# FlexCAD: Unified and Versatile Controllable CAD Generation with Fine-tuned Large Language Models

arXiv: https://arxiv.org/abs/2411.05823 · HTML: https://mlsanity.com/paper/2411.05823/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 39 references that are in the map.
- First posted on arXiv 5 Nov 2024.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data](https://mlsanity.com/paper/1911.00359/) (2019)
- [MotionGPT: Human Motion as a Foreign Language](https://mlsanity.com/paper/2306.14795/) (2023)
- [DeepCAD: A Deep Generative Network for Computer-Aided Design Models](https://mlsanity.com/paper/2105.09492/) (2021)
- [Fine-Tuning LLaMA for Multi-Stage Text Retrieval](https://mlsanity.com/paper/2310.08319/) (2023)
- [SkexGen: Autoregressive Generation of CAD Construction Sequences with Disentangled Codebooks](https://mlsanity.com/paper/2207.04632/) (2022)
- [BrepGen: A B-rep Generative Diffusion Model with Structured Latent Geometry](https://mlsanity.com/paper/2401.15563/) (2024)
- [Cheap and Quick: Efficient Vision-Language Instruction Tuning for Large Language Models](https://mlsanity.com/paper/2305.15023/) (2023)
- [Fine-Tuned Language Models Generate Stable Inorganic Materials as Text](https://mlsanity.com/paper/2402.04379/) (2024)
- [Hierarchical Neural Coding for Controllable CAD Model Generation](https://mlsanity.com/paper/2307.00149/) (2023)
- [Computer-Aided Design as Language](https://mlsanity.com/paper/2105.02769/) (2021)
- [SolidGen: An Autoregressive Model for Direct B-rep Synthesis](https://mlsanity.com/paper/2203.13944/) (2022)
- [UCSG-Net -- Unsupervised Discovering of Constructive Solid Geometry Tree](https://mlsanity.com/paper/2006.09102/) (2020)
- [Fine-tuning Multimodal LLMs to Follow Zero-shot Demonstrative Instructions](https://mlsanity.com/paper/2308.04152/) (2023)
- [CAD-SIGNet: CAD Language Inference from Point Clouds using Layer-wise Sketch Instance Guided Attention](https://mlsanity.com/paper/2402.17678/) (2024)
- [CAPRI-Net: Learning Compact CAD Shapes with Adaptive Primitive Assembly](https://mlsanity.com/paper/2104.05652/) (2021)
- [CSG-Stump: A Learning Friendly CSG-Like Representation for Interpretable Shape Parsing](https://mlsanity.com/paper/2108.11305/) (2021)
- [SECAD-Net: Self-Supervised CAD Reconstruction by Learning Sketch-Extrude Operations](https://mlsanity.com/paper/2303.10613/) (2023)
- [How Can Large Language Models Help Humans in Design and Manufacturing?](https://mlsanity.com/paper/2307.14377/) (2023)
- [D2CSG: Unsupervised Learning of Compact CSG Trees with Dual Complements and Dropouts](https://mlsanity.com/paper/2301.11497/) (2023)
- [DataGen: Unified Synthetic Dataset Generation via Large Language Models](https://mlsanity.com/paper/2406.18966/) (2024)
- [Neural Face Identification in a 2D Wireframe Projection of a Manifold Object](https://mlsanity.com/paper/2203.04229/) (2022)
- [LLaMA-Excitor: General Instruction Tuning via Indirect Feature Interaction](https://mlsanity.com/paper/2404.00913/) (2024)

