When production models drift and retraining isn't an option, a new PyTorch approach offers a solution. This article details how a self-healing neural network detects distribution shifts, adapts in real time using a lightweight adapter layer, and recovers 27.8% accuracy without retraining or downtime. The system works by keeping the backbone frozen while updating only a small component.

Model degradation often strikes silently. In one example, a fraud detection model maintained 92.9% accuracy for two months before transaction patterns shifted. By the time dashboards turned red, accuracy had collapsed to 44.6%. Traditional solutions like retraining require labeled data and hours of downtime—resources unavailable during critical production failures.

The architecture centers on a ReflexiveLayer that sits between the frozen backbone and output head. Using residual connections, the adapter can only correct, never overwrite, the backbone's learned representations. A background thread processes updates asynchronously, ensuring inference never stops. The system combines distribution monitoring (FIDI) with symbolic rule conflicts to trigger healing only when necessary.