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, compare systems on the leaderboard, or read the Performance Index methodology.
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| Attribute | Value |
|---|---|
| Model | OASIS |
| Lab / Organization | Decart |
| Category | Generative World Model |
| Subtype | Real-Time Interactive Simulator |
| World Model Type | Real-time playable world model |
| Primary Domain | Real-time game simulation |
| Architecture | Spatial autoencoder + latent diffusion transformer with real-time action conditioning |
| Modality | Actions → Video (real-time) |
| Training Method | Large-scale gameplay video training with action-conditioned diffusion |
| Status | active |
| Year | 2024 |
| Performance Index | 66/100 (medium confidence, v1.1) |
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OASIS demonstrates that a neural network can serve as an entire game engine, generating real-time interactive Minecraft-like environments at 20+ frames per second. Using a spatial autoencoder and a diffusion-based backbone, OASIS takes user inputs (keyboard/mouse) and generates the next frame in real-time, creating a fully playable world model without any traditional game engine.
OASIS is a real-time playable world model developed by Decart / Etched in 2024 for real-time game simulation.
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| Signal | Value |
|---|---|
| Definition | OASIS is a real-time playable world model developed by Decart / Etched in 2024 for real-time game simulation. |
| Short Description | An open-source real-time interactive world model that generates playable game environments at 20+ FPS entirely from a neural network. |
| Benchmark Rows | 1 |
| FAQ Entries | 1 |
| Related Models | 3 |
| Related Guides | 0 |
| Related Research Topics | 0 |
| Last Updated | 2026-03-12 |
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Published benchmark evidence attached to this model record.
| Benchmark | Metric | Result | Source |
|---|---|---|---|
| Real-time Generation | FPS 20 FPS | 20+ FPS | Source |
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| Reference | Link |
|---|---|
| Decart, 2024. OASIS: A Universe in a Transformer. | Open source |
Side-by-side comparisons already connected to this model.
| Comparison | Matchup | Summary |
|---|---|---|
| Sora vs Genie 2 | Sora (OpenAI) vs Genie 2 (DeepMind) | Sora and Genie 2 both generate video from prompts, but they approach world simulation very differently. Sora generates passive, high-fidelity videos from text; Genie 2 generates interactive, controllable 3D environments from images. |
| OASIS vs GameNGen | OASIS (Decart) vs GameNGen (Google Research) | OASIS and GameNGen both demonstrate neural networks functioning as real-time game engines, but they target different games and use different architectures. They represent the emerging frontier of neural game engines. |
| OASIS vs DIAMOND | OASIS vs DIAMOND | Both use diffusion models as world models for interactive environments, but OASIS generates real-time playable Minecraft-like worlds while DIAMOND uses diffusion for model-based RL training in Atari. |
| Genie vs Genie 2 | Genie (v1) vs Genie 2 | Genie pioneered unsupervised interactive environment generation from video. Genie 2 massively scales this approach to generate persistent, interactive 3D worlds from single images. |
| Pandora vs OASIS | Pandora vs OASIS | Both generate interactive game-like worlds, but Pandora produces multi-domain video simulations with narrative control, while OASIS focuses on high-fidelity real-time open-world generation trained on Minecraft. |
| GameNGen vs DIAMOND | GameNGen vs DIAMOND | Both simulate game environments in real-time, but with radically different approaches: GameNGen uses a fine-tuned diffusion model for photorealistic DOOM simulation, while DIAMOND uses a diffusion-based world model for Atari with reinforcement learning. |
| IRIS vs DIAMOND | IRIS vs DIAMOND | Two approaches to learning game simulators: IRIS uses discrete tokenization with a GPT-like transformer, while DIAMOND leverages diffusion models for higher visual fidelity. |
| OASIS vs Pandora | OASIS vs Pandora | Two real-time neural game engines: OASIS generates Minecraft-like worlds at 20+ FPS using latent diffusion, while Pandora creates diverse game worlds using a hybrid autoregressive-diffusion architecture. |
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Not yet for production games, but it demonstrates the concept of a neural network acting as a complete game engine, generating environments in real-time from player input.
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Published by world-models.io editorial board.
Lead editor Tyler D. - Technical editor, methodology and benchmark analysis.
This model page synthesizes primary papers, official model pages, benchmark evidence, and related world-models.io context into a reference resource.
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: 2026-03-12.
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