Learning-augmented paging has been extensively studied in recent years. A key advantage over naive ML-based approaches is \emph{bounded robustness}, which guarantees worst-case performance even when predictions are inaccurate, making these algorithms valuable for real-world systems. Prior work achieves robustness bounds of $2H_k + O(1)$ in the randomized setting, leaving a gap to the optimal competitive ratio $H_k$. In this paper, we study how to close this gap. We begin by reviewing online optimality and proving a new property of the latest $H_k$-competitive algorithm, which facilitates our analysis in the learning-augmented setting. Then, we review existing learning-augmented paging algorithms and introduce a unifying primitive, the \emph{relative prediction budget}, which captures the essence of establishing robustness and reveals that prior algorithms either overuse or underutilize predictions. Guided by the above analysis, we develop a new framework that achieves the best-possible robustness up to an additive constant for learning-augmented paging: $H_k + O(1)$. Experiments further demonstrate strong practical performance.

翻译：学习增强分页近年来已被广泛研究。相较于朴素的基于机器学习方法，其关键优势在于*有界鲁棒性*——即使在预测不准确的情况下也能保证最坏情况性能，从而使这些算法在现实系统中具有重要价值。现有工作已在随机化设定下实现了$2H_k + O(1)$的鲁棒性界，与最优竞争比$H_k$之间存在差距。本文研究如何弥合这一差距。我们首先回顾在线最优性，并证明最新$H_k$-竞争算法的一个新性质，该性质有助于我们在学习增强设定下进行分析。随后，我们回顾现有学习增强分页算法，并引入统一原语*相对预测预算*，该原语揭示了建立鲁棒性的本质，并表明现有算法要么过度使用、要么未能充分利用预测。基于上述分析，我们开发了一个新框架，为学习增强分页实现了可达的最优鲁棒性（至多相差一个加性常数）：$H_k + O(1)$。实验进一步证明了其强大的实际性能。