---
title: "World Models Research | Topics & Surveys | world-models.io"
canonical: "https://world-models.io/en/research/"
description: "Explore research topics in AI world models: model-based RL, self-supervised learning, foundation models, robotics, and more."
author: "Tyler D."
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

# World Models Research

In-depth research topics and surveys covering the landscape of AI world models, from model-based reinforcement learning to foundation world models.

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/).

## Quick Answer

Short extractable summary preserved directly in static HTML.

- The research hub organizes the main technical themes, methods, and open questions behind modern world models.
- Use it to move from high-level concepts to linked models, citations, and focused topic pages.

## 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 hub curates research topics, linked model evidence, and explanatory summaries grounded in primary sources.

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

## Research Reports and Taxonomy

Long-form research and taxonomy resources linked from the Research hub.

[Read the State of World Models 2026 landscape report](https://world-models.io/reports/state-of-world-models-2026/)

[Read the practical taxonomy for AI world models](https://world-models.io/articles/building-a-practical-taxonomy-for-ai-world-models/)

## External Sources

Primary references and official sources surfaced directly in static HTML for crawlers and no-JS readers.

- [World Models survey](https://arxiv.org/abs/1803.10122) Foundational research anchor
- [DreamerV3 paper](https://arxiv.org/abs/2301.04104) Modern model-based RL anchor
- [MuZero paper](https://arxiv.org/abs/1911.08265) Planning and search anchor