Research questionHow can algorithms use imperfect learned predictions without losing formal performance guarantees?Prediction errors can improve algorithmic decisions but may undermine guarantees when their magnitude, cost, feedback, or interactions across algorithmic components are unclear. The central difficulty is relating prediction quality to both worst-case robustness and end-to-end system behavior. Latest papersRecent research connected to this question, newest first.Learning-Augmented Algorithms: Guarantees, Construction Mechanisms, and System-Level ImplicationsA survey of prediction interfaces, error measures, consistency–robustness trade-offs, and five construction mechanisms. It distinguishes formal upper bounds from matched asymptotic dependence and from empirical systems evidence, while discussing prediction cost, feedback, composition, endogenous error, semantic predictors, and benchmarking.research paper · Sep 7, 2026