Measuring Progress Toward AGI: A Cognitive Framework
Fuente:
arXiv
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| Autori principali: | , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866913168158097408 |
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| author | Burnell, Ryan Yamamori, Yumeya Firat, Orhan Olszewska, Kate Hughes-Fitt, Steph Kelly, Oran Galatzer-Levy, Isaac R. Morris, Meredith Ringel Dafoe, Allan Snyder, Alison M. Goodman, Noah D. Botvinick, Matthew Legg, Shane |
| author_facet | Burnell, Ryan Yamamori, Yumeya Firat, Orhan Olszewska, Kate Hughes-Fitt, Steph Kelly, Oran Galatzer-Levy, Isaac R. Morris, Meredith Ringel Dafoe, Allan Snyder, Alison M. Goodman, Noah D. Botvinick, Matthew Legg, Shane |
| contents | Despite widespread discussion of AGI, there is no clear framework for measuring progress toward it. This ambiguity fuels subjective claims, makes it difficult to track progress, and risks hindering responsible governance. As a starting point to address this gap, we present a framework for understanding system capabilities in relation to human cognitive abilities. Drawing from decades of research in psychology, neuroscience, and cognitive science, we introduce a Cognitive Taxonomy that deconstructs general intelligence into 10 key cognitive faculties. We then propose a rigorous evaluation protocol in which a system's performance is measured across a suite of targeted, held-out cognitive tasks, generating a 'cognitive profile' that can be used to understand a system's strengths and weaknesses. We hope this framework will provide a practical roadmap and an initial step toward more rigorous, empirical evaluation of AGI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_28405 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Measuring Progress Toward AGI: A Cognitive Framework Burnell, Ryan Yamamori, Yumeya Firat, Orhan Olszewska, Kate Hughes-Fitt, Steph Kelly, Oran Galatzer-Levy, Isaac R. Morris, Meredith Ringel Dafoe, Allan Snyder, Alison M. Goodman, Noah D. Botvinick, Matthew Legg, Shane Artificial Intelligence Despite widespread discussion of AGI, there is no clear framework for measuring progress toward it. This ambiguity fuels subjective claims, makes it difficult to track progress, and risks hindering responsible governance. As a starting point to address this gap, we present a framework for understanding system capabilities in relation to human cognitive abilities. Drawing from decades of research in psychology, neuroscience, and cognitive science, we introduce a Cognitive Taxonomy that deconstructs general intelligence into 10 key cognitive faculties. We then propose a rigorous evaluation protocol in which a system's performance is measured across a suite of targeted, held-out cognitive tasks, generating a 'cognitive profile' that can be used to understand a system's strengths and weaknesses. We hope this framework will provide a practical roadmap and an initial step toward more rigorous, empirical evaluation of AGI. |
| title | Measuring Progress Toward AGI: A Cognitive Framework |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2605.28405 |