Large Language Models for Zero Touch Network Configuration Management
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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.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Aug 2024.
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Citing papers per year: 2025: 4; 2026: 6.
10 papers.
- 2025DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Surveycited by 6
- 2025LAMeTA: Intent-Aware Agentic Network Optimization via a Large AI Model-Empowered Two-Stage Approachcited by 5
- 2026NetAgentBench: A State-Centric Benchmark for Evaluating Agentic Network Configurationcited by 5
- 2026SADE: Symptom-Aware Diagnostic Escalation for LLM-Based Network Troubleshootingcited by 4
- 2025KP-A: A Unified Network Knowledge Plane for Catalyzing Agentic Network Intelligencecited by 1
- 2025Prompting for Performance: Exploring LLMs for Configuring Softwarecited by 1
- 2026Uncertainty Signals for Network Intent Translation: Risk Ranking and Ambiguity Localizationcited by 1
- 2026TopoEdge: Topology-Grounded Agentic Framework for Edge Networking Code Generation and Repaircited by 0
- 2026TopoIntent: Compiling Security Intent into Executable, Compliance-Checked Network Topologiescited by 0
- 2026SoK: Semantic Decision Engines in Network Control Loopscited by 0
- 2026SoK: Semantic Decision Engines in Network Control Loops5 Oct 2026
- 2026Uncertainty Signals for Network Intent Translation: Risk Ranking and Ambiguity Localization3 Sep 2026
- 2026TopoIntent: Compiling Security Intent into Executable, Compliance-Checked Network Topologies13 Aug 2026
- 2026SADE: Symptom-Aware Diagnostic Escalation for LLM-Based Network Troubleshooting6 May 2026
- 2026NetAgentBench: A State-Centric Benchmark for Evaluating Agentic Network Configuration3 Apr 2026
- 2026TopoEdge: Topology-Grounded Agentic Framework for Edge Networking Code Generation and Repair28 Feb 2026
- 2025Prompting for Performance: Exploring LLMs for Configuring Software13 Jul 2025
- 2025KP-A: A Unified Network Knowledge Plane for Catalyzing Agentic Network Intelligence10 Jul 2025
- 2025LAMeTA: Intent-Aware Agentic Network Optimization via a Large AI Model-Empowered Two-Stage Approach18 May 2025
- 2025DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey13 Mar 2025