{"total":46,"rows":[{"id":"2501.04697","title":"Grokking at the Edge of Numerical Stability","created":"2025-01-08","cited_by_count":46,"area":null},{"id":"2406.06158","title":"Get rich quick: exact solutions reveal how unbalanced initializations promote rapid feature learning","created":"2024-06-10","cited_by_count":42,"area":null},{"id":"2407.12332","title":"Why Do You Grok? A Theoretical Analysis of Grokking Modular Addition","created":"2024-07-17","cited_by_count":36,"area":null},{"id":"2604.21691","title":"There Will Be a Scientific Theory of Deep Learning","created":"2026-04-23","cited_by_count":29,"area":null},{"id":"2406.03495","title":"Grokking Modular Polynomials","created":"2024-06-05","cited_by_count":18,"area":null},{"id":"2509.21519","title":"Provable Scaling Laws of Feature Emergence from Learning Dynamics of Grokking","created":"2025-09-25","cited_by_count":15,"area":null},{"id":"2402.16726","title":"Towards Empirical Interpretation of Internal Circuits and Properties in Grokked Transformers on Modular Polynomials","created":"2024-02-26","cited_by_count":14,"area":null},{"id":"2502.07998","title":"Adaptive kernel predictors from feature-learning infinite limits of neural networks","created":"2025-02-11","cited_by_count":13,"area":null},{"id":"2410.04489","title":"Grokking at the Edge of Linear Separability","created":"2024-10-06","cited_by_count":12,"area":null},{"id":"2406.16689","title":"Coding schemes in neural networks learning classification tasks","created":"2024-06-24","cited_by_count":11,"area":null},{"id":"2502.03210","title":"From Kernels to Features: A Multi-Scale Adaptive Theory of Feature Learning","created":"2025-02-05","cited_by_count":10,"area":null},{"id":"2502.21009","title":"Position: Solve Layerwise Linear Models First to Understand Neural Dynamical Phenomena (Neural Collapse, Emergence, Lazy/Rich Regime, and Grokking)","created":"2025-02-28","cited_by_count":9,"area":null},{"id":"2505.24849","title":"Statistical mechanics of extensive-width Bayesian neural networks near interpolation","created":"2025-05-30","cited_by_count":9,"area":null},{"id":"2505.11411","title":"Is Grokking a Computational Glass Relaxation?","created":"2025-05-16","cited_by_count":8,"area":null},{"id":"2405.17088","title":"Phase Transitions in the Output Distribution of Large Language Models","created":"2024-05-27","cited_by_count":7,"area":null},{"id":"2310.19470","title":"Bridging Lottery Ticket and Grokking: Understanding Grokking from Inner Structure of Networks","created":"2023-10-30","cited_by_count":6,"area":null},{"id":"2606.17120","title":"Noise-Driven Escape from Metastable Phases explains Grokking in Deep Neural Networks","created":"2026-06-15","cited_by_count":5,"area":null},{"id":"2406.02105","title":"Can Kernel Methods Explain How the Data Affects Neural Collapse?","created":"2024-06-04","cited_by_count":4,"area":null},{"id":"2509.06931","title":"Learning words in groups: fusion algebras, tensor ranks and grokking","created":"2025-09-08","cited_by_count":4,"area":null},{"id":"2509.20829","title":"Explaining Grokking and Information Bottleneck through Neural Collapse Emergence","created":"2025-09-25","cited_by_count":4,"area":null},{"id":"2512.04165","title":"Mitigating the Curse of Detail: Scaling Arguments for Feature Learning and Sample Complexity","created":"2025-12-03","cited_by_count":4,"area":null},{"id":"2603.01192","title":"A Basin-Selection Perspective on Grokking via Singular Learning Theory","created":"2026-03-01","cited_by_count":4,"area":null},{"id":"2605.09724","title":"Model Capacity Determines Grokking through Competing Memorisation and Generalisation Speeds","created":"2026-05-10","cited_by_count":4,"area":null},{"id":"2507.04448","title":"Transfer Learning in Infinite Width Feature Learning Networks","created":"2025-07-06","cited_by_count":3,"area":null},{"id":"2602.05184","title":"Towards Worst-Case Guarantees with Scale-Aware Interpretability","created":"2026-02-05","cited_by_count":3,"area":null},{"id":"2603.24746","title":"Grokking as a Falsifiable Finite-Size Transition","created":"2026-03-25","cited_by_count":3,"area":null},{"id":"2407.03652","title":"Over the Edge of Chaos? Excess Complexity as a Roadblock to Artificial General Intelligence","created":"2024-07-04","cited_by_count":2,"area":null},{"id":"2410.04264","title":"Decoupling Dynamical Richness from Representation Learning: Towards Practical Measurement","created":"2024-10-05","cited_by_count":2,"area":null},{"id":"2502.01739","title":"Grokking vs. Learning: Same Features, Different Encodings","created":"2025-02-03","cited_by_count":2,"area":null},{"id":"2506.12284","title":"GrokAlign: Geometric Characterisation and Acceleration of Grokking","created":"2025-06-14","cited_by_count":2,"area":null},{"id":"2604.19740","title":"Generalization at the Edge of Stability","created":"2026-04-21","cited_by_count":2,"area":null},{"id":"2606.20299","title":"Statistical Properties of Training & Generalization","created":"2026-06-18","cited_by_count":2,"area":null},{"id":"2607.13749","title":"Algebraic Representability as the Limiting Regime of Grokking: An Exactly Solvable Model with Holomorphic Activations","created":"2026-07-15","cited_by_count":2,"area":null},{"id":"2507.05263","title":"Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization","created":"2025-06-20","cited_by_count":1,"area":null},{"id":"2511.04760","title":"When Data Falls Short: Grokking Below the Critical Threshold","created":"2025-11-06","cited_by_count":1,"area":null},{"id":"2606.18465","title":"What Does the Weight Norm Control in Grokking? Logit-Scale Mediation under Cross-Entropy","created":"2026-06-16","cited_by_count":1,"area":null},{"id":"2608.22513","title":"From Symmetry to Invariance: Learning Galois Equivalent Representations in Finite Fields","created":"2026-08-23","cited_by_count":1,"area":null},{"id":"2505.06597","title":"Phase Transitions between Accuracy Regimes in L2 regularized Deep Neural Networks","created":"2025-05-10","cited_by_count":0,"area":null},{"id":"2507.08197","title":"Consciousness as a Jamming Phase","created":"2025-07-10","cited_by_count":0,"area":null},{"id":"2510.15174","title":"A simple mean field model of feature learning","created":"2025-10-16","cited_by_count":0,"area":null},{"id":"2510.24616","title":"Statistical physics of deep learning: Optimal learning of a multi-layer perceptron near interpolation","created":"2025-10-28","cited_by_count":0,"area":null},{"id":"2511.12768","title":"Evidence of Phase Transitions in Small Transformer-Based Language Models","created":"2025-11-16","cited_by_count":0,"area":null},{"id":"2605.08464","title":"The Geometric Structure of Models Learning Sparse Data","created":"2026-05-08","cited_by_count":0,"area":null},{"id":"2609.09589","title":"A Function-Space Approach to the Statistical Mechanics of Learning Dynamics","created":"2026-09-09","cited_by_count":0,"area":null},{"id":"2609.25634","title":"An Exploratory Replica-Overlap Probe of the Grokking Transition","created":"2026-09-22","cited_by_count":0,"area":null},{"id":"2609.26679","title":"A Spectral Theory of Grokking: Weight Decay induces Feature Learning","created":"2026-09-22","cited_by_count":0,"area":null}],"next_cursor":null}