{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T18:07:55Z","timestamp":1784138875298,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":65,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T00:00:00Z","timestamp":1784419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,7,20]]},"DOI":"10.1145\/3805712.3809693","type":"proceedings-article","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T14:28:19Z","timestamp":1783693699000},"page":"2150-2161","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Question-Adaptive Graph Learning for Multi-hop Retrieval Augmented Generation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8672-4468","authenticated-orcid":false,"given":"Yuchen","family":"Yan","sequence":"first","affiliation":[{"name":"Department of Computer Science, National University of Singapore, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8691-1846","authenticated-orcid":false,"given":"Peiyan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7330-5601","authenticated-orcid":false,"given":"Zhihua","family":"Liu","sequence":"additional","affiliation":[{"name":"Samsung R&amp;#38;D institute China-Beijing, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5150-5204","authenticated-orcid":false,"given":"Hao","family":"Wang","sequence":"additional","affiliation":[{"name":"Samsung R&amp;#38;D institute China-Beijing, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2368-4084","authenticated-orcid":false,"given":"Yatao","family":"Bian","sequence":"additional","affiliation":[{"name":"Department of Computer Science, National University of Singapore, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4054-5956","authenticated-orcid":false,"given":"Weiming","family":"Li","sequence":"additional","affiliation":[{"name":"Samsung R&amp;#38;D institute China-Beijing, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4209-6695","authenticated-orcid":false,"given":"Xiaoshuai","family":"Hao","sequence":"additional","affiliation":[{"name":"Xiaomi EV, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,7,19]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Llama 3 model card","year":"2024","unstructured":"AI@Meta., 2024. Llama 3 model card. 2024. https:\/\/github.com\/meta-llama\/llama3\/blob\/main\/MODEL_CARD.md."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P15-1034"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2024.09.178"},{"key":"e_1_3_2_1_4_1","volume-title":"Self-rag: Learning to retrieve, generate, and critique through self-reflection.","author":"Asai Akari","year":"2024","unstructured":"Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. 2024. Self-rag: Learning to retrieve, generate, and critique through self-reflection. (2024)."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5747"},{"key":"e_1_3_2_1_6_1","volume-title":"Multi-hop question answering via reasoning chains. arXiv preprint arXiv:1910.02610","author":"Chen Jifan","year":"2019","unstructured":"Jifan Chen, Shih-ting Lin, and Greg Durrett. 2019. Multi-hop question answering via reasoning chains. arXiv preprint arXiv:1910.02610 (2019)."},{"key":"e_1_3_2_1_7_1","volume-title":"Bge m3-embedding: Multi-lingual, multi-functionality, multi-granularity text embeddings through self-knowledge distillation. arXiv preprint arXiv:2402.03216","author":"Chen Jianlv","year":"2024","unstructured":"Jianlv Chen, Shitao Xiao, Peitian Zhang, Kun Luo, Defu Lian, and Zheng Liu. 2024. Bge m3-embedding: Multi-lingual, multi-functionality, multi-granularity text embeddings through self-knowledge distillation. arXiv preprint arXiv:2402.03216 (2024)."},{"key":"e_1_3_2_1_8_1","volume-title":"GRIL: Knowledge graph retrieval-integrated learning with large language models. Machine learning","author":"Chen Jialin","year":"2025","unstructured":"Jialin Chen, Houyu Zhang, Seongjun Yun, Alejandro Mottini, Rex Ying, Xiang Song, Vassilis N Ioannidis, Zheng Li, and Qingjun Cui. 2025. GRIL: Knowledge graph retrieval-integrated learning with large language models. Machine learning, Vol. 1, 2 (2025), 3."