Majority Is The Reference You Never Had

Feature shifts across data sources are prevalent in applications involving healthcare, biomedicine, socioeconomic, financial, surveys, and multi-sensor data. Such shifts can arise from unharmonized heterogeneous data sources, noisy measurements, or inconsistent processing and standardization pipelines, resulting in erroneous features. Localizing shifted features is essential for identifying the underlying causes and correcting or filtering affected data before they degrade downstream analyses. Existing methods typically rely on a prespecified trusted reference to identify feature shifts in query data. In practice, however, a fully trustworthy reference source is often unavailable, making conventional pairwise comparisons unable to determine which data source is responsible for an observed shift. We propose Multi-source Reference-Free Feature Shift Localization (MuRF-FSL), an end-to-end neural network that simultaneously localizes shifted features across multiple data sources without requiring an external clean reference. Under the assumption that, for each feature, a strict majority of sources remain normal, MuRF-FSL treats the pattern shared by this majority as the missing reference and learns to attribute shifts by combining source-specific statistical representations with cross-source deviation patterns. Trained on a large and diverse collection of datasets with simulated shifts, MuRF-FSL generalizes to previously unseen datasets and shift scenarios without retraining. Experiments show that, despite operating in the more challenging reference-free setting, MuRF-FSL achieves state-of-the-art localization performance with highly efficient inference and outperforms reference-based methods provided with clean reference data.