发表机构
King Abdullah University of Science and Technology; Microsoft Research; Nanyang Technological University（阿卜杜拉国王科技大学; 微软研究院; 南洋理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对科学论文图表编辑自动化的挑战，提出SciDiagramEdit框架，通过从arXiv版本历史挖掘数据，采用技能进化的智能体学习，能从自然论文修订中学习，提升编辑准确性。
AI 中文摘要
编辑研究论文中的图表是日常研究工作中常见且耗时的部分。在自然语言指令下自动化此编辑工作流程具有挑战性，因为科学图表是密集的信息图。为此，我们提出了SciDiagramEdit，这是一个基准和技能进化框架，它从自然论文修订中学习，在图形的可编辑矢量源上运行。我们的基准从arXiv版本历史中挖掘前后图形对，通过技能进化采用智能体学习。结果表明自然论文修订是指令驱动图形编辑的有效训练信号。
英文摘要
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.
Comments20 pages
