:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Cagnetta, Francesco, Raventós, Allan, Ganguli, Surya, Wyart, Matthieu
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence
Online Access:	https://arxiv.org/abs/2602.07488
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Deep networks learn to parse uniform-depth context-free languages from local statistics
by: Parley, Jack T., et al.
Published: (2026)

Towards a theory of how the structure of language is acquired by deep neural networks
by: Cagnetta, Francesco, et al.
Published: (2024)

Rethinking Fine-Tuning when Scaling Test-Time Compute: Limiting Confidence Improves Mathematical Reasoning
by: Chen, Feng, et al.
Published: (2025)

Scaling Laws and Representation Learning in Simple Hierarchical Languages: Transformers vs. Convolutional Architectures
by: Cagnetta, Francesco, et al.
Published: (2025)

Get rich quick: exact solutions reveal how unbalanced initializations promote rapid feature learning
by: Kunin, Daniel, et al.
Published: (2024)

Learning curves theory for hierarchically compositional data with power-law distributed features
by: Cagnetta, Francesco, et al.
Published: (2025)

How Compositional Generalization and Creativity Improve as Diffusion Models are Trained
by: Favero, Alessandro, et al.
Published: (2025)

How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model
by: Cagnetta, Francesco, et al.
Published: (2023)

Stochastic Collapse: How Gradient Noise Attracts SGD Dynamics Towards Simpler Subnetworks
by: Chen, Feng, et al.
Published: (2023)

TFGN: Task-Free, Replay-Free Continual Pre-Training Without Catastrophic Forgetting at LLM Scale
by: Ganguli, Anurup
Published: (2026)

An analytic theory of creativity in convolutional diffusion models
by: Kamb, Mason, et al.
Published: (2024)

From Kepler to Newton: Inductive Biases Guide Learned World Models in Transformers
by: Liu, Ziming, et al.
Published: (2026)

Contrastive Concept-Tree Search for LLM-Assisted Algorithm Discovery
by: Leleu, Timothee, et al.
Published: (2026)

On the Optimizer Dependence of Neural Scaling Laws
by: Ramani, Vansh, et al.
Published: (2026)

Towards Neural Scaling Laws on Graphs
by: Liu, Jingzhe, et al.
Published: (2024)

A Hierarchical Language Model with Predictable Scaling Laws and Provable Benefits of Reasoning
by: Gaitonde, Jason, et al.
Published: (2026)

Information-Theoretic Foundations for Neural Scaling Laws
by: Jeon, Hong Jun, et al.
Published: (2024)

Symmetry in language statistics shapes the geometry of model representations
by: Karkada, Dhruva, et al.
Published: (2026)

Towards Neural Scaling Laws for Time Series Foundation Models
by: Yao, Qingren, et al.
Published: (2024)

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks
by: Lee, Dongwoo, et al.
Published: (2025)

Do Neural Scaling Laws Exist on Graph Self-Supervised Learning?
by: Ma, Qian, et al.
Published: (2024)

MARK: Memory Augmented Refinement of Knowledge
by: Ganguli, Anish, et al.
Published: (2025)

Scaling Laws and Symmetry, Evidence from Neural Force Fields
by: Ngo, Khang, et al.
Published: (2025)

Effective Frontiers: A Unification of Neural Scaling Laws
by: Zou, Jiaxuan, et al.
Published: (2026)

Relative-Based Scaling Law for Neural Language Models
by: Yue, Baoqing, et al.
Published: (2025)

A Resource Model For Neural Scaling Law
by: Song, Jinyeop, et al.
Published: (2024)

Derived Fields Preserve Fine-Scale Detail in Budgeted Neural Simulators
by: Wang, Wenshuo, et al.
Published: (2026)

Efficiency optimization of large-scale language models based on deep learning in natural language processing tasks
by: Mei, Taiyuan, et al.
Published: (2024)

Universal Neural Functionals
by: Zhou, Allan, et al.
Published: (2024)

The Neural Pruning Law Hypothesis
by: Barbulescu, Eugen, et al.
Published: (2025)

On Implications of Scaling Laws on Feature Superposition
by: Katta, Pavan
Published: (2024)

Scaling Law Hypothesis for Multimodal Model
by: Sun, Qingyun, et al.
Published: (2024)

Scaling Law for Time Series Forecasting
by: Shi, Jingzhe, et al.
Published: (2024)

Wukong: Towards a Scaling Law for Large-Scale Recommendation
by: Zhang, Buyun, et al.
Published: (2024)

A Neural Scaling Law from Lottery Ticket Ensembling
by: Liu, Ziming, et al.
Published: (2023)

Time Matters: Scaling Laws for Any Budget
by: Inbar, Itay, et al.
Published: (2024)

Distillation Scaling Laws
by: Busbridge, Dan, et al.
Published: (2025)

Hierarchical Concept Geometry in Language Models Emerges from Word Co-occurrence
by: Nava, Andres, et al.
Published: (2026)

Purity Law for Generalizable Neural TSP Solvers
by: Liu, Wenzhao, et al.
Published: (2025)

Scaling Laws for Precision in High-Dimensional Linear Regression
by: Zhang, Dechen, et al.
Published: (2026)