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PhAI Labs, together with Stanford, Oxford, and Princeton, proposed JEPA-Anything, which splits JEPA's single prediction into multiple specialized prediction modules and lets each module differentiate into distinct roles during training, thereby covering seven domains—including physics, robotics, weather, single-cell, and clinical—with a single architecture. In a simplified Pong environment, prediction error dropped 35%, and on unseen intervention combinations it dropped 13%; on the Burgers equation benchmark the error was nearly halved, but after 50 steps the advantage shrank to about 3%; in simulated walking-robot planning, it won two of three environments. The team also screened biological data to identify a candidate liver cancer regimen combining IL-18 with CD73 blockade, and tested it in organoids, patient tissue, and mice; the paper also notes that this does not yet show it can become a therapy, and the code and models have been made public.

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