LLM-Driven Agentic Calibration Outperforms Classical Methods on Grey-Box Simulation Models
A new preprint introduces an agentic calibration approach where a large language model (LLM) serves as the optimizer for grey-box simulation models. The method is evaluated against Nelder-Mead and Bayesian Optimization on an anal cancer simulation model, showing lower error and requiring fewer model evaluations in unconstrained settings, and achieving comparable performance under constraints. Constraint handling is simplified by using plain-language prompts, though the approach incurs higher per-iteration inference costs.
Why it matters: This work suggests that LLMs can act as effective and auditable optimizers for complex simulation models, potentially simplifying calibration workflows and reducing the need for specialized constraint-handling techniques.
Full story at: arXiv Machine Learning ↗