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AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction

Paper · 2026 · Hong Ting Tsang, Jiaxin Bai, Haoyu Huang et al.

Summary

Graph-based RAG systems usually build their knowledge graph once, judge it by intrinsic criteria such as precision or coverage, and then use it unchanged for question answering. The authors argue that such a graph may look sound yet serve the downstream task poorly. Because triple extraction is discrete and non-differentiable, QA accuracy cannot be backpropagated into the extractor. AutoGraph-R1 addresses this with reinforcement learning. An LLM graph constructor is treated as a policy, and its reward reflects how well the graph it produces works inside a frozen RAG pipeline. There are two task-aware rewards, one for each role a graph can play. When the graph acts as a knowledge carrier, an LLM judge checks whether the answer can be deduced from the retrieved triples. When the graph acts as an index for passage retrieval, the reward measures retrieval utility. Across five multi-hop and open-domain QA benchmarks, graphs from AutoGraph-R1 consistently help graph RAG methods beat task-agnostic baseline graphs. The work points toward building graphs for usefulness rather than intrinsic quality.

Key points

  • There are two rewards: R_C, a binary Yes/No LLM-judge check of deducibility for graphs as knowledge carriers, and R_I, for graphs as knowledge indices used by graph-based text retrievers such as HippoRAG and HippoRAG2.
  • Constructors are Qwen2.5 and Llama-3.2 models with 1B–7B parameters. Fine-tuned small models can outperform larger zero-shot baselines.
  • In an ablation, a plain F1 reward fell more than 2.2 average F1 below the zero-shot baseline with the Triples Retriever and degraded HippoRAG/HippoRAG2. The task-specific rewards improved results in every setting tested.
  • Evaluation uses public English datasets, including HotpotQA, Musique, NQ and PopQA. Training requires (query, gold answer, associated passages) triples.
  • Training on 2×H100 takes about 8h38m for the 7B text-retriever setup and about 2h18m for the 3B graph-knowledge setup. Each sample uses a pool of 15 documents for text retrieval and 2–5 for graph retrieval.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🏢MicrosoftRedmond · United States325 works · 142,931 citations🎓Cornell UniversityIthaca · United States120 works · 59,940 citations🎓Hong Kong University of Science and TechnologyHong Kong · China142 works · 25,136 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 1Research subjects and languages the work is about.Knowledge graphs & ontologies · 192 works

Cite

Cite as Hong Ting Tsang, Jiaxin Bai, Haoyu Huang, Qiao Xiao, Tianshi Zheng, Baixuan Xu, Shujie Liu and Yangqiu Song. 2026. AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.1070

@inproceedings{tsang2026autograph,
  title = {{AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction}},
  author = {Hong Ting Tsang, Jiaxin Bai, Haoyu Huang, Qiao Xiao, Tianshi Zheng, Baixuan Xu, Shujie Liu and Yangqiu Song},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1070},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1070}
}

References

84 references, 29 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Gabor Angeli, Melvin Jose Johnson Premkumar, and Christopher D. Manning. 2015. Leveraging Linguistic Structure For Open Domain Information Extraction. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Lang…
  2. Jiaxin Bai, Wei Fan, Qi Hu, Qing Zong, Chunyang Li, Hong Ting Tsang, Hongyu Luo, Yauwai Yim, Haoyu Huang, Xiao Zhou, Feng Qin, Tianshi Zheng, Xi Peng, Xin Yao, Huiwen Yang, Leijie Wu, Yi Ji, Gong Zhang, Renhai Chen, and Yangqiu Song. 2025a. AutoSchemaKG: Autonomous Knowledge Graph Construction throu… find
