Dissecting Agentic RAG: A Component Ablation for Multi-Hop QA with a Local 7B Model

• URL: http://arxiv.org/abs/2606.21553v1
• Date: Fri, 19 Jun 2026 15:50:35 GMT
• Title: Dissecting Agentic RAG: A Component Ablation for Multi-Hop QA with a Local 7B Model
• Abstract summary: We run a controlled ablation study of a full agentic RAG pipeline evaluated on 5,000 questions.<n>We find that fixed hybrid retrieval via reciprocal rank fusion consistently outperforms rule-based adaptive routing.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Agentic retrieval-augmented generation (RAG) systems combine iterative reasoning loops, query decomposition, and adaptive retrieval to tackle multi-hop question answering. However, the contribution of each component remains poorly understood, particularly under resource-constrained settings using only local language models. Many agentic designs add adaptive retrieval routing and deeper retrieval loops on the assumption that the added complexity helps. To test whether it does, we run a controlled ablation study of a full agentic RAG pipeline evaluated on 5,000 questions from the HotpotQA distractor development set using a local 7B parameter model (Qwen2.5-7B-Instruct). Our full pipeline achieves EM=53.2% and F1=61.6%, compared to a single-pass dense-retrieval baseline of EM=43.1% and F1=54.0%. Across eight ablation conditions, we find that: (1) fixed hybrid retrieval via reciprocal rank fusion consistently outperforms rule-based adaptive routing (+1.8 EM, +1.9 F1), as the routing heuristic over-routes to BM25 by firing on named entities present in nearly all multi-hop sub-questions; (2) two retrieval iterations over the decomposed sub-questions capture 95% of the gains of five, with no meaningful benefit from deeper loops; and (3) query decomposition and cross-encoder reranking each contribute statistically significant but smaller gains (p<0.01 and p<0.001 respectively). Taken together, on a fixed local-model budget, the simpler and fixed choices turn out to be competitive with or better than their adaptive versions: most of the gain comes from running a short retrieval loop, not from adaptive routing or from many iterations. We use no proprietary APIs or large-scale compute.

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