# LINKs: Large Language Model Integrated Management for 6G Empowered Digital Twin NetworKs

arXiv: https://arxiv.org/abs/2412.19811 · HTML: https://mlsanity.com/paper/2412.19811/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 20 references that are in the map.
- First posted on arXiv 9 Dec 2024.

## Builds on (most cited first, 10 of 10)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [A decoder-only foundation model for time-series forecasting](https://mlsanity.com/paper/2310.10688/) (2023)
- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [Long-Term Mobile Traffic Forecasting Using Deep Spatio-Temporal Neural Networks](https://mlsanity.com/paper/1712.08083/) (2017)
- [Towards Intent-Based Network Management: Large Language Models for Intent Extraction in 5G Core Networks](https://mlsanity.com/paper/2403.02238/) (2024)

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

- [LAMeTA: Intent-Aware Agentic Network Optimization via a Large AI Model-Empowered Two-Stage Approach](https://mlsanity.com/paper/2505.12247/) (2025)
- [MX-AI: Agentic Observability and Control Platform for Open and AI-RAN](https://mlsanity.com/paper/2508.09197/) (2025)
- [A Tutorial on Cognitive Biases in Agentic AI-Driven 6G Autonomous Networks](https://mlsanity.com/paper/2510.19973/) (2025)
- [Towards 6G Native-AI Edge Networks: A Semantic-Aware and Agentic Intelligence Paradigm](https://mlsanity.com/paper/2512.04405/) (2025)
- [From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications](https://mlsanity.com/paper/2505.22311/) (2025)
- [Toward an Unbiased Collective Memory for Efficient LLM-Based Agentic 6G Cross-Domain Management](https://mlsanity.com/paper/2509.26200/) (2025)
- [LLM-Driven Adaptive 6G-Ready Wireless Body Area Networks: Survey and Framework](https://mlsanity.com/paper/2508.08535/) (2025)

## Abstract (arXiv)

In the rapidly evolving landscape of digital twins (DT) and 6G networks, the integration of large language models (LLMs) presents a novel approach to network management. This paper explores the application of LLMs in managing 6G-empowered DT networks, with a focus on optimizing data retrieval and communication efficiency in smart city scenarios. The proposed framework leverages LLMs for intelligent DT problem analysis and radio resource management (RRM) in fully autonomous way without any manual intervention. Our proposed framework -- LINKs, builds up a lazy loading strategy which can minimize transmission delay by selectively retrieving the relevant data. Based on the data retrieval plan, LLMs transform the retrieval task into an numerical optimization problem and utilizing solvers to build an optimal RRM, ensuring efficient communication across the network. Simulation results demonstrate the performance improvements in data planning and network management, highlighting the potential of LLMs to enhance the integration of DT and 6G technologies.