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Progress in scientific discovery is rarely the result of a single "Eureka" moment, but is rather the product of hundreds of scientists incrementally working together toward a common goal.
The structure of scientific revolutions
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Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
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Arxiv at 20
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Novelty search and the problem with objectives
Joel Lehman and Kenneth O Stanley · 2011
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What is this thing called science?
Alan Chalmers · 2013
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World of bits: An open-domain platform for web-based agents
Tianlin Shi, Andrej Karpathy, Linxi Fan, Jonathan Hernandez, and Percy Liang · 2017
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Attention is all you need
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Improving language understanding by generative pre-training
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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New preprint server for medical research, 2019
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biorxiv: the preprint server for biology
Richard Sever, Ted Roeder, Samantha Hindle, Linda Sussman, Kevin-John Black, Janet Argentine, Wayne Manos, and John R Inglis · 2019
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Language models are few-shot learners
Tom B Brown · 2020
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Human ownership of artificial creativity
Jason K Eshraghian · 2020
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Social biases in nlp models as barriers for persons with disabilities
Ben Hutchinson, Ajaya Smart, Alex Hanna, Emily Denton, Shu Kuo, Kristen Thomson, Sarah Denuyl, and Larry Heck · 2020
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On the dangers of stochastic parrots: Can language models be too big?
Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell · 2021
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde De Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021
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Advancing mathematics by guiding human intuition with ai
Alex Davies, Petar Veličković, Lars Buesing, Sam Blackwell, Daniel Zheng, Nenad Tomašev, Richard Tanburn, Peter Battaglia, Charles Blundell, András Juhász, et al · 2021
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Automl: A survey of the state-of-the-art
Xin He, Kaiyong Zhao, and Xiaowen Chu · 2021
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What disease does this patient have? a large-scale open domain question answering dataset from medical exams
Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hung Weng, Hanyi Fang, and Peter Szolovits · 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 Žídek, Anna Potapenko, et al · 2021
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Ethical and social risks of harm from language models
Laura Weidinger, John Mellor, Maribeth Rauh, and et al · 2021
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Discovering faster matrix multiplication algorithms with reinforcement learning
Alhussein Fawzi, Matej Balog, Aja Huang, Thomas Hubert, Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Francisco J R Ruiz, Julian Schrittwieser, Grzegorz Swirszcz, et al · 2022
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Large language models can self-improve
Jiaxin Huang, Shixiang Shane Gu, Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu, and Jiawei Han · 2022
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2022
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Codegen: An open large language model for code with multi-turn program synthesis
Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong · 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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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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Autonomous chemical research with large language models
Daniil A Boiko, Robert MacKnight, Ben Kline, and Gabe Gomes · 2023
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Combining data and theory for derivable scientific discovery with ai-descartes
Cristina Cornelio, Sanjeeb Dash, Vernon Austel, Tyler R Josephson, Joao Goncalves, Kenneth L Clarkson, Nimrod Megiddo, Bachir El Khadir, and Lior Horesh · 2023
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A real-world webagent with planning, long context understanding, and program synthesis
Izzeddin Gur, Hiroki Furuta, Austin Huang, Mustafa Safdari, Yutaka Matsuo, Douglas Eck, and Aleksandra Faust · 2023
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Reasoning with language model is planning with world model
Shibo Hao, Yi Gu, Haodi Ma, Joshua Jiahua Hong, Zhen Wang, Daisy Zhe Wang, and Zhiting Hu · 2023
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Swe-bench: Can language models resolve real-world github issues?
