LLM4CAD-Editor: An Intent-Aware Large Language Model Framework for Multi-Level Computer-Aided Design Editing

Abstract

Large language models (LLMs) have recently enabled automatic generation of parametric computer-aided design (CAD) programs from natural language. However, real-world CAD workflows are inherently iterative and require reliable editing rather than one-shot model synthesis. In this work, we propose LLM4CAD-Editor, an LLM-based intent-aware framework for instruction-guided CAD editing based on a structured domain-specific language (LLM4CAD-DSL). The symbolic representation of LLM4CAD-DSL enables robust geometric modification through a feature-level entity selection mechanism, allowing models to reference geometry via feature names instead of coordinates, thus transforming fragile coordinate-based reasoning into natural language-based reasoning that many LLMs can handle. We construct a multimodal CAD editing dataset with over 35,139 instruction-program pairs via DSL-based augmentation and vision-language instruction synthesis, covering functional-, operation-, and parameter-level editing intents. To validate the work, we fine-tuned a 32B-parameter language model for DSL editing generation. Experimental results show high parsing accuracy for parameter-level edits (96.3%) and strong intent satisfaction rates of 82% for functional instructions. The model also achieves an average Intersection-over-Union (IoU) of 0.935 for parameter-level edits, 0.871 for operation-level edits, and 0.708 for functional-level edits, while the corresponding average editing distances are 0.176, 0.579, and 2.859, respectively. Comparative studies further demonstrate a significant improvement in editing robustness by 1.4x over Python-based CAD scripting approaches. These results confirm that LLM4CAD-Editor can reliably perform both low-level parameter modifications and high-level functional edits, maintaining high accuracy and low structural errors across diverse editing tasks.