Abstract
This disclosure describes systems and methods for characterizing when an error in an artificial intelligence trajectory remains correctable and when it becomes structurally irreversible. The disclosure introduces a temporal framework centered on closure, defined as the transition from revisable to non-revisable trajectory dynamics under active constraints. Two error regimes are distinguished: localized error, in which a deviation remains attached to an identifiable and correctable claim or state, and distributed error, in which violations are absorbed into the trajectory as a whole. The disclosure further introduces Aggregation-Induced Error (AIE) as a mechanism by which independent constraints lose separability when compressed into a coherent aggregate, thereby making targeted revision underdetermined. Representative embodiments show that perturbations introduced before closure can remain revisable, while perturbations introduced after closure are more likely to persist, propagate, or be embedded. The key boundary is therefore not correctness alone, but whether the trajectory remains open to revision under constraint.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
pham, thom, "Systems and Methods for Detecting Trajectory Closure and Structural Irreversibility in AI Systems", Technical Disclosure Commons, (March 30, 2026)
https://www.tdcommons.org/dpubs_series/9671