We introduce CADFS, a data-centric framework that enables large vision-language models to generate complex CAD design histories. Existing generative CAD systems are restricted to sketch-extrude operations due to simplified representations and limited datasets. We address this by introducing a FeatureScript-based representation and constructing a dataset of 450k real-world CAD models spanning 15 modeling operations. We obtain the dataset via a new pipeline that reconstructs clean, executable FeatureScript programs and provides multimodal annotations. Fine-tuning a VLM on this representation yields state-of-the-art results in text-conditioned CAD generation and image-based reconstruction, producing more accurate, diverse, and feature-rich designs than prior frameworks. Ablations show that each individual component of our framework, i.e., the FeatureScript representation, the extended operation set, and representation-aligned textual descriptions, significantly improves performance. Our framework substantially broadens the complexity and realism achievable in generative CAD. The CADFS framework and the new dataset are available at https://voyleg.github.io/cadfs/.
@article{arxiv.2605.01925,
title = {CADFS: A Big CAD Program Dataset and Framework for Computer-Aided Design with Large Language Models},
author = {Vladislav Pyatov and Gleb Bobrovskikh and Saveliy Galochkin and Nikita Boldyrev and Oleg Voynov and Alexander Filippov and Gonzalo Ferrer and Peter Wonka and Evgeny Burnaev},
journal= {arXiv preprint arXiv:2605.01925},
year = {2026}
}