COSMO-Agent trains LLMs via tool-augmented RL and a multi-constraint reward to close the CAD-CAE loop, with experiments showing small open-source models outperforming larger ones on feasibility and stability for 25 component categories.
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators.Nature Machine Intelligence, 3:218–229
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cs.AI 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
COSMO-Agent is a tool-augmented RL agent that trains LLMs to complete closed-loop CAD-CAE optimization using a multi-constraint reward and an industry dataset of 25 component categories, improving small models over larger ones.
citing papers explorer
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COSMO-Agent: Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration
COSMO-Agent trains LLMs via tool-augmented RL and a multi-constraint reward to close the CAD-CAE loop, with experiments showing small open-source models outperforming larger ones on feasibility and stability for 25 component categories.
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Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration
COSMO-Agent is a tool-augmented RL agent that trains LLMs to complete closed-loop CAD-CAE optimization using a multi-constraint reward and an industry dataset of 25 component categories, improving small models over larger ones.