The field of natural language processing is witnessing significant developments in efficiently handling long contexts in transformer-based language models. Researchers are exploring innovative approaches to reduce the quadratic time complexity of the attention mechanism while maintaining model quality. One promising direction is the use of caching mechanisms, which have been shown to improve performance and reduce computational requirements. Another area of focus is the development of novel architectures and frameworks that enable efficient length scaling during pre-training and inference. These advancements have the potential to enable the deployment of large language models in resource-constrained environments and improve their overall performance. Noteworthy papers in this area include CacheFormer, which introduces a high attention-based segment caching approach, and CAOTE, which proposes a novel token eviction criterion based on attention output error. KeyDiff is also notable for its key similarity-based KV cache eviction method, while HEMA presents a hippocampus-inspired extended memory architecture for long-context AI conversations. Additionally, Efficient Pretraining Length Scaling introduces the Parallel Hidden Decoding Transformer, which enables efficient length scaling during pre-training.