## Built on by (most cited first, 30 of 34)

- [CSBrain: A Cross-scale Spatiotemporal Brain Foundation Model for EEG Decoding](https://mlsanity.com/paper/2506.23075/) (2025)
- [Text-to-CAD Generation Through Infusing Visual Feedback in Large Language Models](https://mlsanity.com/paper/2501.19054/) (2025)
- [CADmium: Fine-Tuning Code Language Models for Text-Driven Sequential CAD Design](https://mlsanity.com/paper/2507.09792/) (2025)
- [Seek-CAD: A Self-refined Generative Modeling for 3D Parametric CAD Using Local Inference via DeepSeek](https://mlsanity.com/paper/2505.17702/) (2025)
- [cadrille: Multi-modal CAD Reconstruction with Reinforcement Learning](https://mlsanity.com/paper/2505.22914/) (2025)
- [Pointer-CAD: Unifying B-Rep and Command Sequences via Pointer-based Edges & Faces Selection](https://mlsanity.com/paper/2603.04337/) (2026)
- [Text-to-CAD Evaluation with CADTests](https://mlsanity.com/paper/2605.07807/) (2026)
- [Zero-to-CAD: Agentic Synthesis of Interpretable CAD Programs at Million-Scale Without Real Data](https://mlsanity.com/paper/2604.24479/) (2026)
- [Clarify Before You Draw: Proactive Agents for Robust Text-to-CAD Generation](https://mlsanity.com/paper/2602.03045/) (2026)
- [CADReasoner: Iterative Program Editing for CAD Reverse Engineering](https://mlsanity.com/paper/2603.29847/) (2026)
- [GeoCAD: Local Geometry-Controllable CAD Generation with Large Language Models](https://mlsanity.com/paper/2506.10337/) (2025)
- [PR-CAD: Progressive Refinement for Unified Controllable and Faithful Text-to-CAD Generation with Large Language Models](https://mlsanity.com/paper/2604.19773/) (2026)
- [Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents](https://mlsanity.com/paper/2510.17491/) (2025)
- [CADKnitter: Compositional CAD Generation from Text and Geometry Guidance](https://mlsanity.com/paper/2512.11199/) (2025)
- [STEP-LLM: Generating CAD STEP Models from Natural Language with Large Language Models](https://mlsanity.com/paper/2601.12641/) (2026)
- [ComAct: Reframing Professional Software Manipulation via COM-as-Action Paradigm](https://mlsanity.com/paper/2606.13239/) (2026)
- [CAD-Tokenizer: Towards Text-based CAD Prototyping via Modality-Specific Tokenization](https://mlsanity.com/paper/2509.21150/) (2025)
- [MamTiff-CAD: Multi-Scale Latent Diffusion with Mamba+ for Complex Parametric Sequence](https://mlsanity.com/paper/2511.17647/) (2025)
- [BrepLLM: Enabling Large Language Models to Understand Boundary Representations](https://mlsanity.com/paper/2512.16413/) (2025)
- [SOV-CAD: Stepwise Orthographic Views Guided CAD Modeling Sequence Reconstruction](https://mlsanity.com/paper/2607.04119/) (2026)
- [HierCAD: Hierarchical Text-to-CAD Design via Structure Alignment and Parameter Grounding](https://mlsanity.com/paper/2607.11339/) (2026)
- [GraphBrep: Learning B-Rep in Graph Structure for Efficient CAD Generation](https://mlsanity.com/paper/2507.04765/) (2025)
- [CADMorph: Geometry-Driven Parametric CAD Editing via a Plan-Generate-Verify Loop](https://mlsanity.com/paper/2512.11480/) (2025)
- [Learning Hierarchical and Geometry-Aware Graph Representations for Text-to-CAD](https://mlsanity.com/paper/2604.10075/) (2026)
- [Computer-Aided Design Generation by Cascaded Discrete Diffusion Model](https://mlsanity.com/paper/2605.05031/) (2026)
- [Text-to-CAD Retrieval: a Strong Baseline](https://mlsanity.com/paper/2605.05572/) (2026)
- [KDH-CAD: Knowledge-data hybrid CAD learning under data scarcity](https://mlsanity.com/paper/2606.01702/) (2026)
- [Wrong Design Intent Is Worse Than Never Conditioning: A Derangement-Control Diagnosis of Header Conditioning in CAD Program Completion](https://mlsanity.com/paper/2607.23191/) (2026)
- [AIMold: An Autonomous AI-based Pipeline for Complex Mold Design](https://mlsanity.com/paper/2608.00800/) (2026)
- [Test-Time Scaling for CAD Generation via Verifier-Free Consensus Selection](https://mlsanity.com/paper/2608.09706/) (2026)

## Abstract (arXiv)

Recently, there is a growing interest in creating computer-aided design (CAD) models based on user intent, known as controllable CAD generation. Existing work offers limited controllability and needs separate models for different types of control, reducing efficiency and practicality. To achieve controllable generation across all CAD construction hierarchies, such as sketch-extrusion, extrusion, sketch, face, loop and curve, we propose FlexCAD, a unified model by fine-tuning large language models (LLMs). First, to enhance comprehension by LLMs, we represent a CAD model as a structured text by abstracting each hierarchy as a sequence of text tokens. Second, to address various controllable generation tasks in a unified model, we introduce a hierarchy-aware masking strategy. Specifically, during training, we mask a hierarchy-aware field in the CAD text with a mask token. This field, composed of a sequence of tokens, can be set flexibly to represent various hierarchies. Subsequently, we ask LLMs to predict this masked field. During inference, the user intent is converted into a CAD text with a mask token replacing the part the user wants to modify, which is then fed into FlexCAD to generate new CAD models. Comprehensive experiments on public dataset demonstrate the effectiveness of FlexCAD in both generation quality and controllability. Code will be available at https://github.com/microsoft/FlexCAD.