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

Recent advancements in deep learning have actively addressedcomplex challenges within the Computer-Aided Design (CAD) domain.However, most existing approaches rely on task-specific models requir-ing structural modifications for new tasks, and they predominantly focuson point clouds or images rather than the industry-standard BoundaryRepresentation (B-rep) format. To address these limitations, we proposeBrepCoder, a unified Multimodal Large Language Model (MLLM) thatperforms diverse CAD tasks from B-rep inputs. By leveraging the codegeneration capabilities of Large Language Models (LLMs), we convertCAD modeling sequences into Python-like code and align them withB-rep. We then adopt a two-stage training strategy: First, pre-trainingon reverse engineering to learn geometric features and design logic. Sec-ond, effectively extending the model to various downstream tasks such ascompletion, error correction, and CAD-QA. Consequently, by interpret-ing B-rep as structural code, BrepCoder achieves superior generalizationacross diverse tasks, demonstrating its potential as a general-purposeCAD agent.