SOURCE-LINKED INTELLIGENCE
Hi-Q: Hierarchical Evidence-guided Query Refinement for Multi-Hop Question Answering
A central bottleneck in multi-hop Question Answering (QA) is that the granularity at which a question is expressed often differs from the granularity at which corpus evidence is retrievable. Existing methods address this mismatch by imposing fixed graph structures over the corpus, by iteratively reformulating the query, or by executing a generated program over it, but these strategies do not explicitly decide when a query unit is already supported by evidence and when it should be refined. We formulate this bottleneck as retrievable granularity discovery and introduce Hi-Q, an evidence-conditi
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-31T08:55:07.000Z
First collected: 2026-09-21T07:01:58.596Z. This is not the publication date.