Large Vision-Language Models (LVLMs) consistently require new arenas to guide their expanding boundaries, yet their capabilities with hypergraphs remain unexplored. In the real world, hypergraphs have significant practical applications in areas such as life sciences and social communities. Recent advancements in LVLMs have shown promise in understanding complex topologies, yet there remains a lack of a benchmark to delineate the capabilities of LVLMs with hypergraphs, leaving the boundaries of their abilities unclear. To fill this gap, in this paper, we introduce HyperGVL, the first benchmark to evaluate the proficiency of LVLMs in hypergraph understanding and reasoning. HyperGVL provides a comprehensive assessment of 12 advanced LVLMs across 84,000 vision-language question-answering (QA) samples spanning 12 tasks, ranging from basic component counting to complex NP-hard problem reasoning. The involved hypergraphs contain multiscale synthetic structures and real-world citation and protein networks. Moreover, we examine the effects of 12 textual and visual hypergraph representations and introduce a generalizable router WiseHyGR that improves LVLMs in hypergraph via learning adaptive representations. We believe that this work is a step forward in connecting hypergraphs with LVLMs.
@article{arxiv.2604.15648,
title = {HyperGVL: Benchmarking and Improving Large Vision-Language Models in Hypergraph Understanding and Reasoning},
author = {Yanbin Wei and Chun Kang and Siwei Li and Haoxuan Che and Yang Chen and Hua Liu and Jian Liu and Zhuang Liu and Can Ouyang and Fei Xing and Lei Sha and Rui Liu and Yu Zhang and James Kwok},
journal= {arXiv preprint arXiv:2604.15648},
year = {2026}
}