·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Introducing Cosmos 3 Edge1h◆YouTube clarifies policies around AI slop and upsetting videos2h◆China delivers a one-two punch to America’s AI dominance7h◆AI is more likely than humans to form biases when hiring9h◆Learn to Memorize: Scalable Continual Learning in Semiparametric Models with Mixture-of-Neighbors Induction Memory13h◆Echoes Across Vietnam's Highlands, Delta, and Coast: A Multilingual Corpus for Cham, Khmer, and Tay-Nung13h◆Stochastic Reset Pathfinding: Path-Level Regret for Cascading Bandits over Graph Paths13h◆ADS-C: Antidistillation Sampling for Classification13h◆Publicly-Verifiable Certificates for Statistical Algorithms13h◆Do Generative Models Keep Time? A Time-Aware Evaluation of Synthetic Sequential Tabular Data13h◆QUADS: Stabilizing NVFP4 Reinforcement Learning for MoE via QUantization-error Alignment across Dual Sides13h◆CLaC@FinMMEval 2026 Task 3: Sentiment-Augmented Deep Reinforcement Learning for Active Trading -- An Alpha-Reward Approach13h◆DELUGE: Towards Continental-Scale Daily Pluvial Flood Damage Prediction via Interpretable Conditioning on Foundation Model Embeddings13h◆A Physics-Informed Neural Network with a Modified Lorentzian Activation for Nonlocal Gradient-Flow Equations in Dynamic Density Functional Theory13h◆Unsupervised Keypoints for Real-Time Fall Detection: Comparative Analysis Under Real-world Conditions with Predictive Bandwidth Reduction13h◆Closed-Loop Bayesian Bandit Encoder with GRAND Receiver for a Bursty Interference Channel13h◆E3DGS: Unified Geometric-Photometric Equivariance for 3D Gaussian Splatting via Color-as-Geometry Embedding13h◆Testing Distributions Against Bounded Distinguishers13h◆Discovering Generalizable Governing Equations for Graph Dynamical Systems with Interpretable Neural Networks13h◆LLM-Guided Transportation Hub Capacity Planning with Textual Business Inputs13h◆Introducing Cosmos 3 Edge1h◆YouTube clarifies policies around AI slop and upsetting videos2h◆China delivers a one-two punch to America’s AI dominance7h◆AI is more likely than humans to form biases when hiring9h◆Learn to Memorize: Scalable Continual Learning in Semiparametric Models with Mixture-of-Neighbors Induction Memory13h◆Echoes Across Vietnam's Highlands, Delta, and Coast: A Multilingual Corpus for Cham, Khmer, and Tay-Nung13h◆Stochastic Reset Pathfinding: Path-Level Regret for Cascading Bandits over Graph Paths13h◆ADS-C: Antidistillation Sampling for Classification13h◆Publicly-Verifiable Certificates for Statistical Algorithms13h◆Do Generative Models Keep Time? A Time-Aware Evaluation of Synthetic Sequential Tabular Data13h◆QUADS: Stabilizing NVFP4 Reinforcement Learning for MoE via QUantization-error Alignment across Dual Sides13h◆CLaC@FinMMEval 2026 Task 3: Sentiment-Augmented Deep Reinforcement Learning for Active Trading -- An Alpha-Reward Approach13h◆DELUGE: Towards Continental-Scale Daily Pluvial Flood Damage Prediction via Interpretable Conditioning on Foundation Model Embeddings13h◆A Physics-Informed Neural Network with a Modified Lorentzian Activation for Nonlocal Gradient-Flow Equations in Dynamic Density Functional Theory13h◆Unsupervised Keypoints for Real-Time Fall Detection: Comparative Analysis Under Real-world Conditions with Predictive Bandwidth Reduction13h◆Closed-Loop Bayesian Bandit Encoder with GRAND Receiver for a Bursty Interference Channel13h◆E3DGS: Unified Geometric-Photometric Equivariance for 3D Gaussian Splatting via Color-as-Geometry Embedding13h◆Testing Distributions Against Bounded Distinguishers13h◆Discovering Generalizable Governing Equations for Graph Dynamical Systems with Interpretable Neural Networks13h◆LLM-Guided Transportation Hub Capacity Planning with Textual Business Inputs13h◆
News/Disentangling Feature Structure: A Mathematically Provable Two-Stage Training Dynamics in Transformers
arxiv
PublishedJuly 14, 2026 at 4:00 AM
—neutral

Disentangling Feature Structure: A Mathematically Provable Two-Stage Training Dynamics in Transformers

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2502.20681v3 Announce Type: replace-cross Abstract: Transformers may exhibit two-stage training dynamics during the real-world training process. For instance, when training GPT-2 on the Counterfact dataset, the answers progress from syntactically incorrect to syntactically correct to semantica

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivLearn to Memorize: Scalable Continual Learning in Semiparametric Models with Mixture-of-Neighbors Induction Memory13harxivEchoes Across Vietnam's Highlands, Delta, and Coast: A Multilingual Corpus for Cham, Khmer, and Tay-Nung13harxivStochastic Reset Pathfinding: Path-Level Regret for Cascading Bandits over Graph Paths13harxivADS-C: Antidistillation Sampling for Classification13h
The Bubble Brief
WEEKLY

Read AI insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews