Understanding which parts of the retrieved context contribute to a large language model's generated answer is essential for building interpretable and trustworthy generative QA systems. We propose a novel framework that formulates context attribution as a combinatorial multi-armed bandit (CMAB) problem. Each context segment is treated as a bandit arm, and we employ Combinatorial Thompson Sampling (CTS) to efficiently explore the exponentially large space of context subsets under a limited query budget. Our method …