Tool-Augmented Agent for Closed-loop Optimization, Simulation, and Modeling Orchestration
Proposes COSMO-Agent, a tool-augmented reinforcement learning framework that teaches LLMs to complete the closed-loop CAD-CAE process, addressing the semantic gap in iterative industrial design. Experiments show small open-source LLMs outperform larger models in feasibility, efficiency, and stability.
[2605.20190] Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration
[Submitted on 1 Apr 2026]
Title:Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration
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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.
Comments: 8pages,3figures
Subjects:
Artificial Intelligence (cs.AI); Graphics (cs.GR)
Cite as: arXiv:2605.20190 [cs.AI]
(or arXiv:2605.20190v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.20190
arXiv-issued DOI via DataCite
Submission history
From: Liyuan Deng [view email] [v1] Wed, 1 Apr 2026 14:14:09 UTC (1,981 KB)
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