Large language models have become the default interface for code generation, math problem solving, summarization, document understanding, and many other developer workflows. Under the hood, though, most LLMs still generate text one token at a time, with each token depending on all previous tokens. These autoregressive (AR) models consume their own outputs as they generate. The autoregressive approach has been remarkably successful—it's stable to train, simple to serve, and responsible for much of the progress in modern language modeling. But it has a fundamental limitation: every new token...