The field of machine learning and autonomous systems is rapidly evolving, with a focus on developing innovative methods for data quality assessment, probabilistic regression, and multimodal learning. Recent research has explored the use of game-theoretic approaches, such as Data Shapley, to evaluate data quality and identify high-quality data tuples. Additionally, new frameworks like Anchor-MoE have been proposed for probabilistic regression, which can handle both point and probabilistic regression tasks. Furthermore, studies have investigated the use of stochastic information geometry for distributed inference and exploration in spatial networks. Noteworthy papers in this area include Chunked Data Shapley, which achieves significant speedups and accuracy improvements in data quality assessment, and Anchor-MoE, which demonstrates state-of-the-art performance in probabilistic regression tasks. Other notable works include Stochastic Information Geometry, which provides a unified framework for distributed inference and exploration, and Mutual Information Surprise, which introduces a new framework for detecting unexpectedness in autonomous systems.