# Large Language Models for Zero Touch Network Configuration Management

arXiv: https://arxiv.org/abs/2408.13298 · HTML: https://mlsanity.com/paper/2408.13298/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2024.

## Built on by (most cited first, 10 of 10)

- [DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey](https://mlsanity.com/paper/2503.09956/) (2025)
- [LAMeTA: Intent-Aware Agentic Network Optimization via a Large AI Model-Empowered Two-Stage Approach](https://mlsanity.com/paper/2505.12247/) (2025)
- [NetAgentBench: A State-Centric Benchmark for Evaluating Agentic Network Configuration](https://mlsanity.com/paper/2604.09678/) (2026)
- [SADE: Symptom-Aware Diagnostic Escalation for LLM-Based Network Troubleshooting](https://mlsanity.com/paper/2605.04530/) (2026)
- [KP-A: A Unified Network Knowledge Plane for Catalyzing Agentic Network Intelligence](https://mlsanity.com/paper/2507.08164/) (2025)
- [Prompting for Performance: Exploring LLMs for Configuring Software](https://mlsanity.com/paper/2507.09790/) (2025)
- [Uncertainty Signals for Network Intent Translation: Risk Ranking and Ambiguity Localization](https://mlsanity.com/paper/2609.04486/) (2026)
- [TopoEdge: Topology-Grounded Agentic Framework for Edge Networking Code Generation and Repair](https://mlsanity.com/paper/2603.00569/) (2026)
- [TopoIntent: Compiling Security Intent into Executable, Compliance-Checked Network Topologies](https://mlsanity.com/paper/2608.13389/) (2026)
- [SoK: Semantic Decision Engines in Network Control Loops](https://mlsanity.com/paper/2610.06425/) (2026)

## Abstract (arXiv)

The Zero-touch Network & Service Management (ZSM) paradigm, a direct response to the increasing complexity of communication networks, is a problem-solving approach. In this paper, taking advantage of recent advances in generative Artificial Intelligence, we introduce the Network ConFiguration Generator (LLM-NetCFG) that employs Large Language Model and architects ZSM configuration agents by Large Language Models. LLM-NetCFG can automatically generate configurations, verify them, and configure network devices based on intents expressed in natural language. We also show the automation and verification of network configurations with minimum human intervention. Moreover, we explore the opportunities and challenges of integrating LLM in functional areas of network management to fully achieve ZSM.