AI RESEARCH
Asymptotic and Finite-Time Guarantees for Langevin-Based Temperature Annealing in InfoNCE
arXiv CS.LG
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ArXi:2603.12552v1 Announce Type: new The InfoNCE loss in contrastive learning depends critically on a temperature parameter, yet its dynamics under fixed versus annealed schedules remain poorly understood. We provide a theoretical analysis by modeling embedding evolution under Langevin dynamics on a compact Riemannian manifold.