A Psychometric Framework for Artificial General Intelligence

This large collaboration between 29 different institutions proposes a quantifiable framework for defining **Artificial General Intelligence (AGI)**, characterized as an AI matching or exceeding the versatility and proficiency of a well-educated adult. This framework utilizes the **Cattell-Horn-Carroll (CHC) theory** of cognitive abilities, the most empirically validated model of human intelligence, to systematically break down general intelligence into ten core components. These components include **General Knowledge (K)**, **Reading and Writing Ability (RW)**, **Mathematical Ability (M)**, and various forms of **Reasoning** and **Memory**, each weighted equally to ensure a focus on breadth. The analysis reveals that current AI systems, such as GPT-4 and GPT-5, demonstrate a jagged profile of capabilities, excelling in some narrow tasks but substantially lacking in core human cognitive functions like **Long-Term Memory Storage (MS)** and **On-the-Spot Reasoning (R)**. The paper also discusses "capability contortions," where current AI uses inefficient methods like large context windows or external search (RAG) to compensate for these missing foundational abilities, suggesting that achieving AGI requires overcoming significant barriers beyond simple impressive performance.Source:https://www.agidefinition.ai/paper.pdf

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