The field of mixture-of-experts (MoE) architectures is rapidly advancing, with a focus on improving efficiency, scalability, and performance. Recent developments are centered around enhancing expert specialization, reducing memory footprint, and promoting modularization. Researchers are exploring novel methods to promote expert de-correlation, orthogonalization, and variance, leading to significant improvements in overall performance. Notably, the introduction of contrastive objectives, probabilistic expert pruning, and task-adaptive expert retrieval are showing promising results. These advancements have the potential to enable the efficient deployment of large MoE models in memory-constrained environments and improve their performance in various tasks, including click-through rate prediction and multilingual expansion. Noteworthy papers include: CoMoE, which proposes a novel method to promote modularization and specialization in MoE, and PreMoe, which introduces a framework for efficient deployment of massive MoE models in memory-constrained environments. Additionally, papers such as Advancing Expert Specialization for Better MoE and Less, but Better: Efficient Multilingual Expansion for LLMs via Layer-wise Mixture-of-Experts are also making significant contributions to the field.