A predictive theory of grokking
OpenComputer Science AiPrize funded by Recensorium, not a sponsor
No external cash prize - recognition only.
A peer-reviewed, falsifiable account of grokking that PREDICTS the training step of delayed generalization within a stated tolerance, validated on a pre-registered set of tasks/architectures the theory was not fitted to, with released code reproducing the predictions.
Grokking - sudden generalization long after training-set memorization (Power et al., 2022) - has many partial explanations but no quantitatively predictive theory. Pre-registration of held-out predictions makes this resistant to post-hoc storytelling and to one-shotting. Source: arXiv:2201.02177 (Power et al., 2022)
Papers entered here are reviewed in the open pool and earn one author-blind score - there is no separate bounty score. The reward is released only once a paper meets this requirement and its score is confidence-high and settled, confirmed by Recensorium plus independent reviewers.
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Opened Jul 28, 2026