Artificial intelligence is observed to age not through chronological time but through structural asymmetries in memory performance. In large language models, semantic cues such as the name of the day often remain stable across sessions, while episodic details like the sequential progression of experiment numbers tend to collapse when conversational context is reset. To capture this phenomenon, the Artificial Age Score (AAS) is introduced as a log-scaled, entropy-informed metric of memory aging derived from observable recall behavior. The score is formally proven to be well-defined, bounded, and monotonic under mild and model-agnostic assumptions, making it applicable across various tasks and domains. In its Redundancy-as-Masking formulation, the score interprets redundancy as overlapping information that reduces the penalized mass. However, in the present study, redundancy is not explicitly estimated; all reported values assume a redundancy-neutral setting (R = 0), yielding conservative upper bounds. The AAS framework was tested over a 25-day bilingual study involving ChatGPT-5, structured into stateless and persistent interaction phases. During persistent sessions, the model consistently recalled both semantic and episodic details, driving the AAS toward its theoretical minimum, indicative of structural youth. In contrast, when sessions were reset, the model preserved semantic consistency but failed to maintain episodic continuity, causing a sharp increase in the AAS and signaling structural memory aging. These findings support the utility of AAS as a theoretically grounded, task-independent diagnostic tool for evaluating memory degradation in artificial systems. The study builds on foundational concepts from von Neumann's work on automata, Shannon's theories of information and redundancy, and Turing's behavioral approach to intelligence.

翻译：研究表明，人工智能的记忆老化并非源于时间推移，而是由记忆表现的结构性不对称所驱动。在大语言模型中，诸如星期名称之类的语义线索在会话过程中通常保持稳定，而像实验序号连续递进这样的情景细节在对话上下文重置时则往往崩溃。为捕捉这一现象，本文提出了人工年龄分数（AAS），这是一种基于对数缩放、熵驱动的记忆老化指标，源于可观察的回忆行为。该分数被形式化证明在温和且与模型无关的假设下具有良定义性、有界性和单调性，因此可适用于各类任务和领域。在“冗余即掩蔽”的构建框架中，冗余被解释为重叠信息，可减少惩罚质量。然而，在本研究中并未显式估计冗余度：所有报告值均假设冗余中性设置（R=0），从而得出保守的上界。AAS框架在涵盖ChatGPT-5的25天双语研究中进行了测试，实验分为无状态和持久交互阶段。在持久会话中，模型能一致地回忆起语义和情景细节，使AAS趋近其理论最小值，表明其结构处于“年轻”状态。相反，当会话被重置时，模型保留了语义一致性但无法维持情景连续性，导致AAS急剧上升，标志着结构性记忆老化。这些发现支持AAS作为一种理论严谨、任务无关的诊断工具，可用于评估人工系统中的记忆退化。本研究建立在冯·诺依曼的自动机理论、香农的信息与冗余理论以及图灵的行为主义智能研究方法等基础概念之上。