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.
@article{arxiv.2605.10036,
title = {Bridging the Cognitive Gap: A Unified Memory Paradigm for 6G Agentic AI-RAN},
author = {Xijun Wang and Zhaoyang Liu and Chenyuan Feng and Xiang Chen and Howard H. Yang and Tony Q. S. Quek},
journal= {arXiv preprint arXiv:2605.10036},
year = {2026}
}
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This work has been submitted to the IEEE for possible publication