Measuring Progress Toward AGI: A Cognitive Framework

2026-05-27

AI Summary

This study addresses the absence of an objective and reproducible evaluation framework for artificial general intelligence (AGI), which has led to subjective assessments of progress and challenges in governance. Drawing on foundations from psychology, neuroscience, and cognitive science, this work proposes a systematic cognitive taxonomy comprising ten core capabilities grounded in human cognition. It introduces targeted retention tasks designed to evaluate system performance across these dimensions, thereby generating multidimensional cognitive profiles. The approach enables an operational decomposition and empirical measurement of AGI progress, offering an initial benchmark to identify system strengths and weaknesses and to advance the standardization and transparency of AGI research.

Abstract

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.

Problem

• AGI
• measurement
• cognitive framework
• evaluation
• general intelligence

Innovation

• Cognitive Taxonomy
• AGI evaluation
• cognitive profile
• general intelligence
• empirical benchmarking