PR-CAD: Progressive Refinement for Unified Controllable and Faithful Text-to-CAD Generation with Large Language Models

ICLR 2026 Conference Submission18291 Authors

Keywords: Controllable CAD Modeling, Progressive Refinement Framework, Large Language Models, Reinforcement Learning-enhanced Reasoning.

TL;DR: PR-CAD unifies controllable text-to-CAD generation and editing via RL-enhanced reasoning, enabling progressive modeling from both qualitative and quantitative input.

Abstract: The construction of CAD models has traditionally relied on labor-intensive manual operations and specialized expertise. Recent advances in large language models (LLMs) have inspired research into text-to-CAD generation. However, existing approaches typically treat generation and editing as disjoint tasks, limiting their practicality. We propose PR-CAD, a progressive refinement framework that unifies generation and editing for controllable and faithful text-to-CAD modeling. To support this, we curate a high-fidelity interaction dataset spanning the full CAD lifecycle, encompassing multiple CAD representations as well as both qualitative and quantitative descriptions. The dataset systematically defines the types of edit operations and generates highly human-like interaction data. Building on a CAD representation tailored for LLMs, we propose a reinforcement learning–enhanced reasoning framework that integrates intent understanding, parameter estimation, and precise edit localization into a single agent. This enables an “all-in-one” solution for both design creation and refinement. Extensive experiments demonstrate strong mutual reinforcement between generation and editing tasks, and across qualitative and quantitative modalities. On public benchmarks, PR-CAD achieves state-of-the-art controllability and faithfulness in both generation and refinement scenarios, while also proving user-friendly and significantly improving CAD modeling efficiency. The code and dataset are be available at { will be filled in upon acceptance }.

Primary Area: generative models

Submission Number: 18291