# Large-Scale AI in Telecom: Charting the Roadmap for Innovation, Scalability, and Enhanced Digital Experiences

arXiv: https://arxiv.org/abs/2503.04184 · HTML: https://mlsanity.com/paper/2503.04184/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2025.

## Built on by (most cited first, 12 of 12)

- [RF-GPT: Teaching AI to See the Wireless World](https://mlsanity.com/paper/2602.14833/) (2026)
- [MX-AI: Agentic Observability and Control Platform for Open and AI-RAN](https://mlsanity.com/paper/2508.09197/) (2025)
- [Agentic AI in 6G Software Businesses: A Layered Maturity Model](https://mlsanity.com/paper/2508.03393/) (2025)
- [LLMs' Suitability for Network Security: A Case Study of STRIDE Threat Modeling](https://mlsanity.com/paper/2505.04101/) (2025)
- [Bridging Language Models and Formal Methods for Intent-Driven Optical Network Design](https://mlsanity.com/paper/2509.22834/) (2025)
- [Robust MIMO Channel Estimation Using Energy-Based Generative Diffusion Models](https://mlsanity.com/paper/2510.22230/) (2025)
- [TeleCom-Bench: How Far Are Large Language Models from Industrial Telecommunication Applications?](https://mlsanity.com/paper/2605.18025/) (2026)
- [Towards Resilient and Autonomous Networks: A BlueSky Vision on AI-Native 6G](https://mlsanity.com/paper/2605.21395/) (2026)
- [A Conformal Predictive Measure for Assessing Catastrophic Forgetting](https://mlsanity.com/paper/2505.10677/) (2025)
- [Large-Scale Model Enabled Semantic Communication Based on Robust Knowledge Distillation](https://mlsanity.com/paper/2508.02148/) (2025)
- [Accelerating Wireless Distributed Learning via Hybrid Split and Federated Learning Optimization](https://mlsanity.com/paper/2511.19851/) (2025)
- [Free-Text Evaluation of LLMs for 5G Domain Knowledge and Fault Analysis using LLM-as-Judge](https://mlsanity.com/paper/2608.21021/) (2026)

## Abstract (arXiv)

This white paper discusses the role of large-scale AI in the telecommunications industry, with a specific focus on the potential of generative AI to revolutionize network functions and user experiences, especially in the context of 6G systems. It highlights the development and deployment of Large Telecom Models (LTMs), which are tailored AI models designed to address the complex challenges faced by modern telecom networks. The paper covers a wide range of topics, from the architecture and deployment strategies of LTMs to their applications in network management, resource allocation, and optimization. It also explores the regulatory, ethical, and standardization considerations for LTMs, offering insights into their future integration into telecom infrastructure. The goal is to provide a comprehensive roadmap for the adoption of LTMs to enhance scalability, performance, and user-centric innovation in telecom networks.