# Draw with Thought: Unleashing Multimodal Reasoning for Scientific Diagram Generation

arXiv: https://arxiv.org/abs/2504.09479 · HTML: https://mlsanity.com/paper/2504.09479/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 24 of its 65 references that are in the map.
- First posted on arXiv 13 Apr 2025.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)

## Builds on (most cited first, 24 of 24)

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity](https://mlsanity.com/paper/2104.08786/) (2021)
- [WebWalker: Benchmarking LLMs in Web Traversal](https://mlsanity.com/paper/2501.07572/) (2025)
- [LongWriter: Unleashing 10,000+ Word Generation from Long Context LLMs](https://mlsanity.com/paper/2408.07055/) (2024)
- [ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via Chart-to-Code Generation](https://mlsanity.com/paper/2406.09961/) (2024)
- [GoT: Unleashing Reasoning Capability of Multimodal Large Language Model for Visual Generation and Editing](https://mlsanity.com/paper/2503.10639/) (2025)
- [StarVector: Generating Scalable Vector Graphics Code from Images and Text](https://mlsanity.com/paper/2312.11556/) (2023)
- [ChartCoder: Advancing Multimodal Large Language Model for Chart-to-Code Generation](https://mlsanity.com/paper/2501.06598/) (2025)
- [Vision-Language Models Can Self-Improve Reasoning via Reflection](https://mlsanity.com/paper/2411.00855/) (2024)
- [SciGraphQA: A Large-Scale Synthetic Multi-Turn Question-Answering Dataset for Scientific Graphs](https://mlsanity.com/paper/2308.03349/) (2023)
- [Empowering LLMs to Understand and Generate Complex Vector Graphics](https://mlsanity.com/paper/2412.11102/) (2024)
- [LayerTracer: Cognitive-Aligned Layered SVG Synthesis via Diffusion Transformer](https://mlsanity.com/paper/2502.01105/) (2025)
- [MMCode: Benchmarking Multimodal Large Language Models for Code Generation with Visually Rich Programming Problems](https://mlsanity.com/paper/2404.09486/) (2024)
- [LongProc: Benchmarking Long-Context Language Models on Long Procedural Generation](https://mlsanity.com/paper/2501.05414/) (2025)
- [LoTLIP: Improving Language-Image Pre-training for Long Text Understanding](https://mlsanity.com/paper/2410.05249/) (2024)
- [Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code](https://mlsanity.com/paper/2412.02764/) (2024)
- [Plot2Code: A Comprehensive Benchmark for Evaluating Multi-modal Large Language Models in Code Generation from Scientific Plots](https://mlsanity.com/paper/2405.07990/) (2024)
- [PDF-WuKong: A Large Multimodal Model for Efficient Long PDF Reading with End-to-End Sparse Sampling](https://mlsanity.com/paper/2410.05970/) (2024)
- [ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation?](https://mlsanity.com/paper/2412.02368/) (2024)

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

- [Perception, Reason, Think, and Plan: A Survey on Large Multimodal Reasoning Models](https://mlsanity.com/paper/2505.04921/) (2025)
- [ASCD: Attention-Steerable Contrastive Decoding for Reducing Hallucination in MLLM](https://mlsanity.com/paper/2506.14766/) (2025)
- [From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence](https://mlsanity.com/paper/2511.18538/) (2025)
- [VCG-Bench: Towards A Unified Visual-Centric Benchmark for Structured Generation and Editing](https://mlsanity.com/paper/2605.15677/) (2026)
- [Reason-SVG: Enhancing Structured Reasoning for Vector Graphics Generation with Reinforcement Learning](https://mlsanity.com/paper/2505.24499/) (2025)
- [DiagramEval: Evaluating LLM-Generated Diagrams via Graphs](https://mlsanity.com/paper/2510.25761/) (2025)
- [GeoSketch: A Neural-Symbolic Approach to Geometric Multimodal Reasoning with Auxiliary Line Construction and Affine Transformation](https://mlsanity.com/paper/2509.22460/) (2025)
- [From Perception to Reasoning: Deep Thinking Empowers Multimodal Large Language Models](https://mlsanity.com/paper/2511.12861/) (2025)
- [Multimodal Information Fusion for Chart Understanding: A Survey of MLLMs -- Evolution, Limitations, and Cognitive Enhancement](https://mlsanity.com/paper/2602.10138/) (2026)
- [EduIllustrate: Towards Scalable Automated Generation Of Multimodal Educational Content](https://mlsanity.com/paper/2604.05005/) (2026)
- [Math-Vision Diagrams: A Comprehensive Benchmark for Evaluating LLM Mathematical Diagram Generation Capabilities](https://mlsanity.com/paper/2608.08964/) (2026)
- [PathWISE: Multi-Agent Cancer Pathway Triaging Ontology Learning from Clinical Flowcharts](https://mlsanity.com/paper/2605.25970/) (2026)
- [Paint What You See: Benchmarking Dexterous Visual Tool Use in Multimodal Agents](https://mlsanity.com/paper/2608.25417/) (2026)
- [VectorHarness: Recovering Editable, Relation-Preserving Structure from Scientific Graphics](https://mlsanity.com/paper/2609.13294/) (2026)

## Abstract (arXiv)

Scientific diagrams are vital tools for communicating structured knowledge across disciplines. However, they are often published as static raster images, losing symbolic semantics and limiting reuse. While Multimodal Large Language Models (MLLMs) offer a pathway to bridging vision and structure, existing methods lack semantic control and structural interpretability, especially on complex diagrams. We propose Draw with Thought (DwT), a training-free framework that guides MLLMs to reconstruct diagrams into editable mxGraph XML code through cognitively-grounded Chain-of-Thought reasoning. DwT enables interpretable and controllable outputs without model fine-tuning by dividing the task into two stages: Coarse-to-Fine Planning, which handles perceptual structuring and semantic specification, and Structure-Aware Code Generation, enhanced by format-guided refinement. To support evaluation, we release Plot2XML, a benchmark of 247 real-world scientific diagrams with gold-standard XML annotations. Extensive experiments across eight MLLMs show that our approach yields high-fidelity, semantically aligned, and structurally valid reconstructions, with human evaluations confirming strong alignment in both accuracy and visual aesthetics, offering a scalable solution for converting static visuals into executable representations and advancing machine understanding of scientific graphics.