Retriever Portfolios: A Principled Approach to Adaptive RAG

• URL: http://arxiv.org/abs/2605.31176v1
• Date: Fri, 29 May 2026 11:43:05 GMT
• Title: Retriever Portfolios: A Principled Approach to Adaptive RAG
• Abstract summary: We propose a method that automatically selects a small, diverse subset of retrievers from a large pool of candidates, to cover different regions of the target query distribution.<n>Across multiple QA benchmarks, our learned portfolios and router pipeline consistently outperform single-retriever and naive multi-retriever baselines.
• Score: 30.668406304237404
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Pre-retrieval Query Clustering for Adaptive Top-k Document Retrieval in RAG Systems [7.440012812118169]
  RAG systems commonly retrieve a fixed number of documents to ground generation.<n>simple queries suffer over-retrieval (adding noise and cost) while complex queries are under-retrieved.<n>We propose a practical, general framework for query-adaptive retrieval depth.
  arXiv  Detail & Related papers  (2026-09-11T19:57:12Z)
• Can QPP Choose the Right Query Variant? Evaluating Query Variant Selection for RAG Pipelines [48.34524144147475]
  Large Language Models (LLMs) have made query reformulation ubiquitous in modern retrieval and Retrieval-Augmented Generation (RAG) pipelines.<n>We investigate Query Performance Prediction (QPP) as a mechanism for variant selection across ad-hoc retrieval and end-to-end RAG.
  arXiv  Detail & Related papers  (2026-04-24T15:36:40Z)
• R$^3$AG: Retriever Routing for Retrieval-Augmented Generation [59.47703698994575]
  R$3$AG decomposes retriever capability into two learnable dimensions: retrieval quality and generation utility.<n>R$3$AG consistently outperforms both the best individual retrievers and state-of-the-art static routing methods.
  arXiv  Detail & Related papers  (2026-04-22T06:51:20Z)
• Hybrid Deep Searcher: Integrating Parallel and Sequential Search Reasoning [57.78245296980122]
  We introduce HDS-QA (Hybrid Deep Search QA), a dataset automatically generated from Natural Questions.<n>It comprises hybrid-hop questions that combine parallelizable independent subqueries (executable simultaneously) and sequentially dependent subqueries (requiring step-by-step resolution)<n>We name the model HybridDeepSearcher, which outperforms state-of-the-art baselines across multiple benchmarks.
  arXiv  Detail & Related papers  (2025-08-26T15:15:17Z)
• LTRR: Learning To Rank Retrievers for LLMs [53.285436927963865]
  We show that routing-based RAG systems can outperform the best single-retriever-based systems.<n>Performance gains are especially pronounced in models trained with the Answer Correctness (AC) metric.<n>As part of the SIGIR 2025 LiveRAG challenge, our submitted system demonstrated the practical viability of our approach.
  arXiv  Detail & Related papers  (2025-06-16T17:53:18Z)
• Beyond Prompting: An Efficient Embedding Framework for Open-Domain Question Answering [22.121850498642008]
  Large language models have recently pushed open domain question answering to new frontiers.<n> prevailing retriever-reader pipelines often depend on multiple rounds of prompt level instructions.<n>We propose EmbQA, an embedding-level framework that enhances both the retriever and the reader.
  arXiv  Detail & Related papers  (2025-03-03T14:41:35Z)
• Fast or Better? Balancing Accuracy and Cost in Retrieval-Augmented Generation with Flexible User Control [52.405085773954596]
  Retrieval-Augmented Generation has emerged as a powerful approach to mitigate large language model hallucinations.<n>Existing RAG frameworks often apply retrieval indiscriminately,leading to inefficiencies-over-retrieving.<n>We introduce a novel user-controllable RAG framework that enables dynamic adjustment of the accuracy-cost trade-off.
  arXiv  Detail & Related papers  (2025-02-17T18:56:20Z)
• Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity [59.57065228857247]
  Retrieval-augmented Large Language Models (LLMs) have emerged as a promising approach to enhancing response accuracy in several tasks, such as Question-Answering (QA) We propose a novel adaptive QA framework, that can dynamically select the most suitable strategy for (retrieval-augmented) LLMs based on the query complexity. We validate our model on a set of open-domain QA datasets, covering multiple query complexities, and show that ours enhances the overall efficiency and accuracy of QA systems.
  arXiv  Detail & Related papers  (2024-03-21T13:52:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.