Emergent Symbolic Structure in Health Foundation Models: Extraction, Alignment, and Cross-Modal Transfer

Abstract:We show that information can be transferred post-hoc across independently trained health foundation models (FMs), each pretrained on ~20M minutes of wearable sensor data from ~172K participants, by aligning their data-dependent coordinate systems. From frozen embeddings we extract candidate symbol-like components using linear decomposition methods, and align them across models with simple linear maps. Aligned symbols associate selectively with health conditions and physiological attributes, with associations similar across modalities and architectures. A classifier trained on one model's symbols and applied to another retains more than 95% of its in-domain performance, with similar retention in both directions. Overall, our results indicate that independently trained health FMs converge toward a common representation of the same underlying physiology.

Machine Learning (cs.LG)

arXiv:2605.07407 [cs.LG]

(or arXiv:2605.07407v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2605.07407 Focus to learn more arXiv-issued DOI via DataCite

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