Do-JEPA: From Masking to Intervention in Latent World Models

arXiv:2609.37378 · cs.CV, cs.AI, cs.LG · Submitted 2026-09-29 · Read on arXiv

cs.CV, cs.AI, cs.LG

Submitted: 2026-09-29

Updated: 2026-09-29

Terminology

• AUROC — 56× in this paper · explained in 2 episodes
• Benchmark — 30× in this paper · explained in 2 episodes
• World Model — 19× in this paper · explained in 7 episodes
• Accuracy — 17× in this paper · explained in 1 episode
• Fine-Tuning — 10× in this paper · explained in 5 episodes
• Latent World Models — 8× in this paper · explained in 2 episodes
• Latent Space — 6× in this paper · explained in 7 episodes
• Representation Learning — 6× in this paper · explained in 2 episodes
• Sparsity — 4× in this paper · explained in 4 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Catastrophic Forgetting — 3× in this paper · explained in 5 episodes
• In International Conference on Machine Learning — 3× in this paper
• Self-Supervised Learning — 3× in this paper · explained in 8 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Transfer Learning — 2× in this paper · explained in 10 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Causal Discovery — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Linear probe — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Structural Causal Model (SCM) — 1× in this paper · explained in 4 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning
• V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
• LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics
• Mastering Diverse Domains through World Models
• Variational Causal Dynamics: Discovering Modular World Models from Interventions
• stable-worldmodel: A Platform for Reproducible World Modeling Research and Evaluation
• LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels
• Causal-JEPA: Learning World Models through Object-Level Latent Masking
• DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning

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