Frontier labs

Chris Olah

Anthropic

Why we track: Interpretability research

Career moves

No published career moves.

Papers

Paper2026-05-28 · arXiv.org

Extracting easy-to-understand operational units from inside large artificial intelligence

Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet

Chris Olah +25 more

What: Researchers utilized sparse autoencoders to extract interpretable features from the intermediate layers of the commercial AI model Claude 3 Sonnet.
Key result: The extracted features exhibited multilingual and multimodal characteristics, ranging from famous figures and abstract concepts to potential risks, confirming they can be used to control model behavior.
Why it matters: This demonstrates a technological advancement that can contribute to interpreting the internal workings of AI models and identifying potential risks.AI summary

Safety & policy

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