# Intent-Based Network for RAN Management with Large Language Models

arXiv: https://arxiv.org/abs/2507.14230 · HTML: https://mlsanity.com/paper/2507.14230/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 17 Jul 2025.

## Builds on (most cited first, 3 of 3)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Large Language Models for Software Engineering: Survey and Open Problems](https://mlsanity.com/paper/2310.03533/) (2023)
- [A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches](https://mlsanity.com/paper/2208.09125/) (2022)

## Built on by (most cited first, 8 of 8)

- [Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning](https://mlsanity.com/paper/2511.02748/) (2025)
- [The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network](https://mlsanity.com/paper/2509.10478/) (2025)
- [From Intents to Actions: Agentic AI in Autonomous Networks](https://mlsanity.com/paper/2602.01271/) (2026)
- [IntAgent: NWDAF-Based Intent LLM Agent Towards Advanced Next Generation Networks](https://mlsanity.com/paper/2601.13114/) (2026)
- [Intent2Tc: Automated Intent-to-Traffic Control Translation with Language Models](https://mlsanity.com/paper/2609.31397/) (2026)
- [Agentic-V2X: Small Language Model Agents for Deadline-Aware V2X Scheduling in 5G/6G Networks](https://mlsanity.com/paper/2607.04290/) (2026)
- [Z2-ACT: End-to-End Verifiable Agentic Intent Control for Open 6G RAN](https://mlsanity.com/paper/2608.21049/) (2026)
- [SoK: Semantic Decision Engines in Network Control Loops](https://mlsanity.com/paper/2610.06425/) (2026)

## Abstract (arXiv)

Advanced intelligent automation becomes an important feature to deal with the increased complexity in managing wireless networks. This paper proposes a novel automation approach of intent-based network for Radio Access Networks (RANs) management by leveraging Large Language Models (LLMs). The proposed method enhances intent translation, autonomously interpreting high-level objectives, reasoning over complex network states, and generating precise configurations of the RAN by integrating LLMs within an agentic architecture. We propose a structured prompt engineering technique and demonstrate that the network can automatically improve its energy efficiency by dynamically optimizing critical RAN parameters through a closed-loop mechanism. It showcases the potential to enable robust resource management in RAN by adapting strategies based on real-time feedback via LLM-orchestrated agentic systems.