This topic investigates unsupervised anomaly detection for merge conflict resolution in software engineering. Existing approaches generally formulate conflict resolution as supervised classification over predefined resolution labels. However, even a large labeled dataset may contain rare, unusual, or incorrectly reconstructed cases, and the labels may not adequately represent the full diversity of merge behavior. This project will develop methods to identify anomalous merge conflicts using the available conflict representations while initially ignoring the nine resolution labels. The objective is to determine whether anomaly scores can identify unusual conflict structures, rare resolution patterns, or cases that are difficult for conventional classifiers.