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Understanding the world and explaining it with scientific theories is a central aspiration of artificial intelligence research.
Variations and fluctuations of the number of individuals in animal species living together
Vito Volterra · 1928
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A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955
John McCarthy, Marvin L Minsky, Nathaniel Rochester, and Claude E Shannon · 1955
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G. E. P. Box and William G. Hunter · 1962
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Science and statistics
George EP Box · 1976
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Sampling and Bayes’ Inference in Scientific Modelling and Robustness
George E. P. Box · 1980
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Probabilistic models for some intelligence and attainment tests
Georg Rasch · 1993
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Bayesian Experimental Design: A Review
Kathryn Chaloner and Isabella Verdinelli · 1995
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Hybrid grammar-based approach to nonlinear dynamical system identification from biological time series
B. A. McKinney, J. E. Crowe, H. U. Voss, P. S. Crooke, N. Barney, and J. H. Moore · 2006
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Automated reverse engineering of nonlinear dynamical systems
Josh C. Bongard and Hod Lipson · 2007
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Structure discovery in nonparametric regression through compositional kernel search
David Duvenaud, James Lloyd, Roger Grosse, Joshua Tenenbaum, and Ghahramani Zoubin · 2013
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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A tutorial on adaptive design optimization
Jay I. Myung, Daniel R. Cavagnaro, and Mark A. Pitt · 2013
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Bayesian inference and online experimental design for mapping neural microcircuits
Ben Shababo, Brooks Paige, Ari Pakman, and Liam Paninski · 2013
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Affective cognition: Exploring lay theories of emotion
Desmond C Ong, Jamil Zaki, and Noah D Goodman · 2015
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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The moral machine experiment
Edmond Awad, Sohan Dsouza, Richard Kim, Jonathan Schulz, Joseph Henrich, Azim Shariff, Jean-François Bonnefon, and Iyad Rahwan · 2018
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Analysis of survival data
David Roxbee Cox · 2018
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On Nesting Monte Carlo Estimators
Tom Rainforth, Robert Cornish, Hongseok Yang, Andrew Warrington, and Frank Wood · 2018
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Variational bayesian optimal experimental design
Adam Foster, Martin Jankowiak, Elias Bingham, Paul Horsfall, Yee Whye Teh, Thomas Rainforth, and Noah Goodman · 2019
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Emergent complexity and zero-shot transfer via unsupervised environment design
Michael Dennis, Natasha Jaques, Eugene Vinitsky, Alexandre Bayen, Stuart Russell, Andrew Critch, and Sergey Levine · 2020
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Deep adaptive design: Amortizing sequential bayesian experimental design
Adam Foster, Desi R Ivanova, Ilyas Malik, and Tom Rainforth · 2021
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Metaicl: Learning to learn in context
Sewon Min, Mike Lewis, Luke Zettlemoyer, and Hannaneh Hajishirzi · 2021
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Social simulacra: Creating populated prototypes for social computing systems
Joon Sung Park, Lindsay Popowski, Carrie Cai, Meredith Ringel Morris, Percy Liang, and Michael S Bernstein · 2022
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Aaron Jaech, Adam Kalai, Adam Lerer, Adam Richardson, Ahmed El-Kishky, Aiden Low, Alec Helyar, Aleksander Madry, Alex Beutel, Alex Carney, et al · 2024
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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 R Narasimhan · 2024
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Beyond a*: Better planning with transformers via search dynamics bootstrapping
Lucas Lehnert, Sainbayar Sukhbaatar, DiJia Su, Qinqing Zheng, Paul Mcvay, Michael Rabbat, and Yuandong Tian · 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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Abulhair Saparov and He He · 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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Using large language models to simulate multiple humans and replicate human subject studies
Gati V Aher, Rosa I Arriaga, and Adam Tauman Kalai · 2023
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The impact of large language models on scientific discovery: a preliminary study using gpt-4, 2023
Microsoft Research AI4Science and Microsoft Azure Quantum · 2023
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Out of one, many: Using language models to simulate human samples
Lisa P Argyle, Ethan C Busby, Nancy Fulda, Joshua R Gubler, Christopher Rytting, and David Wingate · 2023
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Strategic reasoning with language models
Kanishk Gandhi, Dorsa Sadigh, and Noah D Goodman · 2023
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posteriordb: a set of posteriors for Bayesian inference and probabilistic programming, October 2023
Måns Magnusson, Paul Bürkner, and Aki Vehtari · 2023
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Allen Nie, Yi Su, Bo Chang, Jonathan N Lee, Ed H Chi, Quoc V Le, and Minmin Chen · 2024
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Hello, GPT-4
OpenAI · 2024
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Phenomenal yet puzzling: Testing inductive reasoning capabilities of language models with hypothesis refinement
Linlu Qiu, Liwei Jiang, Ximing Lu, Melanie Sclar, Valentina Pyatkin, Chandra Bhagavatula, Bailin Wang, Yoon Kim, Yejin Choi, Nouha Dziri, and Xiang Ren · 2024
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Testing the general deductive reasoning capacity of large language models using ood examples
Abulhair Saparov, Richard Yuanzhe Pang, Vishakh Padmakumar, Nitish Joshi, Mehran Kazemi, Najoung Kim, and He He · 2024
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Mastering board games by external and internal planning with language models
John Schultz, Jakub Adamek, Matej Jusup, Marc Lanctot, Michael Kaisers, Sarah Perrin, Daniel Hennes, Jeremy Shar, Cannada Lewis, Anian Ruoss, et al · 2024
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Hypothesis search: Inductive reasoning with language models
Ruocheng Wang, Eric Zelikman, Gabriel Poesia, Yewen Pu, Nick Haber, and Noah D. Goodman · 2024
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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, Kai Dang, Keming Lu, Keqin Bao, Kexin Yang, Le Yu, Mei Li, Mingfeng Xue, Pei Zhang, Qin Zhu, Rui Men, Runji Lin, Tianhao Li, Tingyu Xia, Xingzhang Ren, Xuancheng Ren, Yang Fan, Yang Su, Yichang Zhang, Yu Wan, Yuqiong Liu, Zeyu Cui, Zhenru Zhang, and Zihan Qiu · 2024
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Discovering symbolic cognitive models from human and animal behavior
Pablo Samuel Castro, Nenad Tomasev, Ankit Anand, Navodita Sharma, Rishika Mohanta, Aparna Dev, Kuba Perlin, Siddhant Jain, Kyle Levin, Noémi Éltető, Will Dabney, Alexander Novikov, Glenn C Turner, Maria K Eckstein, Nathaniel D Daw, Kevin J Miller, and Kimberly L Stachenfeld · 2025
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Cognitive behaviors that enable self-improving reasoners, or, four habits of highly effective stars
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Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
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Automated hypothesis validation with agentic sequential falsifications, 2025
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Open Thoughts, January 2025
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