Text-to-CAD Generation

Follow this topic Ask about this topic

Momentum

2 papers in the last four weeks, down 50% on the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 33

All topics

DepthBenchCAD: When Does Deeper Auditing Yield More Reliable Conclusions?
Sep 14, 2026Hongye Yang, Zhihao Xie, Shengjun Xiong +1Benchmark Auditing Text-to-CAD Generation
College of Computing Georgia Institute of Technology · Independent Researcher
CIT-CAD: Constraint Intent Tree-based CAD Code Generation and Verification
Sep 7, 2026Yali Du, Hui Sun, San-Zhuo Xi +1Parametric CAD Modeling Text-to-CAD Generation
National Key Laboratory for Novel Software Technology, School of Artificial Intelligence, Nanjing University, China
VisCAD: A Foundation Model Suite with Multimodal Industrial CAD Intelligence
Sep 3, 2026JoyIndustrial VisCAD Team, Linxin Cai, Qiuhe Hong +10Part-Level 3D Generation Parametric CAD Modeling
JoyIndustrial VisCAD Team
RealCADBench: Benchmarking Parametric CAD Modeling from Industrial Design Intents
Sep 3, 2026JoyIndustrial VisCAD Team, Linxin Cai, Qiuhe Hong +10Benchmark Design Parametric CAD Modeling
JoyIndustrial-VisCAD Team
Test-Time Scaling for CAD Generation via Verifier-Free Consensus Selection
Aug 10, 2026Aaron Haag, Altay Kaçan, Bertram Fuchs +1Parametric CAD Modeling Test-Time Scaling
Siemens AG, Foundational Technologies, Munich, Germany
CADEngBench: It Looks Like CAD, but Does It Work? Evaluating Parametric Design, Assembly Reasoning, and Physics Simulation
Aug 10, 2026Harmanjot Singh, Abhra Dubey, Jorge Alejandro Amador HerreraBenchmark Construction Parametric CAD Modeling
Mohamed Bin Zayed University of Artificial Intelligence · Abu Dhabi, UAE
RA-CAD: Learning Post-Execution Critique for State-Aware Text-to-CAD Generation
Aug 6, 2026Shuhao Yan, Changhao He, Peng Hu +1RL for Code Generation Code Generation
College of Computer Science, Sichuan University, Chengdu, China · School of Artificial Intelligence, Sichuan University, Chengdu, China · National Key Laboratory of Fundamental Algorithms and Models for Engineering Numerical Simulation, Sichuan University, Chengdu, China
TraceCAD: Trace-Guided Repair for Agentic CAD Generation
Aug 4, 2026Fengxiao Fan, Jingzhe Ni, Fan Sang +5Automated Program Repair Parametric CAD Modeling
Zhejiang University, China
CADIR: A Cross-Backend Editable Intermediate Representation for Agentic CAD Generation
Aug 1, 2026Yu Liu, Jingzhe Ni, Yiming Chen +4Parametric CAD Modeling Text-to-CAD Generation
Zhejiang University, China
IndustryForge-27B: A Domain-Enhanced Multimodal Foundation Model for Industrial CAD
Jul 30, 2026Nianchen Deng, Jiaxin Ai, Tao Hu +10Parametric CAD Modeling Text-to-CAD Generation
Shanghai Artificial Intelligence Laboratory · Wuhan University · Shanghai Innovation Institute +4
HierCAD: Hierarchical Text-to-CAD Design via Structure Alignment and Parameter Grounding
Jul 13, 2026Jimin Xu, Tianbao Wang, Tao Jin +1Parametric CAD Modeling Text-to-CAD Generation
College of Computer Science and Technology, Zhejiang University, Hangzhou, China
ArtisanCAD: An Industrial-Level CAD Agent with Expert-Grounded Knowledge Distillation
Jul 7, 2026Yunhan Xu, Qifeng Wu, Xunjin Li +10Parametric CAD Modeling Text-to-CAD Generation
Peking University, Beijing 100871, China · TenFong Technology Co., Ltd., Shenzhen 518000, Guangdong, China · Eastern Institute of Technology, Ningbo 315200, Zhejiang, China +2
Foundation Models for Automatic CAD Generation
Jul 6, 2026J de Curtò, Victoria Guillén, I. de ZarzàParametric CAD Modeling Large Vision-Language Models
Department of Computer Applications in Science & Engineering, BARCELONA Supercomputing Center, 08034 Barcelona, Spain · Escuela T´ecnica Superior de Ingenier´ıa (ICAI), Universidad Pontificia Comillas, 28015 Madrid, Spain · Department of Artificial Intelligence, Chung-Ang University, Seoul, Republic of Korea +1
ASSEMCAD: Production-Ready CAD Assembly Generation from Natural Language
Jul 6, 2026Yurui Dong, Shu Zou, Siqi Li +7Parametric CAD Modeling Program Synthesis
Shanghai Artificial Intelligence Laboratory · Fudan University · The Australian National University +2
Embodied CAD: Solver-Grounded LLM Agents for Parametric B-Rep Assembly Modeling
Jun 30, 2026Fumin Liu, Haoyu Zhou, Fei Hao +1Parametric CAD Modeling LLM Agent Skill Learning
Nanjing University
Arko-T: A Foundation Model for Text-to-Structured 3D Generation
Jun 29, 2026Liang Wang, Zhaoyang Xi, Zekai Xiang +53D Foundation Models Parametric CAD Modeling
