We finally have a 'thermometer' that tells us exactly when a model has truly understood a pattern instead of just memorizing the data.
April 17, 2026
Original Paper
A Bayesian Perspective on the Role of Epistemic Uncertainty for Delayed Generalization in In-Context Learning
arXiv · 2604.12434
The Takeaway
Grokking—the sudden jump from memorization to generalization—is often impossible to predict or detect. This Bayesian perspective shows that epistemic uncertainty collapses sharply at the exact moment grokking occurs. By monitoring this internal signal, practitioners can detect in real-time whether a model is learning the 'right' thing without needing labeled test data. It turns training from a guess-and-check process into a measurable science of generalization. This could drastically reduce wasted compute by telling you exactly when to stop training.
From the abstract
In-context learning enables transformers to adapt to new tasks from a few examples at inference time, while grokking highlights that this generalization can emerge abruptly only after prolonged training. We study task generalization and grokking in in-context learning using a Bayesian perspective, asking what enables the delayed transition from memorization to generalization. Concretely, we consider modular arithmetic tasks in which a transformer must infer a latent linear function solely from i