CAD-HLLM: Generating Executable CAD from Text with Hierarchical LLM Planning

Zhuo Zuo, Yantao Gan, Junfeng Long, Xianggen Liu

Abstract: Translating natural language into precise and executable Computer-Aided Design (CAD) programs remains a challenging task, requiring both semantic understanding and geometric fidelity. In this paper, we present CAD-HLLM, a hierarchical LLM framework for structured CAD command generation. Our approach decomposes the task into two stages: a Plan Generator that infers high-level symbolic plans from text, and a Parameter Completor that generates detailed parametric commands conditioned on both the original description and the inferred plan. To enhance robustness, we introduce a lightweight ensemble selection mechanism that ranks and selects among multiple candidates based on model log-likelihoods. Experiments on benchmark datasets show that our method outperforms existing baselines in both parametric precision and 3D shape similarity, demonstrating the effectiveness of hierarchical reasoning and LLM-based planning in bridging the gap between human design intent and executable CAD sequences.

Submission Number: 224