Beyond Accuracy Rankings: Relational Competence in Object Detection

Two object detectors can achieve nearly identical scalar performance while succeeding on different instances. We ask whether these differences are incidental or reflect reproducible structure in how models solve the task. We introduce relational competence, a population-relative characterization of detector behavior based on residual co-observability after exact control of detector coverage and instance observability with a fixed-margin null. Across eight detectors, residual relational structure remains after this marginal control. Independent retrainings also recover design-associated relational neighborhoods: Faster R-CNN and RetinaNet checkpoints matched to within \(0.009\) AP have mean relational distances of \(0.0113\) within design and \(0.1537\) across designs, yielding \(\Delta D=0.1424\) with a 95% bootstrap interval of \([0.09,0.16]\). Under controlled resolution and JPEG degradation, population-relative signature changes are likewise reproducible across retrainings, but a predefined same- versus cross-intervention comparison does not establish intervention-specific directions. These results distinguish scalar performance, relational organization, retraining reproducibility, and response to intervention as separate aspects of model behavior. Relational competence therefore complements conventional evaluation by measuring not only how often models succeed, but how their successes are organized relative to a model population.