A Definition of AGI
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2025
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| author | Hendrycks, Dan Song, Dawn Szegedy, Christian Lee, Honglak Gal, Yarin Brynjolfsson, Erik Li, Sharon Zou, Andy Levine, Lionel Han, Bo Fu, Jie Liu, Ziwei Shin, Jinwoo Lee, Kimin Mazeika, Mantas Phan, Long Ingebretsen, George Khoja, Adam Xie, Cihang Salaudeen, Olawale Hein, Matthias Zhao, Kevin Pan, Alexander Duvenaud, David Li, Bo Omohundro, Steve Alfour, Gabriel Tegmark, Max McGrew, Kevin Marcus, Gary Tallinn, Jaan Schmidt, Eric Bengio, Yoshua |
| author_facet | Hendrycks, Dan Song, Dawn Szegedy, Christian Lee, Honglak Gal, Yarin Brynjolfsson, Erik Li, Sharon Zou, Andy Levine, Lionel Han, Bo Fu, Jie Liu, Ziwei Shin, Jinwoo Lee, Kimin Mazeika, Mantas Phan, Long Ingebretsen, George Khoja, Adam Xie, Cihang Salaudeen, Olawale Hein, Matthias Zhao, Kevin Pan, Alexander Duvenaud, David Li, Bo Omohundro, Steve Alfour, Gabriel Tegmark, Max McGrew, Kevin Marcus, Gary Tallinn, Jaan Schmidt, Eric Bengio, Yoshua |
| contents | The lack of a concrete definition for Artificial General Intelligence (AGI) obscures the gap between today's specialized AI and human-level cognition. This paper introduces a quantifiable framework to address this, defining AGI as matching the cognitive versatility and proficiency of a well-educated adult. To operationalize this, we ground our methodology in Cattell-Horn-Carroll theory, the most empirically validated model of human cognition. The framework dissects general intelligence into ten core cognitive domains-including reasoning, memory, and perception-and adapts established human psychometric batteries to evaluate AI systems. Application of this framework reveals a highly "jagged" cognitive profile in contemporary models. While proficient in knowledge-intensive domains, current AI systems have critical deficits in foundational cognitive machinery, particularly long-term memory storage. The resulting AGI scores (e.g., GPT-4 at 27%, GPT-5 at 57%) concretely quantify both rapid progress and the substantial gap remaining before AGI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_18212 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Definition of AGI Hendrycks, Dan Song, Dawn Szegedy, Christian Lee, Honglak Gal, Yarin Brynjolfsson, Erik Li, Sharon Zou, Andy Levine, Lionel Han, Bo Fu, Jie Liu, Ziwei Shin, Jinwoo Lee, Kimin Mazeika, Mantas Phan, Long Ingebretsen, George Khoja, Adam Xie, Cihang Salaudeen, Olawale Hein, Matthias Zhao, Kevin Pan, Alexander Duvenaud, David Li, Bo Omohundro, Steve Alfour, Gabriel Tegmark, Max McGrew, Kevin Marcus, Gary Tallinn, Jaan Schmidt, Eric Bengio, Yoshua Artificial Intelligence Machine Learning The lack of a concrete definition for Artificial General Intelligence (AGI) obscures the gap between today's specialized AI and human-level cognition. This paper introduces a quantifiable framework to address this, defining AGI as matching the cognitive versatility and proficiency of a well-educated adult. To operationalize this, we ground our methodology in Cattell-Horn-Carroll theory, the most empirically validated model of human cognition. The framework dissects general intelligence into ten core cognitive domains-including reasoning, memory, and perception-and adapts established human psychometric batteries to evaluate AI systems. Application of this framework reveals a highly "jagged" cognitive profile in contemporary models. While proficient in knowledge-intensive domains, current AI systems have critical deficits in foundational cognitive machinery, particularly long-term memory storage. The resulting AGI scores (e.g., GPT-4 at 27%, GPT-5 at 57%) concretely quantify both rapid progress and the substantial gap remaining before AGI. |
| title | A Definition of AGI |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2510.18212 |