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This paper proposes a multi-agent framework that enables LLM agents to conduct controlled experiments using simulation models for pharmaceutical process design, yielding more specific and actionable recommendations than language-only reasoning.
This paper introduces an agentic calibration method that uses a large language model as an optimizer for calibrating grey-box simulation models in cost-effectiveness analysis. The LLM-driven approach achieves competitive performance with substantially fewer model evaluations compared to traditional methods like Nelder-Mead and Bayesian optimization.
This paper presents an experimental study on using AI to find simulation models via natural language queries, evaluating data representations, embedding models, and retrieval strategies, finding that open-source embeddings and reranking methods significantly improve performance.