The fields of deep learning, semantic communications, and wireless systems are experiencing significant developments, driven by the need for more efficient, effective, and robust models and systems. In deep learning, researchers are focusing on regularization techniques and continual learning, with a growing understanding of the challenges associated with loss of plasticity in deep continual learning. Noteworthy papers in this area include DIM, which proposes a new regularization method to enforce domain-informed monotonicity in deep neural networks, and Activation Function Design Sustains Plasticity in Continual Learning, which introduces two drop-in nonlinearities to mitigate plasticity loss. In semantic communications and multimodal processing, researchers are exploring innovative approaches to reduce the computational demands and storage requirements of large AI models, while maintaining or improving their performance. The development of lightweight deployment strategies, token-based multimodal interactive coding frameworks, and sparse Gaussian representations for dataset distillation are notable advancements in this area. In collaborative perception, researchers are developing more efficient and effective communication strategies for multi-agent systems, leveraging information theory and semantic-aware paradigms. The development of rate-distortion optimized communication frameworks, instance-level interaction architectures, and prediction-powered communication methods with distortion guarantees are significant innovations in this area. In wireless communication, researchers are exploring innovative approaches to enhance spatial flexibility, sensing accuracy, and energy efficiency in future wireless systems. The integration of unmanned aerial vehicles (UAVs) and fluid antenna systems is showing promising results in achieving low-altitude economy missions. The use of machine learning techniques, such as Monte Carlo Candidate-Likelihood Estimation, is also being investigated for wireless localization, providing a more accurate and robust approach to position estimation. Overall, these advancements have the potential to significantly improve the accuracy, robustness, and efficiency of various systems and applications, from autonomous vehicles and edge inference to wireless communication and signal processing.