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arXiv · 2610.19571

LED-BENCH: A Benchmark for Large-format Engineering Drawing Understanding

Ke Li·Tianyi Wang·Enning You·Chaoqun Wan·Zhen Huang·Shuxian Liang·Xian-Sheng Hua
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Abstract

Real-world engineering drawings are often large-format and information-dense. Answering practical engineering questions therefore requires both fine-grained understanding of engineering information and complex reasoning over objects, relations, and evidence distributed across different regions or views. Existing benchmarks cover a range of engineering-drawing understanding tasks involving local regions, individual views, specific elements, or more constrained visual settings. However, systematic evaluation of understanding over realistic, large-format engineering drawings remains limited. To address this gap, we introduce LED-BENCH, a multidisciplinary benchmark designed to evaluate fine-grained understanding and complex reasoning over large-format engineering drawings, spanning the mechanical, architectural, and transportation domains. Extensive evaluation of 11 representative multimodal large language models, including 8 proprietary models and 3 open-source models, reveals substantial remaining difficulty: GPT-5.6 Sol achieves 52.65% average accuracy, performance varies across engineering domains, and all evaluated models perform worse on questions requiring broader evidence integration. These results highlight fine-grained understanding, evidence integration, and complex reasoning over large-format engineering drawings as persistent challenges for current multimodal models.

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Ke Li, Tianyi Wang, Enning You, Chaoqun Wan, Zhen Huang, Shuxian Liang, Xian-Sheng Hua. 2026-10-14. LED-BENCH: A Benchmark for Large-format Engineering Drawing Understanding. https://arxiv.org/abs/2610.19571

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