Iterative industrial design-simulation optimization is bottlenecked by the CAD-CAE semantic gap: translating simulation feedback into valid geometric edits under diverse, coupled constraints. To fill this gap, we propose COSMO-Agent (Closed-loop Optimization, Simulation, and Modeling Orchestration), a tool-augmented reinforcement learning (RL) framework that teaches LLMs to complete the closed-loop CAD-CAE process. Specifically, we cast CAD generation, CAE solving, result parsing, and geometry revision as an interactive RL environment, where an LLM learns to orchestrate external tools and revise parametric geometries until constraints are satisfied. To make this learning stable and industrially usable, we design a multi-constraint reward that jointly encourages feasibility, toolchain robustness, and structured output validity. In addition, we contribute an industry-aligned dataset that covers 25 component categories with executable CAD-CAE tasks to support realistic training and evaluation. Experiments show that COSMO-Agent training substantially improves small open-source LLMs for constraint-driven design, exceeding large open-source and strong closed-source models in feasibility, efficiency, and stability.

翻译：迭代式工业设计-仿真优化受限于CAD-CAE语义鸿沟：即在多样且耦合的约束条件下，将仿真反馈转化为有效的几何编辑。为填补这一鸿沟，我们提出了COSMO-Agent（闭环优化、仿真与建模编排系统）——一种工具增强型强化学习框架，该框架教导大语言模型完成CAD-CAE闭环流程。具体而言，我们将CAD生成、CAE求解、结果解析与几何修正建模为一个交互式强化学习环境，在该环境中大语言模型学习编排外部工具并修正参数化几何体，直至满足约束条件。为确保学习过程的稳定性及工业实用性，我们设计了一种多约束奖励机制，该机制共同鼓励可行性、工具链鲁棒性及结构化输出有效性。此外，我们贡献了一个面向工业的数据集，该数据集涵盖25个组件类别并包含可执行的CAD-CAE任务，以支持真实场景下的训练与评估。实验表明，COSMO-Agent训练显著提升了小型开源大语言模型在约束驱动设计中的性能，在可行性、效率及稳定性方面均超越了大型开源模型及强大的闭源模型。