arXiv cs.AI· Michael J. Richardson, Manuel Varlet, Ayeh Alhasan, Cassandra Crone, M. Paula Diaz Monfort, Patrick Nalepka, Mark Dras, Rachel W. Kallen, David M. Kaplan·· 5 小时前AI 评分40
过程构成智能：人类与机器智能的共同判据
Process-Constituted Intelligence: A Shared Criterion for Humans and Machines
AI 导读
arXiv 论文提出智能由生成输出的迭代过程构成，而非输出本身；生成式 AI 只是在人类认知痕迹分布上重采样，与人类认知仅是弱等价。作者定义了可对人类与机器评估的七项过程特征，以刻画强等价，并提出过程审计方法使其可检验。论文还讨论了外包生成过程导致人类关键能力未建立的双向风险，以及保留人类判断与创造力的设计原则。
正文 · 原文
Abstract: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.
| Comments: | v2: revised throughout for clarity and consistency; supplementary cross-references corrected |
| Subjects: | Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET) |
| Cite as: | arXiv:2608.16213 [cs.AI] |
| (or arXiv:2608.16213v3 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2608.16213 arXiv-issued DOI via DataCite |
Submission history
From: Michael Richardson Professor [view email]
[v1]
Mon, 17 Aug 2026 07:46:15 UTC (1,275 KB)
[v2]
Mon, 7 Sep 2026 22:37:41 UTC (1,266 KB)
[v3]
Thu, 29 Oct 2026 05:56:21 UTC (66 KB)
来源：arXiv cs.AI · arxiv.org