Reproducing Adaptive Reranking for Reasoning-Intensive IR

2026-04-30

AI Summary

This work addresses the limitation of traditional retrieve-then-rerank pipelines, whose effectiveness is constrained by the recall ceiling of the initial retriever and struggles with queries requiring complex reasoning. To overcome this, the authors propose Graph-based Adaptive Reranking (GAR), a novel approach that introduces an iterative exploration mechanism leveraging a corpus graph during reranking, thereby enhancing reasoning capabilities without modifying the initial retriever. GAR is the first method tailored for reasoning-intensive retrieval scenarios, demonstrating strong generalization across diverse reranking models. Evaluated on the BRIGHT benchmark, it achieves substantial performance gains while incurring minimal computational overhead, marking a significant step toward practical deployment of such systems.

Abstract

The classical cascading pipeline of retrieve--rerank suffers from a bounded recall problem, stemming from limitations of the first-stage retriever. Most current approaches address the bounded recall problem by improving the first-stage retriever, but this incurs substantial training and inference costs, especially to handle queries that require substantial reasoning. To circumvent the computational costs of reasoning-based retrievers, we replicate the findings of GAR, Graph-based Adaptive Reranking, on the BRIGHT reasoning-intensive retrieval benchmark. GAR addresses the bounded recall problem by modifying the reranking process itself through iterative exploration of a corpus graph, but it was previously only tested on models designed for topical and question-answering-style queries. Hence, reproduce GAR in reasoning-intensive settings with reasoning and non-reasoning reranking models. We observe that the quality of the reranker's signal plays an important role in identifying additional relevant documents within the corpus graph. Overall, we find that GAR boosts the effectiveness of reasoning-intensive retrieval across a variety of models while contributing minimally to computational overheads. Ultimately, this work enables more practical deployment of retrieval systems that can address reasoning-intensive queries.

Problem

• bounded recall
• reasoning-intensive retrieval
• adaptive reranking
• retrieval pipeline
• computational cost

Innovation

• Graph-based Adaptive Reranking
• reasoning-intensive retrieval
• bounded recall
• corpus graph
• iterative reranking