DrawingVQA: A Real-World Benchmark for Multi-Depth Visual-Textual Reasoning on Construction Drawings
DrawingVQA is the first benchmark for evaluating multimodal large language models on real-world construction drawings. It features 33 'Issued for Construction' drawings and 92 expert-curated question-answer pairs across three reasoning depths. A dual categorization framework maps engineering workflows to AI capabilities, revealing a significant performance gap between state-of-the-art MLLMs and experts, especially at higher reasoning levels.
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[Submitted on 16 Jul 2026]
Title:DrawingVQA: A Real-World Benchmark for Multi-Depth Visual-Textual Reasoning on Construction Drawings
View a PDF of the paper titled DrawingVQA: A Real-World Benchmark for Multi-Depth Visual-Textual Reasoning on Construction Drawings, by Yoonhwa Jung and 2 other authors
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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. Unlike natural images or schematic floor plans, construction drawings fuse abstract geometry, symbolic notation, tabular data, annotations, and domain-specific text, forming a uniquely complex visual-textual domain core to engineering workflows. DrawingVQA bridges this gap with 33 "Issued for Construction" drawings and 92 expertly curated question-answer pairs, spanning three reasoning depths: perceptual understanding, contextual interpretation, and domain-expert reasoning. To evaluate model capabilities, we present a dual categorization framework to jointly analyze performance across seven construction-engineering and four MLLM capability dimensions -- the first to explicitly map engineering workflows to AI reasoning competencies. Evaluations of state-of-the-art MLLMs reveal a substantial gap between model and expert performance, particularly at higher reasoning depths. This benchmark lays a foundation for domain-specialized multimodal reasoning to allow for advancement on integration of AI-driven understanding and real-world engineering workflows.
Comments: CVPR 2026 Findings accepted paper
Subjects:
Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.15418 [cs.AI]
(or arXiv:2607.15418v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.15418
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yoonhwa Jung [view email] [v1] Thu, 16 Jul 2026 19:44:03 UTC (9,031 KB)
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