Fetching the paper…
Reading the bibliography…
One of the grand challenges of artificial general intelligence is developing agents capable of conducting scientific research and discovering new knowledge.
How We Think
J. Dewey · 1910
Earlier work this paper cites.
Automated theory formation in mathematics
Douglas B Lenat · 1977
Earlier work this paper cites.
Dendral and meta-dendral: Their applications dimension
Bruce G Buchanan and Edward A Feigenbaum · 1981
Earlier work this paper cites.
Why am and eurisko appear to work
Douglas B Lenat and John Seely Brown · 1984
Earlier work this paper cites.
Integrating quantitative and qualitative discovery: the abacus system
Brian C Falkenhainer and Ryszard S Michalski · 1986
Earlier work this paper cites.
Scientific discovery: Computational explorations of the creative processes
Pat Langley · 1987
Earlier work this paper cites.
A robust approach to numeric discovery
Bernd Nordhausen and Pat Langley · 1990
Earlier work this paper cites.
Curious model-building control systems
Jürgen Schmidhuber · 1991
Earlier work this paper cites.
Automated discovery of empirical laws
Jan M Zytkow · 1996
Earlier work this paper cites.
The hutter prize, 2006
Marcus Hutter · 2006
Earlier work this paper cites.
Exploiting open-endedness to solve problems through the search for novelty
Joel Lehman, Kenneth O Stanley, et al · 2008
Earlier work this paper cites.
Automating science
David Waltz and Bruce G Buchanan · 2009
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
Earlier work this paper cites.
Artificial scientists & artists based on the formal theory of creativity
Jürgen Schmidhuber · 2010
Earlier work this paper cites.
Formal theory of creativity, fun, and intrinsic motivation (1990–2010)
Jürgen Schmidhuber · 2010
Earlier work this paper cites.
Towards robot scientists for autonomous scientific discovery
Andrew Sparkes, Wayne Aubrey, Emma Byrne, Amanda Clare, Muhammed N Khan, Maria Liakata, Magdalena Markham, Jem Rowland, Larisa N Soldatova, Kenneth E Whelan, et al · 2010
Earlier work this paper cites.
About the test data, 2011
Matt Mahoney · 2011
Earlier work this paper cites.
When creative machines overtake man, 2012
Jürgen Schmidhuber · 2012
Earlier work this paper cites.
Twenty years of mixture of experts
Seniha Esen Yuksel, Joseph N Wilson, and Paul D Gader · 2012
Earlier work this paper cites.
What is this thing called science?
Alan Chalmers · 2013
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Auto-Encoding Variational Bayes
Diederik P. Kingma and Max Welling · 2014
Earlier work this paper cites.
Probabilistic machine learning and artificial intelligence
Zoubin Ghahramani · 2015
Earlier work this paper cites.
The unreasonable effectiveness of recurrent neural networks, 2015
Andrej Karpathy · 2015
Earlier work this paper cites.
A survey of research on cloud robotics and automation
Ben Kehoe, Sachin Patil, Pieter Abbeel, and Ken Goldberg · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Earlier work this paper cites.
Why greatness cannot be planned: The myth of the objective
Kenneth O Stanley and Joel Lehman · 2015
Earlier work this paper cites.
Minimal criterion coevolution: a new approach to open-ended search
Jonathan C Brant and Kenneth O Stanley · 2017
Earlier work this paper cites.
Open-endedness: The last grand challenge you’ve never heard of
Kenneth O Stanley, Joel Lehman, and Lisa Soros · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Semantic scholar
Suzanne Fricke · 2018
Earlier work this paper cites.
Jia-Bin Huang · 2018
Earlier work this paper cites.
Jeff Clune · 2019
Earlier work this paper cites.
Automated machine learning: methods, systems, challenges
Frank Hutter, Lars Kotthoff, and Joaquin Vanschoren · 2019
Earlier work this paper cites.
Improving generalization in meta reinforcement learning using learned objectives
Louis Kirsch, Sjoerd van Steenkiste, and Jürgen Schmidhuber · 2019
Earlier work this paper cites.
Why open-endedness matters
Kenneth O Stanley · 2019
Cited alongside, same era.
CommonsenseQA: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant · 2019
Cited alongside, same era.
Meta-learning curiosity algorithms
Ferran Alet, Martin F Schneider, Tomas Lozano-Perez, and Leslie Pack Kaelbling · 2020
Cited alongside, same era.
Language models are few-shot learners, 2020
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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
Later among the works it cites.
Gpt-4 technical report, 2023
OpenAI · 2023
Later among the works it cites.
tiny-diffusion, 2023
Tanel Pärnamaa · 2023
Later among the works it cites.
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
Later among the works it cites.
Language to rewards for robotic skill synthesis
Wenhao Yu, Nimrod Gileadi, Chuyuan Fu, Sean Kirmani, Kuang-Huei Lee, Montse Gonzalez Arenas, Hao-Tien Lewis Chiang, Tom Erez, Leonard Hasenclever, Jan Humplik, et al · 2023
Later among the works it cites.
The claude 3 model family: Opus, sonnet, haiku, 2024
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
The surprising creativity of digital evolution: A collection of anecdotes from the evolutionary computation and artificial life research communities
Joel Lehman, Jeff Clune, Dusan Misevic, Christoph Adami, Lee Altenberg, Julie Beaulieu, Peter J Bentley, Samuel Bernard, Guillaume Beslon, David M Bryson, et al · 2020
Cited alongside, same era.
