# A Definition of AGI

arXiv: https://arxiv.org/abs/2510.18212 · HTML: https://mlsanity.com/paper/2510.18212/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2025.

## Built on by (most cited first, 27 of 27)

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- [Establishing Construct Validity in LLM Capability Benchmarks Requires Nomological Networks](https://mlsanity.com/paper/2603.15121/) (2026)
- [Measuring Progress Toward AGI: A Cognitive Framework](https://mlsanity.com/paper/2605.28405/) (2026)
- [Large Language Model Agents Are Not Always Faithful Self-Evolvers](https://mlsanity.com/paper/2601.22436/) (2026)
- [Epidemiology of Large Language Models: A Benchmark for Observational Distribution Knowledge](https://mlsanity.com/paper/2511.03070/) (2025)
- [An Operational Kardashev-Style Scale for Autonomous AI - Towards AGI and Superintelligence](https://mlsanity.com/paper/2511.13411/) (2025)
- [Psychometric Tests for AI Agents and Their Moduli Space](https://mlsanity.com/paper/2511.19262/) (2025)
- [The Rise and Fall of G in AGI](https://mlsanity.com/paper/2604.09911/) (2026)
- [Can LLMs Threaten Human Survival? Benchmarking Potential Existential Threats from LLMs via Prefix Completion](https://mlsanity.com/paper/2511.19171/) (2025)
- [Benchmarks Are Not That Out of Distribution: Word Overlap Predicts Performance](https://mlsanity.com/paper/2602.10657/) (2026)
- [Deep Research of Deep Research: From Transformer to Agent, From AI to AI for Science](https://mlsanity.com/paper/2603.28361/) (2026)
- [Task-Focused Memorization for Multimodal Agents](https://mlsanity.com/paper/2605.31075/) (2026)
- [On the Measure of a Model: From Intelligence to Generality](https://mlsanity.com/paper/2511.11773/) (2025)
- [Precautionary Governance of Autonomous AI: Legal Personhood as Functional Instrument](https://mlsanity.com/paper/2605.12505/) (2026)
- [Enhanced and Efficient Reasoning in Large Learning Models](https://mlsanity.com/paper/2605.14036/) (2026)
- [Generative Models Erode Human Temporal Learning Through Market Selection](https://mlsanity.com/paper/2606.06572/) (2026)
- [Definitional alignment before capability alignment: a Design-Science framework for adjudicating claims about AGI](https://mlsanity.com/paper/2606.12713/) (2026)
- [The Boundaries of Automation: A Theory of Persistent Human Participation](https://mlsanity.com/paper/2607.21547/) (2026)
- [When benchmark inferences do not compose: Projectibility in AI evaluation](https://mlsanity.com/paper/2607.26159/) (2026)
- [Position: Reasoning is a Learnable Rule-Based Process](https://mlsanity.com/paper/2608.12325/) (2026)
- [Learn2Play Bench: How Well Do LLM Agents Learn from Experience in Unfamiliar Environments?](https://mlsanity.com/paper/2610.08215/) (2026)

## Abstract (arXiv)

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.