The field of Mixture-of-Experts (MoE) models is rapidly evolving, with a focus on improving scalability, efficiency, and performance. Recent developments have centered around designing novel training frameworks, routing mechanisms, and scaling laws to unlock the full potential of MoE models. Notably, researchers have been exploring ways to enable elastic inference-time expert utilization, allowing models to adapt to varying computational budgets. Additionally, there is a growing interest in understanding the internal mechanisms of MoE models, including expert-level behaviors and routing dynamics. These advancements have led to significant improvements in model performance, efficiency, and robustness. Some noteworthy papers in this regard include Elastic MoE, which introduces a novel training framework for scalable MoE models, and Dynamic Experts Search, which proposes a test-time scaling strategy for enhancing reasoning in MoE models. Other notable works, such as Towards a Comprehensive Scaling Law of Mixture-of-Experts and Bayesian Mixture-of-Experts, have made important contributions to our understanding of MoE models and their potential applications. The integration of MoE models with other areas, such as continual learning, multi-task learning, and multimodal learning, has also shown promising results. For instance, the use of MoE architectures in task-aware time series analytics has enabled more efficient and specialized processing of diverse data types. Furthermore, advances in multimodal learning have enabled the integration of multiple data sources and modalities, enhancing the accuracy and robustness of models in real-world applications. Overall, these developments are driving progress in various fields, including healthcare, finance, and IoT applications, where accurate and reliable time series analytics and multimodal learning are crucial. Other areas, such as anomaly detection, representation learning, and treatment effect estimation, are also witnessing significant advancements, driven by innovations in novel loss formulations, evaluation metrics, and training objectives. As research in these areas continues to evolve, we can expect to see even more exciting developments and applications in the future.