{"success":true,"data":{"query":"retrieval augmented generation","count":5,"papers":[{"source":"openalex","title":"Active Retrieval Augmented Generation","authors":["Zhengbao Jiang","Frank F. Xu","Luyu Gao","Zhiqing Sun","Qian Liu"],"year":2023,"venue":"Conference on Empirical Methods in Natural Language Processing (EMNLP)","doi":"10.18653/v1/2023.emnlp-main.495","url":"https://doi.org/10.18653/v1/2023.emnlp-main.495","pdf_url":"https://aclanthology.org/2023.emnlp-main.495.pdf","citations":459,"abstract":"Zhengbao Jiang, Frank Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, Graham Neubig. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023."},{"source":"arxiv","title":"AR-RAG: Autoregressive Retrieval Augmentation for Image Generation","authors":["Jingyuan Qi","Zhiyang Xu","Qifan Wang","Lifu Huang"],"year":2025,"venue":"arXiv","doi":null,"url":"http://arxiv.org/abs/2506.06962v3","pdf_url":"https://arxiv.org/pdf/2506.06962v3","citations":null,"abstract":"We introduce Autoregressive Retrieval Augmentation (AR-RAG), a novel paradigm that enhances image generation by autoregressively incorporating knearest neighbor retrievals at the patch level. Unlike prior methods that perform a single, static retrieval before generation and condition the entire generation on fixed reference images, AR-RAG performs context-aware retrievals at each generation step, using prior-generated patches as queries to retrieve and incorporate the most relevant patch-level visual references, enabling the model to respond to evolving generation needs while avoiding limitat…"},{"source":"pubmed","title":"Retrieval augmented generation for large language models in healthcare: A systematic review","authors":["Amugongo LM","Mascheroni P","Brooks S","Doering S","Seidel J"],"year":2025,"venue":"PLOS digital health","doi":"10.1371/journal.pdig.0000877","url":"https://pubmed.ncbi.nlm.nih.gov/40498738/","pdf_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12157099/pdf/","citations":null,"abstract":null},{"source":"openalex","title":"Corrective Retrieval Augmented Generation","authors":["Shi-Qi Yan","Jia-Chen Gu","Yun Zhu","Zhen-Hua Ling"],"year":2025,"venue":"SSRN Electronic Journal","doi":"10.2139/ssrn.5267341","url":"https://doi.org/10.2139/ssrn.5267341","pdf_url":"https://doi.org/10.2139/ssrn.5267341","citations":121,"abstract":null},{"source":"arxiv","title":"Intelligent Interaction Strategies for Context-Aware Cognitive Augmentation","authors":[" Xiangrong"," Zhu","Yuan Xu","Tianjian Liu","Jingwei Sun"],"year":2025,"venue":"arXiv","doi":null,"url":"http://arxiv.org/abs/2504.13684v1","pdf_url":"https://arxiv.org/pdf/2504.13684v1","citations":null,"abstract":"Human cognition is constrained by processing limitations, leading to cognitive overload and inefficiencies in knowledge synthesis and decision-making. Large Language Models (LLMs) present an opportunity for cognitive augmentation, but their current reactive nature limits their real-world applicability. This position paper explores the potential of context-aware cognitive augmentation, where LLMs dynamically adapt to users' cognitive states and task environments to provide appropriate support. Through a think-aloud study in an exhibition setting, we examine how individuals interact with multi-…"}],"sources":["openalex","arxiv","pubmed"],"source_errors":{},"searched_at":"2026-10-07T17:59:43.418Z"},"trial":{"free":true,"used_example_input":true,"input_used":{"query":"retrieval augmented generation","limit":5},"remaining_today":0,"resets_at":"2026-10-08T00:00:00.000Z","note":"Real output for the example input. The paid endpoint returns the same format for your own input."},"paid_endpoint":"https://agentsvc.io/api/v1/proxy/paper-search","price_usdc":0.008,"payment":"x402, USDC on Base or Solana mainnet, no account needed","docs":"https://agentsvc.io/docs"}