Agentic World Modeling
A massive 40-author survey lands the cleanest taxonomy of world models in agent research released so far. The paper proposes a "levels by laws" framework spanning three capability levels and four law regimes, then synthesizes 400+ works and 100+ representative systems across model-based RL, video generation, web and GUI agents, multi-agent simulation, and scientific discovery. As agents shift from chatbots to goal-accomplishers, the bottleneck moves from language to environment, and this is the first paper that gives builders a shared vocabulary across communities that have been working in isolation.
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Three capability levels: L1 Predictors handle one-step transitions, L2 Simulators do multi-step action-conditioned rollouts, and L3 Evolvers self-revise as the world changes. The hierarchy makes it easy to place existing systems and identify where capability gaps actually live.
Four law regimes: Physical, digital, social, and scientific laws each impose different constraints on what a world model needs to capture. The framework treats them as orthogonal axes, which clarifies why a strong physics simulator can still fail at social or digital tasks.
Failure-mode catalog: The survey extracts recurring failure patterns across 100+ systems, including misaligned reward shaping, drift under non-stationarity, and brittle transfer across regimes. Each failure mode is mapped to a level and law combination, so the diagnosis is grounded.
Evaluation principles per level: The authors propose evaluation criteria specific to each capability level rather than a single benchmark. This is the right move because L1 prediction accuracy and L3 self-revision quality are not measurable on the same axis.