Spatial Design Intelligence Lab, BitInf Ltd., Shanghai 200003, China · School of Computer Science, Wuhan University, Wuhan 430000, Hubei, China · College of Computer and Information Engineering, Nanjing Tech University, Nanjing 211800, Jiangsu, China
Enhancing Creativity in 3D Generative Design via a TRIZ-Inspired Text-to-CAD Framework
Jun 19, 2026Dongeon Lee, Leekyo Jeong, Soyoung Yoo +2LLM Prompting Computational Creativity
Cho Chun Shik Graduate School of Mobility, KAIST, Daejeon, Republic of Korea · Samsung Electronics, Suwon, Republic of Korea · Department of Mechanical Engineering, Hanyang University, Ansan, Republic of Korea +1
BIM-Edit: Benchmarking Large Language Models for IFC-Based Building Information Modeling
Jun 18, 2026Bharathi Kannan Nithyanantham, Clemens Kujat, Tobias Sesterhenn +5LLM Evaluation Building Information Modeling
University of Rostock · Clausthal University of Technology
IterCAD: An Iterative Multimodal Agent for Visually-Grounded CAD Generation and Editing
Jun 11, 2026Tao Hu, Jiaxin Ai, Licheng Wen +12Parametric CAD Modeling Text-to-CAD Generation
Shanghai Artificial Intelligence Laboratory · University of Science and Technology of China · Shanghai Innovation Institute +4
GuideCAD: A Lightweight Multimodal Framework for 3D CAD Model Generation via Prefix Embedding
Jun 5, 2026Minseong Kim, Jinyeong Park, Sungho Park +13D Asset Generation Parametric CAD Modeling
Convergence Research Center for Insect Vectors Incheon, South Korea · Department of Computer Science and Engineering Incheon National University Incheon, 22012, South Korea · Department of Computer Science and Engineering Center for Brain-Machine Interface Incheon National University Incheon, South Korea
UniCAD: A Unified Benchmark and Universal Model for Multi-Modal Multi-Task CAD
Jun 3, 2026Jingyuan Chen, Sheng Jin, Haopeng Sun +23D Reconstruction Multimodal Large Language Models
SenseTime Research and Tetras.AI
pcbGPT: Automatic PCB Schematic Synthesis from Natural Language Requirements
May 31, 2026Tobias King, Steven Kehrberg, Michael Beigl +1Electronic Design Automation LLM-Based Program Synthesis
Karlsruhe Institute of Technology, Germany · Bosch Sensortec GmbH, Germany
MUSE: Benchmarking Manufacturable, Functional, and Assemblable Text-to-CAD Generation
May 27, 2026Xiaoyu Dong, Zhi Li, Xiao-Ming WuLLM Evaluation Text-to-CAD Generation
1The Hong Kong Polytechnic University · 2Curvature Flow Co., Limited
Memory-Augmented Reinforcement Learning Agent for CAD Generation
May 19, 2026Yin Xiaolong, Liu Yu, Shen Jiahang +4Text-to-CAD Generation RL for Generative Models
Zhejiang University · Ocean University of China
Physics-in-the-Loop: A Hybrid Agentic Architecture for Validated CAD Engineering Design
May 19, 2026Elias Berger, Muhammad Usama, Jan Mehlstäubl +2Text-to-CAD Generation
Dresden University of Technology, Dresden, Germany · MAN Truck & Bus SE, Munich, Germany · German Research Center for Artificial Intelligence, Kaiserslautern, Germany +1
Text2CAD-Bench: A Benchmark for LLM-based Text-to-Parametric CAD Generation
May 18, 2026Liang Wang, Heng Meng, Zekai Xiang +4Benchmark Design Parametric CAD Modeling
School of Computer Science, Wuhan University, Wuhan 430000, Hubei, China · Spatial Design Intelligence Lab, BitInf Ltd., Shanghai 200003, China · College of Computer and Information Engineering, Nanjing Tech University, Nanjing 211800, Jiangsu, China +1
Self-Improving CAD Generation Agents with Finite Element Analysis as Feedback
May 17, 2026Guijin Son, Jehyun Park, Seyeon Park +2Parametric CAD Modeling Text-to-CAD Generation
Seoul National University · OneLineAI · Sungkyunkwan University
BenchCAD: A Comprehensive, Industry-Standard Benchmark for Programmatic CAD
May 11, 2026Haozhe Zhang, Kaichen Liu, Miaomiao Chen +4Benchmark Construction Parametric CAD Modeling
University of Virginia · University of California, San Diego · Rice University
Text-to-CAD Evaluation with CADTests
May 8, 2026Dimitrios Mallis, Marco Wang, Ahmet Serdar Karadeniz +3Text-to-CAD Generation Automated Evaluation
SnT, University of Luxembourg · University of Trento · Fondazione Bruno Kessler
CADFS: A Big CAD Program Dataset and Framework for Computer-Aided Design with Large Language Models
May 3, 2026Vladislav Pyatov, Gleb Bobrovskikh, Saveliy Galochkin +6Parametric CAD Modeling Program Synthesis
1Applied AI Institute · 2AI Foundation and Algorithm Lab · 3AXXX +1

