A Three-Layer Runtime Constraint Verification Framework with Self-Correction for AI-Generated Parametric CAD Models

Large language models (LLMs) frequently generate physically impossible parametric specifications in computer-aided design (CAD). This paper presents FMforME, a three-layer runtime constraint verification framework that integrates LLM-based generation with six engineering standard-grounded defect detection predicates (D1–D6) and execution in Autodesk Fusion 360. To address detected violations, a Self-Examine subsystem iteratively provides structured, standard-referenced feedback to the LLM, enabling verifier-guided error correction. We evaluated the framework across 564 test cases using four LLM backends from three model families. The system showed consistent buildability within the evaluated benchmark: across all models, every specification validated by the Monitor successfully built in Fusion 360 (548/548). Across the four evaluated backends, realized seeded defect detection ranged from 95.5% to 95.8%, and three backends showed an observed 100% natural pass rate in this benchmark. Crucially, this runtime safety barrier adds little computational overhead relative to LLM inference, averaging only 0.1 ms per in-memory verification check. These results show that standards-referenced runtime verification with structured self-correction can serve as a practical safeguard for LLM-generated CAD specifications within the tested single-part workflows, while broader validation on complex assemblies remains future work.