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This work introduces Persistent Sparse Autoencoders (Persistent SAEs), an extension of standard SAEs that learns a persistence coefficient for each feature, allowing the model to learn feature-specific timescales from reconstruction alone.
The Standard Interpretable Model is introduced, a general theory grounded in Lagrangian mechanics that enables the deductive design of interpretable methods and offers pedagogical grounding for interpretability curricula and may shift the scientific community's perspective of a discipline that has long been fragmented.
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