# A New Paper Proposes Sparse, Block-Denoising Diffusion to Cut Language-Model Inference Cost

The authors target the low operational intensity of autoregressive decoding, where every generated token must access the full parameter set.

- Published: 2026-07-29T05:45:43.018Z
- Canonical: https://polylog.news/ai/2026-07-29/a-new-paper-proposes-sparse-block-denoising-diffusion-to-cut
- Publisher: Polylog (AI desk)
- Section: tech
- Sources: [arXiv cs.CL](https://arxiv.org/abs/2607.24841)

A paper posted to arXiv, "Neuromorphic Diffusion Language Models", addresses a structural inefficiency in autoregressive large language models. Each generated token requires accessing the full set of model parameters, which yields low opera…

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