Hierarchical-JEPA self-supervised framework for multivariate ECG time series

arXiv·low signal

A lightweight self-supervised JEPA framework learns from large unlabeled multivariate time series (ECG) to help models trained on small labeled medical datasets. It is a narrow, domain-specific representation-learning result rather than an agent-infrastructure development. Included as new primary-source ML research; low relevance to agent builders.