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：大型语言模型能够将研究者的意图转换为可运行的matplotlib代码，但生成的图表在未经多轮人工修订的情况下很少能直接用于论文。我们认为当前开放问题并非图表代码生成，而是图表发表：使输出看起来像顶级会议/期刊的图表、适应目标排版布局、并能响应作者精确的编辑需求。我们提出chart-plot——一个弥合这最后一公里的智能编排框架，包含三个组件：(1) 风格感知代码生成器，通过从目标会议已收录图表中提炼的文本化风格技能进行条件约束；(2) 部署感知渲染循环，在目标LaTeX上下文中编译图表并进行修订直至满足排版约束；(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。我们报告了三种图表类型案例研究（分组柱状图、比例变化折线图、配对分布图）的初步结果及小规模用户研究。