Group-Aware Adaptive Retrieval for Evidence Navigation

2026-09-02

AI Summary

为解决复杂查询的低召回问题，提出GAREN方法，通过语义组扩展而非单文档扩展来导航和检索相关证据，提高多步推理查询的准确性。

Abstract

Reasoning-intensive retrieval addresses queries whose relevance cannot be identified by surface-level matching, thereby requiring multi-step reasoning. Because relevant documents rarely appear in the initial candidate set, retrieval systems suffer from the bounded recall problem. Existing methods iteratively expand a candidate pool at the document level over a corpus graph, examining each neighbor in isolation and drifting toward a narrow region of the corpus. To address this problem, we propose Group-Aware Adaptive Retrieval for Evidence Navigation (GAREN), which explores the corpus graph through group-level expansion. GAREN organizes documents into semantically coherent and distinguishable groups based on their connections in the corpus graph. The information in each group indicates what can be accessed by expanding through it, providing guidance beyond individual document-level signals. At each iteration, GAREN uses a group-level navigator to identify promising expansion directions, retrieves documents from the selected groups, and applies a document-level reranker to evaluate the updated candidate set. Extensive experiments show that GAREN achieves up to 8.0% improvement over the strongest baseline on BRIGHT. The source code is available at https://github.com/KJ12124/GAREN

Problem

• reasoning-intensive retrieval
• bounded recall problem
• corpus graph

Innovation

• Group-Aware
• Adaptive Retrieval
• Corpus Graph Expansion
• Multi-step Reasoning
• Bounded Recall