Chapter 7

Scaling MI to LLMs

You will be able to describe the scale problem in LLM interpretability, connect sparse autoencoder work to Claude and GPT-style feature dictionaries, explain automated circuit and feature workflows, estimate practical costs, and identify open research limits.

• 1. Mechanistic Interpretability in LLMs→
• 2. Scaling Sparse Autoencoders and Challenges→