Every major language model in production today — GPT, Claude, Gemini, Llama — generates text the same way: left to right, one token at a time. That sequential assumption has been so productive for so long that most researchers treat it as fixed. A team at UC Berkeley and the University of Illinois just published dLLM: Simple Diffusion Language Modeling, a unified open-source framework that refuses to take autoregression for granted. Diffusion language models generate entire sequences through iterative denoising — bidirectionally, in parallel — and dLLM is the infrastructure that lets the field measure, compare, and build on them systematically for the first time.
