AI RESEARCH
AURORA: Contextual Orthogonalization for Geometric Representation Learning in Healthcare Foundation Models
arXiv CS.LG
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ArXi:2605.17765v1 Announce Type: new Recent healthcare foundation models have achieved strong predictive performance through large scale self supervised learning, yet their latent representations frequently entangle physiologic severity, intervention intensity, observational structure, and institutional workflow into shared embedding directions. While effective for downstream prediction, such representations remain semantically opaque and unstable under contextual shift. We