JEPA-Anything: Learning Predictive Models across Different Worlds

arXiv:2609.20800 · cs.CL · Submitted 2026-09-17 · Read on arXiv

cs.CL

Submitted: 2026-09-17

Updated: 2026-09-17

Comments: Code: https://github.com/Gen-Verse/JEPA-Anything

Code: https://github.com/Gen-Verse/JEPA-Anything

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• World Model — 16× in this paper · explained in 7 episodes
• Benchmark — 11× in this paper · explained in 2 episodes
• Core — 9× in this paper · explained in 2 episodes
• Redundancy — 6× in this paper · explained in 2 episodes
• Regularization — 6× in this paper · explained in 3 episodes
• Representation Learning — 5× in this paper · explained in 2 episodes
• Clustering — 4× in this paper · explained in 2 episodes
• Latent Dynamics — 4× in this paper · explained in 2 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Downstream Tasks — 3× in this paper · explained in 2 episodes
• External Validation — 3× in this paper · explained in 2 episodes
• Foundation Model — 3× in this paper · explained in 3 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Latent World Models — 3× in this paper · explained in 2 episodes
• Self-Supervised Learning — 3× in this paper · explained in 8 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Foundation Models — 2× in this paper · explained in 7 episodes
• Tokenization — 2× in this paper · explained in 2 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Closed-Loop Control — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
• Cell-JEPA: Latent Representation Learning for Single-Cell Transcriptomics
• ScienceIDE: Turning World's Scientific Codebase into Agent Learnable Environments
• DINOv3
• Joint Embedding Predictive Architectures Focus on Slow Features
• DeepMind Control Suite
• SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
• ScienceBuddy: Recursive-in-Recursive Self-Improvement for Interactive Scientific Agents
• Discovery Foundation Models: Toward Open-Ended Discovery Intelligence

Related papers

• Exploring Solution Divergence and Its Effect on Large Language Model Problem Solving
• Ishigaki-IDS-Bench: A Benchmark for Generating Information Delivery Specification from BIM Information Requirements
• Subliminal Steering: Stronger Encoding of Hidden Signals
• MedStruct-S: A Benchmark for Key Discovery, Key-Conditioned QA and Semi-Structured Extraction from OCR Clinical Reports
• The End of Transformers? On Challenging Attention and the Rise of Sub-Quadratic Architectures
• Untangling the Mechanisms of Misleading Context in Medical Question Answering