Paper proposes redundancy-adjusted framework to bound structural aging in long-run AI systems

A new arXiv preprint proposes a theoretical framework to analyze whether artificial intelligence systems can persist indefinitely without incurring unbounded structural aging. The paper, titled “A Long-Run Persistence Theory for AI Systems under the Redundancy-Adjusted Artificial Age Score (AAS),” introduces a cycle-level extension of the Artificial Age Score (AAS) that models structural age across repeated cycles of interaction, adaptation, and update.

The author defines structural age at each cycle using a weighted, redundancy-aware logarithmic penalty over component consistency levels. Within this framework, the cycle-level age is shown to be well defined and uniformly bounded, thereby excluding the possibility of explosive pointwise aging over time.

The paper establishes a hierarchy of asymptotic regimes—burdened persistence, zero-burden persistence, oscillatory persistence, and cumulative terminal burden—each describing different long-run behaviors of structural age under repeated operation.

The analysis includes 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. Under stronger regularity conditions, marginal aging vanishes, and in the strongest regime, the cycle-level burden converges to zero.

The framework reframes long-run artificial persistence as a problem of bounded structural burden rather than inevitable cumulative deterioration, offering a formal basis for evaluating system longevity and design choices.