Self-Correcting Multi-Agent LLM for Physics Simulation

This paper introduces a self-correcting multi-agent LLM framework for language-based physics simulation and explanation. The system enables natural language queries to generate physics simulations while providing explanations of the underlying physical phenomena. - **Multi-agent architecture:** The framework employs multiple specialized LLM agents that collaborate to translate natural language descriptions into accurate physics simulations, with each agent handling distinct aspects of the simulation pipeline. - **Self-correction mechanism:** Built-in self-correction capabilities allow the system to identify and fix errors in generated simulations, improving accuracy without requiring human intervention or additional training. - **Language-based interface:** Users can describe physics scenarios in natural language, making complex simulation tools accessible to non-experts while maintaining scientific accuracy in the outputs. - **Explanation generation:** Beyond simulation, the system generates natural language explanations of the physics principles at work, serving both educational and research applications. - **Validation across domains:** The framework demonstrates effectiveness across multiple physics domains, showing generalization capability beyond narrow task-specific applications.