{"ID":2876268,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.00877","arxiv_id":"2509.00877","title":"EviNote-RAG: Enhancing RAG Models via Answer-Supportive Evidence Notes","abstract":"Retrieval-Augmented Generation (RAG) has advanced open-domain question answering by incorporating external information into model reasoning. However, effectively leveraging external information to enhance reasoning presents the following challenges: (1) low signal-to-noise ratio, where answer-supportive external information is diluted by irrelevant material, and (2) error accumulation, which arises in multi-hop reasoning when incomplete or misleading information is incorporated. To address these challenges, we introduce EviNote-RAG, a framework that follows a retrieve-note-answer workflow. Instead of reasoning directly over raw external information, the model first produces Supportive-Evidence Notes (SENs), which concisely preserve answer-critical information and explicitly mark key and uncertainty information to improve accuracy. We further design an entailment-based Evidence Quality Reward (EQR) to ensure that SENs are logically sufficient to derive the final answer, thereby enhancing SENs' quality. Experiments on both in-domain and out-of-domain QA benchmarks show that EviNote-RAG achieves state-of-the-art performance, improving answer accuracy, training stability, robustness, and efficiency. In particular, it yields relative F1 gains of 20% on HotpotQA (+0.093), 40% on Bamboogle (+0.151), and 91% on 2Wiki (+0.256), benefiting from improvements in the reasoning process.","short_abstract":"Retrieval-Augmented Generation (RAG) has advanced open-domain question answering by incorporating external information into model reasoning. However, effectively leveraging external information to enhance reasoning presents the following challenges: (1) low signal-to-noise ratio, where answer-supportive external inform...","url_abs":"https://arxiv.org/abs/2509.00877","url_pdf":"https://arxiv.org/pdf/2509.00877v3","authors":"[\"Yuqin Dai\",\"Guoqing Wang\",\"Yuan Wang\",\"Kairan Dou\",\"Kaichen Zhou\",\"Zhanwei Zhang\",\"Shuo Yang\",\"Fei Tang\",\"Jun Yin\",\"Pengyu Zeng\",\"Zhenzhe Ying\",\"Can Yi\",\"Changhua Meng\",\"Yuchen Zhou\",\"Yongliang Shen\",\"Shuai Lu\"]","published":"2025-08-31T14:44:45Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"RAG\"]","has_code":false}