Beyond Pixels: A Vector-to-Graph Framework for Reliable Schematic Auditing

Abstract:Multimodal Large Language Models (MLLMs) have shown remarkable progress in visual understanding, yet they suffer from a critical limitation: structural blindness. Even state-of-the-art models fail to capture topology and symbolic logic in engineering schematics, as their pixel-driven paradigm discards the explicit vector-defined relations needed for reasoning. To overcome this, we propose a Vector-to-Graph (V2G) pipeline that converts CAD diagrams into property graphs where nodes represent components and edges encode connectivity, making structural dependencies explicit and machine-auditable. On a diagnostic benchmark of electrical compliance checks, V2G yields large accuracy gains across all error categories, while leading MLLMs remain near chance level. These results highlight the systemic inadequacy of pixel-based methods and demonstrate that structure-aware representations provide a reliable path toward practical deployment of multimodal AI in engineering domains. To facilitate further research, we release our benchmark and implementation at this https URL.

4 pages, 3 figures. Published in ICASSP 2026

Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2602.11678 [cs.AI]

(or arXiv:2602.11678v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2602.11678 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1109/ICASSP55912.2026.11460425 Focus to learn more DOI(s) linking to related resources

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