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AEC-Bench: A Multimodal Benchmark for Agentic Systems in Architecture, Engineering, and Construction

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arXiv:2603.29199v1 Announce Type: new Abstract: The AEC-Bench is a multimodal benchmark for evaluating agentic systems on real-world tasks in the Architecture, Engineering, and Construction (AEC) domain. The benchmark covers tasks requiring drawing understanding, cross-sheet reasoning, and construction project-level coordination. This report describes the benchmark motivation, dataset taxonomy, evaluation protocol, and baseline results across several domain-specific foundation model harnesses. W

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    Computer Science > Artificial Intelligence [Submitted on 31 Mar 2026] AEC-Bench: A Multimodal Benchmark for Agentic Systems in Architecture, Engineering, and Construction Harsh Mankodiya, Chase Gallik, Theodoros Galanos, Andriy Mulyar The AEC-Bench is a multimodal benchmark for evaluating agentic systems on real-world tasks in the Architecture, Engineering, and Construction (AEC) domain. The benchmark covers tasks requiring drawing understanding, cross-sheet reasoning, and construction project-level coordination. This report describes the benchmark motivation, dataset taxonomy, evaluation protocol, and baseline results across several domain-specific foundation model harnesses. We use AEC-Bench to identify consistent tools and harness design techniques that uniformly improve performance across foundation models in their own base harnesses, such as Claude Code and Codex. We openly release our benchmark dataset, agent harness, and evaluation code for full replicability at this https URL under an Apache 2 license. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2603.29199 [cs.AI]   (or arXiv:2603.29199v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2603.29199 Focus to learn more Submission history From: Andriy Mulyar [view email] [v1] Tue, 31 Mar 2026 03:10:28 UTC (7,806 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv AI
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    ◬ AI & Machine Learning
    Published
    Apr 01, 2026
    Archived
    Apr 01, 2026
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