# Bridging Physical and Digital Worlds: Embodied Large AI for Future Wireless Systems

arXiv: https://arxiv.org/abs/2506.24009 · HTML: https://mlsanity.com/paper/2506.24009/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 14 references that are in the map.
- First posted on arXiv 30 Jun 2025.

## Builds on (most cited first, 11 of 11)

- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [A Survey of Embodied AI: From Simulators to Research Tasks](https://mlsanity.com/paper/2103.04918/) (2021)
- [Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities](https://mlsanity.com/paper/2405.10825/) (2024)
- [Less Data, More Knowledge: Building Next Generation Semantic Communication Networks](https://mlsanity.com/paper/2211.14343/) (2022)
- [Digital Twin of Wireless Systems: Overview, Taxonomy, Challenges, and Opportunities](https://mlsanity.com/paper/2202.02559/) (2022)
- [Generative AI Agents with Large Language Model for Satellite Networks via a Mixture of Experts Transmission](https://mlsanity.com/paper/2404.09134/) (2024)
- [Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems](https://mlsanity.com/paper/2402.01748/) (2024)
- [Wireless Network Digital Twin for 6G: Generative AI as A Key Enabler](https://mlsanity.com/paper/2311.17451/) (2023)
- [Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networks](https://mlsanity.com/paper/2309.13223/) (2023)
- [Implementation of Big AI Models for Wireless Networks with Collaborative Edge Computing](https://mlsanity.com/paper/2404.17766/) (2024)
- [Robust Deep Learning-Based Physical Layer Communications: Strategies and Approaches](https://mlsanity.com/paper/2505.01234/) (2025)

## Built on by (most cited first, 4 of 4)

- [Large Model Enabled Embodied Intelligence for 6G Integrated Perception, Communication, and Computation Network](https://mlsanity.com/paper/2512.15109/) (2025)
- [World Model-Enabled Causal Digital Twins for Semantic Communications in Physical AI Systems](https://mlsanity.com/paper/2605.16547/) (2026)
- [RadioMaster: Multi-Agent System for Autonomous Radio Signal Generation](https://mlsanity.com/paper/2606.01862/) (2026)
- [Joint Movement and Compression Ratio Design for Mobile Embodied AI Networks (MEAN)](https://mlsanity.com/paper/2610.02334/) (2026)

## Abstract (arXiv)

Large artificial intelligence (AI) models offer revolutionary potential for future wireless systems, promising unprecedented capabilities in network optimization and performance. However, current paradigms largely overlook crucial physical interactions. This oversight means they primarily rely on offline datasets, leading to difficulties in handling real-time wireless dynamics and non-stationary environments. Furthermore, these models often lack the capability for active environmental probing. This paper proposes a fundamental paradigm shift towards wireless embodied large AI (WELAI), moving from passive observation to active embodiment. We first identify key challenges faced by existing models, then we explore the design principles and system structure of WELAI. Besides, we outline prospective applications in next-generation wireless. Finally, through an illustrative case study, we demonstrate the effectiveness of WELAI and point out promising research directions for realizing adaptive, robust, and autonomous wireless systems.