mlsanityData to 7 Oct 2026

Circuit Synchronization Precedes Generalization: A Causal Precursor to Grokking

arXiv 2606.12966 ↗First posted 11 Jun 2026cs.LG cs.NEcited by 2

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Abstract (arXiv)

Grokking is the delayed generalisation phenomenon where a transformer trained on modular arithmetic abruptly transitions from near-chance to near-perfect validation accuracy. It has been attributed to a Fourier-based algorithmic circuit, but its timing, causal structure, and controllability remain poorly understood. We introduce the Frequency Synchronization Degree (FSD), a normalised, permutation-tested metric for Fourier circuit synchronisation requiring no prior knowledge of the circuit. Across nine modular addition configurations (five primes, three seeds), FSD reaches its post-grokking level 500 to 3000 steps before grokking (mean lead 1722 steps, every configuration positive, sign-test p approx 0.004), and synchronises before a restricted-logit loss baseline in all nine cases, making it the earliest available predictor. We give direct causal evidence that the inter-phase gap is a regularisation phenomenon: forking training at the FSD-ceiling step and varying weight decay lambda produces monotonically earlier grokking, with delta-t proportional to 1/lambda. This law replicates across three primes (R-squared 0.89 to 0.99 on seed-averaged delta-t); per-run R-squared is unstable due to the chaotic transition, so we report error bars rather than single runs. Grokking occurs at a near-constant memorisation norm across lambda, grounding the constant in a threshold mechanism. This is not an artefact of applying a Fourier detector to a Fourier circuit: on the non-abelian group S5, a basis-faithful generalisation of FSD precedes grokking on all six seeds, while the original Fourier FSD does not. Using the FSD ceiling to schedule a weight-decay increase also accelerates grokking over a fixed schedule without destabilising training. An attention-only variant groks with a strong FSD precursor while an MLP-only model never groks.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 21 of its 22 references that are in the map. First posted on arXiv 11 Jun 2026.

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21 of 22 references.

References · 21

  1. 2022Locating and Editing Factual Associations in GPTcited by 2,609
  2. 2018On the Spectral Bias of Neural Networkscited by 1,321
  3. 2022In-context Learning and Induction Headscited by 930
  4. 2023Progress measures for grokking via mechanistic interpretabilitycited by 822
  5. 2023Towards Automated Circuit Discovery for Mechanistic Interpretabilitycited by 700
  6. 2024KAN: Kolmogorov-Arnold Networkscited by 634
  7. 2022Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasetscited by 599
  8. 2021Causal Abstractions of Neural Networkscited by 401
  9. 2022Towards Understanding Grokking: An Effective Theory of Representation Learningcited by 229
  10. 2022Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limitcited by 184
  11. 2023The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networkscited by 153
  12. 2022Omnigrok: Grokking Beyond Algorithmic Datacited by 142
9 more
  1. 2023A Toy Model of Universality: Reverse Engineering How Networks Learn Group Operationscited by 141
  2. 2023Explaining grokking through circuit efficiencycited by 105
  3. 2023Grokking as the Transition from Lazy to Rich Training Dynamicscited by 102
  4. 2022The Slingshot Mechanism: An Empirical Study of Adaptive Optimizers and the Grokking Phenomenoncited by 90
  5. 2023A Tale of Two Circuits: Grokking as Competition of Sparse and Dense Subnetworkscited by 78
  6. 2023Grokking modular arithmeticcited by 77
  7. 2023Unifying Grokking and Double Descentcited by 49
  8. 2024Grokfast: Accelerated Grokking by Amplifying Slow Gradientscited by 38
  9. 2023Grokking Group Multiplication with Cosetscited by 22

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Citing papers per year: 2026: 2.

2 papers.