COSMO-Agent: Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration

2026-04-07

AI Summary

This work addresses the semantic gap between CAD and CAE in industrial design—specifically, how to effectively translate simulation feedback into geometric modifications that satisfy multiple constraints. To bridge this gap, the authors propose a tool-augmented reinforcement learning framework that enables large language models to orchestrate a closed-loop workflow encompassing CAD modeling, CAE simulation, result interpretation, and geometric refinement. A multi-constraint joint reward mechanism ensures solution feasibility and robustness of the toolchain, while a novel executable CAD-CAE dataset covering 25 categories of industrial components supports training in realistic scenarios. Experimental results demonstrate that the proposed approach significantly enhances the performance of small open-source large language models in constraint-driven design, outperforming both leading open-source and proprietary models in terms of feasibility, efficiency, and stability.

Abstract

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.

Problem

• CAD-CAE semantic gap
• iterative design-simulation optimization
• geometric editing under constraints
• closed-loop optimization
• constraint-driven design

Innovation

• tool-augmented reinforcement learning
• closed-loop CAD-CAE optimization
• LLM-based design automation
• multi-constraint reward
• parametric geometry revision