The field of natural language processing is moving towards more efficient and effective attention mechanisms. Researchers are exploring alternative architectures and techniques to improve the performance of large language models while reducing computational costs. Notable developments include the introduction of novel attention variants, such as Mamba and differential Mamba, which have shown promising results in terms of efficiency and accuracy. Additionally, hybrid architectures that combine different attention mechanisms are being investigated, with some models achieving state-of-the-art performance on language modeling and recall tasks. Some papers are particularly noteworthy, including the proposal of Mamba-based SIP models for hearing-impaired listeners, which achieves competitive performance while maintaining a relatively small number of parameters. The introduction of differential Mamba also shows improved retrieval capabilities and superior performance over vanilla Mamba. The systematic analysis of hybrid linear attention highlights the importance of selective gating, hierarchical recurrence, and controlled forgetting for effective hybrid models.