# EdgeFlow: Edge-Map Augmented VLM-Based Flowchart Processing for Industrial Requirements Engineering

arXiv: https://arxiv.org/abs/2605.27332 · HTML: https://mlsanity.com/paper/2605.27332/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 26 May 2026.

## Builds on (most cited first, 10 of 10)

- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [FlowVQA: Mapping Multimodal Logic in Visual Question Answering with Flowcharts](https://mlsanity.com/paper/2406.19237/) (2024)
- [FlowLearn: Evaluating Large Vision-Language Models on Flowchart Understanding](https://mlsanity.com/paper/2407.05183/) (2024)
- [Guidelines for Empirical Studies in Software Engineering involving Large Language Models](https://mlsanity.com/paper/2508.15503/) (2025)
- [Model Generation with LLMs: From Requirements to UML Sequence Diagrams](https://mlsanity.com/paper/2404.06371/) (2024)
- [Sketches and Diagrams in Practice](https://mlsanity.com/paper/1706.09172/) (2017)
- [Flowchart2Mermaid: A Vision-Language Model Powered System for Converting Flowcharts into Editable Diagram Code](https://mlsanity.com/paper/2512.02170/) (2025)
- [Arrow-Guided VLM: Enhancing Flowchart Understanding via Arrow Direction Encoding](https://mlsanity.com/paper/2505.07864/) (2025)
- [Structured Extraction from Business Process Diagrams Using Vision-Language Models](https://mlsanity.com/paper/2511.22448/) (2025)
- [Leveraging Large Language Models for Use Case Model Generation from Software Requirements](https://mlsanity.com/paper/2511.09231/) (2025)

## Built on by (most cited first, 1 of 1)

- [One Figure, Every Canvas: Editable Flowchart Relayout via Agentic Pipeline](https://mlsanity.com/paper/2610.06852/) (2026)

## Abstract (arXiv)

Flowcharts are widely used in industrial requirements, but usually remain embedded as static images. Vision Language Models (VLMs) show promise in the conversion of these flowcharts into machine-readable models for RE activities, yet, when directly applied to flowchart conversion, they often fail on topology-critical visual details. To address this, we propose EdgeFlow that augments a VLM's original input with a deterministically extracted Canny edge map-acting as a structural prior-to improve flowchart-to-Mermaid conversion, without requiring annotated training data or domain-specific model fine-tuning. We evaluate EdgeFlow on IndusReqFlow, a dataset sourced from real-world requirements. Compared with off-the-shelf VLMs, EdgeFlow improves node-level F1 by 17.39 percentage points and edge-level F1 by 16.94 percentage points. At the path level, EdgeFlow improves path F1 by 11.06 percentage points, enabling better support for model-based testing. These results demonstrate that EdgeFlow provides a practical, training-free means to improve topology-preserving flowchart-to-Mermaid conversion for industrial RE. Cross-dataset evaluation results on a public synthetic benchmark show no significant improvement; this highlights the need for diverse benchmarks incorporating industrial data for the comprehensive evaluation of future VLM-based RE tools.