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The automation of scientific discovery has been a long-standing goal within the research community, driven by the potential to accelerate knowledge creation.
Zero: Memory optimizations toward training trillion parameter models, 2020
Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, and Yuxiong He · 1910
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Automated theory formation in mathematics
Douglas B Lenat · 1977
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Dendral and meta-dendral: Their applications dimension
Bruce G Buchanan and Edward A Feigenbaum · 1981
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Eurisko: a program that learns new heuristics and domain concepts: the nature of heuristics iii: program design and results
Douglas B Lenat · 1983
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Scientific discovery: Computational explorations of the creative processes
P Langley · 1987
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Towards a universal theory of artificial intelligence based on algorithmic probability and sequential decisions
Marcus Hutter · 2001
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Peer review: a flawed process at the heart of science and journals
Richard Smith · 2006
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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The prevalence of statistical reporting errors in psychology (1985–2013)
Michèle B Nuijten, Chris HJ Hartgerink, Marcel ALM Van Assen, Sacha Epskamp, and Jelte M Wicherts · 2016
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A supervised approach to extractive summarisation of scientific papers
Ed Collins, Isabelle Augenstein, and Sebastian Riedel · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
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Research integrity and peer review—past highlights and future directions, 2018
Stephanie L Boughton, Maria K Kowalczuk, Joerg J Meerpohl, Elizabeth Wager, and Elizabeth C Moylan · 2018
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich K"uttler, Mike Lewis, Wen-tau Yih, Tim Rockt"aschel, Sebastian Riedel, and Douwe Kiela · 2020
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Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters
Jeff Rasley, Samyam Rajbhandari, Olatunji Ruwase, and Yuxiong He · 2020
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Predicting paper acceptance via interpretable decision sets
Peng Bao, Weihui Hong, and Xuanya Li · 2021
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Highly accurate protein structure prediction with alphafold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Z’ıdek, Anna Potapenko, et al · 2021
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Teaching science as a process, not a set of facts: A case-study of a first-year science seminar
Gunilla "Oberg, Alice Campbell, Joanne Fox, Marcia Graves, Tara Ivanochko, Linda Matsuchi, Isobel Mouat, and Ashley Welsh · 2022
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Accelerating materials discovery using artificial intelligence, high performance computing and robotics
Edward O Pyzer-Knapp, Jed W Pitera, Peter WJ Staar, Seiji Takeda, Teodoro Laino, Daniel P Sanders, James Sexton, John R Smith, and Alessandro Curioni · 2022
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Investigating fairness disparities in peer review: A language model enhanced approach
Jiayao Zhang, Hongming Zhang, Zhun Deng, and Dan Roth · 2022
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The impact of large language models on scientific discovery: a preliminary study using gpt-4
Microsoft Research AI4Science and Microsoft Azure Quantum · 2023
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Fighting reviewer fatigue or amplifying bias? considerations and recommendations for use of chatgpt and other large language models in scholarly peer review
Mohammad Hosseini and Serge PJM Horbach · 2023
Cited alongside, same era.
Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al · 2023
Cited alongside, same era.
Reviewergpt? an exploratory study on using large language models for paper reviewing
Ryan Liu and Nihar B Shah · 2023
Cited alongside, same era.
Scaling deep learning for materials discovery
Amil Merchant, Simon Batzner, Samuel S Schoenholz, Muratahan Aykol, Gowoon Cheon, and Ekin Dogus Cubuk · 2023
Cited alongside, same era.
Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn · 2023
Ai4r: The fifth scientific research paradigm
Guojie LI · 2024
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Mlr-copilot: Autonomous machine learning research based on large language models agents, 2024
Ruochen Li, Teerth Patel, Qingyun Wang, and Xinya Du · 2024
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Can large language models provide useful feedback on research papers? a large-scale empirical analysis
Weixin Liang, Yuhui Zhang, Hancheng Cao, Binglu Wang, Daisy Yi Ding, Xinyu Yang, Kailas Vodrahalli, Siyu He, Daniel Scott Smith, Yian Yin, et al · 2024
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The ai scientist: Towards fully automated open-ended scientific discovery
Chris Lu, Cong Lu, Robert Tjarko Lange, Jakob Foerster, Jeff Clune, and David Ha · 2024
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SimPO: Simple preference optimization with a reference-free reward
Yu Meng, Mengzhou Xia, and Danqi Chen · 2024
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alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Gpt4 is slightly helpful for peer-review assistance: A pilot study
Zachary Robertson · 2023
Cited alongside, same era.
