Sparse autoencoder analysis of PatchTST FFN activations shows sparse, stable representations with no empirical support for superposition on standard time series forecasting tasks.
ICLR 2026 Workshop on Time Series in the Age of Large Models (TSALM) , year=
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Superposition Is Not Necessary: A Mechanistic Interpretability Analysis of Transformer Representations for Time Series Forecasting
Sparse autoencoder analysis of PatchTST FFN activations shows sparse, stable representations with no empirical support for superposition on standard time series forecasting tasks.