SOURCE-LINKED INTELLIGENCE
Diagnosing the Fact-Grounding Gap in Multi-Hop Question Answering
Multi-hop question answering requires combining information from multiple documents to answer complex questions. These systems have grown increasingly capable, yet when they fail, the error is typically attributed to not finding the right documents. Whether this holds at the level of individual reasoning steps remains largely unexamined. We investigate this across three standard multi-hop QA benchmarks and find that failures decompose into two distinct modes: retrieval failures, where the needed passage was not retrieved, and extraction failures, where the passage was retrieved but the needed
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-15T11:51:52.000Z
First collected: 2026-09-20T08:40:59.508Z. This is not the publication date.