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=Rupdate−Rhold. 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.