The field of adaptive learning and stochastic systems is moving towards developing more robust and efficient algorithms for handling complex, real-world data. Researchers are focusing on creating frameworks that can combine offline and online learning, allowing for better adaptation to changing environments and improved prediction performance. Additionally, there is a growing interest in stochastic data-driven tasks, such as smoothing, prediction, and control, which can be addressed through unified Bayesian frameworks. Another key area of research is the development of methods for handling partial observability and perturbations in reinforcement learning, where techniques like causal state representation and diffusion-based methods are showing promising results. Noteworthy papers in this area include the proposal of a two-stage learning framework that integrates offline and online algorithms for nonlinear stochastic dynamical systems, and the introduction of a framework that enhances reinforcement learning algorithms by uncovering the underlying causal structure of partially observable Markov decision processes. Furthermore, researchers are exploring the use of Bayesian dynamic scheduling for multipurpose batch processes, and the development of accelerated probabilistic response-time analysis methods for safety-critical real-time applications.