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 | MatrixGame3 |
| Lab / Organization | Skywork |
| Category | Generative World Model |
| Subtype | Action-Conditioned Interactive Game World Model |
| World Model Type | Action-Conditioned Interactive Game World Model |
| Primary Domain | Interactive Gaming |
| Architecture | Action-conditioned real-time streaming world model |
| Modality | Image + Keyboard/Mouse Actions -> Interactive Video |
| Training Method | Autoregressive interactive world modeling |
| Status | emerging |
| Year | 2026 |
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MatrixGame3 generates an evolving environment conditioned on action input. It is included as a native interactive system rather than a text-to-video model wrapped in WBench's chained-context protocol.
MatrixGame3 is an action-conditioned interactive game world model developed by Skywork AI in 2026 for interactive gaming.
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| Signal | Value |
|---|---|
| Definition | MatrixGame3 is an action-conditioned interactive game world model developed by Skywork AI in 2026 for interactive gaming. |
| Short Description | MatrixGame3 is a native action-conditioned interactive world model evaluated by WBench. |
| Benchmark Rows | 1 |
| FAQ Entries | 1 |
| Related Models | 2 |
| Related Guides | 0 |
| Related Research Topics | 0 |
| Last Updated | 2026-08-31 |
Key capabilities associated with this model.
Representative applications attached to this model record.
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Published benchmark evidence attached to this model record.
| Benchmark | Metric | Result | Source |
|---|---|---|---|
| WBench | Five-dimension average 71.3 /100 | 71.3 | Source |
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| Reference | Link |
|---|---|
| Skywork AI. MatrixGame3. Primary project source. | Open source |
| Ying et al., 2026. WBench: A Comprehensive Multi-turn Benchmark for Interactive Video World Model Evaluation. | Open source |
Nearby models linked from the current editorial record.
| Model | Category | World Model Type | Index v1.1 |
|---|---|---|---|
| Matrix-Game 2.0 | Generative World Model | Open-source action-conditioned streaming video world model | N/A |
| Infinite-World | Generative World Model | Action-Conditioned Infinite World Model | N/A |
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MatrixGame3 exposes native action conditioning and generates an evolving interactive environment; WBench evaluates that interface directly.
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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: 2026-08-31.
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