CIT-CAD: Constraint Intent Tree-based CAD Code Generation and Verification
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Abstract (arXiv)
Natural-language Computer-Aided Design (CAD) code generation aims to turn design intent into executable and editable parametric programs. Large language models (LLMs) make this goal increasingly practical, but useful systems must preserve the construction process behind the rendered geometry. Existing benchmarks and methods mostly focus on how closely the generated CAD model matches the reference geometry, often using metrics such as Intersection over Union (IoU). Such metrics can miss errors in part decomposition, construction hierarchy, Boolean operations, sketch structure, and geometric relations. This gap calls for a representation that makes design intent explicit and lets a system check generated code against that intent. We propose CIT-CAD, a framework that infers a Constraint Intent Tree (CIT) from the input description to represent the intended entities, hierarchy, operations, and relations. The tree has two roles: it guides CAD code generation and defines expected constraints for verification. The framework extracts actual constraints from the generated program, compares them with the expected constraints, and uses mismatches to localize and repair design violations. Experiments show that the framework improves CAD generation performance, with larger gains on more complex multi-entity designs. By turning design intent into an explicit and checkable object, this work is the first attempt to move text-to-CAD generation beyond rendered-geometry matching toward construction-aware synthesis, verification, and repair.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 61 of its 76 references that are in the map. First posted on arXiv 7 Sep 2026.
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References · 61
- 2025Qwen3 Technical Reportcited by 8,820
- 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,442
- 2020CodeBERT: A Pre-Trained Model for Programming and Natural Languagescited by 1,310
- 2024DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligencecited by 1,181
- 2020GraphCodeBERT: Pre-training Code Representations with Data Flowcited by 609
- 2018ABC: A Big CAD Model Dataset For Geometric Deep Learningcited by 341
- 2021DeepCAD: A Deep Generative Network for Computer-Aided Design Modelscited by 271
- 2024RepairAgent: An Autonomous, LLM-Based Agent for Program Repaircited by 232
- 2023Large Language Models for Software Engineering: Survey and Open Problemscited by 181
- 2023Software Testing with Large Language Models: Survey, Landscape, and Visioncited by 166
- 2023Structured Chain-of-Thought Prompting for Code Generationcited by 138
- 2017CSGNet: Neural Shape Parser for Constructive Solid Geometrycited by 106
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- 2022Coder Reviewer Reranking for Code Generationcited by 84
- 2021Computer-Aided Design as Languagecited by 82
- 2024CAD-MLLM: Unifying Multimodality-Conditioned CAD Generation With MLLMcited by 81
- 2020SketchGraphs: A Large-Scale Dataset for Modeling Relational Geometry in Computer-Aided Designcited by 72
- 2019Learning to Infer and Execute 3D Shape Programscited by 68
- 2024CAD-Recode: Reverse Engineering CAD Code from Point Cloudscited by 66
- 2023On the Robustness of Code Generation Techniques: An Empirical Study on GitHub Copilotcited by 64
- 2024Generating CAD Code with Vision-Language Models for 3D Designscited by 62
- 2024Exploring and Unleashing the Power of Large Language Models in Automated Code Translationcited by 57
- 2025CAD-Coder: Text-to-CAD Generation with Chain-of-Thought and Geometric Rewardcited by 56
- 2024LLM-Based Test-Driven Interactive Code Generation: User Study and Empirical Evaluationcited by 49
- 2023Exploring the Potential of ChatGPT in Automated Code Refinement: An Empirical Studycited by 44
- 2023Large Language Model-Aware In-Context Learning for Code Generationcited by 43
- 2025CAD-Coder: An Open-Source Vision-Language Model for Computer-Aided Design Code Generationcited by 43
- 2024GenCAD: Image-Conditioned Computer-Aided Design Generation with Transformer-Based Contrastive Representation and Diffusion Priorscited by 38
- 2023ChatCAD+: Towards a Universal and Reliable Interactive CAD using LLMscited by 35
- 2023ChatGPT vs SBST: A Comparative Assessment of Unit Test Suite Generationcited by 32
- 2023Outline, Then Details: Syntactically Guided Coarse-To-Fine Code Generationcited by 27
- 2023Chain-of-Thought in Neural Code Generation: From and For Lightweight Language Modelscited by 27
- 2024OpenECAD: An Efficient Visual Language Model for Editable 3D-CAD Designcited by 26
- 2025CADmium: Fine-Tuning Code Language Models for Text-Driven Sequential CAD Designcited by 25
- 2023Domain Knowledge Matters: Improving Prompts with Fix Templates for Repairing Python Type Errorscited by 22
- 2024Hybrid Automated Program Repair by Combining Large Language Models and Program Analysiscited by 22
- 2023How Effective Are They? Exploring Large Language Model Based Fuzz Driver Generationcited by 21
- 2024The Current Challenges of Software Engineering in the Era of Large Language Modelscited by 21
- 2024Efficient and Green Large Language Models for Software Engineering: Literature Review, Vision, and the Road Aheadcited by 17
- 2024From 2D CAD Drawings to 3D Parametric Models: A Vision-Language Approachcited by 15
- 2024A Large-scale Empirical Study on Fine-tuning Large Language Models for Unit Testingcited by 15
- 2024LiCoEval: Evaluating LLMs on License Compliance in Code Generationcited by 14
- 2024The Fact Selection Problem in LLM-Based Program Repaircited by 12
- 2025From Intent to Execution: Multimodal Chain-of-Thought Reinforcement Learning for Precise CAD Code Generationcited by 12
- 2024Natural Is The Best: Model-Agnostic Code Simplification for Pre-trained Large Language Modelscited by 10
- 2025CAD-Judge: Toward Efficient Morphological Grading and Verification for Text-to-CAD Generationcited by 10
- 2025CAD-Coder:Text-Guided CAD Files Code Generationcited by 9
- 2023Pre-training Code Representation with Semantic Flow Graph for Effective Bug Localizationcited by 8
- 2024LLMorpheus: Mutation Testing using Large Language Modelscited by 8
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