Pipeline choices, not SAE architecture, dominate the variance of autointerpretability scores across four metrics and two models, making cross-paper score comparisons unreliable without standardization.
Learning multi-level features with matryoshka sparse autoencoders
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Building Fast, Evaluating Slow: Pipeline Choices Dominate Autointerpretability Score Variance
Pipeline choices, not SAE architecture, dominate the variance of autointerpretability scores across four metrics and two models, making cross-paper score comparisons unreliable without standardization.