@inproceedings{wei-etal-2026-hypergvl,
title = "{H}yper{GVL}: Benchmarking and Improving Large Vision-Language Models in Hypergraph Understanding and Reasoning",
author = "Wei, Yanbin and
Kang, Chun and
Li, Siwei and
Che, Haoxuan and
Chen, Yang and
Liu, Hua and
Liu, Jian and
Liu, Zhuang and
Ouyang, Can and
Xing, Fei and
Sha, Lei and
Liu, Rui and
Zhang, Yu and
Kwok, James",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1901/",
pages = "43325--43344",
ISBN = "979-8-89176-459-0",
abstract = "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 $\texttt{HyperGVL}$, the first benchmark to evaluate the proficiency of LVLMs in hypergraph understanding and reasoning. $\texttt{HyperGVL}$ 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 $\texttt{WiseHyGR}$ that improves LVLMs in hypergraph via learning adaptive representations. We believe that this work is a step forward in connecting hypergraphs with LVLMs."
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T HyperGVL: Benchmarking and Improving Large Vision-Language Models in Hypergraph Understanding and Reasoning
%A Wei, Yanbin
%A Kang, Chun
%A Li, Siwei
%A Che, Haoxuan
%A Chen, Yang
%A Liu, Hua
%A Liu, Jian
%A Liu, Zhuang
%A Ouyang, Can
%A Xing, Fei
%A Sha, Lei
%A Liu, Rui
%A Zhang, Yu
%A Kwok, James
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wei-etal-2026-hypergvl
%X 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.
%U https://aclanthology.org/2026.emnlp-main.1901/
%P 43325-43344
Markdown (Informal)
[HyperGVL: Benchmarking and Improving Large Vision-Language Models in Hypergraph Understanding and Reasoning](https://aclanthology.org/2026.emnlp-main.1901/) (Wei et al., EMNLP 2026)
ACL
- Yanbin Wei, Chun Kang, Siwei Li, Haoxuan Che, Yang Chen, Hua Liu, Jian Liu, Zhuang Liu, Can Ouyang, Fei Xing, Lei Sha, Rui Liu, Yu Zhang, and James Kwok. 2026. HyperGVL: Benchmarking and Improving Large Vision-Language Models in Hypergraph Understanding and Reasoning. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 43325–43344, Budapest, Hungary. Association for Computational Linguistics.