# RadioSim Agent: Combining Large Language Models and Deterministic EM Simulators for Interactive Radio Map Analysis

arXiv: https://arxiv.org/abs/2511.05912 · HTML: https://mlsanity.com/paper/2511.05912/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2025.

## Built on by (most cited first, 1 of 1)

- [AIMS: An Agentic AI Framework for Sim-to-Real Multi-Modal ISAC](https://mlsanity.com/paper/2609.39964/) (2026)

## Abstract (arXiv)

Deterministic electromagnetic (EM) simulators provide accurate radio propagation modeling but often require expert configuration and lack interactive flexibility. We present RadioSim Agent, an agentic framework that integrates large language models (LLMs) with physics-based EM solvers and vision-enabled reasoning to enable interactive and explainable radio map generation. The framework encapsulates ray-tracing models as callable simulation tools, orchestrated by an LLM capable of interpreting natural language objectives, managing simulation workflows, and visually analyzing resulting radio maps. Demonstrations in urban UAV communication scenarios show that the agent autonomously selects appropriate propagation mechanisms, executes deterministic simulations, and provides semantic and visual summaries of pathloss behavior. The results indicate that RadioSim Agent provides multimodal interpretability and intuitive user interaction, paving the way for intelligent EM simulation assistants in next-generation wireless system design.