PairAlign: A Framework for Autoregressive Tokenization via Self-Alignment with Applications to Audio Tokenization

Modern learning systems represent perceptual signals with continuous vectors, but comparison, retrieval, memory, alignment, and reasoning are often symbolic. In language, tokens provide this interface; for speech and audio, it must be learned. Existing audio tokenizers rely on local quantization, cl