},{"key":"e_1_3_2_1_9_1","volume-title":"International conference on machine learning. PmLR, 1597-1607","author":"Chen Ting","year":"2020","unstructured":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020a. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PmLR, 1597-1607."},{"key":"e_1_3_2_1_10_1","volume-title":"DROP: A reading comprehension benchmark requiring discrete reasoning over paragraphs. arXiv preprint arXiv:1903.00161","author":"Dua Dheeru","year":"2019","unstructured":"Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner. 2019. DROP: A reading comprehension benchmark requiring discrete reasoning over paragraphs. arXiv preprint arXiv:1903.00161 (2019)."},{"key":"e_1_3_2_1_11_1","volume-title":"Robert Osazuwa Ness, and Jonathan Larson","author":"Edge Darren","year":"2024","unstructured":"Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Truitt, Dasha Metropolitansky, Robert Osazuwa Ness, and Jonathan Larson. 2024. From local to global: A graph rag approach to query-focused summarization. arXiv preprint arXiv:2404.16130 (2024)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/1409360.1409378"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671470"},{"key":"e_1_3_2_1_14_1","volume-title":"KiRAG: Knowledge-Driven Iterative Retriever for Enhancing Retrieval-Augmented Generation. arXiv preprint arXiv:2502.18397","author":"Fang Jinyuan","year":"2025","unstructured":"Jinyuan Fang, Zaiqiao Meng, and Craig Macdonald. 2025. KiRAG: Knowledge-Driven Iterative Retriever for Enhancing Retrieval-Augmented Generation. arXiv preprint arXiv:2502.18397 (2025)."},{"key":"e_1_3_2_1_15_1","volume-title":"Hierarchical graph network for multi-hop question answering. arXiv preprint arXiv:1911.03631","author":"Fang Yuwei","year":"2019","unstructured":"Yuwei Fang, Siqi Sun, Zhe Gan, Rohit Pillai, Shuohang Wang, and Jingjing Liu. 2019. Hierarchical graph network for multi-hop question answering. arXiv preprint arXiv:1911.03631 (2019)."},{"key":"e_1_3_2_1_16_1","volume-title":"Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997","author":"Gao Yunfan","year":"2023","unstructured":"Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yixin Dai, Jiawei Sun, Haofen Wang, and Haofen Wang. 2023. Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997, Vol. 2, 1 (2023)."},{"key":"e_1_3_2_1_17_1","volume-title":"Proceedings of the fourteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, 315-323","author":"Glorot Xavier","year":"2011","unstructured":"Xavier Glorot, Antoine Bordes, and Yoshua Bengio. 2011. Deep sparse rectifier neural networks. In Proceedings of the fourteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, 315-323."},{"key":"e_1_3_2_1_18_1","volume-title":"Lightrag: Simple and fast retrieval-augmented generation. arXiv preprint arXiv:2410.05779","author":"Guo Zirui","year":"2024","unstructured":"Zirui Guo, Lianghao Xia, Yanhua Yu, Tu Ao, and Chao Huang. 2024. Lightrag: Simple and fast retrieval-augmented generation. arXiv preprint arXiv:2410.05779 (2024)."},{"key":"e_1_3_2_1_19_1","volume-title":"From rag to memory: Non-parametric continual learning for large language models. arXiv preprint arXiv:2502.14802","author":"Guti\u00e9rrez Bernal Jim\u00e9nez","year":"2025","unstructured":"Bernal Jim\u00e9nez Guti\u00e9rrez, Yiheng Shu, Weijian Qi, Sizhe Zhou, and Yu Su. 2025. From rag to memory: Non-parametric continual learning for large language models. arXiv preprint arXiv:2502.14802 (2025)."},{"key":"e_1_3_2_1_20_1","volume-title":"Inductive representation learning on large graphs. Advances in neural information processing systems","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.52202\/075280-1662"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01818"},{"key":"e_1_3_2_1_23_1","volume-title":"Dual alignment domain adaptation for unsupervised video-text retrieval. ACM Transactions on Multimedia Computing, Communications and Applications","author":"Hao Xiaoshuai","year":"2025","unstructured":"Xiaoshuai Hao, Haimei Zhao, Yunfeng Diao, Rong Yin, Guangyin Jin, Jing Zhang, Wanqian Zhang, and Wei Zhou. 2025. Dada++: Dual alignment domain adaptation for unsupervised video-text retrieval. ACM Transactions on Multimedia Computing, Communications and Applications (2025)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.52202\/079017-4224"},{"key":"e_1_3_2_1_25_1","volume-title":"Saku Sugawara, and Akiko Aizawa.","author":"Ho Xanh","year":"2020","unstructured":"Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, and Akiko Aizawa. 