The field of natural language processing is moving towards improved long-context modeling and reasoning capabilities. Researchers are exploring new methods to enhance language models' ability to process and understand longer contexts, leading to better performance in tasks such as question answering, reading comprehension, and reasoning. A key direction is the development of more efficient and effective position encoding schemes, allowing models to capture longer-range dependencies and relationships between tokens. Additionally, there is a growing interest in understanding the role of long-context capacity in reasoning, with studies showing that models with stronger long-context capacity achieve higher accuracy on reasoning benchmarks. Noteworthy papers in this area include: Longer Context, Deeper Thinking, which reveals a consistent trend of models with stronger long-context capacity achieving higher accuracy on reasoning benchmarks. What Makes a Good Reasoning Chain, which presents an automated framework for analyzing the internal structures of reasoning chains and identifying critical thought patterns that drive or predict the correctness of final answers.