ScanRefer Multiple queries require spatial disambiguation: several instances of the target class are present, and the referencer must pick one.
When per-query logits were never logged, a common substitute is cross-epoch agreement.
On the full ScanRefer validation set (9,508 queries; GeoClassifier with ground-truth proposals), object-level softmax is similarly miscalibrated on Unique and Multiple (ECE 0.068 vs. 0.102; the scene-bootstrap gap interval includes zero).
Fixed-final agreement on the same checkpoints reports ECE 0.041 vs. 0.272 (gap +0.231).
Modal-vote agreement is 0.045 vs. 0.318.
Most Multiple errors are same-class wrong-instance (45.6%); geometric miss (H2) is 59.6%, and wrong-category (H1) is 16.7%.
A non-monotone H1 pattern does not appear under the default fixed-final protocol, and we retract it.
Logit-free thresholds rank spatially hard queries poorly.
If only agreement was logged, calibrate abstention on Multiple and report wrong-instance rates with H1.