{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T13:26:42Z","timestamp":1777728402709,"version":"3.51.4"},"reference-count":44,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T00:00:00Z","timestamp":1763337600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Intelligenza Artificiale"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:p>\n                    Retrieval-Augmented Generation (RAG) enhances language model responses by incorporating external knowledge. However, its effectiveness heavily depends on the quality of the retrieved documents. Using a fixed number of retrieved documents\n                    <jats:inline-formula>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\" overflow=\"scroll\">\n                        <mml:mi>K<\/mml:mi>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    often fails to adapt to varying query complexity, leading either to irrelevant retrievals or to missing crucial evidence. To address this issue, we propose DyRAG, a hybrid retrieval framework that dynamically adjusts\n                    <jats:inline-formula>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\" overflow=\"scroll\">\n                        <mml:mi>K<\/mml:mi>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    based on query characteristics while maintaining computational efficiency. Our method improves retrieval performance by maximizing relevant information and minimizing noise. We evaluate DyRAG across recommendation, question answering, and fact-checking tasks, where it consistently outperforms fixed-\n                    <jats:inline-formula>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\" overflow=\"scroll\">\n                        <mml:mi>K<\/mml:mi>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    and hybrid baselines. Compared to traditional approaches, DyRAG demonstrates greater robustness and adaptability across diverse domains.\n                  <\/jats:p>","DOI":"10.1177\/17248035251395128","type":"journal-article","created":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T18:17:06Z","timestamp":1763403426000},"page":"16-28","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Boosting RAG Efficiency With dyRAG: Dynamic Candidate Selection for Optimal Retrieval"],"prefix":"10.1177","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3900-7858","authenticated-orcid":false,"given":"Zoubida Asmaa","family":"Boudjenane","sequence":"first","affiliation":[{"name":"University of Mascara"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7052-5978","authenticated-orcid":false,"given":"Mohammed","family":"Salem","sequence":"additional","affiliation":[{"name":"University of Mascara"}]}],"member":"179","published-online":{"date-parts":[[2025,11,17]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"publisher","DOI":"10.63180\/jcsra.thestap.2025.1.4"},{"key":"e_1_3_2_3_1","unstructured":"Bai Y. 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