Beyond Confidence? Auditing Routing Entropy in Attention-Residual Transformers

Internal routing traces may correlate with errors without improving uncertainty estimates beyond what a model's output already reveals. We test this increment for routing entropy in Attention-Residual (AR) variants of Swin-Tiny and DeiT-Small trained on CIFAR-10 and CIFAR-100 with a soft-binned calibration auxiliary loss. Three checks ask whether a routing signal appears at fixed confidence, whether it replicates across training seeds, and whether a held-out predictor can exploit it against output-only and shuffled-trace controls; a sensitivity audit injects effects of known size to measure how much of them each probe recovers. None of 30 binned diagnostic tests survives multiplicity correction, and neither a nominal hit nor a borderline result recurs in its sibling seeds. Across 24 paired runs, a scalar routing probe gives no pooled improvement in routing-stratified calibration. An entropy-profile probe predicts correctness better than the same probe given shuffled profiles, yet worse than a confidence-only predictor in both binary log-loss and Brier score: its gain over shuffled traces does not become a gain over the output. Conditioning on the complete logit vector leaves the corresponding comparison unresolved. The audit shows how far these non-detections can be read: at an injected effect of 0.010 nats, the profile probe recovers 24–59% of the oracle gain, and a reference-preserving correction probe recovers 8% and 23% in the two CIFAR-100 settings, below the threshold we set for applying it to real labels. The results establish control-dependent gains and incomplete estimator recovery, not the absence of conditional routing information.