The neurips 2021 consistency experiment
Alina Beygelzimer, Yann Dauphin, Percy Liang, and Jennifer Wortman Vaughan · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Automl: A survey of the state-of-the-art
Xin He, Kaiyong Zhao, and Xiaowen Chu · 2021
Cited alongside, same era.
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
Cited alongside, same era.
grokking
Charlie Snell · 2021
Cited alongside, same era.
Think global and act local: Bayesian optimisation over high-dimensional categorical and mixed search spaces
Xingchen Wan, Vu Nguyen, Huong Ha, Binxin Ru, Cong Lu, and Michael A Osborne · 2021
Cited alongside, same era.
Anthropic · 2024
Closest in time.
Jinheon Baek, Sujay Kumar Jauhar, Silviu Cucerzan, and Sung Ju Hwang · 2024
Closest in time.
Iclr2022-openreviewdata, 2024
Federico Berto · 2024
Closest in time.
Quality-diversity through ai feedback
Herbie Bradley, Andrew Dai, Hannah Benita Teufel, Jenny Zhang, Koen Oostermeijer, Marco Bellagente, Jeff Clune, Kenneth Stanley, Gregory Schott, and Joel Lehman · 2024
Closest in time.
Marg: Multi-agent review generation for scientific papers, 2024
Mike D’Arcy, Tom Hope, Larry Birnbaum, and Doug Downey · 2024
Closest in time.
Quality diversity through human feedback: Towards open-ended diversity-driven optimization
Li Ding, Jenny Zhang, Jeff Clune, Lee Spector, and Joel Lehman · 2024
Closest in time.
Symbolicai: A framework for logic-based approaches combining generative models and solvers, 2024
Marius-Constantin Dinu, Claudiu Leoveanu-Condrei, Markus Holzleitner, Werner Zellinger, and Sepp Hochreiter · 2024
Closest in time.
Maxence Faldor, Jenny Zhang, Antoine Cully, and Jeff Clune · 2024
Closest in time.
aider, 2024
Paul Gauthier · 2024
Closest in time.
Diffit: Diffusion vision transformers for image generation, 2024
Ali Hatamizadeh, Jiaming Song, Guilin Liu, Jan Kautz, and Arash Vahdat · 2024
Closest in time.
Simulating 500 million years of evolution with a language model
Tomas Hayes, Roshan Rao, Halil Akin, Nicholas J Sofroniew, Deniz Oktay, Zeming Lin, Robert Verkuil, Vincent Q Tran, Jonathan Deaton, Marius Wiggert, et al · 2024
Closest in time.
Mlagentbench: Evaluating language agents on machine learning experimentation
Qian Huang, Jian Vora, Percy Liang, and Jure Leskovec · 2024
Closest in time.
Autonomous llm-driven research from data to human-verifiable research papers, 2024
Tal Ifargan, Lukas Hafner, Maor Kern, Ori Alcalay, and Roy Kishony · 2024
Closest in time.
Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed · 2024
Closest in time.
Swe-bench: Can language models resolve real-world github issues?, 2024
Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan · 2024
Closest in time.
Large language models as evolution strategies
Robert Tjarko Lange, Yingtao Tian, and Yujin Tang · 2024
Closest in time.
Integrated systems for computational scientific discovery
Pat Langley · 2024
Closest in time.
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
Closest in time.
Large language models as in-context ai generators for quality-diversity
Bryan Lim, Manon Flageat, and Antoine Cully · 2024
Closest in time.
The llama 3 herd of models, 2024
Llama Team · 2024
Closest in time.
Discoverybench: Towards data-driven discovery with large language models, 2024
Bodhisattwa Prasad Majumder, Harshit Surana, Dhruv Agarwal, Bhavana Dalvi Mishra, Abhijeetsingh Meena, Aryan Prakhar, Tirth Vora, Tushar Khot, Ashish Sabharwal, and Peter Clark · 2024
Closest in time.
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
Closest in time.
Toolformer: Language models can teach themselves to use tools
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom · 2024
Closest in time.
Reflexion: Language agents with verbal reinforcement learning
Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao · 2024
Closest in time.
Xingyou Song, Yingtao Tian, Robert Tjarko Lange, Chansoo Lee, Yujin Tang, and Yutian Chen · 2024
Closest in time.
Large language models for automated open-domain scientific hypotheses discovery, 2024
Zonglin Yang, Xinya Du, Junxian Li, Jie Zheng, Soujanya Poria, and Erik Cambria · 2024
Closest in time.
OMNI: Open-endedness via models of human notions of interestingness
Jenny Zhang, Joel Lehman, Kenneth Stanley, and Jeff Clune · 2024
Closest in time.
Judging llm-as-a-judge with mt-bench and chatbot arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al · 2024
Closest in time.
Deepseek-coder-v2: Breaking the barrier of closed-source models in code intelligence
Qihao Zhu, Daya Guo, Zhihong Shao, Dejian Yang, Peiyi Wang, Runxin Xu, Y Wu, Yukun Li, Huazuo Gao, Shirong Ma, et al · 2024
Closest in time.