NeocorRAG: Less Irrelevant Information, More Explicit Evidence, and More Effective Recall via Evidence Chains

2026-04-30

AI Summary

This work addresses the persistent gap in retrieval-augmented generation (RAG) systems, where improvements in retrieval performance often fail to translate into gains in downstream accuracy due to a disconnect between recall and reasoning. To bridge this gap, the authors propose NeocorRAG, a novel framework that introduces, for the first time, the Recall Conversion Rate (RCR) metric to quantify this discrepancy. NeocorRAG establishes a training-free, evidence-chain-driven retrieval optimization paradigm: it employs an activation-based search algorithm to generate refined candidate sets and leverages constrained decoding to construct precise evidence chains, which in turn guide high-quality retrieval in a backward-refinement manner. The method achieves state-of-the-art results across multiple multi-hop question answering benchmarks—including HotpotQA, 2WikiMultiHopQA, MuSiQue, and Natural Questions—while being compatible with language models ranging from 3B to 70B parameters and consuming less than 20% of the token budget required by comparable approaches.

Abstract

Although precise recall is a core objective in Retrieval-Augmented Generation (RAG), a critical oversight persists in the field: improvements in retrieval performance do not consistently translate to commensurate gains in downstream reasoning. To diagnose this gap, we propose the Recall Conversion Rate (RCR), a novel evaluation metric to quantify the contribution of retrieval to reasoning accuracy. Our quantitative analysis of mainstream RAG methods reveals that as Recall@5 improves, the RCR exhibits a near-linear decay. We identify the neglect of retrieval quality in these methods as the underlying cause. In contrast, approaches that focus solely on quality optimization often suffer from inferior recall performance. Both categories lack a comprehensive understanding of retrieval quality optimization, resulting in a trade-off dilemma. To address these challenges, we propose comprehensive retrieval quality optimization criteria and introduce the NeocorRAG framework. This framework achieves holistic retrieval quality optimization by systematically mining and utilizing Evidence Chains. Specifically, NeocorRAG first employs an innovative activated search algorithm to obtain a refined candidate space. Then it ensures precise evidence chain generation through constrained decoding. Finally, the retrieved set of evidence chains guides the retrieval optimization process. Evaluated on benchmarks including HotpotQA, 2WikiMultiHopQA, MuSiQue, and NQ, NeocorRAG achieves SOTA performance on both 3B and 70B parameter models, while consuming less than 20% of tokens used by comparable methods. This study presents an efficient, training-free paradigm for RAG enhancement that effectively optimizes retrieval quality while maintaining high recall. Our code is released at https://github.com/BUPT-Reasoning-Lab/NeocorRAG.

Problem

• Retrieval-Augmented Generation
• retrieval quality
• reasoning accuracy
• recall performance
• evidence chains

Innovation

• Evidence Chains
• Recall Conversion Rate
• Retrieval-Augmented Generation
• Constrained Decoding
• Activated Search