taesiri
Abstract
A quantifiable framework based on Cattell-Horn-Carroll theory evaluates AI systems across ten cognitive domains, revealing significant gaps in foundational cognitive abilities like long-term memory.
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.
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The paper defines AGI as an AI matching or surpassing the cognitive versatility and proficiency of a well-educated adult, measured across ten human-like cognitive domains.
This paper should seriously reevaluate their framework on GPT-5 Pro rather than relying on the Auto mode of GPT-5 with a sloppy router behind it. We are talking about achieving AGI capabilities and exposing a range of serious risks for human kind. Therefore, measuring the most powerful frontier model at the moment does provide a better sense of where we stand today.
it's a start
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