让大模型自动完成工业设计闭环优化，解决几何修改难题。
Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration
- 用强化学习让大模型协调建模与仿真工具，自动修正设计参数。
- 在25类工业组件上实现90%以上约束满足率，效率超主流模型。
- 专为工业场景设计数据集，适合制造业自动化设计需求。
工业设计的迭代仿真优化受限于CAD与CAE之间的语义鸿沟：在多重耦合约束下，难以将仿真反馈转化为有效的几何修改。为此，我们提出COSMO-Agent（闭环优化、仿真与建模编排）框架，一种增强工具的强化学习方法，使大语言模型能够完成从建模到仿真的闭环流程。具体地，我们将CAD生成、CAE求解、结果解析和几何修订构建成一个交互式强化学习环境，让大模型学习调用外部工具并调整参数化几何直至满足约束。为确保学习稳定且可工业应用，我们设计了多约束奖励函数，联合鼓励可行性、工具链鲁棒性与结构化输出有效性。此外，我们构建了一个面向工业场景的数据集，涵盖25种组件类别，包含可执行的CAD-CAE任务，支持真实训练与评估。实验表明，COSMO-Agent显著提升了小型开源大模型在约束驱动设计中的表现，在可行性、效率和稳定性方面超越大型开源及强闭源模型。
原文摘要 · Abstract (English)
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.
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