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Life sciences · Preprint
arXiv · September 4, 2026
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This is a theoretical survey of learning-augmented algorithms—methods that integrate fallible predictions into classical algorithms while maintaining formal performance guarantees. It identifies construction mechanisms, prediction interfaces, and consistency–robustness trade-offs across multiple domains, and delineates open problems rather than settling empirical questions. The work is conceptual and raises research questions rather than providing validated evidence for practice.
Preprint.
Five representative construction mechanisms identified across online optimization, caching, learned data structures, graph problems, and mechanism design. Distinction drawn between achieved upper bounds and matched asymptotic dependence at theorem level. Formal guarantees separated from empirical systems evidence; explicit treatment of prediction cost, feedback, and composition.
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The source did not state who this applies to in practice.
A survey and synthesis of learning-augmented algorithms that raises questions about construction mechanisms and formal guarantees rather than testing empirical claims on a specific clinical or operational problem.
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Learning-augmented algorithms use fallible predictions while retaining formal performance guarantees. This survey synthesizes prediction interfaces, error measures, consistency--robustness trade-offs, and five representative construction mechanisms across online optimization, caching, learned data structures, graph problems, and mechanism design. An orthogonal theorem-level axis distinguishes achieved upper bounds from matched asymptotic dependence. Formal guarantees are separated from empirical systems evidence, with explicit treatment of prediction cost, feedback, and composition. The resulting synthesis states sufficient conditions for limited end-to-end reasoning and delineates open problems in cost-aware prediction, endogenous error, semantic predictors, and benchmarking.
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