Oscillatory State Persistence and Stable Video Infinity

A system can make each local step look plausible while progressively losing the identity of the process it is supposed to preserve. In long video generation, a face changes, a room drifts, an object's geometry mutates, or the storyline ceases to follow its prompt. Stable Video Infinity (SVI) treats this as a mismatch between training on clean histories and testing on histories already contaminated by the model's own errors. Its Error-Recycling Fine-Tuning exposes the model to historical errors, estimates corrective differences, stores them in Error Replay Memory, and learns a velocity from corrupted latent states toward clean targets. This paper asks what general principle can be extracted from that engineering mechanism and whether a related problem exists in biological cognition. Its conclusions are limited to the assumptions, evidence, and testing conditions stated in the manuscript. It belongs to Micah Blumberg's governed research-paper corpus. This is a corrected preprint edition released under the Creative Commons Attribution 4.0 license.