JEPA-Anything: Learning Predictive Models across Different Worlds
Researchers introduce JEPA-Anything, a domain-agnostic predictive world-modeling framework using orthogonal predictive factorization, validated across seven domains including biology and weather.
JEPA-Anything extends joint-embedding predictive architectures through orthogonal predictive factorization (OPF), which decomposes latent targets into complementary factors learned via dedicated pathways. It is evaluated across seven domains: vision, biology, clinical trajectories, control, molecular dynamics, physical fields, and weather. Against matched JEPA baselines it improves metrics on all 10 dynamics tasks, cuts Interventional Pong single-intervention error by 34.8%, and achieves the lowest one-step and 100-step molecular errors across four systems. A factor-nominated biological intervention received experimental support in cell co-cultures, patient-derived organoids, tumor fragments, and mice.
- Introduces orthogonal predictive factorization extending JEPA to heterogeneous domains
- Evaluated on vision, biology, clinical, control, molecular, field, and weather tasks
- Reduces Interventional Pong error 34.8% and wins all 10 dynamics tasks
- Latent orbital modes recover Keplerian scaling exponent (slope -1.4991)
- Biological intervention validated in organoids, tumor fragments, and mice
- Organizations
- Gen-Verse
- AI models
- JEPA-Anything
From the original article
1 min read at arxiv.orgWorld 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).
By Taoyong Cui · arxiv.org
A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.