GRAFT: Graph-Distilled Generative Retrieval for Facet-Aware Scientific Literature Exploration
Scientific papers may relate by problem, method, result, or contribution, but document-level retrievers collapse these into a single similarity score without saying why they are related. Citation- and similarity-based retrieval alone also confines search to the neighbourhood of what is already known, whereas generative retrieval generates document identifiers directly, enabling the exploratory retrieval that scientific discovery depends on. We connect papers in a graph whose edges are typed by these four facets, derived from facet items and citation signals, and distil it into a generative retriever whose identifiers are the papers' own facet text. Two graph properties do not survive naiv...
| Authors | Italo Luis da Silva, Hanqi Yan, Yujing Wang et al. |
|---|---|
| Primary category | cs.IR |
| Published | 23 August 2026 |
| Last updated at source | 23 August 2026 |
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