We address the problem of learning-augmented online caching in the scenario when each request is accompanied by a prediction of the next occurrence of the requested page. We improve currently known bounds on the competitive ratio of the BlindOracle algorithm, which evicts a page predicted to be requested last. We also prove a lower bound on the competitive ratio of any randomized algorithm and show that a combination of the BlindOracle with the Marker algorithm achieves a competitive ratio that is optimal up to some constant.

翻译：本文研究学习增强型在线缓存问题，其中每个请求均附有被请求页面下一次出现时刻的预测。我们改进了当前已知的BlindOracle算法（该算法驱逐被预测为最晚被请求的页面）的竞争比界限。同时，我们证明了任意随机算法的竞争比下界，并展示了BlindOracle算法与Marker算法相结合可获得在常数因子内最优的竞争比。