Functional renormalization group applied to nearly continuous spectra yields a scale-dependent canonical dimension that undergoes a dimensional phase transition at signal-to-noise ratios below the BBP threshold, correlating with symmetry breaking and eigenvector deviations.
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Diffusion models suffer critical slowing down when sampling near criticality in the O(n) model but deeper local architectures reduce training-time scaling from quadratic to logarithmic in system size.
Global Annealing Monte Carlo with ML global moves plus local updates outperforms Simulated Annealing and is more robust than Population Annealing on 3D Ising spin glasses without hyperparameter tuning.
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Functional Renormalization for Signal Detection: Dimensional Analysis and Dimensional Phase Transition for Nearly Continuous Spectra Effective Field Theory
Functional renormalization group applied to nearly continuous spectra yields a scale-dependent canonical dimension that undergoes a dimensional phase transition at signal-to-noise ratios below the BBP threshold, correlating with symmetry breaking and eigenvector deviations.
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The critical slowing down in diffusion models
Diffusion models suffer critical slowing down when sampling near criticality in the O(n) model but deeper local architectures reduce training-time scaling from quadratic to logarithmic in system size.
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Demonstrating Real Advantage of Machine-Learning-Enhanced Monte Carlo for Combinatorial Optimization
Global Annealing Monte Carlo with ML global moves plus local updates outperforms Simulated Annealing and is more robust than Population Annealing on 3D Ising spin glasses without hyperparameter tuning.