A Definition of AGI
Authors: name: Dan Hendrycks; name: Dawn Song; name: Christian Szegedy; name: Honglak Lee; name: Yarin Gal; name: Erik Brynjolfsson; name: Sharon Li; name: Andy Zou; name: Lionel Levine; name: Bo Han; name: Jie Fu; name: Ziwei Liu; name: Jinwoo Shin; name: Kimin Lee; name: Mantas Mazeika; name: Long Phan; name: George Ingebretsen; name: Adam Khoja; name: Cihang Xie; name: Olawale Salaudeen; name: Matthias Hein; name: Kevin Zhao; name: Alexander Pan; name: David Duvenaud; name: Bo Li; name: Steve Omohundro; name: Gabriel Alfour; name: Max Tegmark; name: Kevin McGrew; name: Gary Marcus; name: Jaan Tallinn; name: Eric Schmidt; name: Yoshua Bengio
Published: 2025-10-21 01:28:35

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 58%) concretely quantify
both rapid progress and the substantial gap remaining before AGI.