做CAD自动生成或智能制造AI的团队，这个框架解决了长序列建模和几何约束的痛点，强化学习+记忆库的设计值得参考，可以直接复现实验。
本文提出一种记忆增强的强化学习框架，用于自动生成计算机辅助设计（CAD）模型。现有基于大语言模型的方法在处理长操作序列、多样操作类型和强几何约束的复杂CAD时，常因推理链断裂和缺乏纠错机制而失败。新框架将底层几何内核封装为可调用的结构化工具链，构建了从意图理解、全局规划、执行到多维验证的闭环机制，并设计了包含案例库和技能库的双轨记忆模块及动态效用检索算法。通过引入强化学习优化检索与策略，智能体可避免语义相似但几何不可行的检索陷阱，实现在线自我纠错和持续进化，无需额外大规模标注数据。实验表明，该方法在复杂CAD生成任务上显著提升了成功率和几何一致性。
Memory-Augmented Reinforcement Learning Agent for CAD Generation
Automatic generation of computer-aided design (CAD) models is a core technology for enabling intelligence in advanced manufacturing. Existing generation methods based on large language models (LLMs) often fall short when handling complex CAD models characterized by long operation sequences, diverse operation types, and strong geometric constraints, primarily because reasoning chains break and effective error-correction mechanisms are lacking. To address this problem, this paper proposes a memory-augmented reinforcement learning framework for CAD generation agents. The framework encapsulates the underlying geometric kernel into a structured toolchain callable by the agent and builds a closed-loop mechanism of design intent understanding, global planning, execution, and multi-dimensional verification. It also designs a dual-track memory module consisting of a case library and a skill library, and proposes a dynamic utility retrieval algorithm. By introducing reinforcement learning into retrieval and policy optimization, the agent can effectively avoid retrieval traps in which examples are semantically similar but geometrically infeasible, enabling online self-correction and continual evolution without additional large-scale annotated data. Experiments show that the proposed method significantly improves both the success rate and geometric consistency on complex CAD model generation tasks.