ADAPTIVE MULTI-STAGE VECTOR RETRIEVAL FOR RETRIEVAL-AUGMENTED GENERATION

Abstract

Retrieval-Augmented Generation (RAG) is the standard for grounding Large Language Models (LLMs), but its parts' embeddings, lexical scoring, query expansion, reranking, and adaptive sizing do not always compose additively. Under default configurations, adding naïve hybrid retrieval with Reciprocal Rank Fusion degrades NDCG@10 by up to 5.6% on scientific corpora; temperature-sampled query expansion with a small LLM further degrades it by 16.8%. To address this, we propose the Adaptive Multi-Stage Vector Retrieval (AMSVR) framework, prioritising weighted, drift-resistant composition over uniform fusion. Rather than a one-sizefits-all system, AMSVR offers tailored configurations: AMSVR-Scientific (dense + tuned hybrid) peaks at NDCG@10 = 0.7570 on SciFact, while AMSVR-Full (seven stages) targets broader, noisier corpora where Recall@100 matters most. We evaluate on three BEIR benchmarks (SciFact, NFCorpus, FIQA), release an offline configuration-diagnostic tool, and provide a per-corpus recipe for selecting which stages to enable.