Diffusion Language Model
A language model that generates entire blocks of tokens by iterative denoising rather than one token at a time.
Plain English
The text condenses out of noise a chunk at a time instead of being typed out word by word.
Example
An in-editor completion built on a dLLM fills a 50-line function almost instantly, so the UI needs a reveal animation rather than a typing effect.
Why it matters
The first serious architectural break from autoregressive generation in commercial LLMs - Gemini Diffusion samples at ~1,479 tokens per second, and streaming-style UIs stop making sense.