  3. Jiaxin Bai, Yicheng Wang, Tianshi Zheng, Yue Guo, Xin Liu, and Yangqiu Song. 2024. Advancing Abductive Reasoning in Knowledge Graphs through Complex Logical Hypothesis Generation. arXiv preprint. ArXiv:2312.15643 [cs]. find
  4. Jiaxin Bai, Zihao Wang, Yukun Zhou, Hang Yin, Weizhi Fei, Qi Hu, Zheye Deng, Jiayang Cheng, Tianshi Zheng, Hong Ting Tsang, Yisen Gao, Zhongwei Xie, Yufei Li, Lixin Fan, Binhang Yuan, Wei Wang, Lei Chen, Xiaofang Zhou, and Yangqiu Song. 2025b. Top Ten Challenges Towards Agentic Neural Graph Database… — [arXiv:2501.14224](https://arxiv.org/abs/2501.14224) find
  5. Shengyuan Chen, Yunfeng Cai, Huang Fang, Xiao Huang, and Mingming Sun. Differentiable Neuro-Symbolic Reasoning on Large-Scale Knowledge Graphs. find
  6. Shengyuan Chen, Qinggang Zhang, Junnan Dong, Wen Hua, Jiannong Cao, and Xiao Huang. 2024a. Neuro-Symbolic Entity Alignment via Variational Inference. arXiv preprint. ArXiv:2410.04153 [cs]. — [arXiv:2410.04153](https://arxiv.org/abs/2410.04153) find
  7. Shengyuan Chen, Qinggang Zhang, Junnan Dong, Wen Hua, Qing Li, and Xiao Huang. 2024b. Entity Alignment with Noisy Annotations from Large Language Models. arXiv preprint. ArXiv:2405.16806 [cs]. — [arXiv:2405.16806](https://arxiv.org/abs/2405.16806) find
  8. DeepSeek-AI, Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, and 1 others. 2025a. DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning. arXiv preprint. ArXiv:2501.12948 [cs].
  9. DeepSeek-AI, Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, Damai Dai, Daya Guo, Dejian Yang, Deli Chen, Dongjie Ji, Erhang Li, Fangyun Lin, Fucong Dai, and 181 others. 2025b. DeepSeek-V3 Technical Report. arXiv preprint.… find
  10. Pierre Dognin, Inkit Padhi, Igor Melnyk, and Payel Das. 2021. ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 1084–1099, Online and Punta Cana, Domi… find
  11. Hanze Dong, Wei Xiong, Deepanshu Goyal, Yihan Zhang, Winnie Chow, Rui Pan, Shizhe Diao, Jipeng Zhang, Kashun Shum, and Tong Zhang. 2023a. RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment. arXiv preprint. ArXiv:2304.06767 [cs].
  12. Junnan Dong, Qinggang Zhang, Xiao Huang, Qiaoyu Tan, Daochen Zha, and Zhao Zihao. 2023b. Active Ensemble Learning for Knowledge Graph Error Detection. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, pages 877–885, Singapore Singapore. ACM. find
  13. Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Truitt, Dasha Metropolitansky, Robert Osazuwa Ness, and Jonathan Larson. 2025. From Local to Global: A Graph RAG Approach to Query-Focused Summarization. arXiv preprint. ArXiv:2404.16130 [cs]. find
  14. Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Meng Wang, and Haofen Wang. 2024a. Retrieval-augmented generation for large language models: A survey. Preprint, arXiv:2312.10997.
  15. Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Meng Wang, and Haofen Wang. 2024b. Retrieval-Augmented Generation for Large Language Models: A Survey. arXiv preprint. ArXiv:2312.10997 [cs].
  16. Michael Gubanov, Anna Pyayt, and Aleksandra Karo-lak. 2024. CancerKG.ORG A Web-scale, Interactive, Verifiable Knowledge Graph-LLM Hybrid for Assisting with Optimal Cancer Treatment and Care. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, pages 4497–4… — [arXiv:2501.00223](https://arxiv.org/abs/2501.00223) find
  17. Zirui Guo, Lianghao Xia, Yanhua Yu, Tu Ao, and Chao Huang. 2025. LightRAG: Simple and Fast Retrieval-Augmented Generation. arXiv preprint. ArXiv:2410.05779 [cs].
  18. Bernal Jiménez Gutiérrez, Yiheng Shu, Yu Gu, Michihiro Yasunaga, and Yu Su. 2025a. HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models. arXiv preprint. ArXiv:2405.14831 [cs].
