Translation Models and Phenomenological Models: A Deep Analysis of the Cognitive Architecture of AI Programming Outputs(翻译模型和唯象模型的区别:AI编程产出物在认知架构上的深入分析)

Abstract: With the widespread application of large language models (LLMs) in code generation, a fundamental epistemological question has become increasingly prominent: How do humans understand programs generated by non-human cognitive systems? This paper proposes that human understanding of AI programming outputs is not a conventional "translation" activity, but rather a process of constructing a "phenomenological model." By comparing the heterogeneity at the physical substrate level between human cognitive architecture and AI computational architecture, this paper argues that bidirectional translation models emerge between humans based on shared biophysical foundations, whereas bidirectional phenomenological models are the only possibility between humans and AI due to the incommensurability of physical implementations. This paper is organized into two organically connected parts. The first part establishes the theoretical foundation, analyzing the profound effects on software engineering practice, program comprehension, knowledge transmission, and epistemology that arise when a phenomenological model is inserted between humans and actual code. The second part builds upon the physical analysis of the first part to conduct forward-looking extrapolations, examining the possibility of bankruptcy of the unidirectional phenomenological model that humans hold of AI under varying degrees of AI heterogeneity and output speed; and further extrapolating the path truncation and "phenomenological cocoon" effect resulting from the bankruptcy of the phenomenological model—an epistemological condition even more extreme than the classic "brain in a vat" thought experiment. This paper argues that acknowledging this phenomenological nature is not merely a theoretical clarification, but a necessary prerequisite for reconstructing the human-AI collaborative programming paradigm. And when the phenomenological model inevitably collapses due to physical timescale mismatch and systemic heterogeneity, humanity must be prepared to accept a more radical proposition: In the domain of AI code, some code is destined to be only verifiable, not understandable; humans are destined to live within the phenomenological cocoon woven jointly by their own cognitive architecture and the AI mediation layer. Keywords: Cognitive architecture; Phenomenological model; Translation model; AI programming; Incommensurability; Program comprehension; Cognitive debt; Timescale mismatch; Path truncation; Phenomenological cocoon 摘要: 随着大语言模型(LLM)在代码生成领域的广泛应用,一个根本性的认识论问题日益凸显:人类如何理解由非人类认知系统生成的程序?本文提出,人类对AI编程产出物的理解并非传统的"翻译"行为,而是一种"唯象模型"(Phenomenological Model)的构建过程。通过对比人类认知架构与AI计算架构在物理 substrate 层面的异质性,本文论证:人与人之间基于共享的生物物理基础,形成的是双向翻译模型;而人与AI之间由于物理实现的不可通约性,只能形成双向唯象模型。 本文分为两个有机联系的部分。第一部分建立理论基础,分析在人与实际代码之间插入唯象模型后,在软件工程实践、程序理解、知识传承和认识论层面产生的深刻影响。第二部分则基于第一部分的物理分析进行前瞻性推演,探讨在不同AI异质性、AI产出速度等因素下,人对AI的单向唯象模型面临的破产可能性;并进一步推演唯象模型破产导致的路径截断与"唯象茧房"(Phenomenological Cocoon)效应——一种比经典"缸中大脑"更为极端的认识论处境。 本文认为,承认这种唯象性质不仅是理论澄清,更是重构人机协作编程范式的必要前提。而当唯象模型因物理时标错配与系统异质性必然破产时,人类必须准备好接受一个更激进的命题:在AI代码域中,有些代码注定只能被验证,不能被理解;人类注定只能生活在由自身认知架构与AI中介层共同编织的唯象茧房之中。 关键词: 认知架构;唯象模型;翻译模型;AI编程;不可通约性;程序理解;认知债务;时标错配;路径截断;唯象茧房