Self-Healing Neural Networks in PyTorch: Fix Model Drift in Real Time Without Retraining

A self-healing neural network architecture addresses model drift in real-time without requiring full retraining or downtime. This approach utilizes a `ReflexiveLayer`, a trainable adapter component inserted into the network while the main model backbone remains frozen. Healing events are triggered by statistical drift detection and conflicts with symbolic rules, with updates processed asynchronously so model inference is not interrupted. Implemented in PyTorch, this method prevents catastrophic forgetting by isolating gradient updates to the adapter, allowing the model to adapt to new data distributions autonomously.