一个AI框架，同时预测天气、分子和病人
同一个预测原则，能同时用在天气、分子、病人轨迹、物理场这些完全不同的世界上——这听起来像吹牛，但论文给出了硬证据：一个叫 JEPA-Anything 的框架，把预测拆成几个互补的因子分别学、再合起来，在视觉、生物、临床、控制、分子、物理、天气七个领域都跑赢了同类方法。分子动力学上，它预测 100 步的误差在所有四个系统里都是最低；在干预预测上，它把 Pong 游戏的单步干预误差降了 34.8%。更狠的是，它预测出的一个生物干预因子，居然在细胞共培养、类器官、肿瘤碎片和小鼠身上都得到了实验验证。这不是一个能让你明天就用上的工具，但它指向一个可能：世界模型也许不需要每个领域各造一套，一个共同原则就够了。
📄 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