The field of large language models is rapidly advancing with a focus on improving efficiency and performance. Recent developments have centered around the Mixture of Experts (MoE) paradigm, which enables the selective activation of parameter subsets for each input token. This approach has shown great promise in reducing computational costs while maintaining model accuracy. Researchers are exploring various innovations, including novel routing mechanisms, sparse expertise allocation, and decentralized learning strategies, to further enhance the efficiency and scalability of MoE models. Notably, the integration of MoE with other techniques, such as quantization and metasurface-enabled wireless communication, is also being investigated. These advancements have the potential to significantly impact the deployment of large language models in real-world applications. Noteworthy papers include: USMoE, which proposes a unified competitive learning framework to improve the performance of existing SMoEs. S2MoE, which introduces a robust sparse mixture of experts via stochastic learning to mitigate representation collapse. MiLo, which augments highly quantized MoEs with a mixture of low-rank compensators to recover accuracy loss from extreme quantization.