AI RESEARCH
Retrieve-then-Steer: Online Success Memory for Test-Time Adaptation of Generative VLAs
arXiv CS.AI
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ArXi:2605.10094v1 Announce Type: cross Vision-Language-Action (VLA) models show strong potential for general-purpose robotic manipulation, yet their closed-loop reliability often degrades under local deployment conditions. Existing evaluations typically treat test episodes as independent zero-shot trials. However, real robots often operate repeatedly in the same or slowly changing environments, where successful executions provide environment-verified evidence of reliable behavior patterns.