AGENTIC-SYSTEMSMay 22, 20261 min read10 views

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

Researchers have introduced COSMO-Agent, a tool-augmented reinforcement learning framework that enables LLMs to automate the closed-loop CAD-CAE process. This framework addresses the semantic gap between design and simulation, outperforming existing large-scale models in industrial design optimization.

Computer Science > Artificial Intelligence

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

View PDF HTML (experimental)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.

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Source: arXiv cs.AI Recent

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