diffusion-language-models

Coverage of an alternative to autoregressive text generation, where models refine entire sequences in parallel rather than predicting one token at a time. Expect practical analysis of how diffusion approaches affect inference speed, controllability, and parallel decoding, alongside their trade-offs against transformer baselines. Content tracks where these architectures fit in real deployment, including local inference and specialized routing, and where they still fall short.