RAGRouter-Bench: A Dataset and Benchmark for Adaptive RAG Routing
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866917383469268992 |
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| author | Wang, Ziqi Zhu, Xi Lin, Shuhang Xue, Haochen Guo, Minghao Zhang, Yongfeng |
| author_facet | Wang, Ziqi Zhu, Xi Lin, Shuhang Xue, Haochen Guo, Minghao Zhang, Yongfeng |
| contents | Retrieval-augmented generation (RAG) has evolved into a family of paradigms with distinct performance profiles and resource demands, turning paradigm selection into a multi-criteria, context-dependent decision problem. Nevertheless, existing studies largely focus on isolated method improvements or query-only benchmarking, without systematically examining how RAG paradigms behave across diverse query-corpus contexts and effectiveness-efficiency trade-offs. In this work, we introduce RAGRouter-Bench, the first dataset and benchmark for adaptive RAG routing. Grounded in query-corpus compatibility, the benchmark integrates three canonical query types, fine-grained corpus indicators capturing structural and semantic properties, and a unified protocol for evaluating both generation quality and resource consumption. Then, we implement standardized RAG paradigms with multiple backbone LLMs across all query-corpus combinations, constructing a comprehensive benchmark with quantitative metrics and LLM-as-a-Judge evaluations to inform context-aware and cost-effective RAG routing decisions. We further formulate routing as context-dependent paradigm selection and benchmark a range of query-corpus routers on the constructed dataset. Extensive experiments demonstrate that no one-size-fits-all paradigm exists across query-corpus pairs, and that adaptive routing yields more favorable effectiveness-efficiency trade-offs than fixed paradigm selection. These findings establish query-corpus compatibility as a central principle for adaptive RAG routing and position RAGRouter-Bench as a systematic testbed for next-generation RAG systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_00296 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RAGRouter-Bench: A Dataset and Benchmark for Adaptive RAG Routing Wang, Ziqi Zhu, Xi Lin, Shuhang Xue, Haochen Guo, Minghao Zhang, Yongfeng Information Retrieval Retrieval-augmented generation (RAG) has evolved into a family of paradigms with distinct performance profiles and resource demands, turning paradigm selection into a multi-criteria, context-dependent decision problem. Nevertheless, existing studies largely focus on isolated method improvements or query-only benchmarking, without systematically examining how RAG paradigms behave across diverse query-corpus contexts and effectiveness-efficiency trade-offs. In this work, we introduce RAGRouter-Bench, the first dataset and benchmark for adaptive RAG routing. Grounded in query-corpus compatibility, the benchmark integrates three canonical query types, fine-grained corpus indicators capturing structural and semantic properties, and a unified protocol for evaluating both generation quality and resource consumption. Then, we implement standardized RAG paradigms with multiple backbone LLMs across all query-corpus combinations, constructing a comprehensive benchmark with quantitative metrics and LLM-as-a-Judge evaluations to inform context-aware and cost-effective RAG routing decisions. We further formulate routing as context-dependent paradigm selection and benchmark a range of query-corpus routers on the constructed dataset. Extensive experiments demonstrate that no one-size-fits-all paradigm exists across query-corpus pairs, and that adaptive routing yields more favorable effectiveness-efficiency trade-offs than fixed paradigm selection. These findings establish query-corpus compatibility as a central principle for adaptive RAG routing and position RAGRouter-Bench as a systematic testbed for next-generation RAG systems. |
| title | RAGRouter-Bench: A Dataset and Benchmark for Adaptive RAG Routing |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2602.00296 |