A Falsification Study of Coupled-Constraint Telemetry Diagnostics in Multi-Agent Learning

We test whether three telemetry signatures from a prior constrained multi-agent learning diagnostic survive progressively less favorable evaluations before their labels are mistaken for causes or safety certificates. The study moves from definitional construct checks through held-out generator stress and independently scheduled controller faults to natural future outcomes. Under selective-verdict scoring, the fixed T1-T3 instrument resolves 67.1% of independently faulted runs with .870 accuracy given resolution and a .0875 wrong-resolution rate; it does not significantly outperform learned baselines under the earlier forced scoring. Its apparent .2021 advantage over a no-fit severity rule comes entirely from obstruction recall, so we claim no general classifier advantage. The unfavorable boundary is stronger: on naturally generated budget telemetry the instrument abstains on 95-100% of windows, while a no-fit current-cost persistence baseline is informative on the same future-outcome target. Calibrated conjunctions equal a calibrated violation-only rule on both the original 2,876 windows and a 17,040-window extension, providing no demonstrated incremental value from residual or dual gates; T2 remains unavailable. Exact identifiability, split-conformal, detection-delay, and unlabeled-target results formalize the required assumptions. This is a case-specific falsification study, not natural causal diagnosis, a generally validated methodology, or a safety certificate.