MUSE: Benchmarking Manufacturable, Functional, and Assemblable Text-to-CAD Generation

• URL: http://arxiv.org/abs/2605.28579v2
• Date: Thu, 04 Jun 2026 01:52:56 GMT
• Title: MUSE: Benchmarking Manufacturable, Functional, and Assemblable Text-to-CAD Generation
• Abstract summary: MUSE is a Text-to-CAD benchmark focused on complex, editable boundary representation assemblies.<n>MUSE evaluates generated models through a three-stage protocol: code check, geometric check, and design-intent alignment.<n> Experiments on closed-source and open-source LLMs reveal a clear failure cascade from executable code to valid geometry.
• Score: 17.467056775887944
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have recently advanced text-driven 3D generation, yet Text-to-CAD remains far from supporting industrial product design. Existing benchmarks focus primarily on generating single-part CAD models and evaluate them using geometric similarity metrics that fail to capture functionality, manufacturability, and assemblability. To address this gap, we introduce MUSE, a Text-to-CAD benchmark focused on complex, editable boundary representation (B-Rep) assemblies. MUSE pairs practical design instances with structured Design Specifications and evaluates generated models through a three-stage protocol: code check, geometric check, and design-intent alignment. The final stage uses design-specific rubrics to assess functionality, manufacturability, and assemblability, moving beyond shape matching toward practical design quality. To enable scalable evaluation, we use a rubric-based visual language model (VLM) judge and validate its reliability through human annotation. Experiments on closed-source and open-source LLMs reveal a clear failure cascade from executable code to valid geometry and finally to engineering-ready design, with even the strongest models achieving limited success on fine-grained engineering criteria. Together, MUSE provides a realistic benchmark and evaluation framework for advancing Text-to-CAD from geometric generation toward true engineering design. Our project website, including the leaderboard, dataset, and code, is available at https://dong7313.github.io/muse-benchmark/.

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