---
license: apache-2.0
paper_ids:
- '2602.11389'
pipeline_tag: image-feature-extraction
datasets:
- clevrer
- pusht
tags:
- object-centric
- world-models
- causal-inference
- jepa
- representation-learning
- vision
---

# C-JEPA: Causal-JEPA

This repository contains the weights and code for **Causal-JEPA (C-JEPA)**, a simple and flexible object-centric world model architecture presented in the paper [Causal-JEPA: Learning World Models through Object-Level Latent Interventions](https://huggingface.co/papers/2602.11389).

*   **Paper:** [Causal-JEPA: Learning World Models through Object-Level Latent Interventions](https://huggingface.co/papers/2602.11389)
*   **Project Page:** [https://hazel-heejeong-nam.github.io/cjepa/](https://hazel-heejeong-nam.github.io/cjepa/)
*   **Code:** [https://github.com/galilai-group/cjepa](https://github.com/galilai-group/cjepa)

## Summary
World models require robust relational understanding to support prediction, reasoning, and control. While object-centric representations provide a useful abstraction, they are not sufficient to capture interaction-dependent dynamics. C-JEPA is a simple and flexible object-centric world model that extends masked joint embedding prediction from image patches to object-centric representations. By applying object-level masking that requires an object's state to be inferred from other objects, C-JEPA induces latent interventions with counterfactual-like effects and prevents shortcut solutions, making interaction reasoning essential.

Empirically, C-JEPA demonstrates:
*   **Improved Visual Reasoning:** Consistent gains in visual question answering, with an absolute improvement of about 20% in counterfactual reasoning compared to the same architecture without object-level masking on benchmarks like CLEVRER.
*   **Efficient Planning:** Substantially more efficient planning in agent control tasks (e.g., Push-T), using only 1% of the total latent input features required by patch-based world models while achieving comparable performance.
*   **Causal Inductive Bias:** A formal analysis demonstrates that object-level masking induces a causal inductive bias via latent interventions.

## Architecture
![architecture](https://hazel-heejeong-nam.github.io/cjepa/static/architecture.png)

## Setup and Usage
C-JEPA relies on object-centric encoders (like VideoSAUR or SAVi) to extract representations. For detailed environment setup, dataset preparation, and training/evaluation scripts, please refer to the [official GitHub repository](https://github.com/galilai-group/cjepa). The repository also provides model checkpoints and pre-extracted slot representations for various configurations.

## Citation
If you find this work useful, please consider citing:
```bibtex
@article{nam2026causal,
  title={Causal-JEPA: Learning World Models through Object-Level Latent Interventions},
  author={Nam, Heejeong and Le Lidec, Quentin and Maes, Lucas and LeCun, Yann and Balestriero, Randall},
  journal={arXiv preprint arXiv:2602.11389},
  year={2026}
}
```