FLACE: Explaining Frozen Classifiers through Signed Feature-Space Bottlenecks

Explaining a frozen classifier through intermediate coordinates requires an explicit link between those coordinates and its prediction. FLACE builds this link by learning a signed dictionary from the classifier's own features with classical semi-NMF, inferring nonnegative reconstruction coordinates, and reusing the original linear head. Each coordinate has an exact signed contribution to the reconstructed class logit, equal to the logit change on deleting that coordinate and its zero-baseline integrated gradient. This gives a direct interface for inspecting and editing reconstructed predictions without concept labels or an external text dictionary. On CIFAR-100, CUB, and ImageNet-100 with EfficientNet-B3, the evaluated native prediction paths show lower within-basis correlations and higher insertion AUC for FLACE than for the compared CBMs, with mixed deletion results. The coordinates weight numerical feature directions; optional names aid visual inspection without defining their meaning. Core implementation code is provided in the supplementary material.