  19. Bernal Jiménez Gutiérrez, Yiheng Shu, Weijian Qi, Sizhe Zhou, and Yu Su. 2025b. From RAG to Memory: Non-Parametric Continual Learning for Large Language Models. arXiv preprint. ArXiv:2502.14802 [cs]. find
  20. Haoyu Han, Harry Shomer, Yu Wang, Yongjia Lei, Kai Guo, Zhigang Hua, Bo Long, Hui Liu, and Jiliang Tang. 2025. RAG vs. GraphRAG: A Systematic Evaluation and Key Insights. arXiv preprint. ArXiv:2502.11371 [cs]. — [arXiv:2502.11371](https://arxiv.org/abs/2502.11371) find
  21. Jiuzhou Han, Nigel Collier, Wray Buntine, and Ehsan Shareghi. 2024. PiVe: Prompting with Iterative Verification Improving Graph-based Generative Capability of LLMs. arXiv preprint. ArXiv:2305.12392 [cs]. find
  22. Mengliang He, Aimin Zhou, and Xiaoming Shi. Enhancing Textbook Question Answering with Knowledge Graph-Augmented Large Language Models. find
  23. Xiaoxin He, Yijun Tian, Yifei Sun, Nitesh V. Chawla, Thomas Laurent, Yann LeCun, Xavier Bresson, and Bryan Hooi. 2024. G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering. arXiv preprint. ArXiv:2402.07630 [cs].
  24. 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 [cs].
  25. Jingcheng Hu, Yinmin Zhang, Qi Han, Daxin Jiang, Xiangyu Zhang, and Heung-Yeung Shum. 2025. Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model. arXiv preprint. ArXiv:2503.24290 [cs].
  26. Haoyu Huang, Chong Chen, Zeang Sheng, Yang Li, and Wentao Zhang. 2025a. Can LLMs be Good Graph Judge for Knowledge Graph Construction? arXiv preprint. ArXiv:2411.17388 [cs]. find
  27. Haoyu Huang, Hong Ting Tsang, Jiaxin Bai, Xi Peng, Gong Zhang, and Yangqiu Song. 2026. At-lasKV: Augmenting LLMs with Billion-Scale Knowledge Graphs in 20GB VRAM. arXiv preprint. ArXiv:2510.17934 [cs]. find
  28. Jiatan Huang, Mingchen Li, Zonghai Yao, Zhichao Yang, Yongkang Xiao, Feiyun Ouyang, Xiaohan Li, Shuo Han, and Hong Yu. 2024. RiTeK: A Dataset for Large Language Models Complex Reasoning over Textual Knowledge Graphs. arXiv preprint. ArXiv:2410.13987 [cs]. — [arXiv:2410.13987](https://arxiv.org/abs/2410.13987) find
  29. Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, and Ting Liu. 2025b. A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions. ACM Transactions on Information Systems…
  30. Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, and Edouard Grave. 2022. Atlas: Few-shot Learning with Retrieval Augmented Language Models. arXiv preprint. ArXiv:2208.03299 [cs].
  31. Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Yejin Bang, Delong Chen, Wenliang Dai, Ho Shu Chan, Andrea Madotto, and Pascale Fung. 2023. Survey of Hallucination in Natural Language Generation. ACM Computing Surveys, 55(12):1–38. ArXiv:2202.03629 [cs].
  32. Pengcheng Jiang, Jiacheng Lin, Lang Cao, Runchu Tian, SeongKu Kang, Zifeng Wang, Jimeng Sun, and Jiawei Han. 2025a. DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning. arXiv preprint. ArXiv:2503.00223 [cs]. — [arXiv:2503.00223](https://arxiv.org/abs/2503.00223) find
  33. Pengcheng Jiang, Xueqiang Xu, Jiacheng Lin, Jinfeng Xiao, Zifeng Wang, Jimeng Sun, and Jiawei Han. 2025b. s3: You Don’t Need That Much Data to Train a Search Agent via RL. arXiv preprint. ArXiv:2505.14146 [cs].
