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This paper studies whether passages retrieved during multi-hop question answering contain the specific relational facts required at each reasoning step. Using a Self-Ask pipeline across MuSiQue, HotpotQA, and 2WikiMultihopQA, it distinguishes retrieval failures from extraction failures, where a gold passage is retrieved but the required fact remains unavailable. It validates an LLM fact-presence judge, trains a DeBERTa predictor of deficient hops, and evaluates targeted augmentation and reranking. The reported results show substantial extraction failures, weak diagnostic value from retrieval recall alone, selective benefits from re-retrieval on retrieval failures, and pronounced variation across datasets and question types.
Finding the right document is not the same as finding the fact needed to answer a reasoning step. The paper argues that standard document-recall metrics hide this difference, so improving retrieval alone can leave many multi-hop failures untouched. A lightweight predictor can target extra retrieval more efficiently on some datasets, but the effect depends on the dataset, question type, retriever, and reasoning model; the work does not solve cases where the corpus passage itself omits the needed relation.
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