrange panels, and restyle visuals as they revise their manuscripts. Automating this editing workflow under a natural-language instruction, however, is challenging, because a scientific figure is a dense infographic in which heterogeneous visual elements such as schematics, plots, photos, captions, and arrows are composed under a tight visual grammar to advance a specific argument. To address this, we present SciDiagramEdit, a benchmark and skill-evolution framework that learns from natural paper revisions and operates on the figure's editable vector source, where users can inspect and co-edit individual primitives alongside the agent. Our benchmark mines before/after figure pairs from arXiv version histories, each grounded in the authors' own revision intent. To accommodate the diversity of editing instructions, we adopt agentic learning via skill evolution: an agentic proposer continually refines the agent's skill specification from execution traces over multiple epochs. The resulting skill progressively lifts edit accuracy on a held-out validation set, providing evidence that natural paper revisions are an effective training signal for instruction-driven figure editing.