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JEPA-Anything: Learning Predictive Models across Different Worlds | AI Learning Platform

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JEPA-Anything: Learning Predictive Models across Different Worlds

Taoyong Cui, Zhongyao Wang, Xinyue Xu et al.1 min readarXivSep 17, 2026

Key Takeaway for Students

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-embedd

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Career Insight

high demand

Why it matters

This paper introduces a novel framework for domain-agnostic world modeling, which is crucial for developing more general and adaptable AI systems. ML engineers can leverage these principles to build predictive models that generalize across diverse data types and tasks, moving towards more unified AI architectures. Understanding this approach will be key for designing future-proof AI agents capable of learning from and interacting with various environments.

Skills to learn

Implementing joint-embedding predictive architectures (JEPA)Designing orthogonal predictive factorization (OPF) componentsDeveloping latent space decomposition techniquesEvaluating predictive models across diverse data modalities (e.g., vision, time-series, molecular dynamics)Applying world modeling concepts to agent design

Interview questions

Explain the core concept of Orthogonal Predictive Factorization (OPF) and how it extends traditional JEPA models. Provide an example of how you would apply OPF to a multimodal prediction task.
How does JEPA-Anything address the challenge of domain-specific predictive models, and what are the implications for building truly general AI agents?
Design a system architecture for a JEPA-Anything based world model that can learn from and predict outcomes in a complex, multi-agent simulation environment, considering data ingestion, model training, and inference serving at scale.

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