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At-Grok Is Not Converged:A Measurement-Validity Audit for Grokking Representation Metrics
A new arXiv preprint audits the validity of 'grokking' representation metrics in neural networks, revealing that compression in embeddings lags generalization by tens of thousands of steps and that common metrics overstate convergence—challenging assumptions used to interpret when models truly learn.
Jul 10, 2026
Cross-Trajectory Chimera Interventions Reveal Dissociable Roles of Weight Magnitude and Direction in Grokking
Researchers introduce 'cross-trajectory chimera interventions' to isolate and test the causal portability of weight direction versus magnitude across independently trained neural networks on grokking tasks, finding direction encodes transferable circuit identity while norm governs susceptibility to overwrite.
Jul 10, 2026