The field of probabilistic modeling and inference is rapidly advancing, with a focus on developing innovative methods for complex data analysis and simulation. Recent research has emphasized the importance of robust and efficient algorithms for inverse problems, Bayesian inference, and stochastic processes. Notably, the development of new criteria for rapid mixing of Glauber dynamics and the application of Gaussian process tilted nonparametric density estimation have shown promise in addressing long-standing challenges in statistical physics and machine learning. Furthermore, advances in diffusion models, such as the Gaussian mixture flow matching model and dimension-free convergence of diffusion models, have demonstrated improved performance in generative tasks and high-dimensional sampling. Overall, the field is moving towards more efficient, scalable, and accurate methods for probabilistic modeling and inference. Noteworthy papers include: Rapid Mixing on Random Regular Graphs beyond Uniqueness, which confirms a conjecture on the mixing behavior of the hardcore model on random regular graphs. Gaussian Mixture Flow Matching Models, which proposes a novel model that generalizes previous diffusion and flow matching models.