Sep 14, 2026, cs.SEJ/K move · Enter open · S save

DepthBenchCAD: When Does Deeper Auditing Yield More Reliable Conclusions?

Hongye Yang, Zhihao Xie, Shengjun Xiong, Boxiao Huang

College of Computing Georgia Institute of Technology · Independent Researcher

Benchmark Auditing Text-to-CAD Generation Code Generation Evaluation

Open paper AskPDF arXiv

Generative CAD models are expected to remain behaviorally correct after parameter edits, so increasing the number of edit checks is often treated as a direct route to more reliable evaluation. Under a fixed budget, however, auditing each program more thoroughly reduces the number of tasks and independent generations that can be evaluated, which can ultimately make model-level estimates less accurate. We study this phenomenon and the conditions under which it arises. We decompose behavioral evaluation into three evidence levels: task templates, stochastic generations, and within-program edits. We define an average failure risk that is invariant to audit depth, and combine three-level variance with measured execution costs to analyze the tradeoff between deeper edit auditing and broader independent coverage. Experiments across two CAD environments and five generation systems show that the value of deeper auditing depends on where evaluation uncertainty originates. When template heterogeneity or generation stochasticity dominates, additional edit checks can increase total estimation error; when within-program state variation is large and generation is expensive, deeper auditing is more valuable. Variance and cost estimates from calibration predict the direction of this change and provide a diagnostic basis for allocating evidence on held-out tasks. These results show that the thoroughness of program inspection can diverge from the reliability of model evaluation, and they help determine whether the next unit of budget should be spent on a new task, a new generation, or additional edit checks.

Momentum

Related topics

Latest papers 33