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IQ testing has served as a foundational methodology for evaluating human cognitive capabilities, deliberately decoupling assessment from linguistic background, language proficiency, or domain-specific knowledge to isolate core competencies in abstraction and reasoning.
The topography of ability and learning correlations
RE Snow · 1984
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Raven progressive matrices
Jean Raven · 2003
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Comparing machines and humans on a visual categorization test
François Fleuret, Ting Li, Charles Dubout, Emma K Wampler, Steven Yantis, and Donald Geman · 2011
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On the measure of intelligence
François Chollet · 2019
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Learning to make analogies by contrasting abstract relational structure
Felix Hill, Adam Santoro, David GT Barrett, Ari S Morcos, and Timothy Lillicrap · 2019
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Deepiq: A human-inspired ai system for solving iq test problems
Jacek Mańdziuk and Adam Żychowski · 2019
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Raven: A dataset for relational and analogical visual reasoning
Chi Zhang, Feng Gao, Baoxiong Jia, Yixin Zhu, and Song-Chun Zhu · 2019
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Logiqa: A challenge dataset for machine reading comprehension with logical reasoning
Jian Liu, Leyang Cui, Hanmeng Liu, Dandan Huang, Yile Wang, and Yue Zhang · 2020
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Bongard-logo: A new benchmark for human-level concept learning and reasoning
Weili Nie, Zhiding Yu, Lei Mao, Ankit B Patel, Yuke Zhu, and Anima Anandkumar · 2020
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Learning representations that support extrapolation
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Machine number sense: A dataset of visual arithmetic problems for abstract and relational reasoning
Wenhe Zhang, Chi Zhang, Yixin Zhu, and Song-Chun Zhu · 2020
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Learning transferable visual models from natural language supervision
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How much intelligence is there in artificial intelligence? a 2020 update
Han LJ Van der Maas, Lukas Snoek, and Claire E Stevenson · 2021
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Deep learning methods for abstract visual reasoning: A survey on raven’s progressive matrices
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts
Pan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu, Chunyuan Li, Hannaneh Hajishirzi, Hao Cheng, Kai-Wei Chang, Michel Galley, and Jianfeng Gao · 2023
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Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi
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Cmmmu: A chinese massive multi-discipline multimodal understanding benchmark
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The Claude 3 Model Family: Opus, Sonnet, Haiku
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A review of emerging research directions in abstract visual reasoning
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A survey on multimodal large language models
Shukang Yin, Chaoyou Fu, Sirui Zhao, Ke Li, Xing Sun, Tong Xu, and Enhong Chen · 2023
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Deepseekmath: Pushing the limits of mathematical reasoning in open language models
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