The field of sampling and numerical methods is witnessing significant developments, with a focus on improving the efficiency and accuracy of sampling algorithms. Researchers are exploring new techniques to enhance the exploration of complex distributions, such as the use of tempered diffusion samplers and parallel tempering methods. Additionally, there is a growing interest in developing numerical schemes that can effectively capture the behavior of complex systems, including time-fractional phase-field models and kinetic Langevin dynamics. These advancements have the potential to impact a wide range of applications, from Bayesian inference to molecular dynamics. Noteworthy papers include: Continuously Tempered Diffusion Samplers, which introduces a new sampler that leverages exploration techniques from molecular dynamics, and Bouncy particle sampler with infinite exchanging parallel tempering, which proposes an algorithm that accelerates convergence to a posterior distribution. Linear Relaxation Schemes with Asymptotically Compatible Energy Law for Time-Fractional Phase-Field Models and An explicit splitting SAV scheme for the kinetic Langevin dynamics also present significant contributions to the field.