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·· 14 小时前AI 评分24
过程构成智能：人类与机器的共享判据
Process-Constituted Intelligence: A Shared Criterion for Humans and Machines
AI 导读
一篇 arXiv 论文提出智能由“过程”而非输出构成，认为当前生成式 AI 只是在复现人类认知过程的痕迹，输出虽像推理、解题与创造，但产生这些输出的活动基本缺席，因此仅属弱等价。作者跨七项过程特征定义“强等价”，并提出可检验的过程审计与设计原则，以保留而非侵蚀人的判断力与创造力。
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