这篇论文提出了一个从论文修订数据中学习编辑科学图表的框架，还附带了一个基准，适合想做图表自动化编辑的研究者看看。
SciDiagramEdit是一个从自然论文修订中学习编辑科学图表的基准和技能演化框架。该基准从arXiv版本历史中挖掘前后图表对，每对都附有作者的修订意图。框架采用智能体学习，通过技能演化不断优化代理的技能规范，提升编辑准确性。实验表明，在保留验证集上，技能逐步提高了编辑准确率，证明自然论文修订是有效的指令驱动图表编辑训练信号。
SciDiagramEdit: Learning to Edit Scientific Diagrams from Paper Revisions
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.