Researchers have developed a new self-supervised learning framework called Next-Audio-Patch-Embedding (NAPE) prediction. NAPE uses a causal Transformer to predict the next patch embedding of an audio signal from previous ones, without relying on elaborate pre-training recipes or auxiliary regularization losses. In experiments, NAPE achieved state-of-the-art fine-tuning performance on several audio and speech benchmarks, scaled consistently with encoder size, and produced structured attention patterns without explicit supervision. --- Why it matters: This matters to AI researchers because it shows that a simple causal paradigm can yield strong audio learners, potentially simplifying the process of training models for audio tasks. The framework's minimalist design also makes it easier to adapt to different applications and datasets. Source: https://arxiv.org/abs/2608.19863