QuDAR: Query-Wise Dual-Perspective Adaptive Retrieval

Abstract

Retrieval-augmented generation (RAG) systems depend on retrieval modules to supply grounding evidence for large language models. While hybrid approaches combining sparse and dense retrievers improve performance, most rely on fixed weights that ignore query-specific and corpus-specific variation. Similarly, query expansion has long been used to enrich recall, but its integration with original queries is usually static and can introduce noise. We present Q U DAR, a dual-perspective adaptive retrieval framework motivated by a systematic analysis of retrieval behavior across retriever type (sparse vs. dense) and query format (original vs. expanded). Leveraging margin-derived confidence (e.g., top-1–top-2 score gaps) and LLM-based relevance scoring, Q U DAR dynamically assigns query-specific weights, enabling effective integration of complementary retrieval signals while mitigating noise. Q U DAR is lightweight, retriever-agnostic, and broadly applicable. Experiments show consistent gains over static baselines, improving retrieval quality by 12 – 16 % and yielding more stable performance across queries.