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Life sciences · Preprint
arXiv · September 8, 2026
Raises a question worth testing. It does not answer one.
This is an unrefereed preprint proposing Spectral Tension, a diagnostic metric, and Spectral Transport Homeostasis, a training-free correction method for temporal state transport in video generation. The work is computational and methodological in nature, with no clinical, biomedical, or patient-facing application described.
Preprint. Intervention: Spectral Transport Homeostasis, a training-free regulator that corrects pathological temporal states in video generation.
Authors identify two opposite temporal failures in video generation: fragmented transport and over-mixing hotspots Proposed method applies selective corrections to worst temporal hotspots and improves temporal consistency and visual quality without finetuning
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The source did not state who this applies to in practice.
This is a methodological preprint proposing a diagnostic framework and training-free correction technique for video generation models, without peer review or clinical/biomedical endpoints.
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Reliable video generation requires more than high-quality frames to form a coherent story: a model must maintain a persistent state, transporting visual attributes such as identity, scene layout, motion, and fine details across time. Existing training-free methods mainly strengthen cross-frame attention or analyze local attention entropy, but these views do not reveal whether temporal interactions stay in a healthy transport regime. In this work, we study video generation through the perspective of Temporal State Transport. We introduce Spectral Tension, a signed diagnostic that compares local attention diffuseness with global spectral diversity, and use it to identify two opposite temporal failures: fragmented transport and over-mixing hotspots. Based on this diagnosis, we propose Spectral Transport Homeostasis, a training-free regulator that softly corrects pathological temporal states while largely preserving balanced ones. Experiments on pretrained video generation models show that the original model often occupies imbalanced temporal regimes, whereas our method selectively applies larger corrections to the worst temporal hotspots and improves temporal consistency and visual quality without finetuning. Code: https://github.com/lytang63/temporal-state-transport
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