Provenance Guided Incremental Learning Under Evolving Concept Definitions

The paper introduces a provenance-guided incremental learning framework that handles rule-induced concept shift, where target definitions are explicitly revised and previously stored instances receive new semantic labels. By compiling concept changes into structured rule deltas, tracing affected records through historical provenance, and selectively re-evaluating only a localized candidate region, the method automatically relabels executable revisions, manages ambiguous cases with selective supervision, and repairs predictors incrementally. Evaluation on the RuleShift-Bench benchmark—covering financial, demographic, cybersecurity, and graph-structured data—shows 92.3% accuracy and 90.2% Macro‑F1, reprocessing only 14.7% of the historical collection and achieving an average update latency of 179 s versus 993 s for full relabeling and retraining.

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