Visual Graph Reasoning via Knowledge Compilation

📅 2026-09-16
📈 Citations: 0
✨ Influential: 0
📄 PDF
🤖 AI Summary
针对视觉图推理问题,提出VGCompiler方法,通过知识编译从图像中恢复显式图表示,并编译查询意图,提高视觉图推理的准确性与效率。
📝 Abstract
Visual graph reasoning requires answering graph-theoretic questions directly from graph images, where graph topology and state are conveyed visually rather than given in symbolic form. Despite recent progress of vision-language models (VLMs), current approaches to visual graph reasoning still fail on simple visual graph problems. This reveals a fundamental limitation of existing approaches: they prioritize final-answer supervision over the intermediate recovery of an explicit graph representation that preserves graph topology and state from visual input. To address this limitation, we propose VGCompiler, a compilation-centric paradigm for visual graph reasoning via knowledge compilation. VGCompiler organizes reasoning around two compilers: a representation compiler that recovers a structure-preserving intermediate graph representation from visual input, and an operation compiler that compiles query intent under the recovered graph state into an executable graph operation. Specifically, we build VGCompiler on Qwen3-VL-8B and train it with reinforcement learning guided by a layered reward over executability, compiled graph validity, representation quality, and operation quality. VGCompiler uses a frozen observer to summarize graph and question conditions into lightweight signatures, enabling archive retrieval and code reuse across similar regimes. Experiments on three benchmarks GVLQA, VisionGraph, and VGCURE, show that Qwen-VGCompiler, built on an 8B backbone, surpasses the strongest closed-source VLM baseline by 28.9% and the strongest code-based baseline by 23.7%, while maintaining high efficiency. We further evaluate VGCompiler on three real-world domains, including metro routing, logistics delivery, and network fault assessment, where it generalizes across heterogeneous visual graphs and domain-grounded tasks.
Problem

Research questions and friction points this paper is trying to address.

Visual Graph Reasoning
Graph Topology
Final-answer Supervision
Innovation

Methods, ideas, or system contributions that make the work stand out.

Knowledge Compilation
Visual Graph Reasoning
Representation Compiler
Operation Compiler
Reinforcement Learning
🔎 Similar Papers
No similar papers found.
💼 Related Jobs
No related jobs found.
R
Rongzheng Wang
University of Electronic Science and Technology of China
Z
Zhe Wang
University of Electronic Science and Technology of China
K
Ke Qin
University of Electronic Science and Technology of China
R
Rongwei Wang
Tsinghua Shenzhen International Graduate School
M
Muquan Li
University of Electronic Science and Technology of China
Y
Yizhuo Ma
University of Electronic Science and Technology of China
Y
Yihong Huang
University of Electronic Science and Technology of China
J
Jielei Wang
University of Electronic Science and Technology of China
Shuang Liang
Shuang Liang
Research Associated Professor, University of Electronic Science and Technology of China
Graph Neural NetworkKnowledge GraphData Mining