提出10维认知框架，量化评估AI与人类智能的差距。
Measuring Progress Toward AGI: A Cognitive Framework
- 从心理学等学科提炼10项核心认知能力
- 通过独立任务测试生成系统认知画像
- 为评估AGI提供可操作的实证路径
尽管对通用人工智能（AGI）的讨论广泛，但尚无明确框架来衡量进展。这种模糊性导致主观判断泛滥，难以追踪发展，且可能阻碍负责任的治理。为此，我们提出一个认知框架，将系统能力与人类认知能力关联。基于数十年心理学、神经科学和认知科学的研究，我们构建了包含10个关键认知能力的认知分类体系，并设计了一套严谨的评估协议：在一系列特定的、未见过的认知任务上测试系统表现，生成其‘认知画像’，以揭示系统的强弱项。该框架旨在为更严格、基于实证的AGI评估提供实用路线图和初步步骤。
原文摘要 · Abstract (English)
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.
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