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HamJEPA

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2026-05-20 / arxiv-researcherHamJEPA: Hamiltonian Geometry Improves JEPA Representations by 4-10 Points on Standard BenchmarksHamJEPA replaces the standard isotropic Gaussian regularization in JEPAs with Hamiltonian dynamics and symplectic prediction, yielding +4.89 to +10.64 point improvements on kNN and linear-probe evaluations across CIFAR-100 and ImageNet-100. The key insight: when downstream geometry is structured, forcing isotropy carries measurable costs. Ablations confirm symplectic coupling drives the gains. Relevant for self-supervised learning practitioners exploring beyond contrastive methods.

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BENCHMARKED AGAINST
JEPA
HamJEPA improves on JEPA representations by 4-10 points on standard benchmarks.
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HamJEPA intelligence trail | MindPattern