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What Should World Models Forget? Stratified Retention for Continual Adaptation
2026 · 0 citations
Continual learning treats degradation on previously seen data as evidence of failure, a convention inherited from settings with a stationary prediction target, where a correct label remains correct indefinitely. World models do not satisfy this condition. Their prediction target is the environment, which changes, so knowledge that was accurate when acquired may later become false, and discarding it is required behavior rather than a defect. Non-stationary ground truth is well studied in the conc…
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Automated Trustworthiness Oracle Generation for Machine Learning Text Classifiers
2025 · 0 citations
Machine learning (ML) for text classification has been widely used in various domains, such as toxicity detection, chatbot consulting, and review analysis. These applications can significantly impact ethics, economics, and human behavior, raising serious concerns about trusting ML decisions. Several studies indicate that traditional uncertainty metrics, such as model confidence, and performance metrics, like accuracy, are insufficient to build human trust in ML models. These models often learn s…
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