Hermes: A Large Language Model Framework on the Journey to Autonomous
  Networks
Authors: name: Fadhel Ayed, name: Ali Maatouk, name: Nicola Piovesan, name: Antonio De Domenico, name: Merouane Debbah, name: Zhi-Quan Luo

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.