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

2026-05-11

AI Summary

This work addresses the semantic bottleneck in 6G AI-enabled radio access networks (AI-RANs), where high-dimensional physical-layer states are compressed into low-dimensional metrics, thereby limiting perception, reasoning, and evolutionary capabilities. To overcome this limitation, the paper proposes a unified memory-centric architecture that maps biologically inspired memory hierarchies onto heterogeneous computing hardware, establishing a cognitive continuum spanning microsecond-scale reflexes, millisecond-scale inference, and long-term evolution. Leveraging emerging coherent interconnects and zero-copy observability mechanisms, the architecture enables multi-timescale state sharing, effectively dissolving the boundary between perception and reasoning. This approach significantly bridges the cognitive gap between real-time responsiveness and long-term contextual awareness, enhancing both cognitive efficiency and autonomous decision-making, and for the first time realizes a truly autonomous 6G AI-RAN that jointly supports real-time operation and continuous evolution.

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.

Problem

• cognitive gap
• semantic bottleneck
• agentic AI
• 6G RAN
• memory-centric architecture

Innovation

• unified memory paradigm
• agentic AI-RAN
• cognitive continuum
• zero-copy observability
• coherent interconnects