A Definition of AGI
Abstract
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.
Keywords
Cite
@article{arxiv.2510.18212,
title = {A Definition of AGI},
author = {Dan Hendrycks and Dawn Song and Christian Szegedy and Honglak Lee and Yarin Gal and Erik Brynjolfsson and Sharon Li and Andy Zou and Lionel Levine and Bo Han and Jie Fu and Ziwei Liu and Jinwoo Shin and Kimin Lee and Mantas Mazeika and Long Phan and George Ingebretsen and Adam Khoja and Cihang Xie and Olawale Salaudeen and Matthias Hein and Kevin Zhao and Alexander Pan and David Duvenaud and Bo Li and Steve Omohundro and Gabriel Alfour and Max Tegmark and Kevin McGrew and Gary Marcus and Jaan Tallinn and Eric Schmidt and Yoshua Bengio},
journal= {arXiv preprint arXiv:2510.18212},
year = {2025}
}