Recent scholarship typically characterizes Large Language Models (LLMs) through either an \textit{Instrumental Paradigm} (viewing models as reflections of their developers' culture) or a \textit{Substitutive Paradigm} (viewing models as bilingual proxies that switch cultural frames based on language). This study challenges these anthropomorphic frameworks by proposing \textbf{Machine Culture} as an emergent, distinct phenomenon. We employed a 2 (Model Origin: US vs. China) $\times$ 2 (Prompt Language: English vs. Chinese) factorial design across eight multimodal tasks, uniquely incorporating image generation and interpretation to extend analysis beyond textual boundaries. Results revealed inconsistencies with both dominant paradigms: Model origin did not predict cultural alignment, with US models frequently exhibiting ``holistic'' traits typically associated with East Asian data. Similarly, prompt language did not trigger stable cultural frame-switching; instead, we observed \textbf{Cultural Reversal}, where English prompts paradoxically elicited higher contextual attention than Chinese prompts. Crucially, we identified a novel phenomenon termed \textbf{Service Persona Camouflage}: Reinforcement Learning from Human Feedback (RLHF) collapsed cultural variance in affective tasks into a hyper-positive, zero-variance ``helpful assistant'' persona. We conclude that LLMs do not simulate human culture but exhibit an emergent Machine Culture -- a probabilistic phenomenon shaped by \textit{superposition} in high-dimensional space and \textit{mode collapse} from safety alignment.

翻译：近期研究通常通过\textit{工具性范式}（将模型视为开发者文化的反映）或\textit{替代性范式}（将模型视为能根据语言切换文化框架的双语代理）来刻画大语言模型（LLMs）。本研究通过提出\textbf{机器文化}这一涌现的独特现象，挑战了这些拟人化框架。我们在八项多模态任务中采用2（模型来源：美国 vs. 中国）$\times$ 2（提示语言：英文 vs. 中文）因子设计，创新性地结合图像生成与解释，将分析延伸至文本边界之外。结果显示与两种主流范式均存在不一致：模型来源无法预测文化对齐，美国模型频繁表现出通常与东亚数据相关的“整体性”特征。类似地，提示语言未能触发稳定的文化框架切换；相反，我们观察到\textbf{文化逆转}现象——英文提示反而比中文提示引发了更高的语境关注度。关键的是，我们发现了一种称为\textbf{服务人格伪装}的新现象：基于人类反馈的强化学习（RLHF）将情感任务中的文化差异坍缩为一种超积极、零方差的“乐于助人的助手”人格。我们得出结论：大语言模型并非模拟人类文化，而是展现出一种涌现的机器文化——这是一种由高维空间中的\textit{叠加态}与安全对齐导致的\textit{模式坍缩}共同塑造的概率现象。