2020. Constructing a multi-hop qa dataset for comprehensive evaluation of reasoning steps. arXiv preprint arXiv:2011.01060 (2020)."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.findings-naacl.232"},{"key":"e_1_3_2_1_27_1","volume-title":"Rag and rau: A survey on retrieval-augmented language model in natural language processing. arXiv preprint arXiv:2404.19543","author":"Hu Yucheng","year":"2024","unstructured":"Yucheng Hu and Yuxing Lu. 2024. Rag and rau: A survey on retrieval-augmented language model in natural language processing. arXiv preprint arXiv:2404.19543 (2024)."},{"key":"e_1_3_2_1_28_1","volume-title":"Unsupervised dense information retrieval with contrastive learning. arXiv preprint arXiv:2112.09118","author":"Izacard Gautier","year":"2021","unstructured":"Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. 2021. Unsupervised dense information retrieval with contrastive learning. arXiv preprint arXiv:2112.09118 (2021)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.495"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.52202\/079017-1902"},{"key":"e_1_3_2_1_31_1","volume-title":"Nv-embed: Improved techniques for training llms as generalist embedding models. arXiv preprint arXiv:2405.17428","author":"Lee Chankyu","year":"2024","unstructured":"Chankyu Lee, Rajarshi Roy, Mengyao Xu, Jonathan Raiman, Mohammad Shoeybi, Bryan Catanzaro, and Wei Ping. 2024. Nv-embed: Improved techniques for training llms as generalist embedding models. arXiv preprint arXiv:2405.17428 (2024)."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.naacl-long.337"},{"key":"e_1_3_2_1_33_1","volume-title":"Towards general text embeddings with multi-stage contrastive learning. arXiv preprint arXiv:2308.03281","author":"Li Zehan","year":"2023","unstructured":"Zehan Li, Xin Zhang, Yanzhao Zhang, Dingkun Long, Pengjun Xie, and Meishan Zhang. 2023. Towards general text embeddings with multi-stage contrastive learning. arXiv preprint arXiv:2308.03281 (2023)."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123335"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3726302.3730066"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3615146"},{"key":"e_1_3_2_1_37_1","volume-title":"Bm25s: Orders of magnitude faster lexical search via eager sparse scoring. arXiv preprint arXiv:2407.03618","author":"Xing Han L\u00f9.","year":"2024","unstructured":"Xing Han L\u00f9. 2024. Bm25s: Orders of magnitude faster lexical search via eager sparse scoring. arXiv preprint arXiv:2407.03618 (2024)."},{"key":"e_1_3_2_1_38_1","volume-title":"Reasoning on graphs: Faithful and interpretable large language model reasoning. arXiv preprint arXiv:2310.01061","author":"Luo Linhao","year":"2023","unstructured":"Linhao Luo, Yuan-Fang Li, Gholamreza Haffari, and Shirui Pan. 2023. Reasoning on graphs: Faithful and interpretable large language model reasoning. arXiv preprint arXiv:2310.01061 (2023)."},{"key":"e_1_3_2_1_39_1","volume-title":"GFM-RAG: graph foundation model for retrieval augmented generation. arXiv preprint arXiv:2502.01113","author":"Luo Linhao","year":"2025","unstructured":"Linhao Luo, Zicheng Zhao, Gholamreza Haffari, Dinh Phung, Chen Gong, and Shirui Pan. 2025. GFM-RAG: graph foundation model for retrieval augmented generation. arXiv preprint arXiv:2502.01113 (2025)."