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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2503.21435 |
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| _version_ | 1866918465323925504 |
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| author | Ai, Qihang Li, Ruizhou Wang, Menghui Jiang, Haiyun |
| author_facet | Ai, Qihang Li, Ruizhou Wang, Menghui Jiang, Haiyun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_21435 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Graph-to-Vision: Multi-graph Understanding and Reasoning using Vision-Language Models Ai, Qihang Li, Ruizhou Wang, Menghui Jiang, Haiyun Artificial Intelligence 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. |
| title | Graph-to-Vision: Multi-graph Understanding and Reasoning using Vision-Language Models |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2503.21435 |