  34. Bowen Jin, Chulin Xie, Jiawei Zhang, Kashob Kumar Roy, Yu Zhang, Zheng Li, Ruirui Li, Xianfeng Tang, Suhang Wang, Yu Meng, and Jiawei Han. 2024. Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs. arXiv preprint. ArXiv:2404.07103 [cs].
  35. Bowen Jin, Hansi Zeng, Zhenrui Yue, Jinsung Yoon, Sercan Arik, Dong Wang, Hamed Zamani, and Jiawei Han. 2025. Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning. arXiv preprint. ArXiv:2503.09516 [cs].
  36. L. P. Kaelbling, M. L. Littman, and A. W. Moore. 1996. Reinforcement Learning: A Survey. arXiv preprint. ArXiv:cs/9605103. find
  37. Timo Kaufmann, Paul Weng, Viktor Bengs, and Eyke Hüllermeier. 2024. A Survey of Reinforcement Learning from Human Feedback. arXiv preprint. ArXiv:2312.14925 [cs]. find
  38. Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019. Natural…
  39. Yassir Lairgi, Ludovic Moncla, Rémy Cazabet, Khalid Benabdeslem, and Pierre Cléau. 2024. iText2KG: Incremental Knowledge Graphs Construction Using Large Language Models. arXiv preprint. ArXiv:2409.03284 [cs]. — [arXiv:2409.03284](https://arxiv.org/abs/2409.03284) find
  40. Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2021. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. arXiv preprint. ArXiv:2005.11401 [cs].
  41. Mufei Li, Siqi Miao, and Pan Li. 2025a. Simple Is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation. arXiv preprint. ArXiv:2410.20724 [cs]. — [arXiv:2410.20724](https://arxiv.org/abs/2410.20724) find
  42. Shilong Li, Yancheng He, Hangyu Guo, Xingyuan Bu, Ge Bai, Jie Liu, Jiaheng Liu, Xingwei Qu, Yangguang Li, Wanli Ouyang, Wenbo Su, and Bo Zheng. 2024a. GraphReader: Building Graph-based Agent to Enhance Long-Context Abilities of Large Language Models. arXiv preprint. ArXiv:2406.14550 [cs]. find
  43. Yangning Li, Weizhi Zhang, Yuyao Yang, Wei-Chieh Huang, Yaozu Wu, Junyu Luo, Yuanchen Bei, Henry Peng Zou, Xiao Luo, Yusheng Zhao, Chunkit Chan, Yankai Chen, Zhongfen Deng, Yinghui Li, Hai-Tao Zheng, Dongyuan Li, Renhe Jiang, Ming Zhang, Yangqiu Song, and Philip S. Yu. 2025b. Towards Agentic RAG wit…
  44. Zhuoqun Li, Xuanang Chen, Haiyang Yu, Hongyu Lin, Yaojie Lu, Qiaoyu Tang, Fei Huang, Xianpei Han, Le Sun, and Yongbin Li. 2024b. StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization. arXiv preprint. ArXiv:2410.08815 [cs]. — [arXiv:2410.08815](https://arxiv.org/abs/2410.08815) find
  45. Xun Liang, Simin Niu, Zhiyu li, Sensen Zhang, Shichao Song, Hanyu Wang, Jiawei Yang, Feiyu Xiong, Bo Tang, and Chenyang Xi. 2024. Empowering Large Language Models to Set up a Knowledge Retrieval Indexer via Self-Learning. arXiv preprint. ArXiv:2405.16933 [cs]. — [arXiv:2405.16933](https://arxiv.org/abs/2405.16933) find
  46. Haochen Liu, Song Wang, Yaochen Zhu, Yushun Dong, and Jundong Li. 2024a. Knowledge Graph-Enhanced Large Language Models via Path Selection. arXiv preprint. ArXiv:2406.13862 [cs]. find
  47. Wei Liu, Ailun Yu, Daoguang Zan, Bo Shen, Wei Zhang, Haiyan Zhao, Zhi Jin, and Qianxiang Wang. 2024b. GraphCoder: Enhancing Repository-Level Code Completion via Code Context Graph-based Retrieval and Language Model. arXiv preprint. ArXiv:2406.07003 [cs]. — [arXiv:2406.07003](https://arxiv.org/abs/2406.07003) find
  48. Xiangyan Liu, Bo Lan, Zhiyuan Hu, Yang Liu, Zhicheng Zhang, Fei Wang, Michael Shieh, and Wenmeng Zhou. 2024c. CodexGraph: Bridging Large Language Models and Code Repositories via Code Graph Databases. arXiv preprint. ArXiv:2408.03910 [cs]. find
  49. Zichen Liu, Changyu Chen, Wenjun Li, Penghui Qi, Tianyu Pang, Chao Du, Wee Sun Lee, and Min Lin. 2025. Understanding R1-Zero-Like Training: A Critical Perspective. arXiv preprint. ArXiv:2503.20783 [cs].
