# RAN Cortex: Memory-Augmented Intelligence for Context-Aware Decision-Making in AI-Native Networks

arXiv: https://arxiv.org/abs/2505.07842 · HTML: https://mlsanity.com/paper/2505.07842/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 32 references that are in the map.
- First posted on arXiv 6 May 2025.

## Builds on (most cited first, 12 of 12)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Improving language models by retrieving from trillions of tokens](https://mlsanity.com/paper/2112.04426/) (2021)
- [Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering](https://mlsanity.com/paper/2007.01282/) (2020)
- [Applications of Deep Reinforcement Learning in Communications and Networking: A Survey](https://mlsanity.com/paper/1810.07862/) (2018)
- [Neural Episodic Control](https://mlsanity.com/paper/1703.01988/) (2017)
- [Model-Free Episodic Control](https://mlsanity.com/paper/1606.04460/) (2016)
- [MANTRA: Memory Augmented Networks for Multiple Trajectory Prediction](https://mlsanity.com/paper/2006.03340/) (2020)
- [ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection](https://mlsanity.com/paper/1905.10750/) (2019)
- [Communication and Computation O-RAN Resource Slicing for URLLC Services Using Deep Reinforcement Learning](https://mlsanity.com/paper/2202.06439/) (2022)
- [Digital Network Twins for Next-generation Wireless: Creation, Optimization, and Challenges](https://mlsanity.com/paper/2410.18002/) (2024)
- [DTRAN: A Special Use Case of RAN Optimization using Digital Twin](https://mlsanity.com/paper/2409.01136/) (2024)

## Built on by (most cited first, 1 of 1)

- [Bridging the Cognitive Gap: A Unified Memory Paradigm for 6G Agentic AI-RAN](https://mlsanity.com/paper/2605.10036/) (2026)

## Abstract (arXiv)

As Radio Access Networks (RAN) evolve toward AI-native architectures, intelligent modules such as xApps and rApps are expected to make increasingly autonomous decisions across scheduling, mobility, and resource management domains. However, these agents remain fundamentally stateless, treating each decision as isolated, lacking any persistent memory of prior events or outcomes. This reactive behavior constrains optimization, especially in environments where network dynamics exhibit episodic or recurring patterns. In this work, we propose RAN Cortex, a memory-augmented architecture that enables contextual recall in AI-based RAN decision systems. RAN Cortex introduces a modular layer composed of four elements: a context encoder that transforms network state into high-dimensional embeddings, a vector-based memory store of past network episodes, a recall engine to retrieve semantically similar situations, and a policy interface that supplies historical context to AI agents in real time or near-real time. We formalize the retrieval-augmented decision problem in the RAN, present a system architecture compatible with O-RAN interfaces, and analyze feasible deployments within the Non-RT and Near-RT RIC domains. Through illustrative use cases such as stadium traffic mitigation and mobility management in drone corridors, we demonstrate how contextual memory improves adaptability, continuity, and overall RAN intelligence. This work introduces memory as a missing primitive in AI-native RAN designs and provides a framework to enable "learning agents" without the need for retraining or centralized inference