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  "path": "/abs/2605.31176v1",
  "publishedAt": "2026-06-01T00:00:00.000Z",
  "site": "https://arxiv.org",
  "tags": [
    "Miltiadis Stouras",
    "Vincent Cohen-Addad",
    "Silvio Lattanzi",
    "Ola Svensson"
  ],
  "textContent": "**Authors:** Miltiadis Stouras, Vincent Cohen-Addad, Silvio Lattanzi, Ola Svensson\n\nRetrieval-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"
}