Boundary-FidelityBench: Probing First-Crossing Fidelity in Action-Conditioned World Models

Action-conditioned world models support planning by predicting the consequences of candidate actions, yet accurate predictions do not guarantee that a model preserves the action conditions under which physical behavior changes. Existing evaluations assess physical consistency and action response, but do not explicitly characterize where the first physical-mode change occurs along controlled action perturbations. We therefore formulate first-crossing boundary fidelity as an evaluation target. We operationalize it through Boundary-FidelityBench, which combines controlled action probing with independent physical references, together with FCRG, a learned readout that uses scale-aware residual evidence for fine-grained estimates of boundary presence, first-crossing location, and supported transition types. Across seven mode-switching systems, 5,629 fresh evaluation rays, and six world-model outputs, boundary-resolved evaluation reveals differences not captured by predictive-quality and behavioral-outcome measures. FCRG achieves 80.76% exact-bin accuracy on fresh simulator responses and separates missed boundaries from incorrect first-interval assignments. First-crossing estimates can guide action-query allocation under a fixed world-model query budget, yielding gains in tested settings while showing that better boundary coverage does not always translate into better decisions. These results establish boundary fidelity as a fine-grained complement to world-model evaluation.