Persistent-State Management for Streaming Test-Time Adaptation in Open-Vocabulary Segmentation

Streaming test-time adaptation (TTA) changes the model while it is being evaluated, so deployment quality depends on the adaptation state carried from earlier samples. We study this persistent state as a first-class object in open-vocabulary semantic segmentation and introduce Profiled State Recovery, a practical framework for managing it. Profiled State Recovery uses a shared monitor–trigger–recovery–resume lifecycle around the native TTA update, while specializing recovery semantics to each adaptation mechanism. A profile is calibrated once on a labeled development stream, frozen, and then deployed label-free without target-stream retuning. Across MLMP, SAR, and TENT, three datasets, two ViT scales, and a ConvNeXt backbone, frozen profiles improve four of five systems, and all four improve on both fresh validation seeds, with mean gains of +4.204, +0.316, +0.491, and +2.881 mIoU points for S1, S2, S3, and S5. All three tested cross-dataset transfers are positive without target-stream retuning: +0.448 (MLMP), +0.602 (SAR), and +0.739 (TENT). The same frozen MLMP profile improves both ViT-L/14 and ViT-B/16. Mechanistic analysis shows that recovery granularity is a central design factor: local/progressive, selective late-state, and global recovery regimes emerge for MLMP, SAR, and TENT, respectively. These results establish persistent adaptation-state management as a structured design axis for streaming TTA and provide a compact, transferable implementation framework.