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New Lattice Framework Unifies Diffusion and Autoregressive Models

By PulseAugur Editorial · [3 sources] ·

Researchers have unified diffusion and autoregressive (AR) models by conceptualizing them as paths on a corruption lattice. This framework allows for the prediction of decoding schedule performance based on data geometry, specifically the graph's treedepth, which dictates the minimum steps for zero-cost schedules. The study proposes a method to rank schedules by estimating pairwise dependence from pretrained weights, verifying these predictions across text, image, and video generation tasks. AI

IMPACT Provides a unified design principle for decoding schedules in future AR, diffusion, and hybrid models.

RANK_REASON The cluster contains an academic paper detailing a new theoretical framework for generative models.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New Lattice Framework Unifies Diffusion and Autoregressive Models

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The cluster contains an academic paper detailing a new theoretical framework for generative models.
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COVERAGE [3]

  1. arXiv cs.CL TIER_1 English(EN) · T. Y. Tsui, Jiatao Gu, Lingjie Liu ·

    The Lattice of Transition Laws

    arXiv:2610.11216v1 Announce Type: cross Abstract: Diffusion and autoregression (AR) have long been seen as different categories of generative models, with diffusion specialising in continuous fields and AR specialising in discrete tokens. Recent work seeks to combine the advantag…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    The Lattice of Transition Laws

    Diffusion and autoregression (AR) have long been seen as different categories of generative models, with diffusion specialising in continuous fields and AR specialising in discrete tokens. Recent work seeks to combine the advantages of the two models, and each hybrid fixes its de…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    The Lattice of Transition Laws

    Diffusion and autoregression (AR) have long been seen as different categories of generative models, with diffusion specialising in continuous fields and AR specialising in discrete tokens. Recent work seeks to combine the advantages of the two models, and each hybrid fixes its de…

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