The field of sequence processing is moving towards the development of more efficient state space models (SSMs) that can effectively capture long-distance dependencies while reducing computational costs. Recent advancements have focused on improving the scalability and performance of SSMs, enabling their deployment on edge devices and cloud services. Notably, innovations in quantization strategies, reconfigurable dataflow units, and novel architectures have led to significant breakthroughs. These advancements have the potential to revolutionize various applications, including image restoration, vision-and-language navigation, and sequence modeling. The development of flexible and robust frameworks that can switch between different mechanisms, such as TransMamba, is also a promising direction. Furthermore, the application of mathematical tools from rough path theory to analyze linear attention models has opened up new avenues for improving sequence modeling architectures. Overall, the field is witnessing a paradigm shift towards more efficient, scalable, and flexible SSMs that can efficiently process long sequences. Noteworthy papers include: Q-MambaIR, which proposes an accurate and efficient quantized Mamba for image restoration tasks, and Quamba2, which presents a robust and scalable post-training quantization framework for selective state space models. TransMamba is also notable for its ability to dynamically switch between Transformer and Mamba mechanisms, offering a scalable solution for next-generation sequence modeling.