The field of wireless communication and sensing is moving towards more adaptive and integrated systems. Researchers are exploring new techniques to enhance the performance of communication systems, such as adaptive environment-aware processing and joint communication and sensing. These innovations have the potential to improve the reliability and efficiency of wireless communication systems, particularly in complex and dynamic environments. Notable papers in this area include: An Adaptive Environment-Aware Transformer Autoencoder for UAV-FSO with Dynamic Complexity Control, which proposes a novel autoencoder framework for UAV-assisted Free Space Optical communications. Polarization-Aware DoA Detection Relying on a Single Rydberg Atomic Receiver, which presents a quantum-enhanced direction-of-arrival detection scheme using a single Rydberg atomic vapor cell. Two-Timescale Learning for Pilot-Free ISAC Systems, which investigates a deep learning-based receiver architecture for pilot-free integrated sensing and communication systems. Quantum-Classical Hybrid Framework for Zero-Day Time-Push GNSS Spoofing Detection, which develops a hybrid quantum-classical autoencoder for detecting zero-day GNSS spoofing attacks. Enhancing Automatic Modulation Recognition With a Reconstruction-Driven Vision Transformer Under Limited Labels, which proposes a unified Vision Transformer framework for automatic modulation recognition with limited labels. Blind Source Separation-Enabled Joint Communication and Sensing in IBFD MIMO Systems, which addresses the challenge of joint communication and sensing in in-band full-duplex MIMO systems. On Secrecy Capacity of Binary Beampointing Channels with Block Memory and Feedback, which investigates the secrecy capacity of binary beampointing channels with block memory and feedback. On the Sensing Capacity of Gaussian Beam-Pointing Channels with Block Memory and Feedback, which explores the sensing capacity of Gaussian beam-pointing channels with block memory and feedback.