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| title: Agentic World Model Explorer | |
| colorFrom: blue | |
| colorTo: purple | |
| sdk: gradio | |
| sdk_version: 4.36.0 | |
| app_file: app.py | |
| pinned: false | |
| # Agentic World Model Explorer | |
| An interactive exploration of the "levels x laws" taxonomy from the Agentic World Modeling paper (2604.22748). | |
| ## What This Does | |
| Demonstrates the three capability levels of world models: | |
| - **L1 Predictor**: One-step local transitions | |
| - **L2 Simulator**: Multi-step action-conditioned rollouts | |
| - **L3 Evolver**: Self-revising models that update from prediction failures | |
| Across four law regimes: | |
| - Physical (object manipulation, physics) | |
| - Digital (web/GUI agents, software) | |
| - Social (multi-agent coordination) | |
| - Scientific (experimental design) | |
| ## Hypothesis | |
| World models with explicit structured state representations (L2+) demonstrate better compositional generalization than pure next-token predictors when evaluated on out-of-distribution scenarios within the same law regime. | |
| ## Findings | |
| See the live demo for interactive examples of state representation strategies and their impact on generalization. | |