Agents Should Replace Narrow Predictive AI as the Orchestrator in 6G AI-RAN

TL;DR

This paper advocates replacing narrow predictive models in 6G AI-RAN with multimodal Large Language Models to improve reasoning, adaptability, and orchestration of network functions.

Contribution

It proposes a novel architecture where LLMs act as cognitive operating systems, orchestrating existing models and translating high-level directives into network policies.

Findings

LLMs can bridge the semantic gap between operator intent and network configurations.

Orchestrating narrow models with LLMs enhances adaptability and anomaly diagnosis.

Calls for foundational research in alignment, quantization, verification, and security for telecom AI.

Abstract

This position paper argues that to achieve Level 5 autonomous 6G networks, the next generation of Artificial Intelligence in Radio Access Networks (AI-RAN) should transition away from fragmented, narrow predictive models and instead adopt multimodal Large Language Models (LLMs) as central reasoning agents. Current AI-RAN architectures rely on disjointed Deep Neural Networks (DNNs) and Deep Reinforcement Learning (DRL) agents that operate in isolated domains. These narrow models suffer from siloed knowledge, severe brittleness to out-of-distribution dynamics, and a fundamental inability to bridge the intent gap the semantic disconnect between high-level, unstructured operator directives and rigid numerical network configurations. We propose elevating LLMs, or domain-adapted Large Telecom Models (LTMs), to act as the cognitive operating system situated within the RAN Intelligent Controller (RIC), the control and orchestration layer of AI-RAN. In this architecture, LLMs do not replace narrow models but orchestrate them as executable subroutines, dynamically translating human intent into concrete policies and utilizing Retrieval-Augmented Generation (RAG) to autonomously diagnose complex, multi-vendor network anomalies. To make this architectural shift a reality, we call upon the machine learning community to prioritize critical foundational research tailored to the strict constraints of telecommunications, specifically focusing on continuous alignment via network-driven feedback (RLNF), extreme sub-8-bit edge quantization, neuro-symbolic verification to curb hallucinations, and securing orchestration frameworks against adversarial prompt injections.