Entropy-Cut Metropolis-Hastings targets high-entropy decision points for resampling, yielding mixing time that scales with the number of decisions and consistent gains over baselines on MATH500, HumanEval, GPQA Diamond, and AIME26.
Blink of an eye: a simple theory for feature localization in generative models
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Analytic solution of full-batch gradient flow for linear and convolutional denoisers in diffusion models yields a universal inverse-variance spectral law for learning times of eigenmodes.
The paper introduces a phase framework for data distributions connected by local denoisers and demonstrates that reverse diffusion consists of trivial and data phases separated by a transition where local score functions must fail, tied to spatial Markovianity.
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Reasoning with Sampling: Cutting at Decision Points
Entropy-Cut Metropolis-Hastings targets high-entropy decision points for resampling, yielding mixing time that scales with the number of decisions and consistent gains over baselines on MATH500, HumanEval, GPQA Diamond, and AIME26.
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An Analytical Theory of Spectral Bias in the Learning Dynamics of Diffusion Models
Analytic solution of full-batch gradient flow for linear and convolutional denoisers in diffusion models yields a universal inverse-variance spectral law for learning times of eigenmodes.
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Local Diffusion Models and Phases of Data Distributions
The paper introduces a phase framework for data distributions connected by local denoisers and demonstrates that reverse diffusion consists of trivial and data phases separated by a transition where local score functions must fail, tied to spatial Markovianity.