The field of human behavior modeling and prediction is rapidly advancing, driven by the increasing availability of large-scale datasets and the development of sophisticated machine learning models. One notable trend is the use of large language models (LLMs) to generate synthetic behavior data, which can help alleviate privacy concerns and improve the accuracy of behavior predictions. Another key direction is the development of frameworks that can simulate human behavior in a realistic and privacy-preserving manner, such as digital twins. Researchers are also exploring the use of LLMs to detect cognitive decline and predict user behavior in various contexts, including clinical settings. Overall, these advances have the potential to revolutionize our understanding of human behavior and improve the development of intelligent assistant services. Noteworthy papers include: Cog-TiPRO, which proposes a framework for detecting cognitive decline using voice assistant commands, and BehaviorSFT, which introduces a novel training strategy for clinical agents using behavioral tokens.