Carlos E Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan · 2023
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The promise and peril of generative ai
A Jo · 2023
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Paperqa: Retrieval-augmented generative agent for scientific research
Jakub Lála, Odhran O’Donoghue, Aleksandar Shtedritski, Sam Cox, Samuel G Rodriques, and Andrew D White · 2023
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Structured chain-of-thought prompting for code generation, 2023
Jia Li, Ge Li, Yongmin Li, and Zhi Jin · 2023
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Encouraging divergent thinking in large language models through multi-agent debate
Tian Liang, Zhiwei He, Wenxiang Jiao, Xing Wang, Yan Wang, Rui Wang, Yujiu Yang, Shuming Shi, and Zhaopeng Tu · 2023
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Lei Lin, Jiayi Fu, Pengli Liu, Qingyang Li, Yan Gong, Junchen Wan, Fuzheng Zhang, Zhongyuan Wang, Di Zhang, and Kun Gai · 2023
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Chameleon: Plug-and-play compositional reasoning with large language models
Pan Lu, Baolin Peng, Hao Cheng, Michel Galley, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, and Jianfeng Gao · 2023
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Can chatgpt be an author? a study of artificial intelligence authorship policies in top academic journals
Brady D. Lund and K. T. Naheem · 2023
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Self-refine: Iterative refinement with self-feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al · 2023
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Scaling deep learning for materials discovery
Amil Merchant, Simon Batzner, Samuel S Schoenholz, Muratahan Aykol, Gowoon Cheon, and Ekin Dogus Cubuk · 2023
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Art: Automatic multi-step reasoning and tool-use for large language models
Bhargavi Paranjape, Scott Lundberg, Sameer Singh, Hannaneh Hajishirzi, Luke Zettlemoyer, and Marco Tulio Ribeiro · 2023
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Chatgpt as research scientist: Probing gpt’s capabilities as a research librarian, research ethicist, data generator, and data predictor
Steven A Lehr, Aylin Caliskan, Suneragiri Liyanage, and Mahzarin R Banaji · 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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Biokgbench: A knowledge graph checking benchmark of ai agent for biomedical science
Xinna Lin, Siqi Ma, Junjie Shan, Xiaojing Zhang, Shell Xu Hu, Tiannan Guo, Stan Z Li, and Kaicheng Yu · 2024
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Acemath: Advancing frontier math reasoning with post-training and reward modeling
Zihan Liu, Yang Chen, Mohammad Shoeybi, Bryan Catanzaro, and Wei Ping · 2024
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Experiential co-learning of software-developing agents
Chen Qian, Yufan Dang, Jiahao Li, Wei Liu, Zihao Xie, Yifei Wang, Weize Chen, Cheng Yang, Xin Cong, Xiaoyin Che, et al · 2023
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Toolllm: Facilitating large language models to master 16000+ real-world apis
Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, et al · 2023
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Whose opinions do language models reflect?
Shibani Santurkar, Esin Durmus, Faisal Ladhak, Cinoo Lee, Percy Liang, and Tatsunori Hashimoto · 2023
Cited alongside, same era.
Toolformer: Language models can teach themselves to use tools
Timo Schick, Jane Dwivedi-Yu, Roberto Dessi, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom · 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.
An autonomous laboratory for the accelerated synthesis of novel materials
Nathan J Szymanski, Bernardus Rendy, Yuxing Fei, Rishi E Kumar, Tanjin He, David Milsted, Matthew J McDermott, Max Gallant, Ekin Dogus Cubuk, Amil Merchant, et al · 2023
Cited alongside, same era.
Chatgpt is fun, but not an author
H. Holden Thorp · 2023
Cited alongside, same era.
Xiaoliang Luo, Akilles Rechardt, Guangzhi Sun, Kevin K Nejad, Felipe Yáñez, Bati Yilmaz, Kangjoo Lee, Alexandra O Cohen, Valentina Borghesani, Anton Pashkov, et al · 2024
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Augmenting large language models with chemistry tools
Andres M. Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew D White, and Philippe Schwaller · 2024
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Discoverybench: Towards data-driven discovery with large language models
Bodhisattwa Prasad Majumder, Harshit Surana, Dhruv Agarwal, Bhavana Dalvi Mishra, Abhijeetsingh Meena, Aryan Prakhar, Tirth Vora, Tushar Khot, Ashish Sabharwal, and Peter Clark · 2024
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Automated social science: Language models as scientist and subjects
Benjamin S Manning, Kehang Zhu, and John J Horton · 2024
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Artificial intelligence and illusions of understanding in scientific research
Lisa Messeri and Molly Crockett · 2024
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Hyperagent: Generalist software engineering agents to solve coding tasks at scale
Huy Nhat Phan, Tien N Nguyen, Phong X Nguyen, and Nghi DQ Bui · 2024
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Citeme: Can language models accurately cite scientific claims?
