Researchers stretch LeCun’s JEPA AI into a universal world model that works from physics to biology

Researchers have expanded the JEPA architecture pioneered by Yann LeCun so it works across seven very different fields. The effort also produced a liver cancer treatment candidate that the team tested in the lab.

World models predict how a system will evolve, whether it’s a robot, a molecule, or a patient’s health. Until now, each domain has typically needed its own model. A team led by PhAI Labs, with collaborators from Stanford, Oxford, and Princeton, wants to show that a single shared principle is enough. Their paper introduces JEPA-Anything, built on Joint-Embedding Predictive Architectures (JEPA). These models don’t reconstruct raw data like pixels but instead predict an abstract summary of a missing or future state, filtering out irrelevant details. The authors see a weakness in the standard approach: everything gets funneled into a single prediction, so easy patterns end up drowning out harder ones.Ad JEPA-Anything breaks the predicted state into several parts, each handled by its own prediction module. An added constraint pushes the modules to capture different aspects rather than learning the same thing, and the model then reassembles their partial predictions into a complete picture.Ad The researchers don’t assign meanings to the parts, letting those roles emerge during training. Instead, they only change how they prepare the data for each field. Big gains in dynamics tests, mixed results for robots The team compared JEPA-Anything against a standard JEPA with the same architecture, trained on the same data under identical conditions. Dynamic systems showed the clearest gains: in a simplified Pong environment with targeted interventions, prediction error dropped by 35 percent. For combinations of interventions the model never saw during training, it fell by 13 percent.Ad JEPA-Anything consistently beat the baseline across ten test tasks spanning physics, robotics, and weather forecasting, according to the authors. On the Burgers equation, a common fluid dynamics benchmark, error fell by nearly half in a separate evaluation. Over 50 prediction steps the advantage held but shrank to about three percent. The method also scored best in simulations of water, quartz, acetaminophen, and benzene, even after 100 steps. For single-cell data, the model assigned cell types more reliably, and on clinical data it predicted more than 1,000 possible disease events slightly better.