VR-JEPA: Learning Contrastive-State Latent Guidance for Generation-based Video Reasoning

arXiv:2609.40129 · cs.CV · Submitted 2026-09-30 · Read on arXiv

cs.CV

Submitted: 2026-09-30

Updated: 2026-09-30

Code: https://github.com/Wan-Video/Wan2.2

Terminology

• Ablation — 14× in this paper · explained in 1 episode
• Localized Contrastive-State Learning — 13× in this paper
• Mixture of Experts — 7× in this paper · explained in 2 episodes
• Transformer — 7× in this paper · explained in 2 episodes
• Diffusion Model — 5× in this paper · explained in 8 episodes
• Diffusion Models — 4× in this paper · explained in 6 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• World Model — 4× in this paper · explained in 7 episodes
• Ablation Studies — 3× in this paper · explained in 1 episode
• Abstraction — 3× in this paper · explained in 1 episode
• Adaptation — 3× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• Generative Model — 3× in this paper · explained in 2 episodes
• Generative Models — 3× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Consistency — 2× in this paper · explained in 3 episodes
• Latent Reasoning — 2× in this paper · explained in 2 episodes
• Latent World Models — 2× in this paper · explained in 2 episodes
• Self-Supervised Learning — 2× in this paper · explained in 7 episodes
• Alignment — 1× in this paper · explained in 1 episode
• CVF International Conference on Computer Vision — 1× in this paper
• Decoupling — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Flow Matching — 1× in this paper · explained in 7 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• JOINT-EMBEDDING PREDICTIVE ARCHITECTURES — 1× in this paper
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes

Sources

• Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture
• V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
• VLMs are Good Teachers for Video Reasoning via Adaptive Test-Time Optimization
• World Models
• LTX-2: Efficient Joint Audio-Visual Foundation Model
• Video Diffusion Models
• HunyuanVideo: A Systematic Framework For Large Video Generative Models
• JEPA-Reasoner: Decoupling Latent Reasoning from Token Generation
• Wan-R1: Verifiable-Reinforcement Learning for Video Reasoning
• V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning
• PhysVideoGenerator: Towards Physically Aware Video Generation via Latent Physics Guidance
• DINOv3
• Seedance 2.0: Advancing Video Generation for World Complexity
• Wan: Open and Advanced Large-Scale Video Generative Models
• A Very Big Video Reasoning Suite
• Video models are zero-shot learners and reasoners
• VISReg: Variance-Invariance-Sketching Regularization for JEPA training
• Aligning Perception, Reasoning, Modeling and Interaction: A Survey on Physical AI
• VBVR-Pro: A Scalable and Verifiable Suite for Native Visual Reasoning

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