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Re$^3$Rank introduces reasoning-conditioned retrieval for improved document reranking

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Re$^3$Rank, a novel listwise reranker designed to improve information retrieval by conditioning retrieval heads on reasoning traces. This method involves a generator creating a reasoning trace over a question and candidate documents, which is then used by selected retrieval heads to score relevance. The system is trained end-to-end using a straight-through estimator to optimize both the generator and scorer for ranking tasks. Experiments show that Re$^3$Rank significantly outperforms existing rerankers, with a 0.6B parameter variant surpassing much larger models and a 4B variant achieving a 3.11 NDCG@10 improvement over the strongest baseline. AI

IMPACT This approach could enhance the accuracy and efficiency of information retrieval systems by enabling more nuanced understanding of query-document relationships.

RANK_REASON The item is a research paper detailing a new method for document reranking. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Re$^3$Rank introduces reasoning-conditioned retrieval for improved document reranking

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The item is a research paper detailing a new method for document reranking. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Ruiyi Yan, Zhuoyuan Mao, Yugo Murawaki, Yiwen Guo ·

    Re$^3$Rank: Listwise Reranking with Reasoning-Conditioned Retrieval Heads

    arXiv:2611.02550v1 Announce Type: new Abstract: Attention-based rerankers score each candidate by the attention mass that retrieval heads route to it from the question, but the question, serving as a fixed query, carries limited information for locating relevant evidence. We prop…

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