Bridging the Cognitive Gap: A Unified Memory Paradigm for 6G Agentic AI-RAN

TL;DR

This paper proposes a unified memory architecture for 6G AI-RAN that integrates sensing and reasoning, enabling real-time responsiveness and long-term evolution through a cognitive continuum.

Contribution

It introduces a memory-centric paradigm inspired by biological hierarchies, leveraging coherent interconnects to unify different time-scale processes in 6G networks.

Findings

Creates a cognitive continuum across microsecond to long-term scales.

Replaces message passing with zero-copy observability.

Enables AI agents to bridge real-time and long-horizon contexts.

Abstract

As 6G evolves, the radio access network must transcend traditional automation to embrace agentic AI capable of perception, reasoning, and evolution. A fundamental cognitive gap persists in current disaggregated architectures, where interfaces force the physical layer to compress high-dimensional states into low-dimensional metrics, trapping reasoning agents behind a semantic bottleneck. This article envisions a shift from interface-bound to memory-centric architectures. We propose a unified memory paradigm that dissolves the boundaries between sensing and reasoning by mapping biological memory hierarchies onto heterogeneous computing fabrics. Enabled by emerging coherent interconnects, this approach creates a cognitive continuum where microsecond-level reflexes, millisecond-level reasoning, and long-term evolution share state across time scales. By replacing message passing with zero-copy observability, we empower AI agents to bridge the gap between real-time responsiveness and long-horizon context for truly autonomous 6G networks.