Retriever Portfolios: A Principled Approach to Adaptive RAG
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866913172609302528 |
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| author | Stouras, Miltiadis Cohen-Addad, Vincent Lattanzi, Silvio Svensson, Ola |
| author_facet | Stouras, Miltiadis Cohen-Addad, Vincent Lattanzi, Silvio Svensson, Ola |
| contents | Retrieval-augmented generation (RAG) systems typically rely on a single retriever and a single set of hyperparameters, despite facing highly heterogeneous queries that range from simple factoid questions to complex multi-hop reasoning. We propose a method that automatically selects a small, diverse subset of retrievers (a portfolio) from a large pool of candidates, to cover different regions of the target query distribution. We formalize this setting via an expected best-of-$k$ objective over the query distribution and show that it admits an efficient portfolio construction algorithm with near-optimal guarantees. Across multiple QA benchmarks, our learned portfolios and router pipeline consistently outperform single-retriever and naive multi-retriever baselines on both retrieval metrics and answer quality. In addition, compared to inference-time hyperparameter tuning approaches, fixed portfolios enable parallel retrieval and LLM calls, achieving comparable (and sometimes better) accuracy with substantially lower latency and token cost. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_31176 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Retriever Portfolios: A Principled Approach to Adaptive RAG Stouras, Miltiadis Cohen-Addad, Vincent Lattanzi, Silvio Svensson, Ola Machine Learning Data Structures and Algorithms Retrieval-augmented generation (RAG) systems typically rely on a single retriever and a single set of hyperparameters, despite facing highly heterogeneous queries that range from simple factoid questions to complex multi-hop reasoning. We propose a method that automatically selects a small, diverse subset of retrievers (a portfolio) from a large pool of candidates, to cover different regions of the target query distribution. We formalize this setting via an expected best-of-$k$ objective over the query distribution and show that it admits an efficient portfolio construction algorithm with near-optimal guarantees. Across multiple QA benchmarks, our learned portfolios and router pipeline consistently outperform single-retriever and naive multi-retriever baselines on both retrieval metrics and answer quality. In addition, compared to inference-time hyperparameter tuning approaches, fixed portfolios enable parallel retrieval and LLM calls, achieving comparable (and sometimes better) accuracy with substantially lower latency and token cost. |
| title | Retriever Portfolios: A Principled Approach to Adaptive RAG |
| topic | Machine Learning Data Structures and Algorithms |
| url | https://arxiv.org/abs/2605.31176 |