Category: Deep Research

Section 1: The Quadratic Wall – Deconstructing the Scaling Limits of Self-Attention The remarkable success of Transformer architectures across a spectrum of artificial intelligence domains is rooted in the self-attention Read More …

The Scaling Barrier: Deconstructing the Transformer’s Quadratic Bottleneck The Transformer architecture, introduced in 2017, has become the cornerstone of modern machine learning, particularly in natural language processing.1 Its success is Read More …

The Paradigm of Progressive Model Growth The predominant paradigm in deep learning has long been centered on static architectures. In this conventional workflow, a neural network’s structure—its depth, width, and Read More …

1. Executive Summary: Navigating the Quantization Frontier The rapid growth in the scale of large language models (LLMs) and other deep neural networks has necessitated a parallel evolution in model Read More …

Executive Summary Cross-lingual transfer learning has emerged as a cornerstone of modern Natural Language Processing (NLP), offering a powerful paradigm to mitigate the profound linguistic inequality prevalent in the digital Read More …

Executive Summary The widespread adoption of large language models (LLMs) has brought a critical challenge to the forefront of inference engineering: managing the Key-Value (KV) cache. While the KV cache Read More …

Introduction: Beyond Classical Knowledge Distillation Knowledge Distillation (KD) has emerged as a cornerstone technique in machine learning, fundamentally addressing the tension between model performance and deployment efficiency.1 As deep neural Read More …

Introduction to Model Over-Parameterization and the Imperative for Efficiency The Challenge of Scaling Deep Learning Models The contemporary landscape of artificial intelligence is dominated by a paradigm of scale. The Read More …

The New Paradigm of Automated Discovery Scientific discovery is undergoing a profound transformation, evolving from an era where artificial intelligence served as a collection of specialized computational tools into a Read More …

Section 1: Foundational Principles: From Recurrence to Parallel Attention The advent of the Transformer architecture in 2017 marked a watershed moment in the field of deep learning, particularly for sequence Read More …