Retrieval-Augmented Generation (RAG) has become a common approach for improving the factuality of Large Language Models (LLMs), yet its reliability remains highly sensitive to how external evidence is retrieved and used. Semantically equivalent queries with different syntactic forms may lead to different retrieval results, while irrelevant or misleading documents can further induce hallucinated answers. Existing multi-path reasoning methods improve robustness by sampling multiple candidate answers and applying voting- or confidence-based selection, but they still face two limitations: diversity is often injected through uncontrollable decoding randomness, and answer evaluation is usually confined to a single query-induced evidence view. To address these limitations, we propose a Cross-Query Consistency Hypothesis: correct answers tend to maintain high confidence across semantically equivalent but syntactically diverse queries, whereas noise-induced hallucinations exhibit unstable confidence under such query variations. Based on this hypothesis, we introduce CQC-RAG, a framework that co-designs query-level diversity injection with cross-query consistency evaluation. CQC-RAG rewrites the original question into diverse but meaning-preserving queries, reranks a shared document pool to construct query-conditioned reasoning contexts, applies an evidence-grounded protocol to extract answer-evidence pairs and selects answers according to their confidence stability across these contexts. This design enables self-evaluation without external supervision and does not rely on expanded retrieval coverage. Experiments on four open-domain question answering benchmarks show that CQC-RAG outperforms the strongest previous multi-query baseline by +4.76 pp EM on TriviaQA and +9.12 pp EM on MuSiQue, validating the effectiveness of cross-query consistency for filtering noise-induced hallucinations.

翻译：检索增强生成（RAG）已成为提升大语言模型（LLM）事实性的常用方法，但其可靠性仍高度依赖于外部证据的检索与利用方式。语义等价但句法形式不同的查询可能导致不同的检索结果，而无关或误导性文档更可能诱发生成幻觉。现有基于多路径推理的方法通过采样多个候选答案并应用投票或置信度选择来增强鲁棒性，但存在两个局限：多样性通常通过不可控的解码随机性注入，且答案评估往往局限于单一查询所引出的证据视角。为解决这些问题，我们提出交叉查询一致性假设：正确答案在不同句法形式但语义等价的查询下通常保持高置信度，而噪声诱发的幻觉在这类查询变化下则表现出不稳定的置信度。基于该假设，我们提出CQC-RAG框架，协同设计查询级多样性注入与跨查询一致性评估。CQC-RAG将原始问题改写为多样化但保持语义的查询，对共享文档池进行重排序以构建查询条件推理上下文，采用基于证据的协议提取答案-证据对，并根据这些上下文中答案的置信度稳定性进行选择。该设计无需外部监督即可实现自我评估，且不依赖扩展检索覆盖范围。在四个开放域问答基准上的实验表明，CQC-RAG在TriviaQA上超越最强先前多查询基线+4.76个EM点，在MuSiQue上超越+9.12个EM点，验证了交叉查询一致性在过滤噪声诱发幻觉方面的有效性。