# Hermes: A Large Language Model Framework on the Journey to Autonomous Networks

arXiv: https://arxiv.org/abs/2411.06490 · HTML: https://mlsanity.com/paper/2411.06490/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 15 references that are in the map.
- First posted on arXiv 10 Nov 2024.

## Builds on (most cited first, 10 of 10)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [On the Planning Abilities of Large Language Models : A Critical Investigation](https://mlsanity.com/paper/2305.15771/) (2023)
- [Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering](https://mlsanity.com/paper/2401.08500/) (2024)
- [How FaR Are Large Language Models From Agents with Theory-of-Mind?](https://mlsanity.com/paper/2310.03051/) (2023)
- [Large Generative AI Models for Telecom: The Next Big Thing?](https://mlsanity.com/paper/2306.10249/) (2023)
- [Large Language Models for Telecom: Forthcoming Impact on the Industry](https://mlsanity.com/paper/2308.06013/) (2023)
- [Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems](https://mlsanity.com/paper/2402.01748/) (2024)
- [Telco-RAG: Navigating the Challenges of Retrieval-Augmented Language Models for Telecommunications](https://mlsanity.com/paper/2404.15939/) (2024)
- [Large Language Models for Power Scheduling: A User-Centric Approach](https://mlsanity.com/paper/2407.00476/) (2024)
- [Adapting Foundation Models for Information Synthesis of Wireless Communication Specifications](https://mlsanity.com/paper/2308.04033/) (2023)

## Built on by (most cited first, 6 of 6)

- [Toward Agentic AI: Generative Information Retrieval Inspired Intelligent Communications and Networking](https://mlsanity.com/paper/2502.16866/) (2025)
- [Managing Procedural Memory in LLM Agents: Control, Adaptation, and Evaluation](https://mlsanity.com/paper/2606.23127/) (2026)
- [TeleMath: A Benchmark for Large Language Models in Telecom Mathematical Problem Solving](https://mlsanity.com/paper/2506.10674/) (2025)
- [Mapping the Landscape of Generative AI in Network Monitoring and Management](https://mlsanity.com/paper/2502.08576/) (2025)
- [SUN: Shared Use of Next-token Prediction for Efficient Multi-LLM Disaggregated Serving](https://mlsanity.com/paper/2603.02599/) (2026)
- [Advanced AI Service Provisioning in O-RAN through LLM Engine Integration](https://mlsanity.com/paper/2605.23809/) (2026)

## Abstract (arXiv)

The drive toward automating cellular network operations has grown with the increasing complexity of these systems. Despite advancements, full autonomy currently remains out of reach due to reliance on human intervention for modeling network behaviors and defining policies to meet target requirements. Network Digital Twins (NDTs) have shown promise in enhancing network intelligence, but the successful implementation of this technology is constrained by use case-specific architectures, limiting its role in advancing network autonomy. A more capable network intelligence, or "telecommunications brain", is needed to enable seamless, autonomous management of cellular network. Large Language Models (LLMs) have emerged as potential enablers for this vision but face challenges in network modeling, especially in reasoning and handling diverse data types. To address these gaps, we introduce Hermes, a chain of LLM agents that uses "blueprints" for constructing NDT instances through structured and explainable logical steps. Hermes allows automatic, reliable, and accurate network modeling of diverse use cases and configurations, thus marking progress toward fully autonomous network operations.