Score regularization is based on the principle that topically related documents should receive similar retrieval scores, extending the cluster hypothesis to score-based information retrieval. This research demonstrates that adjusting retrieval scores to ensure local consistency (autocorrelation) significantly improves ranking performance across various baseline algorithms.