Execution-Supervised Text-to-CAD Generation for Reliable AI-Assisted Parametric Design

Reliable text-to-CAD generation remains challenging because syntactically valid programs may fail during geometric execution or produce inaccurate geometric models. Existing approaches often treat geometric execution primarily as a post-generation validation step, limiting its role in guiding the generation process. To address this limitation, this study proposes a unified execution-supervised framework that incorporates geometric feedback throughout data representation, model optimization, inference-time correction, and self-training. The framework first converts raw boundary representation (B-Rep) data into compact, executable CadQuery programs. A cost-aware hierarchical verification mechanism is then established by combining lightweight Mock-CadQuery (Mock-CQ) screening with authoritative Open CASCADE Technology (OCCT) execution. Furthermore, Chamfer-Distance-guided reinforcement learning, execution-guided iterative repair, and execution-verified self-training are integrated to improve geometric fidelity, execution reliability, and targeted modeling coverage. Experiments demonstrate that the proposed framework significantly reduces the invalid output rate, while also decreasing the mean Chamfer Distance among valid geometric outputs. These findings highlight the effectiveness of integrating execution feedback into CAD program generation and provide a practical approach to producing reliable, executable, and editable parametric models for AI-assisted engineering design.