Unsupervised PCA/ICA/NMF symbol directions in real health foundation models transfer near-losslessly across modalities via linear alignment

Katuwal, Koparkar, Abbaspourazad, Mishra & Kirthivasan (2026) decompose frozen subject-level embeddings from three real pretrained health foundation models (PPG ViT, PPG EfficientNet, Accel ViT; ~172K wearable-sensor participants) into "symbol" directions via linear projections (PCA/ICA/NMF), finding selective association with 23 real health/physiological targets [katuwal-etal-2026-emergent-symbolic-structure-in-health-foundation-models] After linear (CCA/bijective) cross-modal alignment, a linear-classifier-based cross-modal transfer retains over 95% of in-domain AUC (health conditions 98.7%, physiological markers approximately 100%) -- purely linear/affine throughout, with no causal validation [katuwal-etal-2026-emergent-symbolic-structure-in-health-foundation-models]