Parametric Computer-Aided Design (CAD) is fundamental to modern 3D modeling, yet existing methods struggle to generate long command sequences, especially under complex geometric and topological dependencies. Transformer-based architectures dominate CAD sequence generation due to their strong dependency modeling, but their quadratic attention cost and limited context windowing hinder scalability to long programs. We propose GeoFusion-CAD, an end-to-end diffusion framework for scalable and structure-aware generation. Our proposal encodes CAD programs as hierarchical trees, jointly capturing geometry and topology within a state-space diffusion process. Specifically, a lightweight C-Mamba block models long-range structural dependencies through selective state transitions, enabling coherent generation across extended command sequences. To support long-sequence evaluation, we introduce DeepCAD-240, an extended benchmark that increases the sequence length ranging from 40 to 240 while preserving sketch-extrusion semantics from the ABC dataset. Extensive experiments demonstrate that GeoFusion-CAD achieves superior performance on both short and long command ranges, maintaining high geometric fidelity and topological consistency where Transformer-based models degrade. Our approach sets new state-of-the-art scores for long-sequence parametric CAD generation, establishing a scalable foundation for next-generation CAD modeling systems. Code and datasets are available at GitHub.

翻译：参数化计算机辅助设计（CAD）是现代三维建模的基础，但现有方法难以生成长指令序列，尤其在复杂几何与拓扑依赖条件下。基于Transformer的架构凭借其强大的依赖建模能力主导着CAD序列生成，但其二次注意力代价与有限上下文窗口限制了长程序的扩展性。我们提出GeoFusion-CAD，一种面向可扩展与结构感知生成的端到端扩散框架。该方案将CAD程序编码为层次化树结构，在状态空间扩散过程中联合捕获几何与拓扑信息。具体而言，轻量级C-Mamba模块通过选择性状态转换建模长程结构依赖，使扩展指令序列的生成保持连贯性。为支持长序列评估，我们引入DeepCAD-240扩展基准，将序列长度从40提升至240，同时保留ABC数据集的草图-拉伸语义。大量实验表明，GeoFusion-CAD在短指令与长指令范围均表现优异，在Transformer模型性能下降时仍能保持高几何保真度与拓扑一致性。该方法创下了长序列参数化CAD生成的最新最优成绩，为下一代CAD建模系统奠定了可扩展基础。代码与数据集已发布于GitHub。