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Test-Time Scaling for Video Diffusion Models via Diagnosis-Guided Candidate Recycling
Recent video diffusion models have achieved remarkable generation quality, but high-fidelity results still largely depend on closed-source systems or costly large-scale infrastructure. Test-time scaling (TTS) offers a training-free way to improve lightweight generators by spending additional inference compute, yet existing methods mostly remain within a noise-search paradigm: they sample, select, or perturb denoising trajectories and discard low-scoring candidates after expensive generation. This generate-and-discard process wastes not only computation but also the partial motion, layout, or a
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-29T15:08:31.000Z
First collected: 2026-09-21T07:51:58.603Z. This is not the publication date.