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News/MIC: Maximizing Informational Capacity in Adaptive Representations via Isotropic Subspace Alignment
arxiv
PublishedMay 29, 2026 at 4:00 AM
—neutral

MIC: Maximizing Informational Capacity in Adaptive Representations via Isotropic Subspace Alignment

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arXiv:2605.29987v1 Announce Type: cross Abstract: Although multi-scales representation learning enables elastic-dimension embeddings, nested subspaces often suffer from dimensional redundancy and spectral collapse. To address this, we introduce MIC, a framework that optimizes the geometric landscape

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