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

arXiv:2605.29987v1 Announce Type: new 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 of multi-granular embeddings through isotropic subspace alignment. MIC employs Soft Collapse Regularization (SCR) to mitigate redundancy between prefix and residual subspaces via cross-correlation penalties, alongside Spectral Isotropy Regularization (SIR) to ensure hyper-spherical uniformity in low-

Source: arXiv cs.LG — read the full report at the original publisher.

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