KV Caching for dLLMs is Noise Process Agnostic

The typical language model is autoregressive (AR): it predicts one token at a time, left to right, each token conditioned on the ones before it. This bakes in two limitations: it spends one forward pass per token, so latency grows linearly with length regardless of how hard each token is to predict, and it cannot revise earlier tokens if later context makes them appear wrong. Diffusion language…