AI离“理解万物”还有多远？先拿癌细胞和行星轨道试试水

TL;DR - JEPA-Anything is a cross-domain latent world-model framework that shares one predictive core across seven system types while using domain-specific encoders. Its orthogonal factorization improves prediction on most benchmarks and yields interpretable latent structures that supported a validated cancer intervention and recovered Kepler’s third law.

• Orthogonal Predictive Factorization splits target states into complementary, constrained subspaces, reducing interference between signals at different scales.
• Under matched data, architecture, and compute, it improved 9 of 10 prediction tasks, including roughly 40% lower MSE on two PDEBench tasks and 10.5% lower error on WeatherBench 2.
• It achieved the lowest reported one-step and 100-step rollout errors across four molecular systems, though gains narrowed over longer horizons and were inconsistent across control tasks.
• Researchers used learned factors to identify an IL-18 plus CD73-blockade candidate validated in liver-cancer models, while latent orbital modes reproduced the theoretical −1.5 Kepler scaling with a fitted slope of −1.4991.