Oscillatory State Persistence and Stable Video Infinity

Long-horizon generative systems fail when local plausibility does not preserve global state. In video generation, this appears as drift: identity, geometry, motion, style, and prompt controllability degrade across autoregressive continuation. Stable Video Infinity (SVI) provides a concrete engineering response. SVI identifies a training-test hypothesis gap between clean historical trajectories assumed during training and error-bearing histories encountered during autoregressive inference. Its Error-Recycling Fine-Tuning injects historical errors, estimates errors through bidirectional curation, banks them in Error Replay Memory, and trains an error-recycled velocity that points from corrupted state toward clean latent targets. This paper uses SVI as an engineering case study for oscillatory state persistence. It defines semantic decoherence as paper-local drift of an inferred or generated trajectory away from the state it is supposed to preserve. It then maps SVI's error economy to SAN vocabulary: phase-wave differentials, structured dissipation of mismatch, mulling as repeated retrieval-assessment-correction-confirmation, Layer 6 temporal tuning, and a candidate Layer 5-thalamus-TRN-Layer 6 correction loop. The engineering claim is source-backed by SVI. The biological claim remains a constrained research program requiring anatomical checks, falsifiable predictions, and further empirical support.