The field of machine learning and artificial intelligence is moving towards developing more robust and reliable methods for time series forecasting and handling distribution shifts. Recent research has focused on improving the accuracy and practical utility of forecasts, with a particular emphasis on probabilistic forecasting and hierarchical time series forecasting. The use of innovative statistical tools, such as the Kolmogorov-Smirnov distance, has shown promise in measuring distribution shifts and quantifying their impact on AI agent performance. Furthermore, the development of new forecasting frameworks, such as Probabilistic Kolmogorov-Arnold Networks, has demonstrated superior efficiency-risk trade-offs and improved accuracy in certain domains. Noteworthy papers include: The paper on Using Kolmogorov-Smirnov Distance for Measuring Distribution Shift in Machine Learning, which explores the use of KS distance for monitoring and measuring distribution shift. The paper on A Primer on Kolmogorov-Arnold Networks for Probabilistic Time Series Forecasting, which introduces a novel probabilistic extension of Kolmogorov-Arnold Networks for time series forecasting.