},{"key":"e_1_3_2_1_40_1","volume-title":"Deep and faithful large language model reasoning with knowledge-guided retrieval augmented generation. arXiv preprint arXiv:2407.10805","author":"Ma Shengjie","year":"2024","unstructured":"Shengjie Ma, Chengjin Xu, Xuhui Jiang, Muzhi Li, Huaren Qu, Cehao Yang, Jiaxin Mao, and Jian Guo. 2024. Think-on-graph 2.0: Deep and faithful large language model reasoning with knowledge-guided retrieval augmented generation. arXiv preprint arXiv:2407.10805 (2024)."},{"key":"e_1_3_2_1_41_1","volume-title":"ReaRev: Adaptive reasoning for question answering over knowledge graphs. arXiv preprint arXiv:2210.13650","author":"Mavromatis Costas","year":"2022","unstructured":"Costas Mavromatis and George Karypis. 2022. ReaRev: Adaptive reasoning for question answering over knowledge graphs. arXiv preprint arXiv:2210.13650 (2022)."},{"key":"e_1_3_2_1_42_1","volume-title":"Gnn-rag: Graph neural retrieval for large language model reasoning. arXiv preprint arXiv:2405.20139","author":"Mavromatis Costas","year":"2024","unstructured":"Costas Mavromatis and George Karypis. 2024. Gnn-rag: Graph neural retrieval for large language model reasoning. arXiv preprint arXiv:2405.20139 (2024)."},{"key":"e_1_3_2_1_43_1","volume-title":"Hello gpt-4o","author":"AI.","year":"2024","unstructured":"OpenAI., 2024. Hello gpt-4o, 2024a. https:\/\/openai.com\/index\/hello-gpt-4o\/."},{"key":"e_1_3_2_1_44_1","volume-title":"Graph retrieval-augmented generation: A survey. arXiv preprint arXiv:2408.08921","author":"Peng Boci","year":"2024","unstructured":"Boci Peng, Yun Zhu, Yongchao Liu, Xiaohe Bo, Haizhou Shi, Chuntao Hong, Yan Zhang, and Siliang Tang. 2024. Graph retrieval-augmented generation: A survey. arXiv preprint arXiv:2408.08921 (2024)."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/AIxSET62544.2024.00030"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4471-2099-5_24"},{"key":"e_1_3_2_1_47_1","volume-title":"A survey on oversmoothing in graph neural networks. arXiv preprint arXiv:2303.10993","author":"Rusch T Konstantin","year":"2023","unstructured":"T Konstantin Rusch, Michael M Bronstein, and Siddhartha Mishra. 2023. A survey on oversmoothing in graph neural networks. arXiv preprint arXiv:2303.10993 (2023)."},{"key":"e_1_3_2_1_48_1","volume-title":"The Twelfth International Conference on Learning Representations.","author":"Sarthi Parth","year":"2024","unstructured":"Parth Sarthi, Salman Abdullah, Aditi Tuli, Shubh Khanna, Anna Goldie, and Christopher D Manning. 2024. Raptor: Recursive abstractive processing for tree-organized retrieval. In The Twelfth International Conference on Learning Representations."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"e_1_3_2_1_50_1","volume-title":"DRAGIN: dynamic retrieval augmented generation based on the information needs of large language models. arXiv preprint arXiv:2403.10081","author":"Su Weihang","year":"2024","unstructured":"Weihang Su, Yichen Tang, Qingyao Ai, Zhijing Wu, and Yiqun Liu. 2024. DRAGIN: dynamic retrieval augmented generation based on the information needs of large language models. arXiv preprint arXiv:2403.10081 (2024)."},{"key":"e_1_3_2_1_51_1","volume-title":"Think-on-graph: Deep and responsible reasoning of large language model on knowledge graph. arXiv preprint arXiv:2307.07697","author":"Sun Jiashuo","year":"2023","unstructured":"Jiashuo Sun, Chengjin Xu, Lumingyuan Tang, Saizhuo Wang, Chen Lin, Yeyun Gong, Lionel M Ni, Heung-Yeung Shum, and Jian Guo. 2023. Think-on-graph: Deep and responsible reasoning of large language model on knowledge graph. arXiv preprint arXiv:2307.07697 (2023)."},{"key":"e_1_3_2_1_52_1","volume-title":"Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions. arXiv preprint arXiv:2212.10509","author":"Trivedi Harsh","year":"2022","unstructured":"Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2022a. Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions. arXiv preprint arXiv:2212.10509 (2022)."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00475"},{"key":"e_1_3_2_1_54_1","volume-title":"Multi-hop Reasoning via Early Knowledge Alignment. arXiv preprint arXiv:2512.20144","author":"Wang Yuxin","year":"2025","unstructured":"Yuxin Wang, Shicheng Fang, Bo Wang, Qi Luo, Xuanjing Huang, Yining Zheng, and Xipeng Qiu. 2025. Multi-hop Reasoning via Early Knowledge Alignment. arXiv preprint arXiv:2512.20144 (2025)."