This paper introduces online algorithms with unreliable guidance (OAG), a model for ML-augmented online decision-making that cleanly separates the predictive and algorithmic components, thus offering a single, well-defined analysis framework that depends only on the problem at hand. Formulated through the lens of request-answer games, the OAG model brings multiple concepts (predictions from the answer space, guide, anytime competitiveness) which enable learning-augmented algorithms to be analyzed independently of predictor-specific choices - such as prediction semantics, error functions, or probing strategies - that would otherwise restrict the algorithm's generality and applicability. The clean framework of the OAG model allows to build the first generic compiler, the drop-or-trust-blindly (DTB) compiler, that turns almost any standard, prediction-free online algorithm into a learning-augmented one. Although simple, we show that the DTB compiler produces new learning-augmented algorithms with strong consistency-robustness guarantees for three classic online problems: we achieve new trade-offs for bipartite matching with adversarial arrival order, and obtain optimal solutions for caching and uniform metrical task systems.

翻译：本文提出了一种带有不可靠指导的在线算法（OAG）模型，用于机器学习增强的在线决策。该模型将预测组件与算法组件清晰分离，从而提供了一个仅依赖于具体问题的单一、定义明确的分析框架。通过请求-答案博弈的视角构建，OAG模型引入了多个概念（来自答案空间的预测、指导、即时竞争性），使得学习增强算法能够独立于预测器特定选择（如预测语义、误差函数或探测策略）进行分析，这些选择通常会限制算法的通用性和适用性。OAG模型的简洁框架允许构建第一个通用编译器——即“丢弃或盲目信任”（DTB）编译器，该编译器可将几乎任何标准的、无预测的在线算法转化为学习增强算法。尽管简单，我们证明了DTB编译器为三个经典在线问题生成了具有强一致性-鲁棒性保证的新学习增强算法：针对对抗到达顺序的二部图匹配实现了新的权衡，并为缓存和均匀度量任务系统获得了最优解。