The field of networking is experiencing a significant shift towards AI-native architectures, with a focus on enabling autonomous decision-making and improving network intelligence. Recent developments have highlighted the importance of memory and contextual awareness in AI-based decision systems, allowing for more adaptability and continuity in network management. Online learning and deep reinforcement learning are being leveraged to enhance beam management and resource allocation in 6G networks, while AI-driven digital twins are being used to optimize network slicing in 5G/6G Non-Terrestrial Networks. Additionally, researchers are exploring the use of machine learning and signal processing techniques to mitigate mobile jamming and improve spectrum classification in O-RAN architectures. Noteworthy papers in this area include RAN Cortex, which introduces a memory-augmented architecture for contextual recall in AI-based RAN decision systems, and LibIQ, which enables real-time RF spectrum classification using a novel library for RF signals. Online Learning-based Adaptive Beam Switching for 6G Networks also demonstrates the benefits of memory and prioritized learning for robust 6G beam management.