CADEngBench: It Looks Like CAD, but Does It Work? Evaluating Parametric Design, Assembly Reasoning, and Physics Simulation
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Abstract (arXiv)
A CAD model is not engineering-grade merely because it looks correct. It must satisfy design requirements, respond predictably to parameter changes, support controlled edits, match a reference structural response under a declared analysis, and connect to other parts through valid joints. We present CADEngBench, a two-track benchmark for these capabilities. CADEngBench-P evaluates 300 parametric parts, each used for one zero-to-CAD task and one functional-editing task (600 tasks in total), through boundary-representation (B-Rep) validity, engineering and DFM checks, parameter-family perturbations, functional editing, and matched linear-static FEA in CalculiX. CADEngBench-A evaluates 150 body pairs through ranked joint retrieval, exact face-and-edge grounding, joint-frame prediction, and kinematic verification. Across eight multimodal, code-capable models, editing supplied CAD is substantially easier than generating it, while complex edits and matched FEA remain difficult. Assembly predictions often locate the relevant region but fail to recover the recorded joint or mating entities. These results show that CAD evaluation must test engineering behavior rather than appearance alone.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 21 of its 32 references that are in the map. First posted on arXiv 10 Aug 2026.
Builds on
21 of 32 references.
References · 21
- 2018ABC: A Big CAD Model Dataset For Geometric Deep Learningcited by 339
- 2021DeepCAD: A Deep Generative Network for Computer-Aided Design Modelscited by 266
- 2021JoinABLe: Learning Bottom-up Assembly of Parametric CAD Jointscited by 77
- 2025Text-to-CadQuery: A New Paradigm for CAD Generation with Scalable Large Model Capabilitiescited by 43
- 2025CAD-Coder: An Open-Source Vision-Language Model for Computer-Aided Design Code Generationcited by 39
- 2024Text2CAD: Generating Sequential CAD Models from Beginner-to-Expert Level Text Promptscited by 22
- 2023ASAP: Automated Sequence Planning for Complex Robotic Assembly with Physical Feasibilitycited by 17
- 2026Text2CAD-Bench: A Benchmark for LLM-based Text-to-Parametric CAD Generationcited by 11
- 2026Text-to-CAD Evaluation with CADTestscited by 9
- 2026IterCAD: An Iterative Multimodal Agent for Visually-Grounded CAD Generation and Editingcited by 9
- 2026MUSE: Benchmarking Manufacturable, Functional, and Assemblable Text-to-CAD Generationcited by 8
- 2026ArtiCAD: Articulated CAD Assembly Design via Multi-Agent Code Generationcited by 7
9 more
- 2026Zero-to-CAD: Agentic Synthesis of Interpretable CAD Programs at Million-Scale Without Real Datacited by 7
- 2026CADBench: A Multimodal Benchmark for AI-Assisted CAD Program Generationcited by 7
- 2026Towards High-Fidelity CAD Generation via LLM-Driven Program Generation and Text-Based B-Rep Primitive Groundingcited by 6
- 2026CADEvolve: Creating Realistic CAD via Program Evolutioncited by 5
- 2025HistCAD: Constraint-Aware Parametric CAD Histories for Evaluating Editabilitycited by 4
- 2026neuralCAD-Edit: An Expert Benchmark for Multimodal-Instructed 3D CAD Model Editingcited by 3
- 2026Physics-in-the-Loop: A Hybrid Agentic Architecture for Validated CAD Engineering Designcited by 3
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- 2026Self-Improving CAD Generation Agents with Finite Element Analysis as Feedbackcited by 2
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Citing papers per year: 2026: 3.
3 papers.