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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.11678 |
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| _version_ | 1866914324656685056 |
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| author | Ma, Chengwei Tian, Zhen Zhou, Zhou Xu, Zhixian Zhu, Xiaowei Hua, Xia Shi, Si Yu, F. Richard |
| author_facet | Ma, Chengwei Tian, Zhen Zhou, Zhou Xu, Zhixian Zhu, Xiaowei Hua, Xia Shi, Si Yu, F. Richard |
| contents | 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 https://github.com/gm-embodied/V2G-Audit. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_11678 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Beyond Pixels: Vector-to-Graph Transformation for Reliable Schematic Auditing Ma, Chengwei Tian, Zhen Zhou, Zhou Xu, Zhixian Zhu, Xiaowei Hua, Xia Shi, Si Yu, F. Richard Artificial Intelligence Computer Vision and Pattern Recognition 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 https://github.com/gm-embodied/V2G-Audit. |
| title | Beyond Pixels: Vector-to-Graph Transformation for Reliable Schematic Auditing |
| topic | Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.11678 |