超越像素：用于可靠图示审计的向量到图转换
Beyond Pixels: A Vector-to-Graph Framework for Reliable Schematic Auditing
AI总结：
本文提出向量到图转换方法，通过将CAD图转换为属性图，提升工程图示审计的准确性，克服基于像素方法的局限性。
摘要：
多模态大语言模型（MLLMs）在视觉理解方面取得了显著进展，但它们存在一个关键局限：结构盲区。即使是最先进的模型也无法捕捉工程图示中的拓扑结构和符号逻辑，因为其基于像素的范式会丢弃用于推理所需的显式向量定义关系。为了解决这一问题，我们提出了一种向量到图（V2G）流程，将CAD图转换为属性图，其中节点代表组件，边编码连接性，使结构依赖性显式化并可被机器审计。在电气合规性检查的诊断基准测试中，V2G在所有错误类别中均实现了显著的准确性提升，而领先的MLLMs仍接近随机水平。这些结果突显了基于像素的方法的系统不足，并证明了结构感知的表示为在工程领域部署多模态AI提供了可靠的途径。为了促进进一步研究，我们在此发布基准测试和实现：https://github.com/gm-embodied/V2G-Audit。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
