AI RESEARCH
Rethinking Random Transformers as Adaptive Sequence Smoothers for Sleep Staging
arXiv CS.AI
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ArXi:2605.09905v1 Announce Type: cross Automatic sleep staging commonly adopts Transformers under the assumption that they learn complex long-range dependencies. We challenge this view by revealing a neglected property of sleep sequences: strong local temporal continuity. We show that a randomly initialized Transformer, without any