arXiv Machine Learning

When Vision Meets Graphs: A Survey on Graph Reasoning and Learning

The survey titled "When Vision Meets Graphs: A Survey on Graph Reasoning and Learning" reviews how visual depictions of graphs can be used as inputs for graph reasoning and learning. It highlights that while Graph Neural Networks dominate graph machine learning, most pipelines ignore the visual form of graphs, despite scientists routinely interpreting graphs visually. The paper organizes existing work into three threads—vision for graph reasoning, vision for graph learning, and scientific graphs—aiming to clarify current capabilities and chart a path toward foundation models that perceive and reason about graphs like scientists do.

arXiv Computer Vision

Oct 12

GraphVerse: A Comprehensive Visual Graph Reasoning Benchmark for Multimodal Large Language Models

GraphVerse is a new benchmark for multimodal large language models that tests visual graph reasoning by combining perception, graph-based visual inputs, and multi-step reasoning. It introduces Graph-centric Image Editing strategies to actively challenge models while preserving semantics, and offers a VGR-Score metric that evaluates reasoning quality beyond simple answer accuracy. Experiments show current models have limitations in VGR and demonstrate the effectiveness of GraphVerse for broader multimodal reasoning tasks.

By Yuanfu Sun, Yuanhang Ren, Kang Li, Chuanhao Ji, Jiaxi Li, Jiajin Liu, Ninghao Liu, Qiaoyu Tan

llms multimodal benchmarks

arXiv AI

Aug 7

ViSR-KGC: Visual Subgraph Reasoning with Vision-Language Models for Multimodal Knowledge Graph Completion

arXiv:2608. 05833v1 Announce Type: new Abstract: Knowledge graph completion (KGC) aims to infer missing entities or relations from incomplete graph structures, and has evolved into multimodal knowledge graph completion (MMKGC), where entities are associated with multiple modalities such as text and images.

By Jiafan Li, Mengxue Yang, Jiaqi Zhu, Liang Chang, Ying Li, Hongan Wang

llms rag multimodal

arXiv Machine Learning

Jul 22

One Model, Many Graphs: Learning over Attributed Graphs across Heterogeneous Modalities with Vision-Language Models

arXiv:2607. 19128v1 Announce Type: new Abstract: Vision-language models (VLMs) provide a unified representation space for textual and visual information, yet their potential as general-purpose backbones for graph-structured data remains largely unexplored.

By Jiayi Yang, Yifang Chen, Yuanfu Sun, Jiajin Liu, Qiaoyu Tan

llms rag multimodal benchmarks

arXiv AI

Jun 3

Visual Graph Scaffolds for Structural Reasoning in Large Language Models

arXiv:2606. 02673v1 Announce Type: new Abstract: Graphs have been used to enhance large language models (LLMs) for structured reasoning, mostly as external knowledge sources are provided to models at test time.

By Runlin Lei, Xiaokui Xiao, Zhewei Wei

llms nlp fine-tuning efficiency

arXiv Machine Learning

Jun 30

GLIP: Graph and LLM Joint Pretraining for Graph-Level Tasks

arXiv:2606. 29773v1 Announce Type: new Abstract: Graphs are widely used to model relational systems, with applications in domains such as social networks, finance, and biomedicine.

By Haoxin Sun, Yiqing Lin, Yajun Huang, Chenhui Dong, Mingjun Li, Zhongzhi Zhang

llms diffusion fine-tuning benchmarks safety

arXiv AI

Aug 25

VSAL: A Vision Solver with Adaptive Layouts for Graph Property Detection

arXiv:2602.13880v2 Announce Type: replace Abstract: Graph property detection aims to determine whether a graph exhibits certain structural properties, such as being Hamiltonian. Recently, learning-ba...

By Jiahao Xie, Guangmo Tong

benchmarks

arXiv Machine Learning

Jul 17

What Do Temporal Graph Learning Models Learn?

arXiv:2510. 09416v4 Announce Type: replace Abstract: Learning on temporal graphs has become a central topic in graph representation learning, with numerous benchmarks indicating the strong performance of state-of-the-art models.

By Abigail J. Hayes, Tobias Schumacher, Markus Strohmaier

benchmarks safety

arXiv Machine Learning

Jun 3

When Graph Tokens Sink: A Mechanistic Analysis of Graph Language Models

arXiv:2606. 03712v1 Announce Type: new Abstract: Graph Language Models (GLMs) have become a promising direction for adapting Large Language Models (LLMs) to graph learning tasks.

By Ding Zhang, Runtao Zhou, Wenqing Zheng, Rizal Fathony, Bayan Bruss, Chirag Agarwal

llms efficiency multimodal safety

arXiv Computer Vision

Sep 22

Visual Graph Reasoning via Knowledge Compilation

arXiv:2609.22327v1 Announce Type: cross Abstract: Visual graph reasoning requires answering graph-theoretic questions directly from graph images, where graph topology and state are conveyed visually...

By Rongzheng Wang, Zhe Wang, Ke Qin, Rongwei Wang, Muquan Li, Yizhuo Ma, Yihong Huang, Jielei Wang, Shuang Liang

llms reinforcement-learning multimodal benchmarks

arXiv Computation and Language

Oct 12

MVGraph: Benchmarking LVLMs on Evidence Composition across Multi-View Visual Graph

MVGraph is a new benchmark designed to evaluate large vision‑language models on multi‑graph reasoning tasks where evidence is spread across multiple interrelated visual graph views. It contains 8,000 instances covering eight graph‑theoretic tasks derived from partial and temporal graph scenarios, and each instance is provably unsolvable from a single view. The benchmark also offers 29 diagnostic tasks with 64,000 instances to probe four core capabilities—Perception, Alignment, Composition, and Reasoning—providing golden intermediate representations for detailed analysis.

By Yingjie Zhu, Han Cheng, Xuefeng Bai, Youcheng Pan, Yang Xiang, Kehai Chen

llms multimodal benchmarks safety

arXiv AI

Aug 26

ReactBench: A Benchmark for Topological Reasoning in MLLMs on Chemical Reaction Diagrams

ReactBench is a benchmark designed to evaluate the structural reasoning abilities of multimodal large language models (MLLMs) using chemical reaction diagrams. The dataset contains 1,618 expert‑annotated question‑answer pairs that test reasoning across four hierarchical task dimensions, from simple endpoint counting to complex topological analysis. Evaluation of 24 MLLMs shows a performance gap of more than 30% between anchor‑based tasks and holistic structural reasoning tasks, indicating that current models struggle with reasoning over branching, converging, and cyclic structures.

By Qiang Xu, Shengyuan Bai, Yu Wang, He Cao, Leqing Chen, Yuanyuan Liu, Bin Feng, Zijing Liu, Yu Li

llms multimodal benchmarks

arXiv AI

Aug 21

CharTool: Tool-Integrated Visual Reasoning for Chart Understanding

arXiv:2604. 02794v2 Announce Type: replace Abstract: Charts are ubiquitous in scientific and financial literature for presenting structured data.

By Situo Zhang, Yifan Zhang, Zichen Zhu, Da Ma, Lei Pan, Danyang Zhang, Zihan Zhao, Lu Chen, Kai Yu

llms agents reinforcement-learning multimodal benchmarks data-engineering

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