{"ID":6023334,"CreatedAt":"2026-07-08T01:00:23.257252134Z","UpdatedAt":"2026-07-10T01:44:12.350457273Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.05750","arxiv_id":"2607.05750","title":"ArtisanCAD: An Industrial-Level CAD Agent with Expert-Grounded Knowledge Distillation","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.","short_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 t...","url_abs":"https://arxiv.org/abs/2607.05750","url_pdf":"https://arxiv.org/pdf/2607.05750v1","authors":"[\"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\"]","published":"2026-07-07T02:11:50Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.GR\"]","methods":"[]","has_code":false}