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This paper proposes a framework of "process-constituted intelligence" to distinguish between Generative AI outputs and the underlying human cognitive processes they simulate, arguing that current models only achieve weak equivalence by reproducing traces rather than reasoning activities. The authors define seven criteria for strong equivalence and offer design principles aimed at preserving critical human judgment capacities against risks posed by outsourcing generative tasks. By shifting focus from output fidelity to process transparency, this research addresses fundamental questions about the nature of machine cognition that are central to AI theory and safety.
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Process-Constituted Intelligence: A Shared Criterion for Humans and Machines

arXiv2 min read

arXiv:2608.16213v1 Announce Type: new 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.

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.

Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)

arXiv:2608.16213 [cs.AI]

(or arXiv:2608.16213v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.16213 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 248 words

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