Unverified50% confidenceFactExact time
GAS-PFN uses two masked-conditioning forward passes sharing the same Transformer backbone: the first pass models the temporal dynamics specific to each sensor, the second pass focuses on spatiotemporal interactions among neighboring sensors, and the outputs of the two passes are integrated for the final prediction.
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Long-term
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10/28/2026
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