# Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model

arXiv: https://arxiv.org/abs/2412.09041 · HTML: https://mlsanity.com/paper/2412.09041/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2024.

## Built on by (most cited first, 7 of 7)

- [Analogical Learning for Cross-Scenario Generalization: Framework and Application to Intelligent Localization](https://mlsanity.com/paper/2504.08811/) (2025)
- [TokenCom: Vision-Language Model for Multimodal and Multitask Token Communications](https://mlsanity.com/paper/2603.00482/) (2026)
- [Large Model Enabled Embodied Intelligence for 6G Integrated Perception, Communication, and Computation Network](https://mlsanity.com/paper/2512.15109/) (2025)
- [A Token/KV-Cache Communication Media Selection and Resource Allocation Strategy for Multi-Agent Collaboration](https://mlsanity.com/paper/2605.25422/) (2026)
- [E3-Agent: An Executable and Evolving Agent for Resource Management of Edge Generative Inference](https://mlsanity.com/paper/2605.27428/) (2026)
- [Multi-View Wireless Sensing via Conditional Generative Learning: Framework and Model Design](https://mlsanity.com/paper/2505.12664/) (2025)
- [Geometry-Aided Channel Deduction with Partial Channel Estimates and Uncalibrated Digital Twin](https://mlsanity.com/paper/2609.37277/) (2026)

## Abstract (arXiv)

Research on leveraging big artificial intelligence model (BAIM) technology to drive the intelligent evolution of wireless networks is emerging. However, breakthroughs in generalization brought about by BAIM techniques mainly occur in natural language processing. There is a lack of a clear technical direction on how to efficiently apply BAIM techniques to wireless systems, which typically have many additional peculiarities. To this end, this paper reviews recent research on BAIM for wireless systems and assesses the current state of the field. It then analyzes and compares the differences between language intelligence and wireless intelligence on multiple levels, including scientific foundations, core usages, and technical details. It highlights the necessity and scientific significance of developing wireless native BAIM technologies, as well as specific issues that need to be considered for technical implementation. Finally, by synthesizing the evolutionary laws of language models with the particularities of wireless systems, this paper provides several instructive methodologies for developing wireless native BAIM.