# Generative AI-Driven Hierarchical Multi-Agent Framework for Zero-Touch Optical Networks

arXiv: https://arxiv.org/abs/2510.05625 · HTML: https://mlsanity.com/paper/2510.05625/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 34 references that are in the map.
- First posted on arXiv 7 Oct 2025.

## Builds on (most cited first, 7 of 7)

- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [MM-LLMs: Recent Advances in MultiModal Large Language Models](https://mlsanity.com/paper/2401.13601/) (2024)
- [A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT](https://mlsanity.com/paper/2303.04226/) (2023)
- [First Field Trial of LLM-Powered AI Agent for Lifecycle Management of Autonomous Driving Optical Networks](https://mlsanity.com/paper/2409.14605/) (2024)
- [Synergistic Interplay of Large Language Model and Digital Twin for Autonomous Optical Networks: Field Demonstrations](https://mlsanity.com/paper/2411.00473/) (2024)

## Built on by (most cited first, 4 of 4)

- [The Horizon Gap: Planning, Memory, Execution, Training, and Evaluation for Long-Horizon LLM Agents](https://mlsanity.com/paper/2608.06663/) (2026)
- [Mathematical Reasoning Enhanced LLM for Formula Derivation: A Case Study on Fiber NLI Modellin](https://mlsanity.com/paper/2604.13062/) (2026)
- [Agentic AI for IPoDWDM Network Lifecycle Automation: An MCP-Enabled Architecture](https://mlsanity.com/paper/2607.05958/) (2026)
- [Toward Agentic Optical Networks: A Vision of LLM Agent-Driven Autonomous Lifecycle Management](https://mlsanity.com/paper/2609.32226/) (2026)

## Abstract (arXiv)

The rapid development of Generative Artificial Intelligence (GenAI) has catalyzed a transformative technological revolution across all walks of life. As the backbone of wideband communication, optical networks are expecting high-level autonomous operation and zero-touch management to accommodate their expanding network scales and escalating transmission bandwidth. The integration of GenAI is deemed as the pivotal solution for realizing zero-touch optical networks. However, the lifecycle management of optical networks involves a multitude of tasks and necessitates seamless collaboration across multiple layers, which poses significant challenges to the existing single-agent GenAI systems. In this paper, we propose a GenAI-driven hierarchical multi-agent framework designed to streamline multi-task autonomous execution for zero-touch optical networks. We present the architecture, implementation, and applications of this framework. A field-deployed mesh network is utilized to demonstrate three typical scenarios throughout the lifecycle of optical network: quality of transmission estimation in the planning stage, dynamic channel adding/dropping in the operation stage, and system capacity increase in the upgrade stage. The case studies, illustrate the capabilities of multi-agent framework in multi-task allocation, coordination, execution, evaluation, and summarization. This work provides a promising approach for the future development of intelligent, efficient, and collaborative network management solutions, paving the way for more specialized and adaptive zero-touch optical networks.