:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Baek, David D., Liu, Ziming, Tyagi, Riya, Tegmark, Max
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2502.01628
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

GenEFT: Understanding Statics and Dynamics of Model Generalization via Effective Theory
by: Baek, David D., et al.
Published: (2024)

Towards Understanding Distilled Reasoning Models: A Representational Approach
by: Baek, David D., et al.
Published: (2025)

How Do Transformers "Do" Physics? Investigating the Simple Harmonic Oscillator
by: Kantamneni, Subhash, et al.
Published: (2024)

Do Two AI Scientists Agree?
by: Fu, Xinghong, et al.
Published: (2025)

OptPDE: Discovering Novel Integrable Systems via AI-Human Collaboration
by: Kantamneni, Subhash, et al.
Published: (2024)

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

Neural Thermodynamic Laws for Large Language Model Training
by: Liu, Ziming, et al.
Published: (2025)

Investigating Representation Universality: Case Study on Genealogical Representations
by: Baek, David D., et al.
Published: (2024)

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

The Quantization Model of Neural Scaling
by: Michaud, Eric J., et al.
Published: (2023)

Scaling Laws For Scalable Oversight
by: Engels, Joshua, et al.
Published: (2025)

Dual-Branch Convolutional Framework for Spatial and Frequency-Based Image Forgery Detection
by: Tyagi, Naman, et al.
Published: (2025)

Language Models Represent Space and Time
by: Gurnee, Wes, et al.
Published: (2023)

Language Models Use Trigonometry to Do Addition
by: Kantamneni, Subhash, et al.
Published: (2025)

Physics of Skill Learning
by: Liu, Ziming, et al.
Published: (2025)

Survival of the Fittest Representation: A Case Study with Modular Addition
by: Ding, Xiaoman Delores, et al.
Published: (2024)

KAN 2.0: Kolmogorov-Arnold Networks Meet Science
by: Liu, Ziming, et al.
Published: (2024)

Low-Rank Adapting Models for Sparse Autoencoders
by: Chen, Matthew, et al.
Published: (2025)

On the creation of narrow AI: hierarchy and nonlocality of neural network skills
by: Michaud, Eric J., et al.
Published: (2025)

The Geometry of Concepts: Sparse Autoencoder Feature Structure
by: Li, Yuxiao, et al.
Published: (2024)

Decomposing The Dark Matter of Sparse Autoencoders
by: Engels, Joshua, et al.
Published: (2024)

Opening the AI black box: program synthesis via mechanistic interpretability
by: Michaud, Eric J., et al.
Published: (2024)

Not All Language Model Features Are One-Dimensionally Linear
by: Engels, Joshua, et al.
Published: (2024)

KAN: Kolmogorov-Arnold Networks
by: Liu, Ziming, et al.
Published: (2024)

Training AI to be Loyal
by: Oh, Sewoong, et al.
Published: (2025)

Are Sparse Autoencoders Useful? A Case Study in Sparse Probing
by: Kantamneni, Subhash, et al.
Published: (2025)

The Remarkable Robustness of LLMs: Stages of Inference?
by: Lad, Vedang, et al.
Published: (2024)

GraVAC: Adaptive Compression for Communication-Efficient Distributed DL Training
by: Tyagi, Sahil, et al.
Published: (2023)

Efficient Dictionary Learning with Switch Sparse Autoencoders
by: Mudide, Anish, et al.
Published: (2024)

Tula: Optimizing Time, Cost, and Generalization in Distributed Large-Batch Training
by: Tyagi, Sahil, et al.
Published: (2026)

Affordable Precision Agriculture: A Deployment-Oriented Review of Low-Cost, Low-Power Edge AI and TinyML for Resource-Constrained Farming Systems
by: Samanta, Riya, et al.
Published: (2026)

Neural Interpretable PDEs: Harmonizing Fourier Insights with Attention for Scalable and Interpretable Physics Discovery
by: Liu, Ning, et al.
Published: (2025)

Are Your Reasoning Models Reasoning or Guessing? A Mechanistic Analysis of Hierarchical Reasoning Models
by: Ren, Zirui, et al.
Published: (2026)

Energy per Successful Goal: Goal-Level Energy Accounting for Agentic AI Systems
by: Panigrahy, Deepak, et al.
Published: (2026)

Train-before-Test Harmonizes Language Model Rankings
by: Zhang, Guanhua, et al.
Published: (2025)

Dense SAE Latents Are Features, Not Bugs
by: Sun, Xiaoqing, et al.
Published: (2025)

Accelerating Distributed ML Training via Selective Synchronization
by: Tyagi, Sahil, et al.
Published: (2023)

Privacy and Security Implications of Cloud-Based AI Services : A Survey
by: Luqman, Alka, et al.
Published: (2024)

Dynamic Sparse Training of Diagonally Sparse Networks
by: Tyagi, Abhishek, et al.
Published: (2025)

Interpretable Machine Learning in Physics: A Review
by: Wetzel, Sebastian Johann, et al.
Published: (2025)