The State of Mechanistic Interpretability for AI Safety

This survey examines mechanistic interpretability (MI) for AI safety, covering its foundations such as features, polysemanticity, superposition, transformer computation and circuits, along with methods for feature extraction, circuit discovery, representation analysis, learning dynamics and training-data attribution. It characterises six research orientations—Pragmatic, Ambitious, Developmental, Interpretability Across Time, Introspective and Automated Interpretability—defined by their goals, assumptions and trade-offs. It also assesses how mechanistic understanding can support safety interventions including knowledge editing, steering, monitoring and automated interpretability agents, while reviewing their limitations and open problems.