RAG's "more precision means more latency" dilemma, tackled by using topics as a compass 🧭 Title: MCompassRAG: Topic Metadata as a Semantic Compass for Paragraph-Level Retrieval URL: 🧭 Overview A metadata-guided retrieval framework that uses topic-level signals as a "semantic compass" to select relevant evidence at the paragraph level. It aims to improve precision and efficiency at the same time. ❓ Challenges Solved RAG faces a precision-vs-efficiency trade-off. ・Fine-grained chunks raise precision but increase candidates, latency, and cost ・Larger chunks reduce candidates but introduce semantic noise from mixed topics This is acute in deep-research tasks needing fast, precise retrieval over large datasets. 💡 Methodology & Proposed Approach ・It enriches chunk representations with topic metadata within the same embedding space ・It uses LLM-teacher distillation to train a lightweight retriever ・This enables topic-aware retrieval with no additional LLM calls at inference time The core is combining metadata with dense embeddings and distilling into a lightweight retriever. 📊 Experimental Results ・Information efficiency: 8.24% average improvement across six benchmarks ・Latency: over 5x lower than the strongest efficient RAG baselines ・Code is available in a public repository It achieves this precision-and-speed balance without extra LLM calls at inference. #RAG# #Retrieval#