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The Abstraction and Reasoning Corpus (ARC) is a general artificial intelligence benchmark that is currently unsolvable by any Machine Learning method, including Large Language Models (LLMs).
An overview of machine learning
J. G. Carbonell, R. S. Michalski, and T. M. Mitchell · 1983
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Inductive logic programming: Theory and methods
S. Muggleton and L. De Raedt · 1994
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Raven progressive matrices
J. Raven · 2003
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Core knowledge
E. S. Spelke and K. D. Kinzler · 2007
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Bias reformulation for one-shot function induction
D. Lin, E. Dechter, K. Ellis, J. B. Tenenbaum, and S. H. Muggleton · 2014
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Inductive programming meets the real world
S. Gulwani, J. Hernández-Orallo, E. Kitzelmann, S. H. Muggleton, U. Schmid, and B. Zorn · 2015
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Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
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Program synthesis
S. Gulwani, O. Polozov, R. Singh, et al · 2017
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Building machines that learn and think like people
B. M. Lake, T. D. Ullman, J. B. Tenenbaum, and S. J. Gershman · 2017
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Deep learning: A critical appraisal
G. Marcus · 2018
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Meta-interpretive learning from noisy images
S. Muggleton, W.-Z. Dai, C. Sammut, A. Tamaddoni-Nezhad, J. Wen, and Z.-H. Zhou · 2018
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On the measure of intelligence
F. Chollet · 2019
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Fast and flexible: Human program induction in abstract reasoning tasks
A. Johnson, W. K. Vong, B. M. Lake, and T. M. Gureckis · 2021
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Communicating natural programs to humans and machines
S. Acquaviva, Y. Pu, M. Kryven, T. Sechopoulos, C. Wong, G. Ecanow, M. Nye, M. Tessler, and J. Tenenbaum · 2022
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Comparing humans, gpt-4, and gpt-4v on abstraction and reasoning tasks
M. Mitchell, A. B. Palmarini, and A. Moskvichev · 2023
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Assessing gpt4-v on structured reasoning tasks
M. Singh, J. Cambronero, S. Gulwani, V. Le, and G. Verbruggen · 2023
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A divide-align-conquer strategy for program synthesis
J. Witt, S. Rasing, S. Dumančić, T. Guns, and C.-C. Carbon · 2023
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Y. Xu, W. Li, P. Vaezipoor, S. Sanner, and E. B. Khalil · 2023
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Neural networks for abstraction and reasoning: Towards broad generalization in machines
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Inductive logic programming at 30
A. Cropper, S. Dumančić, R. Evans, and S. H. Muggleton · 2022
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What’out-of-distribution’is and is not
S. Farquhar and Y. Gal · 2022
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Ood-bench: Quantifying and understanding two dimensions of out-of-distribution generalization
N. Ye, K. Li, H. Bai, R. Yu, L. Hong, F. Zhou, Z. Li, and J. Zhu · 2022
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J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat, et al · 2023
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M. Bober-Irizar and S. Banerjee · 2024
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Codeit: Self-improving language models with prioritized hindsight replay
N. Butt, B. Manczak, A. Wiggers, C. Rainone, D. Zhang, M. Defferrard, and T. Cohen · 2024
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Out-of-distribution detection with logical reasoning
K. Kirchheim, T. Gonschorek, and F. Ortmeier · 2024
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S. Lee, W. Sim, D. Shin, S. Hwang, W. Seo, J. Park, S. Lee, S. Kim, and S. Kim · 2024
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Generalized planning for the abstraction and reasoning corpus
C. Lei, N. Lipovetzky, and K. A. Ehinger · 2024
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