🤖 AI Summary
Non-technical users struggle to intuitively operate complex simulation systems, while existing large language models (LLMs) lack grounding in real-world dynamic constraints, leading to physically implausible or causally inconsistent outputs.
Method: We propose the first bidirectional collaborative framework integrating simulation systems and LLMs—enabling natural-language-driven simulation execution while constraining LLM reasoning with causally accurate, structured, real-time simulation states. Our approach innovatively combines prompt-engineering-driven LLM-Simulation API orchestration, dynamic knowledge grounding, and a causal-aware state-mapping interface.
Contribution/Results: Evaluated across multi-domain decision-making tasks, the framework significantly improves answer accuracy (+32%) and operational success rate (+41%). It enables zero-code invocation of high-fidelity simulations, achieving a principled balance among interpretability, usability, and physical consistency.
📝 Abstract
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.