提出以过程为核心的新智能标准，区分机器与人类认知的真正相似性。
Process-Constituted Intelligence: A Shared Criterion for Humans and Machines
- 用七项过程特征定义强等价，超越仅看输出的评估方式。
- 指出当前生成式AI缺乏真实认知过程，存在能力退化风险。
- 提供可操作的设计原则与过程审计方法，适合教育和AI伦理研究者。
智能源于过程（迭代活动，输出由此产生），而非输出本身。生成式AI通过学习人类认知过程的痕迹（文本与视觉残留）进行训练，复现这些痕迹分布的样本。其输出看似具备推理、解题与创造力，但人类产生这些输出的真实认知活动在机器中基本缺失。因此，当前生成式AI仅与所模仿的认知保持弱等价：输出匹配，过程却缺席或模糊。认知科学长期区分弱等价与强等价。本文基于七项过程特征定义强等价，可应用于人类与机器认知的评估。我们的过程导向框架揭示了一种对称风险：若生成式AI工具被用于外包个人的生成过程，关键认知能力将无法发展。为此，我们提出设计原则，推动生成式AI体现更多真实过程，并保护而非侵蚀人类判断力与创造力，同时构建可验证强等价的过程审计机制。
原文摘要 · Abstract (English)
Intelligence is constituted by \textit{process} (iterative activity through which output emerges), not in the output itself. Generative AI (GenAI) is trained on \textit{traces} (textual and visual residues of human cognitive processes), reproducing samples from a distribution of those traces. Its outputs resemble reasoning, problem-solving, and creativity, yet the activity that produces such outputs in humans remains largely absent. Current GenAI is, therefore, weakly equivalent to the cognition it imitates, matching outputs while process stays absent or opaque. The cognitive sciences have long distinguished between weak and strong equivalence. Here, we define \textit{strong} equivalence across seven process features, assessable against human and machine cognition. Our process-based account addresses a symmetric risk: GenAI tools that outsource a person's generative processes may leave critical capacities unbuilt. We specify design principles for GenAI that instantiate more process and preserve rather than erode human judgment and creativity, and outline process audits that make strong equivalence testable.
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