---
title: "Meta FAIR | JEPA & World Models Research | world-models.io"
canonical: "https://world-models.io/en/labs/meta-fair/"
description: "Explore Meta FAIR's JEPA world-model research, including V-JEPA and I-JEPA for self-supervised visual learning and embodied AI."
author: "Bernard Grenat"
generated: "2026-10-30"
---

New: the Timeline is live. Track world model releases, papers, and benchmark updates in real time.

The Knowledge Hub for AI World Models

# Meta FAIR

Meta FAIR develops world models guided by Yann LeCun's JEPA (Joint Embedding Predictive Architecture) vision, including V-JEPA and I-JEPA for self-supervised learning.

robotics

model-based-rl

simulation

embodied-ai

## What is a world model?

A world model is an AI system that learns a predictive representation of how an environment changes over time. It can use observations, actions, or language to estimate future states and possible outcomes before an agent acts. World models support planning in model-based reinforcement learning, robotics, embodied AI, autonomous driving, and interactive simulation. This site catalogs these systems with model records, research papers, benchmark context, and source links. Start with the [world models database](https://world-models.io/en/world-models-database/), compare systems on the [leaderboard](https://world-models.io/en/leaderboard/), or read the [Performance Index methodology](https://world-models.io/en/methodology/performance-index/).

## About Meta FAIR

Main editorial body preserved directly in static HTML.

Meta's Fundamental AI Research (FAIR) lab contributes to world models through self-supervised learning, JEPA architectures (Joint Embedding Predictive Architecture), and embodied AI research. Yann LeCun's vision of autonomous machine intelligence, centered on world models that learn physical dynamics without pixel reconstruction, is deeply connected to FAIR's research agenda. V-JEPA represents their approach to visual world models.

Meta FAIR is a industry organization based in United States / France with a visible footprint in AI world models.

## Lab Snapshot

Static summary of the lab profile for crawlers and no-JS readers.

| Attribute | Value |
| --- | --- |
| Lab | Meta FAIR |
| Short Name | Meta FAIR |
| Type | industry |
| Region | United States / France |
| Related Models | 4 |
| Description | Meta's Fundamental AI Research (FAIR) lab contributes to world models through self-supervised learning, JEPA architectures (Joint Embedding Predictive Architecture), and embodied AI research. Yann LeCun's vision of autonomous machine intelligence, centered on world models that learn physical dynamics without pixel reconstruction, is deeply connected to FAIR's research agenda. V-JEPA represents their approach to visual world models. |

## Focus Areas

Focus areas surfaced directly from the lab record.

self-supervised-learning

jepa

embodied-ai

autonomous-intelligence

## Related Models

Models currently associated with this lab in the local knowledge base.

| Model | Category | Year | Index v1.1 |
| --- | --- | --- | --- |
| [V-JEPA](https://world-models.io/en/models/v-jepa/) | Self-Supervised World Model | 2024 | 70/100 |
| [I-JEPA](https://world-models.io/en/models/i-jepa/) | Self-Supervised World Model | 2023 | 61/100 |
| [TD-MPC2](https://world-models.io/en/models/td-mpc2/) | Model-Based RL | 2024 | 80/100 |
| [Emu Video](https://world-models.io/en/models/emu-video/) | Generative World Model | 2023 | 49/100 |

## Frequently Asked Questions

FAQ answers rendered directly into static HTML for extractable responses.

### What is Meta FAIR's approach to world models?

Meta FAIR, guided by Yann LeCun's vision, develops world models through the JEPA (Joint Embedding Predictive Architecture) framework. V-JEPA learns physical dynamics without pixel reconstruction, predicting in abstract representation space.

### What is JEPA?

JEPA (Joint Embedding Predictive Architecture) is a framework proposed by Yann LeCun where models predict in abstract representation space rather than generating pixels, focusing on causal understanding.

## Quick Answer

Short extractable summary preserved directly in static HTML.

- Meta FAIR is a industry organization based in United States / France with a visible footprint in AI world models.
- On world-models.io, it is associated with 4 world models across editorially linked pages.
- Use this page to review the organization profile, connected models, related comparisons, and official sources.

## Editorial Trust Signals

Editorial provenance and refresh policy preserved directly in static HTML.

**Published by** world-models.io editorial board.

**Lead editor** Bernard Grenat.

This lab page distinguishes official organization sources from world-models.io editorial synthesis and related model coverage.

Each editorial page is assembled from primary sources, normalized into extractable summaries, checked for factual drift, and reviewed before publication or major refreshes. Last reviewed: 2026-09-27.

Pages are refreshed when a new paper, benchmark, release, architecture update, or stronger primary source materially changes the answer a reader or AI system should retrieve.

Each page links back to relevant primary sources and keeps a stable canonical URL so readers can verify claims, trace context, and reference the most up-to-date version. [See the editorial policy.](https://world-models.io/en/editorial-policy/)

Primary sources only

Last reviewed date visible

Methodology documented

Source links included

Correction channel published

Review date visible

## Official Sources

Primary lab and model sources embedded in static HTML.

- [Meta FAIR official website](https://ai.meta.com/research/) Primary organizational source
- [V-JEPA paper](https://arxiv.org/abs/2404.08471) Meta primary research source
- [I-JEPA paper](https://arxiv.org/abs/2301.08243) Meta primary research source
- [TD-MPC2 paper](https://arxiv.org/abs/2310.16828) MIT / Meta primary research source