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This paper compares the temporal robustness of expert and imitation-learned policies in dexterous manipulation tasks, finding that imitation-learned policies degrade more sharply with increased execution speeds, primarily due to insertion misalignments.
A study evaluating zero-shot GPT-4o-mini for predicting child stunting from Bangladesh Demographic and Health Survey data, comparing against a random forest baseline and assessing fairness across demographic groups and temporal robustness. Results show comparable balanced accuracy but notable fairness disparities across residence and wealth categories.
This paper investigates temporal concept drift in legal judgment prediction by fine-tuning transformer models on Ukrainian court decisions from three epochs defined by geopolitical disruptions. Findings show severe forward degradation, asymmetry in backward transfer, and that chronological continual learning effectively mitigates forgetting while domain pretraining reduces degradation magnitude.