The paper introduces a unified evaluation protocol for robust counterfactual explanations (CFE), testing six robust methods and two baselines across four tabular datasets under eight types of model change. It shows that robustness scores vary by change type and that methods designed for one change family may not transfer to others, with RobX performing most consistently. The study emphasizes the need for a common protocol that defines model changes, measures their impact, and separates generation performance from robustness.
By Marcin Kostrzewa, Maciej Zi\k{e}ba
arXiv:2606. 01566v1 Announce Type: new Abstract: Small-to-medium scientific datasets place machine learning pipelines under two compounding pressures.
By Amanda S Barnard
arXiv:2607. 10420v1 Announce Type: cross Abstract: In software analytics, rerunning the same analysis twice often yields different models and conclusions.
By Amirali Rayegan, Lunxiao Li, Tim Menzies
arXiv:2608.27704v1 Announce Type: new
Abstract: When machine learning classifiers are retrained, inputs correctly classified by the previous model version may be misclassified by the updated version,...
By Madhusudan Srinivasan, Namith Nishal Raphae
arXiv:2605.30454v2 Announce Type: replace-cross
Abstract: Prompt-injection benchmarks for LLM agents typically test attacks through a single injection surface and report the resulting attack success...
By Syed Nazmus Sakib, Nafiul Haque, Shahrear Bin Amin, Shifat E. Arman
arXiv:2608. 05419v1 Announce Type: cross Abstract: Models trained by empirical risk minimization on data containing spurious correlations achieve high average accuracy while failing on subpopulations where the correlation does not hold.
By Nilesh Kumar