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.

翻译：尽管精确检索是检索增强生成（RAG）的核心目标，但该领域仍存在一个关键性疏忽：检索性能的提升并不能稳定转化为下游推理能力的对应增益。为诊断这一差距，我们提出检索转化率（RCR）这一新型评估指标，用于量化检索对推理准确率的贡献。对主流RAG方法的定量分析表明，随着Recall@5的提升，RCR呈现近线性衰减趋势。我们识别出这些方法对检索质量的忽视是根本原因。与之相反，仅专注于质量优化的方法往往面临检索性能欠佳的问题。两类方法均缺乏对检索质量优化的全面理解，导致陷入两难困境。为解决这些问题，我们提出全面的检索质量优化准则，并引入NeocorRAG框架。该框架通过系统性挖掘和利用证据链实现整体检索质量优化。具体而言，NeocorRAG首先采用创新的激活搜索算法获取优化后的候选空间，随后通过约束解码确保精确的证据链生成，最终利用检索到的证据链集合指导检索优化过程。在HotpotQA、2WikiMultiHopQA、MuSiQue和NQ等基准测试上的评估表明，NeocorRAG在3B和70B参数模型上均达到当前最优性能，同时消耗的令牌数不足同类方法的20%。本研究提供了一种高效、免训练的RAG增强范式，可在保持高召回率的同时有效优化检索质量。我们的代码已开源至https://github.com/BUPT-Reasoning-Lab/NeocorRAG。