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Renormalization Group Flow Matching for Scalable Local Generative Modeling
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generative modelingrenormalization grouplocal computationlong-range correlations
2608.23696
Builder Relevance
3h ago80%
Abstract
This paper introduces a generative framework that utilizes renormalization group flow matching to efficiently generate data while preserving long-range correlations.
Reality Card
Core Claim
The RGFM framework enables local generative modeling that captures long-range structure with a computational cost that scales nearly linearly with system volume.
Method / Result
RGFM reproduces long-range correlations far beyond its receptive field, outperforming conventional local flow matching in generating coherent samples.
Limitations
The paper does not specify limitations or reproducibility concerns.
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