arXiv AI
VSAL: A Vision Solver with Adaptive Layouts for Graph Property Detection
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arXiv Machine Learning
Sep 4
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 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.
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...
arXiv AI
Jul 3
MolSight: A Graph-Aware Vision-Language Model for Unified Chemical Image Understanding
arXiv:2607. 01982v1 Announce Type: cross Abstract: Using molecular large language models (LLMs) as a unified framework for understanding molecular structures and functions is emerging as a new trend in tasks such as molecular design and drug discovery.
arXiv AI
Sep 30
Beyond Pixels: A Vector-to-Graph Framework for Reliable Schematic Auditing
arXiv:2602.11678v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have shown remarkable progress in visual understanding, yet they suffer from a critical limitation: struct...
arXiv Machine Learning
Oct 2
Measuring Structure in Graph Benchmark Datasets Using Graph Invariants
arXiv:2605.06462v2 Announce Type: replace Abstract: Progress in graph learning is hindered by benchmark practices that conflate the contributions of node features and graph structure, making it hard...