},{"key":"e_1_3_2_1_55_1","volume-title":"VQA-GNN: Reasoning with Multimodal Knowledge via Graph Neural Networks for Visual Question Answering. arXiv preprint arXiv:2205.11501","author":"Wang Yanan","year":"2022","unstructured":"Yanan Wang, Michihiro Yasunaga, Hongyu Ren, Shinya Wada, and Jure Leskovec. 2022. VQA-GNN: Reasoning with Multimodal Knowledge via Graph Neural Networks for Visual Question Answering. arXiv preprint arXiv:2205.11501 (2022)."},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"crossref","unstructured":"Thomas Wolf Lysandre Debut Victor Sanh Julien Chaumond Clement Delangue Anthony Moi Pierric Cistac Tim Rault R\u00e9mi Louf Morgan Funtowicz et al. 2019. Huggingface's transformers: State-of-the-art natural language processing. arXiv preprint arXiv:1910.03771 (2019).","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"e_1_3_2_1_57_1","volume-title":"Ming Gong, Linjun Shou, Daxin Jiang, Xing Xie, and Ji-Rong Wen.","author":"Xu Lanling","year":"2022","unstructured":"Lanling Xu, Jianxun Lian, Wayne Xin Zhao, Ming Gong, Linjun Shou, Daxin Jiang, Xing Xie, and Ji-Rong Wen. 2022. Negative sampling for contrastive representation learning: A review. arXiv preprint arXiv:2206.00212 (2022)."},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645620"},{"key":"e_1_3_2_1_59_1","volume-title":"HotpotQA: A dataset for diverse, explainable multi-hop question answering. arXiv preprint arXiv:1809.09600","author":"Yang Zhilin","year":"2018","unstructured":"Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W Cohen, Ruslan Salakhutdinov, and Christopher D Manning. 2018. HotpotQA: A dataset for diverse, explainable multi-hop question answering. arXiv preprint arXiv:1809.09600 (2018)."},{"key":"e_1_3_2_1_60_1","volume-title":"QA-GNN: Reasoning with language models and knowledge graphs for question answering. arXiv preprint arXiv:2104.06378","author":"Yasunaga Michihiro","year":"2021","unstructured":"Michihiro Yasunaga, Hongyu Ren, Antoine Bosselut, Percy Liang, and Jure Leskovec. 2021. QA-GNN: Reasoning with language models and knowledge graphs for question answering. arXiv preprint arXiv:2104.06378 (2021)."},{"key":"e_1_3_2_1_61_1","volume-title":"Auto-rag: Autonomous retrieval-augmented generation for large language models. arXiv preprint arXiv:2411.19443","author":"Yu Tian","year":"2024","unstructured":"Tian Yu, Shaolei Zhang, and Yang Feng. 2024. Auto-rag: Autonomous retrieval-augmented generation for large language models. arXiv preprint arXiv:2411.19443 (2024)."},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591652"},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657720"},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657721"},{"key":"e_1_3_2_1_65_1","volume-title":"A survey on neural open information extraction: Current status and future directions. arXiv preprint arXiv:2205.11725","author":"Zhou Shaowen","year":"2022","unstructured":"Shaowen Zhou, Bowen Yu, Aixin Sun, Cheng Long, Jingyang Li, Haiyang Yu, Jian Sun, and Yongbin Li. 2022. A survey on neural open information extraction: Current status and future directions. arXiv preprint arXiv:2205.11725 (2022)."}],"event":{"name":"SIGIR '26: The 49th International ACM SIGIR Conference on Research and Development in Information Retrieval","location":"Melbourne VIC Australia","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval"],"original-title":[],"deposited":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T17:29:39Z","timestamp":1784136579000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3805712.3809693"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,19]]},"references-count":65,"alternative-id":["10.1145\/3805712.3809693","10.1145\/3805712"],"URL":"https:\/\/doi.org\/10.1145\/3805712.3809693","relation":{},"subject":[],"published":{"date-parts":[[2026,7,19]]},"assertion":[{"value":"2026-07-19","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}