The field of logical frameworks and probabilistic modeling is witnessing significant advancements, driven by the development of innovative techniques and tools. One of the key areas of focus is the creation of efficient and scalable methods for learning Bayesian networks, with researchers exploring ensemble approaches and divide-and-conquer strategies to improve accuracy and reduce computational costs. Additionally, there is a growing interest in the application of logical frameworks to natural language semantics, with diagrammatic calculi and functional models being investigated. Another important area of research is the development of new probabilistic models, such as binned semiparametric Bayesian networks, which offer improved performance and efficiency. Noteworthy papers in this area include the introduction of BayesL, a logical framework for specifying and verifying Bayesian networks, and the development of scalable structure learning algorithms for Bayesian networks. Overall, these advancements have the potential to significantly impact a range of applications, from artificial intelligence and machine learning to natural language processing and decision-making under uncertainty. Notable papers include: BayesL, which introduces a novel logical framework for Bayesian networks, and Scalable Structure Learning of Bayesian Networks by Learning Algorithm Ensembles, which proposes an ensemble approach to learning Bayesian networks.