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Recent advances in large language models (LLMs) have significantly improved multi-hop question answering (QA) through direct Chain-of-Thought (CoT) reasoning.
Multi-hop reading comprehension via deep reinforcement learning based document traversal
Alex Long, Joel Mason, Alan Blair, and Wei Wang. 2019 · 1905
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A truth maintenance system
Jon Doyle. 1979 · 1979
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Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning
Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, and Andrew McCallum. 2018 · 2018
Earlier work this paper cites.
Constructing datasets for multi-hop reading comprehension across documents
Johannes Welbl, Pontus Stenetorp, and Sebastian Riedel. 2018 · 2018
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Hotpotqa: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018 · 2018
Earlier work this paper cites.
Constructing a multi-hop QA dataset for comprehensive evaluation of reasoning steps
Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, and Akiko Aizawa. 2020 · 2020
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R4c: A benchmark for evaluating rc systems to get the right answer for the right reason
Naoya Inoue, Pontus Stenetorp, and Kentaro Inui. 2020 · 2020
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Combining direct trust and indirect trust in multi-agent systems
Elham Parhizkar, Mohammad Hossein Nikravan, Robert C. Holte, and Sandra Zilles. 2020 · 2020
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Improving multi-hop knowledge base question answering by learning intermediate supervision signals
Gaole He, Yunshi Lan, Jing Jiang, Wayne Xin Zhao, and Ji-Rong Wen. 2021 · 2021
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MuSiQue: Multihop questions via single-hop question composition
Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2022 · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc Le, and Denny Zhou. 2022 · 2022
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Evaluating large language models on wikipedia-style survey generation
Fan Gao, Hang Jiang, Rui Yang, Qingcheng Zeng, Jinghui Lu, Moritz Blum, Dairui Liu, Tianwei She, Yuang Jiang, and Irene Li. 2023 · 2023
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Gemini: a family of highly capable multimodal models
Gemini, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, and 1 others. 2023 · 2023
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CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem. 2023 · 2023
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Recprompt: A self-tuning prompting framework for news recommendation using large language models
Dairui Liu, Boming Yang, Honghui Du, Derek Greene, Neil J. Hurley, Aonghus Lawlor, Ruihai Dong, and Irene Li. 2023 · 2023
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Self-refine: Iterative refinement with self-feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Nicholas Lourie, Gabriel Ilharco, Daphne Ippolito, Sam Singh, and 1 others. 2023 · 2023
Cited alongside, same era.
Measuring and narrowing the compositionality gap in language models
Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah Smith, and Mike Lewis. 2023 · 2023
Cited alongside, same era.
Metaqa: Combining expert agents for multi-skill question answering
Haritz Puerto, Gözde Gül Şahin, and Iryna Gurevych. 2023 · 2023
Cited alongside, same era.
Reflexion: Language Agents with Verbal Reinforcement Learning
Noah Shinn, Federico Cassano, Edward Berman, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. 2023 · 2023
Cited alongside, same era.
Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions
Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2023 · 2023
Cited alongside, same era.
Mitigating cognitive biases in clinical decision-making through multi-agent conversations using large language models: Simulation study
Yuhe Ke, Rui Yang, Sui An Lie, Taylor Xin Yi Lim, Yilin Ning, Irene Li, Hairil Rizal Abdullah, Daniel Shu Wei Ting, and Nan Liu. 2024 · 2024
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Debating with More Persuasive LLMs Leads to More Truthful Answers
Akbir Khan, John Hughes, Dan Valentine, Laura Ruis, Kshitij Sachan, Ansh Radhakrishnan, Edward Grefenstette, Samuel R. Bowman, Tim Rocktäschel, and Ethan Perez. 2024 · 2024
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Kag: Boosting llms in professional domains via knowledge augmented generation
Lei Liang, Mengshu Sun, Zhengke Gui, Zhongshu Zhu, Zhouyu Jiang, Ling Zhong, Yuan Qu, Peilong Zhao, Zhongpu Bo, Jin Yang, Huaidong Xiong, Lin Yuan, Jun Xu, Zaoyang Wang, Zhiqiang Zhang, Wen Zhang, Huajun Chen, Wenguang Chen, and Jun Zhou. 2024 · 2024
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Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, and 1 others. 2024 · 2024
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Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2023 · 2023
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou. 2023 · 2023
Cited alongside, same era.
Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate
Kai Xiong, Xiao Ding, Yixin Cao, Ting Liu, and Bing Qin. 2023 · 2023
Cited alongside, same era.
ReAct: Synergizing Reasoning and Acting in Language Models
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. 2023 · 2023
Cited alongside, same era.
Multimodal Chain-of-Thought Reasoning in Language Models
Zhuosheng Zhang, Aston Zhang, Mu Li, Hai Zhao, George Karypis, and Alexander J. Smola. 2023 · 2023
Cited alongside, same era.
Large Language Models Are Human-Level Prompt Engineers
Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis̆, Harris Chan, and Jimmy Ba. 2023 · 2023
Cited alongside, same era.
Reflective multi-agent collaboration based on large language models
Xiaohe Bo, Zeyu Zhang, Quanyu Dai, Xueyang Feng, Lei Wang, Rui Li, Xu Chen, and Ji-Rong Wen. 2024 · 2024
Cited alongside, same era.
Later among the works it cites.
Introducing llama 4: Advancing multimodal intelligence
Meta AI. 2024 · 2024
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OpenAI. 2024 · 2024
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Chain of agents: Large language models collaborating on long-context tasks
Yusen Zhang, Ruoxi Sun, Yanfei Chen, Tomas Pfister, Rui Zhang, and Sercan Ö. Arik. 2024 · 2024
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Longagent: Achieving question answering for 128k-token-long documents through multi-agent collaboration
Jun Zhao, Can Zu, Xu Hao, Yi Lu, Wei He, Yiwen Ding, Tao Gui, Qi Zhang, and Xuanjing Huang. 2024 · 2024
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Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
DeepSeek-AI. 2025 · 2025
Closest in time.
Gemini 2.5: Our most intelligent ai model
Google DeepMind. 2025 · 2025
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Hipporag: Neurobiologically inspired long-term memory for large language models
Bernal Jiménez Gutiérrez, Yiheng Shu, Yu Gu, Michihiro Yasunaga, and Yu Su. 2025 · 2025
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Introducing openai o3 and o4-mini
OpenAI. 2025 · 2025
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Qwen3. 2025 · 2025
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Graphusion: A rag framework for scientific knowledge graph construction with a global perspective
Rui Yang, Boming Yang, Xinjie Zhao, Fan Gao, Aosong Feng, Sixun Ouyang, Moritz Blum, Tianwei She, Yuang Jiang, Freddy Lecue, Jinghui Lu, and Irene Li. 2025b · 2025
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