Crafter：一种从多样输入生成可编辑科学图形的多智能体框架

摘要

科学图表是传达复杂研究思想最有效的手段之一，然而生成达到出版质量的插图仍是论文准备过程中最耗时的环节之一。现有自动化系统均针对单一图表类型和纯文本输入条件，未能涵盖研究人员实际使用的多样类型与条件；其栅格输出结果也无法进行局部修正。由于科学图表是由离散语义组件构成的结构化组合，生成器在此类布局中产生的局部错误需要的不是更强的骨干网络，而是一套约束机制。我们将这一约束机制具体化为两个互补系统：Crafter——一种无需架构修改即可跨图表类型与输入条件进行泛化的多智能体图表生成框架；以及CraftEditor——它运用相同范式将栅格输出转换为可编辑的SVG格式。此外，我们推出了CraftBench基准测试，涵盖三种图表类型与四种输入条件，并配备人工质量标注。实验表明，在PaperBanana-Bench和CraftBench上，Crafter显著优于独立生成器及智能体基线，消融实验证实了各模块的独立贡献；CraftEditor能将输出结果忠实地转换为可编辑SVG，并超越所有基线方法。我们的代码与基准测试已开源在 https://github.com/HaozheZhao/Crafter。

English

Scientific figures are among the most effective means of communicating complex research ideas, yet producing publication-quality illustrations remains one of the most labor-intensive parts of paper preparation. Existing automated systems each target a single figure type under text-only input, leaving the diversity of types and conditions researchers actually use unaddressed; their raster outputs further cannot be locally revised. Because scientific figures are structured compositions of discrete semantic components, the localized errors generators produce on such layouts demand not a stronger backbone but a harness. We instantiate this harness in two complementary systems: Crafter, a multi-agent harness for figure generation that generalizes across figure types and input conditions without architectural changes, and CraftEditor, which applies the same pattern to convert raster outputs into editable SVGs. Moreover, we introduce CraftBench, a benchmark spanning three figure types and four input conditions with human quality annotation. Experiments show that Crafter substantially outperforms both standalone generators and the agentic baseline on PaperBanana-Bench and CraftBench, with ablations confirming each component's independent contribution; CraftEditor faithfully converts outputs into editable SVGs that surpass all baselines. Our code and benchmark are available at https://github.com/HaozheZhao/Crafter.