Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond
- Published
- Source
- arXiv
- Paper number
- 162
- Field
- Agents / Survey
- arXiv ID
- 2604.22748
Key points
- World model concepts are fragmented across AI fields, so definitions and evaluation practices are inconsistent and progress is hard to compare.
- Existing surveys of world models usually lack a coherent capability progression across modalities and domains, or a precise framework for analyzing large generative models as simulators.
- The role of world modeling in emerging agentic AI applications, such as web agents and multi-agent systems, is often underrepresented in unified frameworks.
- The paper introduces a comprehensive level-by-law taxonomy that organizes world-modeling capability into three levels, L1 Predictor, L2 Simulator, and L3 Evolver, and four dominant law domains, physical, digital, social, and scientific.
- It defines clear and verifiable boundary conditions for moving from one level to the next, including long-horizon consistency and intervention sensitivity for L2 and evidence-based diagnosis for L3.
- It proposes a shift toward decision-centered evaluation metrics, such as Action Success Rate and Counterfactual Outcome Deviation, and standardizes evaluation protocols according to the defined capability levels.
Paper links
External research summaries. These are not HDATF publications or measured product results.