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Explicit frequency-domain evidence can dramatically enhance LLM-based time-series anomaly detection, revealing insights that time-domain methods miss.
Forget training: this method detects anomalies by simply measuring how much a query patch *needs* to change to fit in with normal data, using a clever graph Laplacian energy minimization.
Forget hand-labeled bias attributes: BiasEdit automatically detects and edits biases in image datasets, paving the way for fairer visual classifiers without extra training.
