A gated residual KAN framework called Temporal Functional Circuits maps edge functions to input lags, ranks them by activation, and validates faithfulness via interventions showing that learned B-splines add predictive value beyond base activations.
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cs.LG 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
Sparse autoencoder analysis of PatchTST FFN activations shows sparse, stable representations with no empirical support for superposition on standard time series forecasting tasks.
A frozen average of the last two cycles matches or exceeds eight shape-learning alternatives on 97 GIFT-Eval configurations for periodic time series forecasting.
citing papers explorer
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Temporal Functional Circuits: From Spline Plots to Faithful Explanations in KAN Forecasting
A gated residual KAN framework called Temporal Functional Circuits maps edge functions to input lags, ranks them by activation, and validates faithfulness via interventions showing that learned B-splines add predictive value beyond base activations.
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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.
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Don't Learn the Shape: Forecasting Periodic Time Series by Rank-1 Decomposition
A frozen average of the last two cycles matches or exceeds eight shape-learning alternatives on 97 GIFT-Eval configurations for periodic time series forecasting.