发表机构
Victoria University（维多利亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对AI系统能否无限期运行而无界老化的问题，基于AAS开发长期持久性框架，证明无限周期运行可使结构年龄有界，为分析长期人工持久性提供形式基础。
AI 中文摘要
人工智能系统日益被期望在交互、适应和更新的重复周期中运行，而非仅产生孤立的一次性输出。这提出了一个基本理论问题：AI系统能否无限期运行而不产生无界的结构老化？本文基于冗余调整人工年龄评分（AAS），开发了AI系统的长期持久性框架。该模型将AAS从静态评估指标扩展为周期级泛函，用于生成重复运行过程中的年龄序列。在每个周期，结构年龄通过对组件一致性水平的加权、感知冗余的对数惩罚来定义。在该框架内，周期级年龄被证明是良定且一致有界的，从而排除了爆炸性逐点老化。在此基础上，本文定义了渐近状态的层级，包括负担型持久性、零负担型持久性、振荡型持久性和累积终端负担。还建立了比较排序、灵敏度边界、组件稳定下的收敛、有限总变差下的持久性、周期间阻尼扰动下的几何稳定，以及非退化冗余条件下的零负担特征。主要结果表明，无限周期延续不需要无界的结构老化：AI系统可以经历无限多个周期，同时其结构年龄保持有界；在更强的正则条件下，其边际老化消失，在最强状态下，其周期级负担收敛到零。因此，该框架为将长期人工持久性作为有界结构负担而非不可避免的累积恶化问题进行分析提供了形式基础。
英文摘要
Artificial intelligence systems are increasingly expected to operate over repeated cycles of interaction, adaptation, and update rather than through isolated one-shot outputs. This raises a fundamental theoretical question: can an AI system persist indefinitely without incurring unbounded structural aging? This paper develops a long-run persistence framework for AI systems based on the redundancy-adjusted Artificial Age Score (AAS). The model extends AAS from a static evaluative measure into a cycle-level functional that generates an age sequence across repeated operation. At each cycle, structural age is defined through a weighted, redundancy-aware logarithmic penalty over component consistency levels. Within this framework, cycle-level age is shown to be well defined and uniformly bounded, thereby excluding explosive pointwise aging. On this basis, the paper defines a hierarchy of asymptotic regimes, including burdened persistence, zero-burden persistence, oscillatory persistence, and cumulative terminal burden. It also establishes comparative ordering, sensitivity bounds, convergence under componentwise stabilization, persistence under finite total variation, geometric stabilization under damped inter-cycle perturbations, and a zero-burden characterization under nondegenerate redundancy conditions. The main result is that indefinite cyclic continuation does not require unbounded structural aging: an AI system may pass through infinitely many cycles while its structural age remains bounded, while under stronger regularity conditions its marginal aging vanishes and, in the strongest regime, its cycle-level burden converges to zero. The framework thus provides a formal basis for analyzing long-run artificial persistence as a problem of bounded structural burden rather than inevitable cumulative deterioration.
Comments38 pages, no figures, theoretical paper with theorems and proofs
