Mechanistic Interpretability

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  "summary": "Mechanistic interpretability has extracted 34M+ interpretable features from Claude 3 Sonnet with 90% automated labeling accuracy and demonstrated 75-85% success in causal validation, though less than 5% of frontier model computations are currently understood. With \\$75-150M annual investment and a 3-7 year timeline to safety-critical applications, it shows promise for deception detection (25-39% hint rate in reasoning models) but faces significant scalability challenges.",
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