We introduce DeepDrift, a unified internal monitoring framework for deep neural networks based on Semantic Velocity — the ℓ2‑norm of consecutive hidden‑state differences. Key results: • LLM hallucination detection: AUC 0. 891, lead time 7. 2 tokens• RL failure prediction: AUC 0. 985 (DQN), AUC 1. 000 (PPO+noise), lead time 168 steps• ViT semantic OOD (CIFAR‑100 → SVHN): AUROC 0. 817 0. 788, 0. 846• Diffusion memorization: 3× earlier than validation loss divergence• External benchmark on CLIP, DINOv2, ConvNeXt confirms generalization The method requires zero gradient‑based training, operates as a plug‑in PyTorch monitor with <1. 5% overhead, and is available as open‑source software (pip install deepdrift). All experiments are reproducible with provided scripts. GitHub: https: //github. com/Eutonics/DeepDriftPyPI: pip install deepdrift Raw experiment metrics are included as JSON files: - vitₒodₘetrics. json: AUROC, velocity profiles, bootstrap CIs for ViT-B/16 (CIFAR-100 → SVHN) - rlcartpoleₘetrics. json: AUC, Cohen's d, lead time, phase portrait data for CartPole (PPO, DQN, PPO+noise)
Alexey Evtushenko (Thu,) studied this question.