MCompassRAG：主题元数据作为段落级检索的语义指南
MCompassRAG: Topic Metadata as a Semantic Compass for Paragraph-Level Retrieval
摘要
检索增强生成系统在很大程度上取决于文档的分割方式以及搜索策略。细粒度分割可以提升检索精度，但也会扩大搜索范围，从而增加延迟和成本；而大颗粒度的分割则可以减少候选项的数量，但会导致相似性的判断变得不那么可靠，因为每个颗粒度的表示会包含多个主题，从而引入更多语义噪声。这种权衡在复杂的研究任务中尤其重要，因为在这些任务中，检索需要在大型、异质化的数据集中实现快速且准确的操作。我们提出了MCompassRAG这一基于元数据引导的检索框架，它利用主题级别的信号作为语义指南来选择相关的信息。与仅依赖查询与噪声较大的块表示之间的余弦相似度不同，MCompassRAG通过主题元数据来丰富块的表示，并在同一嵌入空间中训练轻量级检索器，同时利用LLM-教师蒸馏技术进行训练。在推理过程中，MCompassRAG能够在不使用额外LLM的情况下实现主题感知检索，从而提升效率和信息质量。在六个复杂的检索测试集上，MCompassRAG平均提升了8.24%的信息效率，其延迟也比最高效的RAG基线方法低5倍以上。代码可访问于https://github.com/AmirAbaskohi/MCompassRAG。
English Abstract
Retrieval-augmented generation (RAG) systems depend critically on how documents are chunked and searched. Fine-grained chunks can improve retrieval precision but expand the search space, increasing latency and cost; larger chunks reduce the number of candidates but make dense similarity less reliable, as the representation for each chunk mixes multiple topics and introduces more semantic noise. This trade-off becomes especially limiting in deep research tasks, where retrieval must be both fast and precise across large, heterogeneous corpora. We introduce MCompassRAG, a metadata-guided retrieval framework that uses topic-level signals as a semantic compass for selecting relevant evidence. Instead of relying only on cosine similarity between queries and noisy chunk embeddings, MCompassRAG enriches chunk representations with topic metadata in the same embedding space and trains a lightweight retriever through LLM-teacher distillation. At inference time, MCompassRAG performs topic-aware retrieval without additional LLM calls, improving both efficiency and evidence quality. Across six complex retrieval benchmarks, MCompassRAG improves information efficiency (IE) by 8.24% on average with over 5 times lower latency than the strongest efficient RAG baselines. Code is available on https://github.com/AmirAbaskohi/MCompassRAG.