arXiv AI
Beyond Pixels: A Vector-to-Graph Framework for Reliable Schematic Auditing
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arXiv AI
Sep 18
VLM-CAD: VLM-Optimized Collaborative Agent Design Workflow for Analog Circuit Sizing
The paper introduces VLM-CAD, a workflow that uses Vision Language Models (VLMs) for analog circuit sizing while mitigating spatial blindness and logical hallucinations. It incorporates a neuro‑symbolic parsing module, Image2Net, to convert schematics into topological graphs and JSON, and an Explainable Trust Region Bayesian Optimization method, ExTuRBO, to guide design decisions with sensitivity evidence. Experiments on 12 sizing tasks across six circuits and four technology platforms show a Strict Pass@1 of 23.3% and a Relaxed Pass@1 of 91.7%.
arXiv AI
Jul 20
DrawingVQA: A Real-World Benchmark for Multi-Depth Visual-Textual Reasoning on Construction Drawings
arXiv:2607. 15418v1 Announce Type: new Abstract: We introduce DrawingVQA, the first benchmark designed to evaluate multimodal large language models (MLLMs) on real-world construction drawings -- a core media in architecture, civil, and many other engineering practices.
arXiv AI
Aug 11
MMArch: Benchmarking Multimodal Reasoning Grounded in Architectural Evidence
arXiv:2608. 09281v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) perform strongly on engineering imagery, yet existing benchmarks mostly test drawing recognition, information extraction, or compliance checking, leaving open whether models can combine distributed visual evidence with engineering principles to reach a conclusion.
arXiv AI
Jun 10
Do VLMs Reason Like Engineers? A Benchmark and a Stage-wise Evaluation
arXiv:2606. 10833v1 Announce Type: new Abstract: Vision-Language Models (VLMs) demonstrate strong performance on general multimodal reasoning benchmarks, yet their ability to perform engineering reasoning remains largely unexplored.
arXiv AI
Aug 7
ViSR-KGC: Visual Subgraph Reasoning with Vision-Language Models for Multimodal Knowledge Graph Completion
arXiv:2608. 05833v1 Announce Type: new Abstract: Knowledge graph completion (KGC) aims to infer missing entities or relations from incomplete graph structures, and has evolved into multimodal knowledge graph completion (MMKGC), where entities are associated with multiple modalities such as text and images.
arXiv AI
Jul 28
ERUnderstand: Evaluating Vision-Language Models on Structured ER Diagrams
arXiv:2607. 24707v1 Announce Type: new Abstract: Entity-Relationship Diagrams (ERDs) are central to conceptual database design, yet they are typically available only as rendered images rather than machine-readable schemas, limiting AI-assisted database engineering.