See or Say Graphs: Agent-Driven Scalable Graph Understanding with Vision-Language Models (2026.findings-acl)
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| Challenge: | Existing studies have explored textual graph descriptions and visual modalities for VLMs to understand graphs. |
| Approach: | They propose a unified framework that enhances both scalability and modality coordination in graph understanding by integrating textual and visual modalities. |
| Outcome: | GraphVista scales to large graphs, 200 larger than those used in existing benchmarks, and consistently outperforms existing textual, visual, and fusion-based methods. |
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| Challenge: | Graph data organizes complex relationships and interactions between objects . Graph neural networks (GNNs) are becoming more popular in graph learning . |
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| Challenge: | Existing methods for enhancing understanding and reasoning abilities in graphbased tasks focus on specific graph types or tasks, posing challenges in designing versatile systems suitable for various tasks and graphs across diverse domains. |
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Graph-Assisted Large Language Models: A Perspective on Mitigating Intrinsic Limitations (2026.findings-acl)
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Haitong Luo, Fali Wang, Weiyao Zhang, Xianren Zhang, Zhiwei Zhang, Tianxiang Zhao, Minhua Lin, Jiahao Zhang, Hui Liu, Xianfeng Tang, Qi He, Suhang Wang, Xuying Meng, Yujun Zhang
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| Challenge: | Graph Neural Networks (GNNs) and graph transformers are inadequate for tasks with limited generalization. |
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| Challenge: | Large language models struggle with comprehending graphical structure information through prompts of graph description sequences, especially as the graph size increases. |
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| Challenge: | Current research typically employs limited setups with small real-world graphs. |
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UnifiedVisual: A Framework for Constructing Unified Vision-Language Datasets (2025.emnlp-main)
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Pengyu Wang, Shaojun Zhou, Chenkun Tan, Xinghao Wang, Wei Huang, Zhen Ye, Zhaowei Li, Botian Jiang, Dong Zhang, Xipeng Qiu
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