Large language models can translate a researcher's intent into runnable matplotlib code, yet the resulting chart rarely lands in a paper without multiple rounds of manual revision. We argue that the open problem is not chart code generation but chart publication: making the output look like a top-venue figure, survive the target layout, and respond to precise author edits. We present chart-plot, an agentic harness that closes this last mile through three components: (1) a style-aware code generator conditioned on a textual style skill distilled from accepted figures at the target venue, (2) a deployment-aware render loop that compiles the chart inside the target LaTeX context and revises until layout constraints are met, and (3) a structured edit layer that exposes every chart element as a directly manipulable handle. We report early results on three chart-type case studies (grouped bar, scaling line, paired distributions) and a small user study.

翻译：大型语言模型可以将研究者的意图转化为可运行的matplotlib代码，但生成的图表往往需要经过多轮手动修改才能用于论文。我们认为，当前的核心问题不在于图表代码生成，而在于图表发布：使输出看起来像顶级期刊的配图，能够适应目标版面布局，并能响应作者精确的修改要求。为此，我们提出chart-plot——一种弥合这一流程最后间距的智能编排框架，其包含三个组件：（1）基于风格感知的代码生成器，该生成器以从目标期刊已录用图表中提炼的文本风格技能为条件；（2）考虑部署的渲染循环，在目标LaTeX上下文中编译图表并持续修订直至满足版面约束；（3）结构化编辑层，将每个图表元素暴露为可直接操控的句柄。我们通过三种图表类型的案例研究（分组柱状图、比例线图、配对分布图）及一项小型用户研究，报告了初步成果。