# Provenance Guided Incremental Learning Under Evolving Concept Definitions

arXiv: https://arxiv.org/abs/2608.23893 · HTML: https://mlsanity.com/paper/2608.23893/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 47 references that are in the map.
- First posted on arXiv 24 Aug 2026.

## Builds on (most cited first, 7 of 7)

- [Fairness Testing: A Comprehensive Survey and Analysis of Trends](https://mlsanity.com/paper/2207.10223/) (2022)
- [A comprehensive analysis of concept drift locality in data streams](https://mlsanity.com/paper/2311.06396/) (2023)
- [UNSW-NB15 Computer Security Dataset: Analysis through Visualization](https://mlsanity.com/paper/2101.05067/) (2021)
- [FAL-CUR: Fair Active Learning using Uncertainty and Representativeness on Fair Clustering](https://mlsanity.com/paper/2209.12756/) (2022)
- [HPCR: Holistic Proxy-based Contrastive Replay for Online Continual Learning](https://mlsanity.com/paper/2309.15038/) (2023)
- [Conjunctive Queries with Free Access Patterns under Updates](https://mlsanity.com/paper/2206.09032/) (2022)
- [Insert-Only versus Insert-Delete in Dynamic Query Evaluation](https://mlsanity.com/paper/2312.09331/) (2023)

## Abstract (arXiv)

Learning systems deployed over long periods must adapt not only to statistical changes in incoming data, but also to revisions of the definitions that generate their prediction targets. Conventional concept-drift methods typically infer such changes from observations or prediction errors, even when the underlying policy, rule, or query has been explicitly modified. This paper studies rule-induced concept shift, where the target-defining concept is revised directly, causing previously stored instances to acquire different semantic labels without requiring any change in their observed data. We introduce a provenance-guided incremental learning framework that compiles consecutive concept definitions into a structured rule delta, traces the changed components through historical provenance, certifies records whose previous labels remain valid, and restricts reevaluation to a localized candidate region. Executable revisions are relabeled automatically, ambiguous cases are handled through selective supervision, and the resulting changes are used for incremental predictor repair. A versioned concept memory further supports recurring definitions. We also introduce RuleShift-Bench, spanning financial, demographic, cybersecurity, and graph-structured data with threshold, predicate, logical, relational, recurring, and mixed concept revisions. Across the benchmark, provenance-guided repair attains 92.3% accuracy and 90.2% Macro-F1 while reprocessing 14.7% of the historical collection and retaining 94.6% of affected records. Its average update latency is 179s compared with 993s for complete relabeling and retraining. The results demonstrate that an explicit concept revision can be exploited as a data-maintenance signal, allowing learning systems to update the supervision and predictive state that depend on the change while preserving knowledge that remains valid.