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VeriVLA: Adaptive Test-Time Verification for Flow-Based VLAs with Latent World Models
Flow-based Vision-Language-Action (VLA) policies are inherently stochastic: different noise seeds can produce action chunks of varying quality from the same observation. At test time, however, the policy typically commits to a single sample without an explicit estimate of its quality. In fine-graine
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