Graph-to-Vision: Multi-graph Understanding and Reasoning using Vision-Language Models

ACL ARR 2026 January Submission8830 Authors

Keywords: Graph-to-Vision、Multi-graph Understanding、Vision-Language Models

Abstract:

Recent advances in Vision-Language Models (VLMs) have shown promising capabilities in interpreting visualized graph data, offering a new perspective for graph-structured reasoning beyond traditional Graph Neural Networks (GNNs).
However, existing studies focus primarily on single-graph reasoning, leaving the critical challenge of multi-graph joint reasoning underexplored.
In this work, we introduce the first comprehensive benchmark designed to evaluate and enhance the multi-graph reasoning abilities of VLMs.
Our benchmark covers four common graph types—knowledge graphs, flowcharts, mind maps, and route maps—and supports both homogeneous and heterogeneous graph groupings with tasks of increasing complexity.
We evaluate several state-of-the-art VLMs under a multi-dimensional scoring framework that assesses graph parsing, reasoning consistency, and instruction-following accuracy.
Additionally, we fine-tune multiple open-source models and observe consistent improvements, confirming the effectiveness of our dataset.
This work provides a principled step toward advancing multi-graph understanding and reveals new opportunities for cross-modal graph intelligence.

Paper Type: Long

Research Area: Multimodality and Language Grounding to Vision, Robotics and Beyond

Research Area Keywords: Multimodality and Language Grounding to Vision, Robotics and Beyond

Contribution Types: NLP engineering experiment, Approaches to low-resource settings

Languages Studied: English

Submission Number: 8830