EW-GET: Access-Conditioned Identifiability of Failure Mechanisms in Sensor-to-Decision Models

Failure diagnosis is fundamentally resolution-limited: which mechanisms can be distinguished depends on what an evaluator can observe. We therefore treat diagnostic resolution, rather than a prespecified failure label, as the primary statistical object. EW-GET formalizes this principle for learned sensor-to-decision systems. Exposed traces are mapped to acquisition-grounded witness distributions for information magnitude, directional fidelity, and decision overclaim. Equality of joint witness laws defines a population access-conditioned failure quotient; at finite resolution, direct operational indistinguishability induces a threshold graph whose connected components define the reportable partition. We prove that richer access refines the population quotient and give finite-sample recovery and calibrated-abstention guarantees for the declared operational partition. Controlled benchmarks recover the predicted Black/Grey/White refinement. In HM3D RGB-D navigation, richer interfaces sharply improve mechanism-label predictability, yet a generic MLP with comparable F1 does not by itself establish evidence-supported diagnostic resolution. Resolution-compatible intervention improves aggregate recovery under a frozen repair library, while adverse representation-family effects show that identifiability does not imply intervention sufficiency. EW-GET thus turns diagnosis into an access-conditioned measurement problem with an explicit population target, finite-sample reporting rule, and bounded downstream use.