Mechanistic Interpretability of Large Language Models

Large Language Models (LLMs) have emerged as transformative tools across numerous domains, yet they remain fundamentally opaque in their internal decision-making processes. This paper presents a comprehensive review of mechanistic interpretability, the nascent discipline dedicated to reverse-engineering the internal computations of neural networks, as applied to state-of-the-art LLMs. We examine the core challenges posed by polysemanticity and superposition, survey the principal techniques currently employed—including sparse autoencoders, activation patching, circuit tracing, and chain-of-thought monitoring—and assess their implications for AI safety, alignment, and governance. Drawing on recent breakthroughs by Anthropic, OpenAI, and Google DeepMind, we argue that mechanistic interpretability represents a critical frontier for ensuring that increasingly powerful AI systems remain understandable, auditable, and safe. We also identify open risks, including the fragility of chain-of-thought monitoring under adversarial optimization, and outline a forward-looking research agenda for the coming years.