The field is witnessing a significant shift towards the development of more efficient model architectures, with a focus on reducing computational overhead and improving performance. This is evident in the growing adoption of state-space models (SSMs) and Mamba-based architectures, which offer linear complexity and superior scalability compared to traditional transformer-based models. Researchers are exploring innovative ways to leverage these architectures, including knowledge distillation, attention-based distillation, and ensemble methods. Noteworthy papers in this area include: Stratos, which proposes an end-to-end distillation pipeline for customized large language models, achieving significant improvements in accuracy and efficiency. VM-BeautyNet, which introduces a synergistic ensemble architecture for facial beauty prediction, achieving state-of-the-art performance on benchmark datasets. StretchySnake, which proposes a flexible training method for SSMs, enabling them to handle videos with varying spatial and temporal resolutions. PUMBA, which improves protein-protein interface evaluation by replacing the Vision Transformer backbone with Vision Mamba, resulting in improved accuracy and efficiency. Mamba4Net, which introduces a novel cross-architecture distillation framework for transferring knowledge from transformer-based models to Mamba-based models, achieving significant efficiency gains. Data Efficient Any Transformer-to-Mamba Distillation via Attention Bridge, which proposes a novel distillation framework for efficiently transferring attention knowledge from transformer teachers to state-space student models. g-DPO, which introduces a scalable preference optimization framework for protein language models, achieving significant speedups while maintaining performance.