The field of stochastic dynamics and generative modeling is witnessing significant advancements, driven by innovative applications of mathematical techniques and machine learning algorithms. Researchers are exploring new frameworks for deriving score functions, designing novel numerical methods for mean field stochastic differential equations, and developing more efficient sampling methods for high-dimensional probability distributions. Notably, the integration of Malliavin calculus and Bismut's formula is providing fresh insights into the smoothness and structure of probability densities, while neural network approaches are being used to improve the performance of Monte Carlo algorithms. Additionally, the application of quantum mechanical systems and message-passing Monte Carlo methods is showing promise in addressing the challenges of high-dimensional sampling and computational complexity. Some noteworthy papers in this area include: The paper on Malliavin-Bismut Score-based Diffusion Models, which introduces a rigorous connection between Malliavin calculus and diffusion generative models. The paper on Low Stein Discrepancy via Message-Passing Monte Carlo, which extends the Message-Passing Monte Carlo framework to sample from general multivariate probability distributions. The paper on Almost Bayesian: The Fractal Dynamics of Stochastic Gradient Descent, which shows that stochastic gradient descent can be regarded as a modified Bayesian sampler that accounts for accessibility constraints induced by the fractal structure of the loss landscape.