The field of unmanned aerial vehicle (UAV) research is moving towards the development of more advanced vision-language models and autonomous systems. Recent work has focused on improving the performance of these models in aerial visual reasoning tasks, such as object counting and spatial scene inference. Researchers are also exploring the use of large language models (LLMs) for UAV applications, including autonomous semantic compression for swarm communication and individual identification via distilled RF fingerprints. Notable papers in this area include UAV-VL-R1, which proposes a lightweight vision-language model for aerial visual reasoning, and AeroDuo, which introduces a novel task called Dual-Altitude UAV Collaborative VLN. Other papers, such as Talk Less, Fly Lighter and UAV Individual Identification via Distilled RF Fingerprints-Based LLM, demonstrate the potential of LLMs for efficient collaborative communication and accurate individual identification.