Family-Conditioned Hidden Markov Models for Cross-Architecture Out-of-Distribution Monitoring

Deep Neural Networks (DNNs) are transforming perception tasks across a wide range of applications, yet their reliability can degrade when inputs at deployment differ from the training distribution. This work presents a family-conditioned runtime monitor for detecting such out-of-distribution (OOD) inputs. The monitor extracts per-layer activation representations, converts them into normalized anomaly scores, and discretizes these scores into ordered severity levels. The resulting sequence of levels across the network is used to train a Hidden Markov Model (HMM) for each structural family, capturing characteristic activation patterns shared across its constituent networks. The central hypothesis is that networks within the same structural family exhibit stronger alignment in their activation behavior than networks from different families. Empirical evaluation across $13$ networks from three structural families supports this hypothesis, with within-family alignment $5.25\times$ better than cross-family alignment. The family-conditioned HMM also generalizes to unseen network architectures: under a leave-one-network-out protocol, the AUROC gap between networks used to train the monitor and held-out networks is at most $0.009$. Furthermore, the proposed monitor achieves OOD detection performance comparable to per-network baseline methods while eliminating the need to construct and calibrate a separate monitor for every network. These results demonstrate the potential of family-level monitoring as a scalable approach to runtime OOD detection across diverse neural network architectures.