  50. Haoran Luo, Haihong E, Guanting Chen, Qika Lin, Yikai Guo, Fangzhi Xu, Zemin Kuang, Meina Song, Xiaobao Wu, Yifan Zhu, and Luu Anh Tuan. 2025. Graph-R1: Towards Agentic GraphRAG Framework via End-to-end Reinforcement Learning. arXiv preprint. ArXiv:2507.21892 [cs]. — [arXiv:2507.21892](https://arxiv.org/abs/2507.21892) find
  51. Shengjie Ma, Chengjin Xu, Xuhui Jiang, Muzhi Li, Huaren Qu, Cehao Yang, Jiaxin Mao, and Jian Guo. 2025. Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation. arXiv preprint. ArXiv:2407.10805 [cs]. — [arXiv:2407.10805](https://arxiv.org/abs/2407.10805) find
  52. Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, and Hannaneh Hajishirzi. 2023. When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories. arXiv preprint. ArXiv:2212.10511 [cs]. find
  53. Belinda Mo, Kyssen Yu, Joshua Kazdan, Proud Mpala, Lisa Yu, Chris Cundy, Charilaos Kanatsoulis, and Sanmi Koyejo. 2025. KGGen: Extracting Knowledge Graphs from Plain Text with Language Models. arXiv preprint. ArXiv:2502.09956 [cs]. — [arXiv:2502.09956](https://arxiv.org/abs/2502.09956) find
  54. Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. 2022. Train…
  55. Cheng Peng, Xi Yang, Aokun Chen, Kaleb E. Smith, Nima PourNejatian, Anthony B. Costa, Cheryl Martin, Mona G. Flores, Ying Zhang, Tanja Magoc, Gloria Lipori, Duane A. Mitchell, Naykky S. Ospina, Mustafa M. Ahmed, William R. Hogan, Elizabeth A. Shenkman, Yi Guo, Jiang Bian, and Yonghui Wu. 2023. A Stu… find
  56. Qwen, An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jingren Zhou, Junyang Lin, and 24 others. 2025. Qwen2.5 Technical Report. arXiv preprint. ArXiv:2… find
  57. John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017. Proximal Policy Optimization Algorithms. arXiv preprint. ArXiv:1707.06347 [cs]. find
  58. Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, Y. K. Li, Y. Wu, and Daya Guo. 2024. DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models. arXiv preprint. ArXiv:2402.03300 [cs].
