JEPA

TL;DR for operators A world model can be reasonably accurate when predicting the next state and still become unreliable once its own predictions are fed back repeatedly. That difference is operationally relevant because planning and control systems often depend on trajectories, not isolated one-step estimates. Semigroup-JEPA: Latent Dynamics Consistency for Zero-Shot Physics Generalization1 tests this problem by training models on a narrow range of gravity values and evaluating them across much wider regimes. SG-JEPA jointly trains its visual representation and temporal predictor using several recursively generated future latents. Its largest advantages generally appear where errors have more opportunity to compound: longer forecast horizons and gravity values outside the training distribution. ...

A patient chart looks like a document because hospitals make it look that way. There are notes, medication lists, lab panels, procedure codes, imaging references, adverse events, survival outcomes, and enough timestamps to make a database administrator feel briefly useful. So it is tempting to treat the electronic health record as a very long piece of text: serialize the events, train a model to predict the next token, extract an embedding, and hope that clinical meaning emerges somewhere inside the transformer fog. ...