This work introduces diffusion-augmented LLMs, a new class of models.

They first suggest that speculative decoding needs a separate draft model.

Diffusion LLMs give up the quality of the model they replace. This work achieves parallel token generation without either cost.

Uno is a class of diffusion augmented LLMs that defines an autoregressive model distribution and uses diffusion to draw multiple tokens in parallel from that same distribution.

The parameters split in two. Autoregressive weights train under standard next token prediction, and a lightweight set of diffusion weights is added by a short distillation phase that adds negligible overhead to an existing training pipeline.

Because the sampler draws from the AR distribution itself, the speedup is lossless. An existing open-weight AR model can be upgraded rather than retrained.

Uno beats leading speculative decoding methods at every evaluated batch size, including the largest the device supports, and reaches up to 3x over the base model. The 8B Uno outperforms the 26B DiffusionGemma and the proprietary Mercury 2 across agentic tool use, coding and long-context reasoning.

Paper: https://arxiv.org/abs/2609.04010

Chat with Paper: https://academy.dair.ai/papers/unlocking-lossless-speedups-in-llms-via-discrete-diffusion-2609.04010