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New framework and benchmark improve diagram-to-graph topology extraction

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced TopoAgent, a novel framework designed to improve the extraction of graph topologies from structural diagrams using large vision-language models. This framework is accompanied by TopoBench-180, a new benchmark dataset featuring 180 diagrams categorized into Web-style and Network-style, complete with human-verified graph annotations. TopoAgent enhances accuracy by integrating grounded perception with global structural priors and topological consistency enforcement, outperforming existing vision-language models and visual reasoning frameworks, particularly in edge extraction. AI

IMPACT Establishes a new benchmark and framework for multimodal structured understanding, potentially advancing AI capabilities in diagram interpretation.

RANK_REASON The cluster describes a new research paper introducing a framework and benchmark for a specific AI task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework and benchmark improve diagram-to-graph topology extraction

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The cluster describes a new research paper introducing a framework and benchmark for a specific AI task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Bangwei Guo, Xujiang Zhao, Yanchi Liu, Wei Cheng, Shengyu Chen, Dongyue Li, Masaharu Morimoto, Takayuki Kuroda, Dimitris Metaxas, Haifeng Chen ·

    TopoAgent: A Structure-Aware Perception-to-Reasoning Framework for Diagram-to-Graph Topology Extraction with Large Vision-Language Models

    arXiv:2608.28701v1 Announce Type: new Abstract: Diagram-to-graph topology extraction aims to extract a graph of entities and their connections from a structural diagram. This task remains challenging for current vision-language models because it requires both fine-grained percept…

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