HyperGVL: Benchmarking and Improving Large Vision-Language Models in Hypergraph Understanding and Reasoning
On the map: LLM reasoning → Multimodal reasoning
Abstract (arXiv)
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 textttHyperGVL, the first benchmark to evaluate the proficiency of LVLMs in hypergraph understanding and reasoning. textttHyperGVL 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 textttWiseHyGR that improves LVLMs in hypergraph via learning adaptive representations. We believe that this work is a step forward in connecting hypergraphs with LVLMs.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Multimodal reasoning, one of 17 directions in LLM reasoning. Builds on 13 of its 44 references that are in the map. First posted on arXiv 17 Apr 2026.
Builds on
13 of 44 references.
Closest ancestors · 2
Same direction, newest first
- 2024Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling6 Dec
- 2024VisionGraph: Leveraging Large Multimodal Models for Graph Theory Problems in Visual Context8 May
Other references · 11
- 2025Qwen2.5-VL Technical Reportcited by 5,587
- 2023Improved Baselines with Visual Instruction Tuningcited by 4,418
- 2025Qwen3-VL Technical Reportcited by 2,793
- 2025InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Modelscited by 1,834
- 2025Gemma 3 Technical Reportcited by 1,824
- 2021DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharingcited by 1,254
- 2024ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Toolscited by 1,121
- 2024DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understandingcited by 572
- 2025A Survey of State of the Art Large Vision Language Models: Alignment, Benchmark, Evaluations and Challengescited by 47
- 2025Hyper-RAG: Combating LLM Hallucinations using Hypergraph-Driven Retrieval-Augmented Generationcited by 23
- 2024LLM-Guided Multi-View Hypergraph Learning for Human-Centric Explainable Recommendationcited by 19
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2024Benchmarking and Improving Large Vision-Language Models for Fundamental Visual Graph Understanding and Reasoning
- 2025ChartMuseum: Testing Visual Reasoning Capabilities of Large Vision-Language Models
- 2026MVGraph: Benchmarking LVLMs on Evidence Composition across Multi-View Visual Graph
- 2024NPHardEval4V: Dynamic Evaluation of Large Vision-Language Models with Effects of Vision
- 2025Graph-to-Vision: Multi-graph Understanding and Reasoning using Vision-Language Models
- 2025Visual Graph Arena: Evaluating Visual Conceptualization of Vision and Multimodal Large Language Models
- 2024UniBench: Visual Reasoning Requires Rethinking Vision-Language Beyond Scaling
- 2026GraphVerse: A Comprehensive Visual Graph Reasoning Benchmark for Multimodal Large Language Models