EVA-0 is a zeroth-order test-time adaptation method that uses scale-invariant loss, anchor-guided optimization, and symmetric two-sided perturbations to enable inference and adaptation in two forward passes, outperforming prior methods on ImageNet-C with ViT-Base.
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2026 2verdicts
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Muon with Nesterov momentum and inexact polar decomposition achieves optimal convergence rates of O(ε^(-(3α-2)/(α-1))) under heavy-tailed noise for ε-stationary points in non-convex settings.
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EVA-0: Test-Time Model Evolution with Only Two Forward Passes per Sample
EVA-0 is a zeroth-order test-time adaptation method that uses scale-invariant loss, anchor-guided optimization, and symmetric two-sided perturbations to enable inference and adaptation in two forward passes, outperforming prior methods on ImageNet-C with ViT-Base.
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Muon with Nesterov Momentum: Heavy-Tailed Noise and (Randomized) Inexact Polar Decomposition
Muon with Nesterov momentum and inexact polar decomposition achieves optimal convergence rates of O(ε^(-(3α-2)/(α-1))) under heavy-tailed noise for ε-stationary points in non-convex settings.