ATCG: Structural Trait Molding for Combinatorial Auditing of Deep Classifiers

Deep classifiers map learned embeddings to class labels through a flat prediction head and achieve high accuracy. However, when classes contain heterogeneous latent subpopulations, the opaque, unstructured embedding space makes the internal basis of predictions difficult to audit. Inspired by the combinatorial richness of modern biological taxonomy, we investigate the feasibility of representing intra-class heterogeneity through the combinatorial composition of “root elements.” We introduce *Artificial-Trait Configurative-Generation* (ATCG), a structured framework that retrofits pretrained backbones with a *Pinball head*: a per-class binary hierarchy that learns a coordinate system of basis traits and induces a combinatorial barcode space that encodes intra-class heterogeneity. To structure this combinatorial space for auditing, ATCG aggregates class evidence from basis traits bottom-up via *Max-Dominance* through a hierarchy of intermediate-layer traits, while a geometric *Molding Bonus* discourages trait collapse by encouraging sibling specialization into complementary patterns, together yielding sparse basis-trait barcodes. The induced barcodes reveal whether each prediction's evidence is *consistent*, *conflicting*, or *insufficient*, and detect intra-class shifts that may be missed by class-label frequencies at the cohort level. We validate this on controlled Stacked-MNIST experiments where barcode-based monitoring identifies intra-class shifts, demonstrate it on Camelyon17-WILDS where barcode auditing relates prediction accuracy to patient-share profiles of basis traits, and show it generalizes to non-vision settings via TCGA RNA-seq classification. ATCG further integrates with Concept Bottleneck Models as a structural backend, exposing multiple latent realizations under a single concept.