Aug 4, 2026 · cs.AIJ/K move · Enter open · S save
Mario Leiva, Yue Ma, Qinru Qiu, Gerardo Simari+1
1DCIC, Universidad Nacional del Sur (UNS) & ICIC (UNS-CONICET), Bahía Blanca, Argentina · 2Syracuse University, Syracuse, NY USA
Deploying pre-trained perception models in novel environments degrades their accuracy under distributional shift, and assembling them alone does not recover it: combiners such as majority voting trade recall for precision and are brittle to coordinated failures. Prior metacognitive methods learn logical rules that flag a model's errors, but rely on hand-authored domain-knowledge cues (object-size priors, segmentation masks) that do not transfer to genuinely novel scenes. We show that this metacognitive layer can be learned without any domain knowledge by exploiting vector-space geometry: per-model Label Vector Pools (LVP), built from each model's own training embeddings, yield error-detection rules from the geometry of detections relative to training-determined prototypes, reaching parity with domain-knowledge rules to within
0.002 every F1 on test set. Because the approach remains neurosymbolic, these geometric rules share a single logical framework and can still be complemented by domain knowledge when available. We frame the fusion of multiple imperfect ViT-based detectors as a consistency-based abduction problem solved at test time by an exact Integer Program (IP) and a polynomial-time heuristic. On an aerial-imagery benchmark of 15 weather-shifted test sets and six ViT detectors, our domain-knowledge-free layer matches the strongest majority-vote variant on clean data (within
0.005 F1) and, unlike every majority-vote baseline, retains its performance under a coordinated label-flipping attack: at a
90% flip rate it averages
0.42 F1 versus
0.35 for MV-Plurality (a
22% relative gain) and attains the highest F1 on \emph{every} test set once the flip rate exceeds
0.4