[2606.13861] Temporal Backtracking Search for Test-time Generative Video Reasoning
[Submitted on 11 Jun 2026]
Title:Temporal Backtracking Search for Test-time Generative Video Reasoning
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Abstract:While test-time scaling has revolutionized reasoning in large language models, generative video reasoning remains bottlenecked by a single-shot paradigm. We demonstrate that searching over denoising steps cannot rescue logically flawed rollouts because spatial trajectories commit early in the diffusion process. Root-level Best-of-N (BoN) sampling is similarly inefficient: reasoning errors cluster early in the temporal axis, and resampling blindly discards verified upstream progress. To unlock effective test-time scaling for video models, we introduce Temporal Backtracking Search (TBS), which shifts the search space to the temporal axis. TBS transforms video generation into an iterative generate-verify-restart loop via three core mechanisms: (1) variable-K conditioning to resume generation from arbitrary clean prefixes; (2) temporal process verification to localize failures and extract valid restart anchors; and (3) prefix-based search to reallocate compute toward extending correct trajectories rather than root resampling. Across algorithmic, navigation, and robotics domains, TBS Pareto-dominates matched-budget BoN. In a strict out-of-distribution setting where one-shot generation collapses (0.7% for BoN), TBS achieves 22.7%, with every solved episode stemming from a restarted branch. Ultimately, TBS reveals that the local reasoning competence of video models far exceeds what single-shot rollouts indicate, providing a scalable test-time framework to unlock it.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.13861 [cs.CV]
(or arXiv:2606.13861v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.13861
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Sejoon Jun [view email] [v1] Thu, 11 Jun 2026 19:41:18 UTC (21,875 KB)
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