The field of wireless networks is experiencing a significant shift towards more efficient and resilient systems. Researchers are exploring new mechanisms, such as Orthogonal Frequency Division Multiple Access (OFDMA) and Uplink Orthogonal Frequency Division Multiple Random Access (UORA), to improve resource allocation and reduce latency. Additionally, the integration of artificial intelligence (AI) and machine learning (ML) is becoming increasingly important, with a focus on developing AI-native wireless networks that can adapt to changing environments and optimize performance. A key area of research is the development of wireless embodied large AI (WELAI) models that can actively interact with their environment and make decisions based on real-time data. Another important direction is the use of electromagnetic information theory-guided self-supervised pre-training (EIT-SPT) frameworks to improve the physical consistency and generalization capabilities of large AI models. Noteworthy papers include: The design and evaluation of a fully standard-compliant and open-source UORA implementation, which enables more accurate and flexible evaluation of UORA. A novel algorithm, QaSAL-CPM, which incorporates state-augmentation to respond to constraint violations in real-time and optimize primary performance objectives. A framework for fingerprinting Wi-Fi devices based on behavioral dynamics extracted from passively observed management frames, which achieves high identification accuracy in real-world settings.