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

arXiv · AI, language, vision and robotics · article · Sep 10, 2026 · UTC

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_{\mathrm{update}} - R_{\mathrm

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