发表机构
School of Software Technology, Zhejiang University; College of Computer Science and Technology, Zhejiang University; School of Computer Science and Engineering, Nanyang Technological University（浙江大学软件学院; 浙江大学计算机科学与技术学院; 南洋理工大学计算机科学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本综述针对学习增强型算法，综合了多领域的构造机制与相关权衡，给出了端到端推理的充分条件，同时划定了相关开放问题。
AI 中文摘要
学习增强型算法会使用可能出错的预测，同时保留形式化性能保证。本综述综合了在线优化、缓存、学习数据结构、图问题与机制设计领域的预测接口、误差度量、一致性-鲁棒性权衡及五种代表性构造机制。正交的定理层面维度区分了已实现的上界与匹配的渐近依赖关系，形式化保证与实证系统证据相分离，明确处理了预测成本、反馈与组合问题。该综合成果给出了有限端到端推理的充分条件，并划定了成本感知预测、内生误差、语义预测器及基准测试方面的开放问题。
英文摘要
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.
