JEPA-Anything: Learning Predictive Models across Different Worlds

Key points

Eliminates interference from monolithic learning by decomposing prediction targets into mutually orthogonal factors, predicting each separately, and recombining them (OPF).
Reused the same core structure across seven domains (vision, biology, clinical, control, molecular, physical fields, weather), improving scores on all 10 matched dynamics tasks against matched JEPA baselines.
Reduced single-intervention prediction error by 34.8% and achieved the lowest errors among compared methods on 100-step molecular rollouts in all four systems.
A biological intervention nominated by the model (IL-18 + CD73 blockade) was confirmed to have antitumor effects in actual co-culture, organoid, and mouse experiments.
From orbital motion data, it recovered the Keplerian scaling exponent (theoretical value -1.5) at -1.4991 without ever being taught the physical law.

External research summaries. These are not HDATF publications or measured product results.