A survey that categorizes RIR benchmarks by domain and modality, proposes a taxonomy for integrating reasoning into retrieval pipelines, and outlines key challenges.
Hockenmaier, and Tong Zhang
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
Active learning applied to noisy LLM pairwise judgments improves NDCG@10 per call in budget-constrained reranking and enables unbiased aggregation via a randomized-direction single-call oracle.
citing papers explorer
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A Survey of Reasoning-Intensive Retrieval: Progress and Challenges
A survey that categorizes RIR benchmarks by domain and modality, proposes a taxonomy for integrating reasoning into retrieval pipelines, and outlines key challenges.
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Active Learners as Efficient PRP Rerankers
Active learning applied to noisy LLM pairwise judgments improves NDCG@10 per call in budget-constrained reranking and enables unbiased aggregation via a randomized-direction single-call oracle.