Querying complex models for precise information (e.g. traffic models, database systems, large ML models) often entails intense computations and results in long response times. Thus, weaker models which give imprecise results quickly can be advantageous, provided inaccuracies can be resolved using few queries to a stronger model. In the fundamental problem of computing a maximum-weight basis of a matroid, a well-known generalization of many combinatorial optimization problems, algorithms have access to a clean oracle to query matroid information. We additionally equip algorithms with a fast but dirty oracle modelling an unknown, potentially different matroid. We design and analyze practical algorithms which only use few clean queries w.r.t. the quality of the dirty oracle, while maintaining robustness against arbitrarily poor dirty matroids, approaching the performance of classic algorithms for the given problem. Notably, we prove that our algorithms are, in many respects, best-possible. Further, we outline extensions to other matroid oracle types, non-free dirty oracles and other matroid problems.

复杂模型的查询通常需要复杂的计算和长时间的响应，因此弱模型能够快速提供不准确的结果，在使用少量查询到更强大的模型解决不准确性时具有优势。本文设计和分析了实用的算法，仅使用少量准确查询来处理低质量的不准确模型，接近于给定问题的经典算法性能，并且在许多方面证明了我们的算法是最佳的。此外，我们还概述了拓展至其他类型的 matroid oracle、非自由的 dirty oracle 以及其他 matroid 问题。

通过快速不精确的预言加速拟阵优化