2026 de Curto Foundation Models Automatic CAD

Recent advances in Large Language Models (LLMs) and Vision-Language Models (VLMs) have opened new pathways for the automatic generation of parametric three-dimensional designs from natural-language specifications. This chapter presents a comprehensive empirical study on the use of modern foundation models for automatic Computer-Aided Design (CAD) generation of mechanical parts, structured around a unified evaluation pipeline and a curated benchmark suite of 97 engineering design problems. We introduce LLMForge, a multi-model text-to-CAD framework that integrates JSON-schema validation, analytic feature scoring, mesh synthesis, and a multi-round iterative refinement loop, studied under two distinct critique regimes. The first regime, IterTracer, employs a Phong-shaded ray-trace renderer coupled with a suite of analytic visual metrics to provide lightweight, geometry-aware feedback across successive generation rounds. The second regime, IterVision, replaces the analytic scorer with a VLM-based semantic critic that evaluates rendered views of each candidate geometry through chain-of-thought visual reasoning, enabling richer assessment of spatial coherence and design intent. Using a benchmark spanning four canonical geometry families, we evaluate seven state-of-the-art foundation models and demonstrate that compact instruction-tuned models can attain reliability competitive with substantially larger systems.