---
title: "Who cites Omnigrok: Grokking Beyond Algorithmic Data"
description: "22 papers of the Elamur Research library cite «Omnigrok: Grokking Beyond Algorithmic Data», found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/papers/liu-2022-omnigrok-grokking-algorithmic-data/cited-by/"
---
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# Who cites «Omnigrok: Grokking Beyond Algorithmic Data»

22 papers of our library cite it (of the 149,439 whose bibliographies we read) · 6 with a page here, the most cited first · [← back to «Omnigrok: Grokking Beyond Algorithmic Data»](https://md.research.elamur.ai/free/en/papers/liu-2022-omnigrok-grokking-algorithmic-data/)

1. [**Mechanistic Interpretability for AI Safety — A Review arXiv:2404.14082**Leonard Bereska, Efstratios Gavves*2024 · 108 citations*](https://md.research.elamur.ai/free/en/papers/bereska-2024-mechanistic-interpretability-ai-safety-review/)
2. **[Towards Understanding Grokking: An Effective Theory of Representation Learning](https://md.research.elamur.ai/free/en/papers/liu-2022-towards-understanding-grokking-effective-theory/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Ziming Liu, Ouail Kitouni, Niklas Nolte, et al.*2022 · 65 citations*
3. **[On Memorization of Large Language Models in Logical Reasoning](https://md.research.elamur.ai/free/en/papers/xie-2025-memorization-large-language-models-logical/) [International Joint Conference on Natural Language Processing (IJCNLP)](https://md.research.elamur.ai/free/en/venues/ijcnlp/)**Chulin Xie, Yangsibo Huang, Chiyuan Zhang, et al.*2025 · 45 citations*
4. [**Grokked Transformers are Implicit Reasoners: A Mechanistic Journey to the Edge of Generalization arXiv:2405.15071**Boshi Wang, Xiang Yue, Yu Su, et al.*2024 · 30 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-grokked-transformers-implicit-reasoners-mechanistic/)
5. **[Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-locate-steer-improve-practical-survey/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hengyuan Zhang, Zhihao Zhang, Ercong Nie, et al.*2026 · 9 citations*
6. [**Towards Understanding Distilled Reasoning Models: A Representational Approach arXiv:2503.03730 · ICLR 2025 Workshop on Building Trust in Language Models and Applications**David D. Baek, Max Tegmark*2025 · 8 citations*](https://md.research.elamur.ai/free/en/papers/baek-2025-towards-understanding-distilled-reasoning-models/)

16 more papers of our library cite it; they have no page here yet.

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