MCompassRAG：主題元數據作為段落級檢索的語義指南針

摘要

检索增强生成（RAG）系统的性能关键取决于文档的切分与检索策略。细粒度切分虽能提升检索精度，但会扩大搜索空间，导致延迟和成本增加；而粗粒度切分虽能减少候选数量，却会使稠密相似度计算的可信度下降——因为每个分块的表征会混合多个主题，引入更多语义噪声。这种权衡在深度研究任务中尤为受限：此类任务需要在大规模异构语料库中同时实现快速且精确的检索。为此，我们提出MCompassRAG——一种元数据引导的检索框架，利用主题级信号作为语义罗盘，精准定位相关证据。不同于仅依赖查询与含噪分块嵌入间的余弦相似度，MCompassRAG通过在相同嵌入空间中用主题元数据丰富分块表征，并通过大语言模型（LLM）教师蒸馏训练轻量级检索器。在推理阶段，MCompassRAG无需额外调用LLM即可实现主题感知检索，同时提升效率与证据质量。在六项复杂检索基准测试中，MCompassRAG的信息效率（IE）平均提升8.24%，且延迟比最强的高效RAG基线降低5倍以上。代码已开源至https://github.com/AmirAbaskohi/MCompassRAG。

English

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.