The field of natural language processing is moving towards developing more efficient and effective models for long-context processing. Recent research has focused on improving the performance of language models on tasks that require processing long sequences of text, such as math and coding domains. One of the key challenges in this area is overcoming the limitations of fixed-size recurrent memory, which can lead to underutilization of long contexts. Researchers are exploring new approaches, including chunk-based inference procedures and compact model architectures, to address this issue. These advancements have the potential to enable more accurate and efficient processing of long documents, with applications in areas such as abstractive summarization and compliance monitoring. Noteworthy papers include: xGen-small Technical Report, which introduces a family of Transformer decoder models optimized for long-context applications. Overflow Prevention Enhances Long-Context Recurrent LLMs, which demonstrates a simple yet effective approach to mitigating recurrent memory failures. Scaling Context, Not Parameters, which presents a compact language model that supports 512K-token context length and achieves competitive performance on long-context benchmarks.