# CADReview: Automatically Reviewing CAD Programs with Error Detection and Correction

arXiv: https://arxiv.org/abs/2505.22304 · HTML: https://mlsanity.com/paper/2505.22304/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 40 references that are in the map.
- First posted on arXiv 28 May 2025.

## Builds on (most cited first, 21 of 21)

- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [LLaVA-OneVision: Easy Visual Task Transfer](https://mlsanity.com/paper/2408.03326/) (2024)
- [MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI](https://mlsanity.com/paper/2311.16502/) (2023)
- [Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling](https://mlsanity.com/paper/2412.05271/) (2024)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Teaching Large Language Models to Self-Debug](https://mlsanity.com/paper/2304.05128/) (2023)
- [CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing](https://mlsanity.com/paper/2305.11738/) (2023)
- [PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding](https://mlsanity.com/paper/1812.02713/) (2018)
- [Self-Edit: Fault-Aware Code Editor for Code Generation](https://mlsanity.com/paper/2305.04087/) (2023)
- [CAD-SIGNet: CAD Language Inference from Point Clouds using Layer-wise Sketch Instance Guided Attention](https://mlsanity.com/paper/2402.17678/) (2024)
- [CAD-GPT: Synthesising CAD Construction Sequence with Spatial Reasoning-Enhanced Multimodal LLMs](https://mlsanity.com/paper/2412.19663/) (2024)
- [CadVLM: Bridging Language and Vision in the Generation of Parametric CAD Sketches](https://mlsanity.com/paper/2409.17457/) (2024)
- [ShapeCoder: Discovering Abstractions for Visual Programs from Unstructured Primitives](https://mlsanity.com/paper/2305.05661/) (2023)
- [CADTalk: An Algorithm and Benchmark for Semantic Commenting of CAD Programs](https://mlsanity.com/paper/2311.16703/) (2023)
- [Joint Multimodal Entity-Relation Extraction Based on Edge-enhanced Graph Alignment Network and Word-pair Relation Tagging](https://mlsanity.com/paper/2211.15028/) (2022)
- [Coffee-Gym: An Environment for Evaluating and Improving Natural Language Feedback on Erroneous Code](https://mlsanity.com/paper/2409.19715/) (2024)
- [Learning to Correction: Explainable Feedback Generation for Visual Commonsense Reasoning Distractor](https://mlsanity.com/paper/2412.07801/) (2024)
- [FIRE: A Dataset for Feedback Integration and Refinement Evaluation of Multimodal Models](https://mlsanity.com/paper/2407.11522/) (2024)

## Built on by (most cited first, 4 of 4)

- [Beyond NL2Code: A Structured Survey of Multimodal Code Intelligence](https://mlsanity.com/paper/2606.15932/) (2026)
- [BrepCoder: A Unified Multimodal Large Language Model for Multi-task B-rep Reasoning](https://mlsanity.com/paper/2602.22284/) (2026)
- [BRepCLIP: Contrastive Multimodal Pretraining on BRep Primitives for CAD Understanding](https://mlsanity.com/paper/2606.05515/) (2026)
- [RA-CAD: Learning Post-Execution Critique for State-Aware Text-to-CAD Generation](https://mlsanity.com/paper/2608.05714/) (2026)

## Abstract (arXiv)

Computer-aided design (CAD) is crucial in prototyping 3D objects through geometric instructions (i.e., CAD programs). In practical design workflows, designers often engage in time-consuming reviews and refinements of these prototypes by comparing them with reference images. To bridge this gap, we introduce the CAD review task to automatically detect and correct potential errors, ensuring consistency between the constructed 3D objects and reference images. However, recent advanced multimodal large language models (MLLMs) struggle to recognize multiple geometric components and perform spatial geometric operations within the CAD program, leading to inaccurate reviews. In this paper, we propose the CAD program repairer (ReCAD) framework to effectively detect program errors and provide helpful feedback on error correction. Additionally, we create a dataset, CADReview, consisting of over 20K program-image pairs, with diverse errors for the CAD review task. Extensive experiments demonstrate that our ReCAD significantly outperforms existing MLLMs, which shows great potential in design applications.