Do all world models work the same way?

No. They share the encode-predict-plan structure, but differ significantly in architecture (RSSM vs Transformer vs abstract models), representation (continuous vs discrete latents), and planning method (actor-critic vs MPC vs MCTS).

How accurate do world model predictions need to be?

Surprisingly, they don't need perfect pixel predictions. What matters is that the latent dynamics capture reward-relevant structure. DreamerV3 succeeds despite imperfect reconstructions because its latent space captures the dynamics that matter for decision-making.

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

world-models.io

The Knowledge Hub for AI World Models

How World Models Work

World models work by learning internal representations of environment dynamics, enabling agents to predict future states, plan actions, and learn from imagined experience.

robotics model-based-rl simulation embodied-ai

world-models.io - The Knowledge Hub for AI World Models

world-models.io is a structured knowledge platform covering AI world models - from model-based reinforcement learning to foundation world models, embodied AI, and simulation engines.

Explore world models like DreamerV3, Genie 3, V-JEPA 2, NVIDIA Cosmos, MuZero, Sora, DIAMOND, OASIS, LeWorldModel, PixVerse R1, and more. Compare architectures, read research, and discover the labs building the future of AI.

Featured World Models

Genie 3 - Google DeepMind's real-time interactive 3D world generation from text at 24fps
V-JEPA 2 - Meta FAIR's self-supervised world model for visual understanding and zero-shot robot control
DreamerV3 - General algorithm mastering diverse domains through world model learning
NVIDIA Cosmos - World foundation model platform for physical AI
Genie 2 - Interactive 3D environment generation from single images
Sora - OpenAI's video generation model with emergent world understanding
MuZero - Superhuman game play through learned planning
LeWorldModel - Compact 15M-parameter JEPA that learns physics on a single GPU
V-JEPA - Self-supervised visual world model by Meta FAIR
DIAMOND - Diffusion-based world model for reinforcement learning
OASIS - Open-source real-time neural game engine
PixVerse R1 - First multiplayer real-time world model with shared worlds
TD-MPC2 - Scalable world model agent for 104+ tasks
IRIS - Autoregressive world model bridging language modeling and RL
GAIA-1 - Generative world model for autonomous driving
Copilot4D - 4D LiDAR world model for self-driving
I-JEPA - Image-based Joint Embedding Predictive Architecture
GameNGen - Neural game engine simulating DOOM in real-time
3D-VLA - 3D vision-language-action model for embodied AI
Pandora - Generative world model for open-ended environments
PlaNet - Deep Planning Network for latent space control
RSSM - Foundational architecture behind the Dreamer family
DreamerV2 - First model-based agent to achieve human-level Atari
World Models (Ha & Schmidhuber) - The seminal 2018 paper that popularized world models
Predictron - End-to-end learning and planning via abstract world models
UniSim - Universal simulator for real-world interactions
I2A - Imagination-augmented agents for deep RL
VPN - Value Prediction Network for abstract planning
Emu Video - Factorized video generation via image conditioning
AMI World Model - Multimodal foundation model for embodied AI
RT-2 - Vision-Language-Action model for robotic control
Gen-3 Alpha - Advanced video generation by Runway
Stable Video Diffusion - Open-source video generation foundation model
MILE - Model-based imitation learning for autonomous driving
LWM - Large World Model processing 1M+ tokens of video and text
Genie - First generative interactive environment from unlabeled video
STEVE-1 - Instruction-following agent for Minecraft

Explore

Home
What Are World Models
Models Database - Browse the full world models database
Leaderboard - Rankings and performance index
Research - Topics and surveys
Comparisons - Side-by-side analyses
Guides - Beginner to advanced tutorials
Labs - Research labs and organizations
Timeline - Latest releases and updates
Glossary - Key terms and definitions
Methodology
Editorial Policy - How content is reviewed and refreshed
Top World Models
World Models for Robotics
Research Papers
Video World Models
Autonomous Driving World Models
Game World Models

Research Topics

Comparisons

Guides

Labs & Organizations

Google DeepMind - Pioneers of DreamerV3, MuZero, Genie 2, and the RSSM architecture
NVIDIA Research - World foundation models for physical AI (Cosmos)
Meta FAIR - JEPA architectures and self-supervised world models
OpenAI - Sora and video generation as world simulation
Wayve - GAIA-1 for autonomous driving
Decart - Real-time neural game engines (OASIS)
Waabi - 4D LiDAR world models for self-driving
Microsoft Research - IRIS and diffusion-based world models
Google Research - Neural game engines (GameNGen)
AMI Labs - Multimodal foundation models for embodied AI
UC Berkeley
Stanford
Carnegie Mellon University
MIT CSAIL
Toyota Research Institute
Stability AI - Open-source generative AI (Stable Video Diffusion)
Runway - AI video generation (Gen-3 Alpha)
UT Austin - Game AI and instruction-following agents