More Context, Same Budget: Dual-Bounded Relational Recall Beyond Top-K Retrieval

Abstract:More context does not require a larger retrieval budget. Under the same ceiling, a retrieval system can recover more of the evidence a question requires by following relationships between evidence that flat top-k ranking leaves behind. We test that proposition with Dual-Bounded Relational Recall (DBRR), which allocates a fixed retrieval budget between relevance-selected seeds and bounded graph-adjacent context, against matched flat top-k retrieval using the same relevance-ranking stage and the same maximum number of retrieval units and tokens. The outcome is complete recovery of the official HotpotQA supporting-evidence set for each question. Across 7,405 FullWiki questions, the Primary DBRR allocation increased complete supporting-evidence recovery by 23.8 percentage points over its matched flat baseline (paired risk difference 0.2377; question-level bootstrap 95% interval 0.2269 to 0.2489). It improved 1,952 questions, tied on 5,261, and harmed 192. Bridge questions drove the effect, with a 28.7-point increase; comparison questions showed a smaller 4.2-point difference. In a prespecified, evaluation-only diagnostic population, real relationships also outperformed random-neighbor and degree-preserving shuffled-graph controls. The result is straightforward: under the same context budget, complete-evidence retrieval depends not only on which items rank highest, but on how context is allocated around them. Relational allocation recovered complete evidence sets that flat top-k retrieval left incomplete.

20 pages, 4 figures. Complete supporting-evidence recovery under a frozen HotpotQA FullWiki retrieval design; not answer accuracy

Information Retrieval (cs.IR); Computation and Language (cs.CL)

arXiv:2608.18448 [cs.IR]

(or arXiv:2608.18448v1 [cs.IR] for this version)

https://doi.org/10.48550/arXiv.2608.18448 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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