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Improving Interpretability Through Better Feature Sets
Ameisen et al. 2025 propose replacement models that swap internal activations for interpretable features from sparse auto-encoders. Current methods suffer from compounding errors due to lossy re-encoding. The author argues that refining feature quality is more effective than adding error nodes. This approach aims to build more accurate feature circuits for model behavior analysis.