Ori Press, Andreas Hochlehnert, Ameya Prabhu, Vishaal Udandarao, Ofir Press, and Matthias Bethge · 2024
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Fine-tuning with divergent chains of thought boosts reasoning through self-correction in language models
Haritz Puerto, Tilek Chubakov, Xiaodan Zhu, Harish Tayyar Madabushi, and Iryna Gurevych · 2024
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Agent q: Advanced reasoning and learning for autonomous ai agents
Pranav Putta, Edmund Mills, Naman Garg, Sumeet Motwani, Chelsea Finn, Divyansh Garg, and Rafael Rafailov · 2024
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Chatdev: Communicative agents for software development
Chen Qian, Wei Liu, Hongzhang Liu, Nuo Chen, Yufan Dang, Jiahao Li, Cheng Yang, Weize Chen, Yusheng Su, Xin Cong, et al · 2024
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Tool learning with large language models: A survey
Changle Qu, Sunhao Dai, Xiaochi Wei, Hengyi Cai, Shuaiqiang Wang, Dawei Yin, Jun Xu, and Ji-Rong Wen · 2024
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Gpqa: A graduate-level google-proof q&a benchmark
David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, and Samuel R Bowman · 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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Agentclinic: a multimodal agent benchmark to evaluate ai in simulated clinical environments
Samuel Schmidgall, Rojin Ziaei, Carl Harris, Eduardo Reis, Jeffrey Jopling, and Michael Moor · 2024
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Introducing weco aide, 2024
Dominik Schmidt, Zhengyao Jiang, and Yuxiang Unknown · 2024
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Deepseekmath: Pushing the limits of mathematical reasoning in open language models
Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, et al · 2024
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Ehragent: Code empowers large language models for few-shot complex tabular reasoning on electronic health records
Wenqi Shi, Ran Xu, Yuchen Zhuang, Yue Yu, Jieyu Zhang, Hang Wu, Yuanda Zhu, Joyce Ho, Carl Yang, and May Dongmei Wang · 2024
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Llm-sr: Scientific equation discovery via programming with large language models
Parshin Shojaee, Kazem Meidani, Shashank Gupta, Amir Barati Farimani, and Chandan K Reddy · 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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Cs-bench: A comprehensive benchmark for large language models towards computer science mastery
Xiaoshuai Song, Muxi Diao, Guanting Dong, Zhengyang Wang, Yujia Fu, Runqi Qiao, Zhexu Wang, Dayuan Fu, Huangxuan Wu, Bin Liang, et al · 2024
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The virtual lab: Ai agents design new sars-cov-2 nanobodies with experimental validation
Kyle Swanson, Wesley Wu, Nash L Bulaong, John E Pak, and James Zou · 2024
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Towards conversational diagnostic ai
Tao Tu, Anil Palepu, Mike Schaekermann, Khaled Saab, Jan Freyberg, Ryutaro Tanno, Amy Wang, Brenna Li, Mohamed Amin, Nenad Tomasev, et al · 2024
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Cycleresearcher: Improving automated research via automated review
Yixuan Weng, Minjun Zhu, Guangsheng Bao, Hongbo Zhang, Jindong Wang, Yue Zhang, and Linyi Yang · 2024
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Hallucination is inevitable: An innate limitation of large language models
Ziwei Xu, Sanjay Jain, and Mohan Kankanhalli · 2024
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Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan · 2024
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Gptswarm: Language agents as optimizable graphs
Mingchen Zhuge, Wenyi Wang, Louis Kirsch, Francesco Faccio, Dmitrii Khizbullin, and Jürgen Schmidhuber · 2024
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Lumi-lab: a foundation model-driven autonomous platform enabling discovery of new ionizable lipid designs for mrna delivery
Haotian Cui, Yue Xu, Kuan Pang, Gen Li, Fanglin Gong, Bo Wang, and Bowen Li · 2025
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Towards an ai co-scientist, 2025
Juraj Gottweis, Wei-Hung Weng, Alexander Daryin, Tao Tu, Anil Palepu, Petar Sirkovic, Artiom Myaskovsky, Felix Weissenberger, Keran Rong, Ryutaro Tanno, Khaled Saab, Dan Popovici, Jacob Blum, Fan Zhang, Katherine Chou, Avinatan Hassidim, Burak Gokturk, Amin Vahdat, Pushmeet Kohli, Yossi Matias, Andrew Carroll, Kavita Kulkarni, Nenad Tomasev, Yuan Guan, Vikram Dhillon, Eeshit Dhaval Vaishnav, Byron Lee, Tiago R D Costa, José R Penadés, Gary Peltz, Yunhan Xu, Annalisa Pawlosky, Alan Karthikesalingam, and Vivek Natarajan · 2025
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All that glitters is not novel: Plagiarism in ai generated research
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Curie: Toward rigorous and automated scientific experimentation with ai agents
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Confidence improves self-consistency in llms
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