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| Format: | Preprint |
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2026
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| Accès en ligne: | https://arxiv.org/abs/2604.09911 |
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| _version_ | 1866915932031418368 |
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| author | Krakauer, David C. |
| author_facet | Krakauer, David C. |
| contents | In the psychological literature the term `general intelligence' describes correlations between abilities and not simply the number of abilities. This paper connects Spearman's $g$-factor from psychometrics, measuring a positive manifold, to the implicit ``$G$-factor'' in claims about artificial general intelligence (AGI) performance on temporally structured benchmarks. By treating LLM benchmark batteries as cognitive test batteries and model releases as subjects, principal component analysis is applied to a models $\times$ benchmarks $\times$ time matrix spanning 39 models (2019--2025) and 14 benchmarks. Preliminary results confirm a strong positive manifold in which all 28 pairwise correlations positive across 8 benchmarks. By analyzing the spectrum of the benchmark correlation through time, PC1 explains 90\% of variance on a 5-benchmark core battery ($n=19$)) reducing to 77\% by 2024. On a four benchmark battery, PC1 is found to peak at 92\% of the variance between 2023--2024 and reduce to 64\% with the arrival of reasoning-specialized models in 2024. This is coincident with a rotation in the G-factor as models outsource `reasoning' to tools. The analysis of partial correlation matrices through time provides evidence for the evolution of specialization beneath the positive manifold of general intelligence (AI-hedgehog) encompassing diverse high dimensional problem solving systems (AI-foxes). In strictly psychometric terms, AI models exhibit general intelligence suppressing specialized intelligences. LLMs invert the ideal of substituting complicated models with parsimonious mechanisms, a `Ptolemaic Succession' of theories, with architectures of increasing hierarchical complication and capability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_09911 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The Rise and Fall of $G$ in AGI Krakauer, David C. Neurons and Cognition Artificial Intelligence In the psychological literature the term `general intelligence' describes correlations between abilities and not simply the number of abilities. This paper connects Spearman's $g$-factor from psychometrics, measuring a positive manifold, to the implicit ``$G$-factor'' in claims about artificial general intelligence (AGI) performance on temporally structured benchmarks. By treating LLM benchmark batteries as cognitive test batteries and model releases as subjects, principal component analysis is applied to a models $\times$ benchmarks $\times$ time matrix spanning 39 models (2019--2025) and 14 benchmarks. Preliminary results confirm a strong positive manifold in which all 28 pairwise correlations positive across 8 benchmarks. By analyzing the spectrum of the benchmark correlation through time, PC1 explains 90\% of variance on a 5-benchmark core battery ($n=19$)) reducing to 77\% by 2024. On a four benchmark battery, PC1 is found to peak at 92\% of the variance between 2023--2024 and reduce to 64\% with the arrival of reasoning-specialized models in 2024. This is coincident with a rotation in the G-factor as models outsource `reasoning' to tools. The analysis of partial correlation matrices through time provides evidence for the evolution of specialization beneath the positive manifold of general intelligence (AI-hedgehog) encompassing diverse high dimensional problem solving systems (AI-foxes). In strictly psychometric terms, AI models exhibit general intelligence suppressing specialized intelligences. LLMs invert the ideal of substituting complicated models with parsimonious mechanisms, a `Ptolemaic Succession' of theories, with architectures of increasing hierarchical complication and capability. |
| title | The Rise and Fall of $G$ in AGI |
| topic | Neurons and Cognition Artificial Intelligence |
| url | https://arxiv.org/abs/2604.09911 |