CADEngBench, a layered benchmark of 600 parametric tasks and 120 assembly pairs, shows current AI models edit supplied CAD far more easily than they generate it, and rarely match reference physics or recover exact assembly joints.
neuralCAD-Edit: An Expert Benchmark for Multimodal-Instructed 3D CAD Model Editing
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abstract
We introduce neuralCAD-Edit, the first benchmark for editing 3D CAD models collected from expert CAD engineers. Instead of text conditioning as in prior works, we collect realistic CAD editing requests by capturing videos of professional designers, interacting directly with CAD models in CAD software, while talking, pointing and drawing. We recruited ten consenting designers to contribute to this contained study. We benchmark leading foundation models against human CAD experts carrying out edits, and find a large performance gap in both automatic metrics and human evaluations. Even the best foundation model (GPT 5.2) scores 53% lower (absolute) than CAD experts in human acceptance trials, demonstrating the challenge of neuralCAD-Edit. We hope neuralCAD-Edit will provide a solid foundation against which 3D CAD editing approaches and foundation models can be developed. Code/data: https://autodeskailab.github.io/neuralCAD-Edit
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CADEngBench: It Looks Like CAD, but Does It Work? Evaluating Parametric Design, Assembly Reasoning, and Physics Simulation
CADEngBench, a layered benchmark of 600 parametric tasks and 120 assembly pairs, shows current AI models edit supplied CAD far more easily than they generate it, and rarely match reference physics or recover exact assembly joints.