The field of natural language processing is moving towards more efficient and effective long-context modeling. Recent research has focused on developing methods that can capture long-range dependencies without increasing computational costs quadratically. One direction is the integration of state-space models with sparse attention mechanisms, which has shown promise in improving the expressiveness of these models. Another approach is the use of novel attention architectures, such as chunked attention and temporal kernels, which can efficiently handle both short-range and long-range dependencies. Furthermore, the incorporation of biologically inspired components, such as gated memory mechanisms and Rotary positional encoding, has led to the development of more efficient and scalable models. Notable papers in this area include those that propose innovative solutions to overcome the limitations of current models, such as the use of hypertokens and holographic computing to improve the precision of large language models. Overall, the field is advancing towards more efficient and effective long-context modeling, with potential applications in natural language processing, forecasting, and beyond. Noteworthy papers include: The paper on Towards practical FPRAS for #NFA, which presents a new algorithm with improved time complexity. The paper on Hypertokens, which introduces a novel symbolic memory framework for large language models. The paper on Overcoming Long-Context Limitations of State-Space Models, which proposes a solution based on integrating state-space models with context-dependent sparse attention.