The field of adaptive systems and collective intelligence is moving towards a more rigorous and formal definition of its core concepts. Researchers are working to establish a common understanding of self-adaptive systems, foundation models, and collective adaptive intelligence, which is expected to facilitate more effective collaboration and innovation. A key trend in this area is the integration of different methodologies and frameworks, such as the combination of extreme programming and CRISP-DM for agile data science projects. Additionally, there is a growing interest in developing more resilient, scalable, and adaptable AI systems, particularly in embodied AI applications. The development of new architectures and frameworks, such as the CASE framework for participatory research and digital health surveillance, is also a notable trend. These advancements have the potential to enable more sustainable and institutionally controlled data collection systems, and to support the development of more robust and adaptive AI systems. Noteworthy papers in this area include: Defining Self-adaptive Systems: A Systematic Literature Review, which provides a comprehensive analysis of the existing formal definitions of self-adaptive systems. Defining Foundation Models for Computational Science: A Call for Clarity and Rigor, which proposes a formal definition of foundation models in computational science and introduces the Data-Driven Finite Element Method (DD-FEM) framework. The CASE Framework -- A New Architecture for Participatory Research and Digital Health Surveillance, which presents a mature and reusable research infrastructure for adaptive, context-aware participatory research and pandemic preparedness.