The field of 6G communications is rapidly evolving, with a focus on integrating Artificial Intelligence (AI) and machine learning to enable more efficient, adaptive, and autonomous networking systems. Recent research has highlighted the potential of AI-driven solutions to enhance resource management, improve network performance, and enable new applications such as extended reality (XR) and autonomous communication systems. A key direction in this field is the development of user-centric cell-free massive Multiple-Input Multiple-Output (UC-CFmMIMO) systems, which can provide seamless and homogeneous connectivity. Additionally, the integration of AI agents and Large Artificial Intelligence Models (LAMs) is expected to play a crucial role in enabling intelligent communication systems. Noteworthy papers in this area include the proposal of an adaptive Implicit-layer DL Channel Estimation Network (ICENet) for balancing computational complexity and channel estimation accuracy, and the introduction of a hybrid reinforcement learning and metaheuristic approach for frequency resource management in 6G UC-CFmMIMO systems. The study on video streaming over QUIC also presents significant findings on the impact of QUIC implementations on user Quality of Experience (QoE). Furthermore, research on ES3A, a novel security architecture for 6G networks, showcases the importance of service-based security, end-to-end protection, and smart security automation.