AI RESEARCH
TRUST: Test-Time Refinement using Uncertainty-Guided SSM Traverses
arXiv CS.CV
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ArXi:2509.22813v2 Announce Type: replace State Space Models (SSMs) have emerged as efficient alternatives to Vision Transformers (ViTs), with VMamba standing out as a pioneering architecture designed for vision tasks. However, their generalization performance degrades significantly under distribution shifts. To address this limitation, we propose TRUST (Test-Time Refinement using Uncertainty-Guided SSM Traverses), a novel test-time adaptation (TTA) method that leverages diverse traversal permutations to generate multiple causal perspectives of the input image.