冗余作为遮蔽：形式化人工年龄评分（AAS）以建模生成AI中的记忆老化
Redundancy-as-Masking: Formalizing the Artificial Age Score (AAS) to Model Memory Aging in Generative AI
AI总结：
本文提出人工年龄评分（AAS）作为衡量生成AI记忆老化的新指标，通过结构不对称性分析，揭示模型在持续交互中的记忆保持与重置后的老化现象。
摘要：
人工智能的衰老并非通过时间流逝，而是通过记忆表现的结构不对称性。在大型语言模型中，语义线索如日期名称在会话间保持稳定，而事件细节如实验编号的序列进展在重置上下文时会崩溃。为此，引入人工年龄评分（AAS）作为基于可观察回忆行为的对数尺度、熵驱动的内存老化度量。该评分在温和且模型无关的假设下被正式证明为良好定义、有界且单调。在冗余作为遮蔽的表述中，评分将冗余解释为重叠信息，减少受惩罚的质量。然而，在本研究中，冗余未被显式估计；所有报告值假设冗余中性设置（R = 0），产生保守的上限。AAS框架在为期25天的双语研究中得到测试，涉及ChatGPT-5，分为无状态和持续交互阶段。在持续会话中，模型一致回忆了语义和事件细节，推动AAS向理论最小值发展，表明结构年轻。相反，当会话重置时，模型保持语义一致性但无法维持事件连续性，导致AAS急剧上升，表明结构记忆老化。这些发现支持AAS作为理论基础、任务无关的诊断工具，用于评估人工系统中的记忆退化。该研究建立在冯·诺依曼关于自动机的基础概念、香农的信息与冗余理论以及图灵的智能行为方法之上。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
