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ArtisanCAD is a skill-guided industrial CAD agent that uses expert-grounded knowledge distillation to convert ambiguous prompts into executable CAD procedures, improving performance on the Text2CAD benchmark and enabling editable B-Rep models for complex automotive components.
The paper introduces COSMO-Agent, a tool-augmented reinforcement learning framework that trains LLMs to perform closed-loop CAD-CAE optimization, iteratively generating parametric geometries and running simulations until constraints are satisfied, with a multi-constraint reward and a new industry-aligned dataset.