The field of sampling and optimization is currently witnessing significant developments, with a focus on improving the efficiency and accuracy of existing algorithms. One of the key directions is the development of new techniques for sampling from complex distributions, such as logconcave densities and discrete spaces. Researchers are also exploring new methods for optimization, including the use of mirror mean-field Langevin dynamics and adaptive importance sampling. Additionally, there is a growing interest in applying these techniques to real-world problems, such as language model control and parameter estimation. Notable papers in this area include those that propose innovative algorithms for fast logconcave sampling, semantic probabilistic control of language models, and entropy-guided sampling of flat modes in discrete spaces. Overall, these advances have the potential to impact a wide range of fields, from machine learning and artificial intelligence to statistics and engineering. Noteworthy papers include: Faster logconcave sampling from a cold start in high dimension, which presents a faster algorithm for generating a warm start for sampling an arbitrary logconcave density. Semantic Probabilistic Control of Language Models, which leverages a verifier's gradient information to efficiently reason over all generations that satisfy the target attribute. Entropy-Guided Sampling of Flat Modes in Discrete Spaces, which proposes a new method for sampling from flat modes in discrete spaces using local entropy.