The field of language modeling is witnessing significant developments, with a focus on improving the efficiency and effectiveness of large language models. Researchers are exploring new architectures and techniques to enhance the expressivity and power of these models, while also reducing their computational requirements. One notable direction is the integration of recurrent neural network (RNN)-based approaches with advanced attention mechanisms, which has shown promise in improving contextual coherence and sequence generation capabilities. Another area of research is the development of methods for extending the context window of large language models, which is essential for applications involving long-form content generation. Techniques such as dimension-wise positional embeddings manipulation and frequency domain key-value compression are being proposed to optimize fine-tuning and inference efficiency. Noteworthy papers in this area include: Leveraging Decoder Architectures for Learned Sparse Retrieval, which investigates the effectiveness of learned sparse retrieval across different transformer-based architectures. WuNeng: Hybrid State with Attention, which introduces a novel approach to enhancing the expressivity and power of large language models by integrating RNN-based RWKV-7 with advanced attention mechanisms. RWKV-X: A Linear Complexity Hybrid Language Model, which achieves linear-time complexity in training and constant-time complexity in inference decoding. FreqKV: Frequency Domain Key-Value Compression for Efficient Context Window Extension, which proposes an efficient compression technique for extending the context window of large language models.