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[2602.22284] BrepCoder: A Unified Multimodal Large Language Model for Multi-task B-rep Reasoning
Summary
BrepCoder is a unified multimodal large language model designed for multi-task reasoning in Computer-Aided Design (CAD), specifically utilizing Boundary Representation (B-rep) inputs to enhance task performance and generalization.
Why It Matters
This research addresses significant limitations in current CAD approaches by proposing a versatile model that can handle multiple tasks without the need for task-specific modifications. It highlights the potential for improved efficiency and accuracy in CAD applications, which are critical in various engineering and design fields.
Key Takeaways
- BrepCoder utilizes B-rep inputs to perform diverse CAD tasks effectively.
- The model employs a two-stage training strategy for enhanced learning.
- It converts CAD modeling sequences into Python-like code, improving task adaptability.
- BrepCoder demonstrates superior generalization across various CAD applications.
- This approach could streamline workflows in engineering and design sectors.
Computer Science > Machine Learning arXiv:2602.22284 (cs) [Submitted on 25 Feb 2026] Title:BrepCoder: A Unified Multimodal Large Language Model for Multi-task B-rep Reasoning Authors:Mingi Kim, Yongjun Kim, Jungwoo Kang, Hyungki Kim View a PDF of the paper titled BrepCoder: A Unified Multimodal Large Language Model for Multi-task B-rep Reasoning, by Mingi Kim and 3 other authors View PDF HTML (experimental) Abstract:Recent advancements in deep learning have actively addressed complex challenges within the Computer-Aided Design (CAD) this http URL, most existing approaches rely on task-specifi c models requiring structural modifi cations for new tasks, and they predominantly focus on point clouds or images rather than the industry-standard Boundary Representation (B-rep) format. To address these limitations, we propose BrepCoder, a unifi ed Multimodal Large Language Model (MLLM) that performs diverse CAD tasks from B-rep inputs. By leveraging the code generation capabilities of Large Language Models (LLMs), we convert CAD modeling sequences into Python-like code and align them with B-rep. We then adopt a two-stage training strategy: First, pre-training on reverse engineering to learn geometric features and design logic. Second, eff ectively extending the model to various downstream tasks such as completion, error correction, and CAD-QA. Consequently, by interpreting B-rep as structural code, BrepCoder achieves superior generalization across diverse tasks, demonstrating its ...