# GenAI-DrawIO-Creator: A Framework for Automated Diagram Generation

arXiv: https://arxiv.org/abs/2601.05162 · HTML: https://mlsanity.com/paper/2601.05162/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 15 references that are in the map.
- First posted on arXiv 8 Jan 2026.

## Builds on (most cited first, 8 of 8)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Fast Inference from Transformers via Speculative Decoding](https://mlsanity.com/paper/2211.17192/) (2022)
- [Prompting Is Programming: A Query Language for Large Language Models](https://mlsanity.com/paper/2212.06094/) (2022)
- [Advancing Requirements Engineering through Generative AI: Assessing the Role of LLMs](https://mlsanity.com/paper/2310.13976/) (2023)
- [DiagrammerGPT: Generating Open-Domain, Open-Platform Diagrams via LLM Planning](https://mlsanity.com/paper/2310.12128/) (2023)
- [Guiding Large Language Models to Generate Computer-Parsable Content](https://mlsanity.com/paper/2404.05499/) (2024)

## Built on by (most cited first, 2 of 2)

- [T2MM: An LLM Supported Architecture For Inquiry-Based Modeling](https://mlsanity.com/paper/2606.11210/) (2026)
- [One Figure, Every Canvas: Editable Flowchart Relayout via Agentic Pipeline](https://mlsanity.com/paper/2610.06852/) (2026)

## Abstract (arXiv)

Diagrams are crucial for communicating complex information, yet creating and modifying them remains a labor-intensive task. We present GenAI-DrawIO-Creator, a novel framework that leverages Large Language Models (LLMs) to automate diagram generation and manipulation in the structured XML format used by draw.io. Our system integrates Claude 3.7 to reason about structured visual data and produce valid diagram representations. Key contributions include a high-level system design enabling real-time diagram updates, specialized prompt engineering and error-checking to ensure well-formed XML outputs. We demonstrate a working prototype capable of generating accurate diagrams (such as network architectures and flowcharts) from natural language or code, and even replicating diagrams from images. Simulated evaluations show that our approach significantly reduces diagram creation time and produces outputs with high structural fidelity. Our results highlight the promise of Claude 3.7 in handling structured visual reasoning tasks and lay the groundwork for future research in AI-assisted diagramming applications.