Reflexion: language agents with verbal reinforcement learning
Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao · 2023
Cited alongside, same era.
Large language models are better reasoners with self-verification
Yixuan Weng, Minjun Zhu, Fei Xia, Bin Li, Shizhu He, Shengping Liu, Bin Sun, Kang Liu, and Jun Zhao · 2023
Cited alongside, same era.
Ai for open science: A multi-agent perspective for ethically translating data to knowledge
Chase Yakaboski, Gregory Hyde, Clement Nyanhongo, and Eugene Santos Jr · 2023
Cited alongside, same era.
Large language models for automated open-domain scientific hypotheses discovery
Zonglin Yang, Xinya Du, Junxian Li, Jie Zheng, Soujanya Poria, and Erik Cambria · 2023
Cited alongside, same era.
Jinheon Baek, Sujay Kumar Jauhar, Silviu Cucerzan, and Sung Ju Hwang · 2024
Cited alongside, same era.
Fast-detectgpt: Efficient zero-shot detection of machine-generated text via conditional probability curvature
Guangsheng Bao, Yanbin Zhao, Zhiyang Teng, Linyi Yang, and Yue Zhang · 2024
Cited alongside, same era.
Iterative reasoning preference optimization
Richard Yuanzhe Pang, Weizhe Yuan, He He, Kyunghyun Cho, Sainbayar Sukhbaatar, and Jason E Weston · 2024
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Scideator: Human-llm scientific idea generation grounded in research-paper facet recombination
Marissa Radensky, Simra Shahid, Raymond Fok, Pao Siangliulue, Tom Hope, and Daniel S Weld · 2024
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Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Machel Reid, Nikolay Savinov, Denis Teplyashin, Dmitry Lepikhin, Timothy Lillicrap, Jean-baptiste Alayrac, Radu Soricut, Angeliki Lazaridou, Orhan Firat, Julian Schrittwieser, et al · 2024
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Mathematical discoveries from program search with large language models
Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Matej Balog, M Pawan Kumar, Emilien Dupont, Francisco JR Ruiz, Jordan S Ellenberg, Pengming Wang, Omar Fawzi, et al · 2024
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Can llms generate novel research ideas? a large-scale human study with 100+ nlp researchers
Chenglei Si, Diyi Yang, and Tatsunori Hashimoto · 2024
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Buxin Su, Jiayao Zhang, Natalie Collina, Yuling Yan, Didong Li, Kyunghyun Cho, Jianqing Fan, Aaron Roth, and Weijie J. Su · 2024
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Tadahiro Taniguchi, Shiro Takagi, Jun Otsuka, Yusuke Hayashi, and Hiro Taiyo Hamada · 2024
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Qwen2.5: A party of foundation models, September 2024
Qwen Team · 2024
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Ai-driven review systems: Evaluating llms in scalable and bias-aware academic reviews
Keith Tyser, Ben Segev, Gaston Longhitano, Xin-Yu Zhang, Zachary Meeks, Jason Lee, Uday Garg, Nicholas Belsten, Avi Shporer, Madeleine Udell, et al · 2024
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Iterative preference learning from human feedback: Bridging theory and practice for RLHF under KL-constraint
Wei Xiong, Hanze Dong, Chenlu Ye, Ziqi Wang, Han Zhong, Heng Ji, Nan Jiang, and Tong Zhang · 2024
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Self-rewarding language models
Weizhe Yuan, Richard Yuanzhe Pang, Kyunghyun Cho, Xian Li, Sainbayar Sukhbaatar, Jing Xu, and Jason E Weston · 2024
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Generative verifiers: Reward modeling as next-token prediction
Lunjun Zhang, Arian Hosseini, Hritik Bansal, Mehran Kazemi, Aviral Kumar, and Rishabh Agarwal · 2024
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Moss: Enabling code-driven evolution and context management for ai agents, 2024
Ming Zhu and Yi Zhou · 2024
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Locking down the finetuned llms safety
Minjun Zhu, Linyi Yang, Yifan Wei, Ningyu Zhang, and Yue Zhang · 2024
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Automated design of agentic systems
Shengran Hu, Cong Lu, and Jeff Clune · 2025
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