Logic Nest https://validator.w3.org/feed/docs/rss2.html Why Do Large Models Develop More Interpretable Heads? Can We Edit Attention Heads to Improve Reasoning? What Causes Attention Patterns to Specialize Understanding Induction Heads: Formation During Pre-Training Why Transformers Prefer Simpler Circuits Early Understanding Grokking and Its Connection to Circuit Formation The Role of Replay Buffer in Grokking Why Do Networks Learn Modular Solutions During Grokking? Can Grokking Predict Emergent Reasoning Capabilities? Understanding the Impact of Batch Size on Grokking Dynamics Exploring the Rarity of Grokking in Natural Datasets Can Weight Decay Speed Up Grokking Convergence? Understanding Phase Transition During Grokking Accelerating Grokking Through Curriculum Learning Understanding the Need for Multiple Epochs in Grokking Algorithmic Data Why Do Wide Networks Show Weaker Double Descent? Can NTK Theory Predict Double Descent in Transformers? Understanding Late Double Descent Through Feature Learning Understanding the Drop in Test Error After the Interpolation Point Understanding the Drop in Test Error After the Interpolation Point Understanding Double Descent in Modern Overparameterized Networks Navigating Loss Geometry with SAM Optimizer Why Do Adversarial Attacks Target Sharp Minima? Can Flatter Minima Improve Out-of-Distribution Robustness? Understanding Flat-Minima Hypothesis and Its Role in Generalization Understanding Sharpness-Aware Minimization: How It Finds Better Minima Why RMSNorm Outperforms Layer Norm in Transformers Understanding Group Normalization: The Advantage of Small Batches Understanding Gradient Explosion in Unnormalized Layers Why Do Plain Deep Networks Collapse Without Normalization? Can Reversible Architectures Enable Infinite-Depth Training? Why Do Deep Equilibrium Models Converge Faster? Understanding Layer Normalization and Its Interaction with Residuals Understanding Why Residual Connections Flatten Loss Landscapes Exploring the Impact of Vector Quantization on Emergent Representations Understanding VQ-VAE: The Path to Discovering Discrete Meaningful Latents Why Autoregressive Models Excel in Likelihood Estimation Understanding Conditional Image Generation with BigGAN Can Diffusion Models Surpass GANs in Reasoning Tasks? Understanding Why StyleGAN Achieves Better Disentanglement Understanding Why Progressive GANs Produce Coherent High-Resolution Images Stabilizing Deep GAN Training with Spectral Normalization Can Elastic Weight Consolidation Preserve Intelligence? Understanding Catastrophic Forgetting in Continual Deep Learning Can Adapter Fusion Create Multi-Task Intelligence? Understanding Prompt Tuning: Scaling with Large Language Models What Makes DORA Outperform LoRA in Low-Rank Adaptation Understanding Qlora: Achieving 4-Bit Fine-Tuning Without Loss Why Does LoRA Preserve More Pre-trained Knowledge? Home