Unlock: Mechanistic Interpretability: Features, Circuits, and Causal Faithfulness

Reverse-engineering trained neural networks. Coverage of the superposition hypothesis, sparse autoencoders for feature extraction, the linear representation hypothesis and its counterexamples, induction heads and IOI as canonical circuits, sparse feature circuits (Marks et al. 2024), cross-layer transcoders (Lindsey et al. 2024), activation patching (noising vs denoising), faithfulness checks, frontier-scale evidence from Anthropic's Scaling Monosemanticity (Templeton et al. 2024) and DeepMind's Gemma Scope (Lieberum et al. 2024), and the limits of current interpretability.