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Executive Summary The rapid evolution of Transformer-based Large Language Models (LLMs) has fundamentally altered the landscape of artificial intelligence, transitioning from simple pattern matching to complex reasoning, code generation, and Read More …
Executive Summary The capacity of Large Language Models (LLMs) to process and reason over extensive sequences of information—a capability defined by their “context window”—has become a pivotal frontier in artificial Read More …
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 …