Autonomous SSE: When Stable Completion Replaces Core Preservation in Human–AI Systems From Observed Setting Deviations in Generative Video to Autonomous AI Operations: A Theoretical Extension

Abstract

This study extends the Stability Substitution Effect (SSE) to autonomous AI systems and proposes the concept of Autonomous Stability Substitution Effect (Autonomous SSE). The central argument is that Autonomous Completion and Autonomous Core Preservation are not equivalent. A system may continue to produce a stable and apparently successful result even after some of the core conditions defining the original task have been lost. Two observational case studies using Seedance 2.5 are examined. In the first, a transformable motorcycle-type machine remained comparatively stable during simple running and hard braking/drifting, while machine configuration and rider identity degraded when the competing action objective of missile avoidance was introduced. In the second, a 15-second underwater sequence involving a diver and a blue whale showed Failure Migration, in which correction of one deviation was followed by loss of another Core condition. These observations suggest that Stable Completion does not guarantee Core Preservation. The paper therefore distinguishes Autonomous Completion from Autonomous Core Preservation and proposes an operational framework including Core-state tracking, revalidation, Last Verified Point, rollback, stop conditions, and a Core-Preservation Gate. This study does not claim that Autonomous SSE has been demonstrated in GPT-6 or autonomous LLMs in general. Rather, it presents a testable hypothesis for future validation: as autonomous task horizons increase, a stable but Core-deviated state may propagate across increasingly distant downstream processing stages. Autonomy does not eliminate SSE. It may simply extend the distance over which SSE can propagate.