The field is moving towards developing personalized and adaptive interventions that can effectively align with users' preferences and needs. This is evident in the increasing use of reinforcement learning and other machine learning techniques to create personalized recommendation systems and health interventions. Researchers are exploring new design strategies and frameworks that can support long-term well-being, behavioral alignment, and socially responsible personalization. Noteworthy papers include:

• Audio-Thinker, which proposes a reinforcement learning framework to enhance the reasoning capabilities of large audio language models.
• PEARL, which demonstrates the potential of a scalable, behaviorally-informed reinforcement learning approach to personalize digital health interventions for physical activity.