  59. Haozhan Shen, Peng Liu, Jingcheng Li, Chunxin Fang, Yibo Ma, Jiajia Liao, Qiaoli Shen, Zilun Zhang, Kangjia Zhao, Qianqian Zhang, Ruochen Xu, and Tiancheng Zhao. 2025a. VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model. arXiv preprint. ArXiv:2504.07615 [cs]. find
  60. Xiangqing Shen, Fanfan Wang, and Rui Xia. 2025b. Reason-Align-Respond: Aligning LLM Reasoning with Knowledge Graphs for KGQA. arXiv preprint. ArXiv:2505.20971 [cs]. — [arXiv:2505.20971](https://arxiv.org/abs/2505.20971) find
  61. Jiashuo Sun, Chengjin Xu, Lumingyuan Tang, Saizhuo Wang, Chen Lin, Yeyun Gong, Lionel M. Ni, Heung-Yeung Shum, and Jian Guo. 2024. Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph. arXiv preprint. ArXiv:2307.07697 [cs]. find
  62. Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2022. MuSiQue: Multi-hop Questions via Single-hop Question Composition. arXiv preprint. ArXiv:2108.00573 [cs]. — [arXiv:2108.00573](https://arxiv.org/abs/2108.00573) find
  63. Boci Peng, Yun Zhu, Yongchao Liu, Xiaohe Bo, Haizhou Shi, Chuntao Hong, Yan Zhang, and Siliang Tang. 2024a. Graph retrieval-augmented generation: A survey. Preprint, arXiv:2408.08921.
  64. Yu Wang, Nedim Lipka, Ryan A. Rossi, Alexa Siu, Ruiyi Zhang, and Tyler Derr. 2023. Knowledge Graph Prompting for Multi-Document Question Answering. arXiv preprint. ArXiv:2308.11730 [cs] version: 3. — [arXiv:2308.11730](https://arxiv.org/abs/2308.11730) find
  65. Boci Peng, Yun Zhu, Yongchao Liu, Xiaohe Bo, Haizhou Shi, Chuntao Hong, Yan Zhang, and Siliang Tang. 2024b. Graph Retrieval-Augmented Generation: A Survey. arXiv preprint. ArXiv:2408.08921 [cs].
  66. Junde Wu, Jiayuan Zhu, Yunli Qi, Jingkun Chen, Min Xu, Filippo Menolascina, and Vicente Grau. 2024. Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generation. arXiv preprint. ArXiv:2408.04187 [cs]. — [arXiv:2408.04187](https://arxiv.org/abs/2408.04187) find
  67. Yunjia Xi, Jianghao Lin, Yongzhao Xiao, Zheli Zhou, Rong Shan, Te Gao, Jiachen Zhu, Weiwen Liu, Yong Yu, and Weinan Zhang. 2025. A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challenges. arXiv preprint. ArXiv:2508.05668 [cs]. — [arXiv:2508.05668](https://arxiv.org/abs/2508.05668) find
  68. Zhishang Xiang, Chuanjie Wu, Qinggang Zhang, Shengyuan Chen, Zijin Hong, Xiao Huang, and Jinsong Su. 2025. When to use Graphs in RAG: A Comprehensive Analysis for Graph Retrieval-Augmented Generation. arXiv preprint. ArXiv:2506.05690 [cs]. — [arXiv:2506.05690](https://arxiv.org/abs/2506.05690) find
  69. Qiao Xiao, Hong Ting Tsang, and Jiaxin Bai. 2025. TERAG: Token-Efficient Graph-Based Retrieval-Augmented Generation. arXiv preprint. ArXiv:2509.18667 [cs]. — [arXiv:2509.18667](https://arxiv.org/abs/2509.18667) find
  70. Bingcong Xue and Lei Zou. 2022. Knowledge Graph Quality Management: a Comprehensive Survey. IEEE Transactions on Knowledge and Data Engineering, pages 1–1. find
  71. 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 [cs].
  72. Qiying Yu, Zheng Zhang, Ruofei Zhu, Yufeng Yuan, Xiaochen Zuo, Yu Yue, Weinan Dai, Tiantian Fan, Gaohong Liu, Lingjun Liu, Xin Liu, Haibin Lin, Zhiqi Lin, Bole Ma, Guangming Sheng, Yuxuan Tong, Chi Zhang, Mofan Zhang, Wang Zhang, and 16 others. 2025. DAPO: An Open-Source LLM Reinforcement Learning S…
  73. Zishun Yu, Yunzhe Tao, Liyu Chen, Tao Sun, and Hongxia Yang. 2024. $\\mathcal{B}$-Coder: Value-Based Deep Reinforcement Learning for Program Synthesis. arXiv preprint. ArXiv:2310.03173 [cs]. — [arXiv:2310.03173](https://arxiv.org/abs/2310.03173) find
  74. Qinggang Zhang, Shengyuan Chen, Yuanchen Bei, Zheng Yuan, Huachi Zhou, Zijin Hong, Junnan Dong, Hao Chen, Yi Chang, and Xiao Huang. 2025a. A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models. arXiv preprint. ArXiv:2501.13958 [cs] version: 1.
