Anthropic's "LLM Genome Project": learning & clamping 34m features on Claude Sonnet

**Anthropic** released their third paper in the MechInterp series, **Scaling Monosemanticity**, scaling interpretability analysis to **34 million features** on **Claude 3 Sonnet**. This work introduces the concept of **dictionary learning** to isolate recurring neuron activation patterns, enabling more interpretable internal states by combining features rather than neurons. The paper reveals abstract features related to code, errors, sycophancy, crime, self-representation, and deception, demonstrating intentional modifiability by clamping feature values. The research marks a significant advance in **model interpretability** and **neural network analysis** at frontier scale.