BrepCoder: A Unified Multimodal Large Language Model for Multi-task B-rep Reasoning

• URL: http://arxiv.org/abs/2602.22284v2
• Date: Mon, 02 Mar 2026 04:18:48 GMT
• Title: BrepCoder: A Unified Multimodal Large Language Model for Multi-task B-rep Reasoning
• Authors: Mingi Kim, Yongjun Kim, Jungwoo Kang, Hyungki Kim,
• Abstract summary: We propose BrepCoder, a Python-like Large Language Model (MLLM) that performs diverse CAD tasks from B-rep inputs.<n>By leveraging the code generation capabilities of LLMs, we convert CAD modeling sequences into Python-like code and align them with B-rep.
• Score: 4.393837288225634
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent advancements in deep learning have actively addressed complex challenges within the Computer-Aided Design (CAD) domain.However, 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 potential as a general-purpose CAD agent.

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