Developmental World Model: A Child-Machine Framework for Heterogeneous World Dynamics

World models are commonly learned for one environment or through a shared parameterization across related tasks. We study heterogeneous worlds whose mature dynamics can differ substantially while following a common organization of how state evolves, behavior is generated, events change state, observations are produced, and autonomous rollout proceeds. Inspired by Turing's child-machine distinction, we introduce Developmental World Model (DWM), which supplies this event-time organization before fitting and learns a separate numerical realization for each world. Development moves from teacher-supported fitting toward autonomous generation, with candidate stages selected by autonomous prediction and a fixed-event-path contraction certificate. A final post-hoc affine-logit calibration adjusts predictive probabilities after autonomous trajectory generation. The mathematical analysis connects finite-dimensional behavioral prediction to the DWM event-time form, establishes contraction along realized event paths, and derives a Wasserstein contraction condition for autonomous transition kernels. Experiments across sequential, temporal, multi-agent, and embodied environments show that the same dynamical organization can develop distinct world-specific dynamics and state semantics. Code: https://anonymous.4open.science/r/DWM-29D4/.