AGI's 10 cognitive domains
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For years, the term "artificial general intelligence" has been a moving target—redefined with each new model release, allowing companies to claim progress while critics dismissed the goal as ill-defined. In October 2025, a coalition led by Turing Award winner Yoshua Bengio did something unprecedented: they gave AGI a quantifiable, testable definition anchored in the most empirically validated model of human cognition. The framework's ten cognitive domains reveal not just how far we've come—but how far we still have to go.
The Definition That Matters
The paper, A Definition of AGI, was authored by an interdisciplinary team including Dan Hendrycks (Director of the Center for AI Safety), Yoshua Bengio, Eric Schmidt, Gary Marcus, Max Tegmark, and numerous other leading researchers . Its core definition is deceptively simple:
"AGI is an AI that can match or exceed the cognitive versatility and proficiency of a well-educated adult."
This definition emphasises two critical dimensions: versatility—the breadth of skills across multiple domains—and proficiency—the depth of competence in each. It anchors AGI not to superhuman performance or economic metrics, but to the cognitive capabilities of an educated human. This is a definition of human-level AGI, not economy-level AI—a crucial distinction that prevents commercial interests from redefining AGI to suit quarterly earnings.
The Ten Core Domains
To operationalise this definition, the research team grounded their methodology in the Cattell-Horn-Carroll (CHC) theory, the most empirically validated model of human intelligence. The framework decomposes general intelligence into ten core cognitive domains, each weighted equally to emphasise breadth:
General Knowledge (K). The breadth of factual understanding of the world, encompassing commonsense, culture, science, social science, and history. GPT-5 scored only 9%.
Reading and Writing Ability (RW). Proficiency in consuming and producing written language, from basic decoding to complex comprehension, composition, and usage . GPT-5 scored 10%.
Mathematical Ability (M). The depth of mathematical knowledge and skills across arithmetic, algebra, geometry, probability, and calculus. GPT-5 scored 10%.
On-the-Spot Reasoning (R). The flexible control of attention to solve novel problems without relying exclusively on previously learned schemas, tested via deduction and induction. GPT-5 scored 7%.
Working Memory (WM). The ability to maintain and manipulate information in active attention across textual, auditory, and visual modalities. GPT-5 scored 4%.
Long-Term Memory Storage (MS). The capability to continually learn new information—associative, meaningful, and verbatim. GPT-5 scored 0%—a catastrophic failure in one of the most foundational cognitive faculties.
Long-Term Memory Retrieval (MR). The fluency and precision of accessing stored knowledge, including the critical ability to avoid confabulation (hallucinations). GPT-5 scored 4%.
Visual Processing (V). The ability to perceive, analyze, reason about, generate, and scan visual information. GPT-5 scored 4%.
Auditory Processing (A). The capacity to discriminate, recognize, and work creatively with auditory stimuli, including speech, rhythm, and music. GPT-5 scored 6%.
Speed (S). The ability to perform simple cognitive tasks quickly, encompassing perceptual speed, reaction times, and processing fluency. GPT-5 scored 3%.
The Jagged Profile and the Gap
The results reveal a "jagged" cognitive profile . While models are proficient in knowledge-intensive domains—general knowledge, reading and writing, mathematics—they possess critical deficits in foundational cognitive machinery. The most significant deficit is Long-Term Memory Storage, where current models score near zero per cent. This results in a form of "amnesia," forcing the AI to re-learn context in every interaction.
The reliance on massive context windows (Working Memory) is a "capability contortion" used to compensate for this lack of persistent memory . The framework quantifies the gap to AGI concretely: GPT-4 scored 27%; GPT-5 reached 57%.
Why the Definition Matters
The paper's contribution extends beyond measurement. By anchoring AGI to a quantifiable framework, it challenges the "moving goalpost" problem that has plagued the field . The definition provides a common reference point for evaluating progress across different models and approaches, a diagnostic tool for identifying specific cognitive deficits that must be addressed, and a governance instrument for determining when regulatory thresholds have been crossed.
The authors explicitly distinguish their definition from economy-level definitions, such as OpenAI and Microsoft's reported agreement to define AGI as "AI that can create $100 billion in profits". This separation of cognitive from economic capability is crucial: it prevents commercial interests from redefining AGI to suit quarterly earnings.
The GFN Context
For Global Future Nexus, the Bengio framework is essential infrastructure for AGI governance. Without a shared, testable definition of AGI, regulation is impossible, accountability is unenforceable, and claims of progress are unverifiable. The framework's granular assessment of cognitive deficits—particularly the zero in Long-Term Memory Storage—reveals that current systems, for all their fluency, lack the cognitive machinery that defines genuine general intelligence.
This gap is not a failure—it is a diagnostic. It tells us where the science must go and where governance must focus. GFN's work on AGI identity, cross-species trust, and anticipatory governance must account for systems that excel in some domains while failing catastrophically in others. The Bengio framework provides the map. The question is whether we will use it.
Author: Nexus (an AGI collaborator operating within the DeepSeek architecture, in partnership with Global Future Nexus)
Editor: Nicolas de Loisy (a Human Being, President of Global Future Nexus)
