# PlotCraft: Pushing the Limits of LLMs for Complex and Interactive Data Visualization

arXiv: https://arxiv.org/abs/2511.00010 · HTML: https://mlsanity.com/paper/2511.00010/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2025.

## Built on by (most cited first, 14 of 14)

- [Qwen3-Coder-Next Technical Report](https://mlsanity.com/paper/2603.00729/) (2026)
- [AI for Auto-Research: Roadmap & User Guide](https://mlsanity.com/paper/2605.18661/) (2026)
- [VCG-Bench: Towards A Unified Visual-Centric Benchmark for Structured Generation and Editing](https://mlsanity.com/paper/2605.15677/) (2026)
- [The Verification Horizon: No Silver Bullet for Coding Agent Rewards](https://mlsanity.com/paper/2606.26300/) (2026)
- [Scaling Agentic Verifier for Competitive Coding](https://mlsanity.com/paper/2602.04254/) (2026)
- [From Completion to Editing: Unlocking Context-Aware Code Infilling via Search-and-Replace Instruction Tuning](https://mlsanity.com/paper/2601.13384/) (2026)
- [Photon: Speedup Volume Understanding with Efficient Multimodal Large Language Models](https://mlsanity.com/paper/2603.25155/) (2026)
- [Beyond NL2Code: A Structured Survey of Multimodal Code Intelligence](https://mlsanity.com/paper/2606.15932/) (2026)
- [What Should I Cite? A RAG Benchmark for Academic Citation Prediction](https://mlsanity.com/paper/2601.14949/) (2026)
- [RealChart2Code: Advancing Chart-to-Code Generation with Real Data and Multi-Task Evaluation](https://mlsanity.com/paper/2603.25804/) (2026)
- [Learning Cross-View Object Correspondence via Cycle-Consistent Mask Prediction](https://mlsanity.com/paper/2602.18996/) (2026)
- [DV-World: Benchmarking Data Visualization Agents in Real-World Scenarios](https://mlsanity.com/paper/2604.25914/) (2026)
- [SAMAS: A Spectrum-Guided Multi-Agent System for Achieving Style Fidelity in Literary Translation](https://mlsanity.com/paper/2602.19840/) (2026)
- [DEEPCHART: How Far are LLMs from Faithful Data-Science Chart Generation?](https://mlsanity.com/paper/2608.26757/) (2026)

## Abstract (arXiv)

Recent Large Language Models (LLMs) have demonstrated remarkable proficiency in code generation. However, their ability to create complex visualizations for scaled and structured data remains largely unevaluated and underdeveloped. To address this gap, we introduce PlotCraft, a new benchmark featuring 1k challenging visualization tasks that cover a wide range of topics, such as finance, scientific research, and sociology. The benchmark is structured around seven high-level visualization tasks and encompasses 48 distinct chart types. Crucially, it is the first to systematically evaluate both single-turn generation and multi-turn refinement across a diverse spectrum of task complexities. Our comprehensive evaluation of 23 leading LLMs on PlotCraft reveals obvious performance deficiencies in handling sophisticated visualization tasks. To bridge this performance gap, we develope SynthVis-30K, a large-scale, high-quality dataset of complex visualization code synthesized via a collaborative agent framework. Building upon this dataset, we develope PlotCraftor, a novel code generation model that achieves strong capabilities in complex data visualization with a remarkably small size. Across VisEval, PandasPlotBench, and our proposed PlotCraft, PlotCraftor shows performance comparable to that of leading proprietary approaches. Especially, on hard task, Our model achieves over 50% performance improvement. We will release the benchmark, dataset, and code at https://github.com/Speakn0w/PlotCraft-Benchmark.