dailyai.report

Categorical Flow Maps (CFMs) enable discrete data generation through a continuous flow matching process between Gaussian and one-hot distributions. Apple researchers demonstrate that this approach offers faster sampling than autoregressive language models. This method allows for tilting and accelerated inference. Practitioners can now generate discrete sequences without the linear bottlenecks of token-by-token prediction.