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 | ABot-World |
| Lab / Organization | Amap |
| Category | Generative World Model |
| Subtype | Action-Conditioned Infinite World Model |
| World Model Type | Action-Conditioned Infinite World Model |
| Primary Domain | Interactive Gaming |
| Architecture | Action-conditioned autoregressive world model optimized for desktop inference |
| Modality | Image + Discrete Actions -> Interactive Video |
| Training Method | Autoregressive interactive world modeling |
| Status | emerging |
| Year | 2026 |
Main editorial body preserved directly in static HTML.
ABot-World 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.
ABot-World is an action-conditioned infinite world model developed by Amap in 2026 for interactive gaming.
Short extractable facts for answer engines and no-JS readers.
| Signal | Value |
|---|---|
| Definition | ABot-World is an action-conditioned infinite world model developed by Amap in 2026 for interactive gaming. |
| Short Description | ABot-World 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 74.7 /100 | 74.7 | Source |
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| Reference | Link |
|---|---|
| Amap. ABot-World. 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 |
|---|---|---|---|
| DreamX-World | Generative World Model | General-Purpose Interactive World Model | N/A |
| MatrixGame3 | Generative World Model | Action-Conditioned Interactive Game World Model | N/A |
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ABot-World 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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