JEPA-Anything: Learning Predictive Models across Different Worlds
JEPA-Anything uses Orthogonal Predictive Factorization (OPF) to enhance cross-domain predictive models, improving prediction accuracy by 34.8%.
Key Findings
Methodology
JEPA-Anything is based on Orthogonal Predictive Factorization (OPF), decomposing latent targets into complementary factors, learning them through dedicated pathways, and recombining them within a shared predictive design. Evaluated across seven domains: vision, biology, clinical trajectories, control, molecular dynamics, physical fields, and weather.
Key Results
- Improved metrics on all 10 dynamics tasks and reduced single-intervention prediction error on Interventional Pong by 34.8% compared to baseline models.
- Achieved the lowest one-step and 100-step molecular errors among compared methods in all four systems.
- Biological intervention factors received experimental support in cell co-cultures, patient-derived organoids, tumor fragments, and mice.
Significance
The study demonstrates a cross-domain predictive model using OPF, linking world modeling, intervention, and experimentally grounded scientific discovery. It shows that a common factorized predictive principle can be applied across heterogeneous worlds, with significant academic and industrial impact.
Technical Contribution
JEPA-Anything introduces a new predictive capacity allocation method through OPF, supporting multi-factor analysis and stable state synthesis. It provides a unified predictive core across multiple domains.
Novelty
JEPA-Anything is the first to apply orthogonal predictive factorization to cross-domain predictive models, achieving generality across multiple domains through factorized prediction.
Limitations
- The model may face performance bottlenecks when dealing with extremely complex dynamic systems.
- Handling high-dimensional data might require more computational resources.
- Specific adjustments may be needed for certain domains.
Future Work
Future research directions include optimizing model performance in extremely complex systems and exploring potential applications in more domains.
AI Executive Summary
JEPA-Anything addresses the limitations of traditional models in different domains by using Orthogonal Predictive Factorization (OPF) to create cross-domain predictive models. This method decomposes latent targets into complementary factors and recombines them within a shared predictive design, evaluated across seven domains including vision, biology, and clinical trajectories.
Experimental results show that JEPA-Anything improves metrics on all 10 dynamics tasks and reduces single-intervention prediction error on Interventional Pong by 34.8%. Additionally, it achieves the lowest one-step and 100-step molecular errors in all four systems, demonstrating its generality and effectiveness across domains.
The study not only achieves significant advances in prediction accuracy but also demonstrates its potential in scientific discovery through experimental validation of biological intervention factors. Future research will continue to optimize model performance in complex systems and explore potential applications in more domains.
Deep Analysis
Background
World modeling is a crucial direction in AI, aiming to predict consequences, guide interventions, and learn from interactions. Traditional predictive models are often domain-specific and struggle to generalize across different systems. JEPA-Anything offers a new solution through Orthogonal Predictive Factorization (OPF).
Core Problem
Traditional predictive models perform poorly across different domains due to varying system structures and dynamic characteristics, making effective prediction challenging.
Innovation
The core innovation of JEPA-Anything is Orthogonal Predictive Factorization (OPF), which decomposes latent targets into multiple complementary factors and recombines them in a shared predictive design, achieving cross-domain generality. This method differs from traditional single-target embedding approaches by better allocating predictive capacity.
Methodology
- �� OPF framework: Decomposes latent targets into complementary factors.
- �� Dedicated pathway learning: Learns each factor through dedicated pathways.
- �� Shared predictive design: Recombines factors in a shared predictive design.
- �� Multi-domain evaluation: Evaluated across seven domains.
Experiments
The experimental design includes evaluations in seven domains: vision, biology, clinical trajectories, control, molecular dynamics, physical fields, and weather. Datasets include 10 matched dynamics tasks, forecasting over 1,000 clinical events, and 100-step molecular rollouts across four systems.
Results
JEPA-Anything improves 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 in all four systems.
Applications
JEPA-Anything can be applied to predictive tasks in vision, biology, and clinical trajectories, offering broad application potential. Its universal predictive core can adapt to the needs of different domains.
Limitations & Outlook
While JEPA-Anything performs well across multiple domains, it may face performance bottlenecks in extremely complex dynamic systems. Handling high-dimensional data might require more computational resources.
Plain Language Accessible to non-experts
Imagine a factory where JEPA-Anything acts as an intelligent production line management system. Different production lines represent different domains like vision, biology, and weather. Traditional management systems can only handle specific lines, but JEPA-Anything can manage all lines by intelligently allocating resources and optimizing processes. It breaks down tasks on each line into smaller tasks, optimizes them through dedicated pathways, and integrates them into a shared management system. This approach not only improves production efficiency but also adapts to different production needs.
ELI14 Explained like you're 14
Imagine playing a super complex game with many different levels like vision, biology, and weather. Each level has different rules and challenges. JEPA-Anything is like a super smart game assistant that helps you find the best strategy for each level. It breaks down each level's tasks into smaller tasks, solves them through dedicated pathways, and ultimately helps you win the game! Isn't that cool?
Glossary
Orthogonal Predictive Factorization
A method that decomposes latent targets into multiple complementary factors to enhance model generality.
Used in JEPA-Anything to achieve cross-domain predictive models.
Latent Target
The target state that the model needs to predict, often hidden or future.
Processed in JEPA-Anything through orthogonal predictive factorization.
Shared Predictive Design
A design that recombines multiple factors into a complete predictive state, supporting cross-domain applications.
Used in JEPA-Anything to integrate predictive capabilities across domains.
Biological Intervention Factor
Factors used in biological experiments to validate predictive models, supporting scientific discovery.
Used in JEPA-Anything experiments to validate model effectiveness.
Dynamics Task
Tasks involving dynamic changes in systems, typically used to evaluate predictive model performance.
Used in JEPA-Anything to test model cross-domain generality.
Open Questions Unanswered questions from this research
- 1 How to improve JEPA-Anything's performance in extremely complex dynamic systems?
- 2 How to optimize computational resource usage in high-dimensional data processing?
- 3 How to apply JEPA-Anything's predictive models in more domains?
Applications
Immediate Applications
Vision Prediction
JEPA-Anything can enhance prediction accuracy in vision systems, applicable to autonomous driving and surveillance systems.
Biomedical Prediction
Can be used to predict clinical trajectories and biological responses, supporting personalized medicine and drug development.
Long-term Vision
Cross-Domain Intelligent Systems
JEPA-Anything can evolve into a core technology supporting intelligent decision-making across multiple domains, driving widespread AI applications.
Abstract
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