Simulations, although powerful in accurately replicating real-world systems, often remain inaccessible to non-technical users due to their complexity. Conversely, large language models (LLMs) provide intuitive, language-based interactions but can lack the structured, causal understanding required to reliably model complex real-world dynamics. We introduce our simulation agent framework, a novel approach that integrates the strengths of both simulation models and LLMs. This framework helps empower users by leveraging the conversational capabilities of LLMs to interact seamlessly with sophisticated simulation systems, while simultaneously utilizing the simulations to ground the LLMs in accurate and structured representations of real-world phenomena. This integrated approach helps provide a robust and generalizable foundation for empirical validation and offers broad applicability across diverse domains.
View on arXiv@article{kleiman2025_2505.13761,
title={ Simulation Agent: A Framework for Integrating Simulation and Large Language Models for Enhanced Decision-Making },
author={ Jacob Kleiman and Kevin Frank and Joseph Voyles and Sindy Campagna },
journal={arXiv preprint arXiv:2505.13761},
year={ 2025 }
}