Auditing Node-Importance Measures on Learned Dynamical Models with Embedded Ground-Truth Probes

Node-importance measures derived from learned dynamical models, including controllability energies, trajectory-level leverage, and attribution through time, are increasingly reported as substantive findings, even though the true influence of any node is unknown in real data. We introduce an audit for such measures based on an embedded ground-truth probe: a small synthetic subsystem added to the analyst's panel and estimated jointly with it using the same model. Because the probe's true multi-step importance is known exactly, the fitted measure can be tested against ground truth under the same estimation conditions and computational pipeline as the real data. A simple diagnostic, the coupling-to-persistence ratio, indicates when the main failure mode is likely to arise, and a theoretical decomposition explains its source. We apply the audit to an existing research pipeline across five empirical panels. The results reveal both a successful validation and three distinct failure modes: the measure succeeds when the estimator can represent the relevant dynamics, inherits the estimator's structural blind spots, becomes dominated by node persistence when coupling is weak, and can be distorted by pooled linearizations evaluated away from the observed trajectory. The audit requires only a few additional channels and no specialized tooling.