The field of natural language processing (NLP) is moving towards more efficient and scalable models for long-sequence processing. Recent developments have focused on overcoming the limitations of traditional Transformer-based models, which suffer from quadratic time and memory complexity. New architectures are being proposed to reduce computational demands and improve performance on tasks such as sentiment analysis, intent detection, and topic classification. Notably, innovations in attention mechanisms, recurrent reasoning, and sparse structured transformers are enabling more effective modeling of long-term contextual dependencies. These advancements have the potential to significantly improve the accuracy and efficiency of NLP models. Noteworthy papers include: ResFormer, which proposes a novel neural network architecture that integrates an reservoir computing network and a conventional Transformer architecture to model varying context lengths efficiently. InfLLM-V2, which introduces a dense-sparse switchable attention framework that seamlessly adapts models from short to long sequences. ReSSFormer, which presents a Recursive Sparse Structured Transformer that integrates recurrent reasoning, adaptive sparse attention, and self-organizing encoder structure for scalable and long-context reasoning. Poolformer, which replaces self-attention with recurrent layers and incorporates pooling operations to reduce sequence length.