# Demonstrating chart-plot: Closing the Last Mile of Academic Chart Generation

arXiv: https://arxiv.org/abs/2606.09174 · HTML: https://mlsanity.com/paper/2606.09174/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2026.

## Built on by (most cited first, 4 of 4)

- [ResearchStudio-Reel: Automate the Last Mile of Research from Paper to Poster, Video, and Blog](https://mlsanity.com/paper/2607.04438/) (2026)
- [DataMagic: Authoring Data Videos through Declarative Multi-Agent Orchestration](https://mlsanity.com/paper/2609.33403/) (2026)
- [FinReportBench: Measuring and Improving Institution-Grade Financial Report Generation](https://mlsanity.com/paper/2608.04374/) (2026)
- [DataVista: Diagnosing Multimodal LLMs on Data Video Understanding](https://mlsanity.com/paper/2610.11993/) (2026)

## Abstract (arXiv)

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.