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From Input to Output: A Flexible Agent for Dual-End Interpretation of Sparse Autoencoder Features
Sparse autoencoders (SAEs) are an important tool for mechanistic interpretability, but interpreting their many features remains challenging. Existing methods characterize input-side activation patterns and output-side intervention effects, yet often leave their functional connection implicit, while
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