The field of research is witnessing significant developments in diffusion models and sampling techniques. Recent studies have focused on improving the efficiency and accuracy of these models, particularly in handling complex data distributions and inverse problems. The introduction of novel frameworks, such as Measurement-Aligned Sampling, and the extension of existing methods, like Stein Variational Gradient Descent, have shown promising results in addressing challenging tasks. Furthermore, the exploration of diffusion models in various applications, including image restoration and optimization problems, has led to innovative solutions and improved performance. Noteworthy papers include: The Effect of Stochasticity in Score-Based Diffusion Sampling, which provides valuable insights into the role of stochasticity in diffusion models. An Exact and Efficient Sampler for Dynamic Discrete Distributions, which presents a groundbreaking approach to sampling from dynamic discrete distributions. Unsupervised Imaging Inverse Problems with Diffusion Distribution Matching, which offers a versatile method for addressing inverse problems in imaging using unpaired datasets.