The field of natural language processing is moving towards more efficient and interpretable language models. Researchers are exploring alternatives to traditional self-attention mechanisms, such as adaptive two-sided Laplace transforms and recurrent attention layers, which can achieve state-of-the-art performance while reducing computational complexity. Another trend is the development of more robust and noise-robust speech coding methods, including variable bitrate residual vector quantization and techniques for accelerating neural speech transcription. Additionally, there is a growing interest in analyzing and understanding the underlying mechanisms of transformer-based language models, using tools such as free probability theory and spectral dictionary token mixers. Noteworthy papers in this area include:

• Adaptive Two Sided Laplace Transforms, which proposes a learnable and scalable replacement for self-attention.
• Early Attentive Sparsification Accelerates Neural Speech Transcription, which accelerates neural speech transcription by sparsifying the hidden state.
• Towards Bitrate-Efficient and Noise-Robust Speech Coding with Variable Bitrate RVQ, which introduces a variable bitrate residual vector quantization framework for noise-robust speech coding.