  75. Xie, Yuyao Yang, Wei-Chieh Huang, Chunyu Miao, Henry Peng Zou, Xiao Luo, Yusheng Zhao, Yankai Chen, Chunkit Chan, Peilin Zhou, Xinyang Zhang, Chenwei Zhang, Jingbo Shang, and 4 others. 2025b. From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents. arXiv preprint. A… — [arXiv:2506.18959](https://arxiv.org/abs/2506.18959) find
  76. Yanzhao Zhang, Mingxin Li, Dingkun Long, Xin Zhang, Huan Lin, Baosong Yang, Pengjun Xie, An Yang, Dayiheng Liu, Junyang Lin, Fei Huang, and Jingren Zhou. 2025c. Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models. arXiv preprint. ArXiv:2506.05176 [cs].
  77. Yichi Zhang, Binbin Hu, Zhuo Chen, Lingbing Guo, Ziqi Liu, Zhiqiang Zhang, Lei Liang, Huajun Chen, and Wen Zhang. 2024b. Multi-domain Knowledge Graph Collaborative Pre-training and Prompt Tuning for Diverse Downstream Tasks. arXiv preprint. ArXiv:2405.13085 [cs] version: 1. — [arXiv:2405.13085](https://arxiv.org/abs/2405.13085) find
  78. Zhiqiang Zhang and Wen Zhao. 2025. A Collaborative Reasoning Framework Powered by Reinforcement Learning and Large Language Models for Complex Questions Answering over Knowledge Graph. In Proceedings of the 31st International Conference on Computational Linguistics, pages 10672–10684, Abu Dhabi, UAE… find
  79. Xinjie Zhao, Moritz Blum, Rui Yang, Boming Yang, Luis Márquez Carpintero, Mónica Pina-Navarro, Tony Wang, Xin Li, Huitao Li, Yanran Fu, Rongrong Wang, Juntao Zhang, and Irene Li. 2024. AGENTi-Graph: An Interactive Knowledge Graph Platform for LLM-based Chatbots Utilizing Private Data. arXiv preprint… — [arXiv:2410.11531](https://arxiv.org/abs/2410.11531) find
  80. Tianshi Zheng, Zheye Deng, Hong Ting Tsang, Weiqi Wang, Jiaxin Bai, Zihao Wang, and Yangqiu Song. 2025. From Automation to Autonomy: A Survey on Large Language Models in Scientific Discovery. arXiv preprint. ArXiv:2505.13259 [cs]. find
  81. Changtai Zhu, Siyin Wang, Ruijun Feng, Kai Song, and Xipeng Qiu. 2025. ConvSearch-R1: Enhancing Query Reformulation for Conversational Search with Reasoning via Reinforcement Learning. arXiv preprint. ArXiv:2505.15776 [cs].
  82. Qinggang Zhang, Junnan Dong, Hao Chen, Daochen Zha, Zailiang Yu, and Xiao Huang. 2024a. KnowGPT: Knowledge Graph based Prompting for Large Language Models. arXiv preprint. ArXiv:2312.06185 [cs]. — [arXiv:2312.06185](https://arxiv.org/abs/2312.06185) find
  83. Qinggang Zhang, Junnan Dong, Keyu Duan, Xiao Huang, Yezi Liu, and Linchuan Xu. 2022. Contrastive Knowledge Graph Error Detection. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pages 2590–2599. ArXiv:2211.10030 [cs]. — [arXiv:2211.10030](https://arxiv.org/abs/2211.10030) find
  84. Weizhi Zhang, Yangning Li, Yuanchen Bei, Junyu Luo, Guancheng Wan, Liangwei Yang, Chenxuan find

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