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CalibDCD reveals that post-training shifts can significantly degrade data contamination detection, but targeted calibration can restore accuracy.
SymCA achieves a remarkable balance between accuracy and interpretability in column annotation, outperforming traditional methods by over 6% in F1 scores.
