Measuring the Value of World-Model Updates: A Counterfactual Utility Protocol for Continual Adaptation

Continual world models must decide whether new data justify changing the model. Fixed replay schedules and prediction-error triggers specify when to update, but neither reveals the value of an individual update: one deployment run cannot show how the same model would have performed at that moment had it held its parameters. We introduce the fork ledger, which branches a deployment stream at pre-registered decision points into matched update and hold continuations under common random numbers. It evaluates both continuations on the same episodes and records ΔR = R update - R hold. Always applying one fixed update mechanism lowers return on all three simulated control tasks: CartPole (-144.0; checkpoint-bootstrap 95% CI [-185.4,-116.1], against a converged return near 650), Walker (-82.8; [-101.1,-61.7]) and Cheetah (-18.6; [-29.0,-6.6]). Divergence is an outcome of applying the update, so the estimand counts every attempted fork; restricted to the 693 of 720 that did not collapse, CartPole and Walker are unchanged in sign (-113.4 and-82.1) and Cheetah becomes unresolved (-3.9; [-17.5,+13.0]). The task is the unit of inference: each contributes 240 attempted forks over five pretrained checkpoints crossed with two drift directions. The ledger makes counterfactual utility observable for a fixed mechanism, allowing triggers to be judged by the updates they select rather than by surprise detection alone.