一个预测框架，横扫七个世界
一个 AI 预测框架，在视觉、生物、临床、控制、分子、物理、天气七个领域通吃，还顺手在细胞实验和老鼠身上验证了它提出的生物干预。核心做法是把预测目标拆成几个互补的因子，各走各的通道学，再拼回同一个预测框架里。在 10 个动力学任务上全面超过同类基线，单次干预预测误差降了 34.8%，分子 100 步推演在四个系统里都是最低误差。更意外的是，它从数据里学到的轨道模式，自己还原出了开普勒定律的指数。这不是你明天能用的工具，但它指向一个可能：不同世界背后，或许真有一套共同的预测法则。
📄 原文摘要(英文)
World modeling enables intelligence to anticipate consequences, guide interventions, and learn from interaction. Yet predictive models remain domain-specific: can a common learning principle support world modeling across radically different systems? We introduce JEPA-Anything, a domain-agnostic framework based on orthogonal predictive factorization (OPF). Extending joint-embedding predictive architectures, OPF decomposes latent targets into complementary factors, learns them through dedicated pathways, and recombines them within a shared predictive design. We evaluate JEPA-Anything across seven domains: vision, biology, clinical trajectories, control, molecular dynamics, physical fields, and weather. Experiments span representation learning, intervention prediction, out-of-distribution generalization, and long-horizon dynamics, including 10 matched dynamics tasks, forecasting of over 1,000 clinical events, and 100-step molecular rollouts across four systems. Against matched JEPA baselines, JEPA-Anything improves reported metrics on all 10 dynamics tasks and reduces single-intervention prediction error on Interventional Pong by 34.8%. It achieves the lowest one-step and 100-step molecular errors among compared methods in all four systems. Beyond prediction, a factor-nominated biological intervention receives experimental support in cell co-cultures, patient-derived organoids, tumor fragments, and mice; latent orbital modes recover the Keplerian scaling exponent with a fitted slope of -1.4991. These results support a common factorized predictive principle across heterogeneous worlds, connecting world modeling with intervention and experimentally grounded scientific discovery. Code: https://github.com/Gen-Verse/JEPA-Anything