ArtisanCAD: An Industrial-Level CAD Agent with Expert-Grounded Knowledge Distillation
Quick Answer
ArtisanCAD introduces a skill-guided industrial CAD agent that utilizes expert-grounded knowledge distillation to enhance CAD generation.
Quick Take
ArtisanCAD introduces a skill-guided industrial CAD agent that utilizes expert-grounded knowledge distillation to enhance CAD generation. By employing a CAD intermediate representation (CAD-IR), it reduces mean Chamfer Distance from 14.83 to 9.88 on the Text2CAD benchmark, effectively bridging ambiguous prompts and executable CAD operations. This innovation allows for the generation of editable CATIA-native B-Rep models from expert recordings.
Key Points
- CAD-IR distills expert procedures into reusable parameterized skills.
- Generates production-ready B-Rep models for new variant requests.
- Improves CAD generation from intermediate prompts significantly.
- Utilizes multi-view visual feedback for iterative refinement.
- Integrates seamlessly with CATIA- backend for execution.
Paper Resources
📖 Reader Mode
~2 min readAuthors:Yunhan Xu, Qifeng Wu, Xunjin Li, Yuanwei Bin, Qingsong Yao, Jianghang Gu, Guan Wang, Weihao Lv, Huiyu Yang, Wenfa Luo, Jiao Xiang, Yuntian Chen, Shiyi Chen
Abstract:Computer-aided design (CAD) for industrial components requires long-horizon procedural modeling, robust feature dependencies, editable parametric geometry, and production-grade B-Rep execution. Existing text-to-CAD methods have made promising progress in generating CAD programs from natural-language descriptions, but they still struggle when user prompts are ambiguous, underspecified, or only describe high-level design intent. They also rarely exploit expert procedural knowledge naturally available in industrial workflows, such as CATIA operation recordings, macro logs, drawing notes, and engineering descriptions. We present \algname, a skill-guided industrial CAD agent with expert-grounded knowledge distillation. The core of \algname is CAD intermediate representation (CAD-IR), an executable procedural representation that encodes parameters, ordered operations, MCP tool bindings, dependencies, generated entities, and verification rules. CAD-IR plays two key roles: it first serves as the carrier for distilling expert CAD procedures into reusable parameterized skills; then it provides a procedural scaffold that turns vague or intermediate-level prompts into complete executable CAD operations. \algname retrieves expert-derived skills, instantiates and revises CAD-IR, executes the resulting procedure through a dedicated CATIA-MCP backend, and uses multi-view visual feedback for iterative refinement, and finally generates production-ready B-Rep models. On the Text2CAD benchmark, CAD-IR improves generation from intermediate prompts by reducing mean Chamfer Distance from $14.83$ to $9.88$, showing its ability to bridge ambiguous textual intent and executable CAD construction. On four complex automotive components, CAD-IR enables expert CATIA recordings to be distilled into reusable skills, allowing \algname to generate editable CATIA-native B-Rep models for new variant requests.
| Subjects: | Artificial Intelligence (cs.AI); Graphics (cs.GR) |
| Cite as: | arXiv:2607.05750 [cs.AI] |
| (or arXiv:2607.05750v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.05750 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yuanwei Bin [view email]
[v1]
Tue, 7 Jul 2026 02:11:50 UTC (2,825 KB)
— Originally published at arxiv.org
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