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Executive Summary The rapid ascendancy of Transformer-based Large Language Models (LLMs) has outpaced our theoretical understanding of their internal operations. While their behavioral capabilities are well-documented, the underlying computational mechanisms—the Read More …
Executive Summary The rapid ascendancy of Transformer-based Large Language Models (LLMs) has outpaced our theoretical understanding of their internal operations. While their behavioral capabilities are well-documented, the underlying computational mechanisms—the Read More …
Introduction: The Opaque Mind of the Machine: From Black Boxes to Mechanistic Understanding The advent of large language models (LLMs) built upon the transformer architecture represents a watershed moment in Read More …
Part I: The Reverse Engineering Paradigm As artificial intelligence systems, particularly deep neural networks, achieve superhuman performance and become integrated into high-stakes domains, the imperative to understand their internal decision-making 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 …