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The paper surveys mechanistic interpretability for AI safety, exploring how studying internal representations and computations of neural networks can help evaluate, predict, and control their behavior. It reviews foundational concepts such as features, polysemanticity, transformer computation, and circuits, and surveys methods for feature extraction, circuit discovery, causal verification, and training-data attribution. The authors propose a unified framework for evaluating mechanistic claims, outline six research orientations, and discuss how mechanistic understanding can aid safety through knowledge editing, steering, and monitoring, while also highlighting limitations and open problems.
arXiv:2608. 12036v1 Announce Type: new Abstract: AI models have achieved remarkable success across diverse domains, yet the mechanisms underlying their capabilities and the risks they may pose remain poorly understood.
arXiv:2606. 26523v1 Announce Type: new Abstract: We develop a framework for interpreting AI systems as agents, drawing on the philosophical tradition of radical interpretation and the tools of mechanistic interpretability.
arXiv:2607. 07316v1 Announce Type: new Abstract: This article offers a comprehensive overview of mechanistic interpretability, an emerging field that seeks to reverse-engineer the internal algorithms of modern neural networks.
arXiv:2410. 22526v2 Announce Type: replace Abstract: To effectively address potential harms from Artificial Intelligence (AI) systems, it is essential to identify and mitigate system-level hazards.
arXiv:2606. 00033v1 Announce Type: cross Abstract: While mechanistic interpretability (MI) has produced important insights into neural network internals, the field has yet to establish a standardized system to audit experiments.