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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.

JEPA-Anything: Learning Predictive Models across Different Worlds

Researchers present JEPA-Anything, a domain-agnostic world-modeling framework improving prediction across vision, biology, control, molecular dynamics, and weather.

JEPA-Anything extends joint-embedding predictive architectures with orthogonal predictive factorization, decomposing latent targets into complementary factors learned through dedicated pathways. It was evaluated across seven domains, including 10 matched dynamics tasks, forecasting of over 1,000 clinical events, and 100-step molecular rollouts across four systems. Against matched JEPA baselines it improves metrics on all 10 dynamics tasks and cuts single-intervention prediction error on Interventional Pong by 34.8%. A factor-nominated biological intervention received experimental support in cell co-cultures, patient-derived organoids, tumor fragments, and mice.

Hugging Face daily papersupdated · 22h agofirst · 1d agoAI research 2 sources

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