The field of complex systems is moving towards a deeper understanding of emergence and synchronization, with a focus on developing predictive laws and frameworks that can capture the dynamics of complex phenomena. Researchers are exploring the use of information theory and machine learning to identify the natural scale of emergence and to design controllers that can synchronize heterogeneous systems. A key direction is the development of data-driven methods that can discover governing equations and closure models from data, enabling the simulation and analysis of complex systems. Notable papers in this area include: A Law of Emergence: Maximum Causal Power at the Mesoscale, which establishes a predictive law for emergence in complex systems. Synchronization Dynamics of Heterogeneous, Collaborative Multi-Agent AI Systems, which presents a novel framework for synchronizing AI agents. Data-driven particle dynamics: Structure-preserving coarse-graining for emergent behavior in non-equilibrium systems, which proposes a framework for coarse-graining particle dynamics while preserving thermodynamic properties.