JEPA-Anything: Learning Predictive Models across Different Worlds

World models learn internal states for predicting how an underlying system changes. Cells, molecules, physical fields, control environments, and clinical trajectories differ in observation geometry, yet require predictions of hidden, intervened, or future states from available context. Joint-embedding predictive architectures (JEPAs) provide a natural framework for this problem, but their single target-prediction pathway does not explicitly organize predictive target coordinates. We therefore introduce \method, a framework that separates domain-specific observations and context–target construction from a shared predictive-state interface. Within this interface, we further propose orthogonal predictive factorization (OPF), which assigns target coordinates to separately parameterized factor-specific output heads and synthesizes them into a complete state for readout, intervention, planning, and multi-step rollout. Across a broad set of visual, biological, clinical, control, physical-field, weather, and molecular settings, domains retain their own encoders and observation geometries while using the same predictive core contract. Matched experiments show improvements in terminal readout, intervention prediction, and out-of-distribution or recursive forecasting; geometric analyses support well-conditioned synthesis, and factor interventions indicate functional use of the learned coordinate blocks. These results support a shared factorized predictive principle for heterogeneous worlds while preserving the structures needed for prediction, interaction, and analysis.