COSMO-Agent: Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration
📰 ArXiv cs.AI
COSMO-Agent is a tool-augmented reinforcement learning framework that teaches LLMs to complete the closed-loop CAD-CAE process
Action Steps
- Identify the CAD-CAE semantic gap in industrial design-simulation optimization
- Develop a tool-augmented reinforcement learning framework to teach LLMs
- Implement COSMO-Agent to complete the closed-loop CAD-CAE process
- Evaluate the performance of COSMO-Agent in various design optimization scenarios
Who Needs to Know This
This benefits cross-functional teams of engineers, designers, and researchers working on complex design optimization problems, as it enables them to automate the CAD-CAE process and improve design efficiency
Key Insight
💡 COSMO-Agent can automate the CAD-CAE process by teaching LLMs to translate simulation feedback into valid geometric edits
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🤖 COSMO-Agent: a tool-augmented RL framework for closed-loop CAD-CAE optimization
Key Takeaways
COSMO-Agent is a tool-augmented reinforcement learning framework that teaches LLMs to complete the closed-loop CAD-CAE process
Full Article
Title: COSMO-Agent: Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration
Abstract:
arXiv:2604.05547v1 Announce Type: new 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 generat
Abstract:
arXiv:2604.05547v1 Announce Type: new 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 generat
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