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Test-Time Optimization Turns Frozen Reconstructors into Imputers for Missing Time-Series Channels

This paper introduces a test-time optimization method that converts a frozen time-series reconstructor into an imputer for complete channel failures, without mask augmentation during training. Missing entries are optimized to minimize reconstruction error on observed coordinates, with an optional temporal-smoothness prior. Across five datasets and four architectures, the method reduces MAE in 19 of 20 combinations versus one-pass reconstruction, though it trails mask-aware imputers on three datasets and requires more inference time.