# Advanced LLM-Enhanced Intent-Based 5G Network Management using Dynamic Semantic Routes

arXiv: https://arxiv.org/abs/2608.22644 · HTML: https://mlsanity.com/paper/2608.22644/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 13 references that are in the map.
- First posted on arXiv 23 Aug 2026.

## Builds on (most cited first, 6 of 6)

- [What do LLMs need to Synthesize Correct Router Configurations?](https://mlsanity.com/paper/2307.04945/) (2023)
- [Towards Intent-Based Network Management: Large Language Models for Intent Extraction in 5G Core Networks](https://mlsanity.com/paper/2403.02238/) (2024)
- [Designing Network Algorithms via Large Language Models](https://mlsanity.com/paper/2404.01617/) (2024)
- [Semantic Routing for Enhanced Performance of LLM-Assisted Intent-Based 5G Core Network Management and Orchestration](https://mlsanity.com/paper/2404.15869/) (2024)
- [Synergistic Interplay of Large Language Model and Digital Twin for Autonomous Optical Networks: Field Demonstrations](https://mlsanity.com/paper/2411.00473/) (2024)
- [LLM-based Continuous Intrusion Detection Framework for Next-Gen Networks](https://mlsanity.com/paper/2411.03354/) (2024)

## Built on by (most cited first, 1 of 1)

- [SoK: Semantic Decision Engines in Network Control Loops](https://mlsanity.com/paper/2610.06425/) (2026)

## Abstract (arXiv)

As the use of Artificial Intelligence (AI) and Large Language Models (LLMs) is becoming common in everyday applications, their ability to interpret natural language has increased significantly. An emerging application of AI is integration with network management and orchestration practices. An instance of this integration is LLM-enhanced intent-based networking, where network operators will control a network using natural language. This work presents the use of dynamic routes with a semantic router to identify an intent from a network operator's prompt and extract necessary details for intent fulfillment in intent-based 5G+ core networks. Furthermore, the performance of static route selection is assessed by evaluating multiple encoders and dynamic route detail extraction accuracy against a series of realistic operator prompts. The presented results show that static and dynamic routes are successful in detail extraction and schema formatting.