Order-Aware Test-Time Adaptation: Leveraging Temporal Dynamics for Robust Streaming Inference
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910004288684032 |
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| author | Kim, Young Kyung Schlesinger, Oded Wu, Qiangqiang Di Martino, J. Matías Sapiro, Guillermo |
| author_facet | Kim, Young Kyung Schlesinger, Oded Wu, Qiangqiang Di Martino, J. Matías Sapiro, Guillermo |
| contents | Test-Time Adaptation (TTA) enables pre-trained models to adjust to distribution shift by learning from unlabeled test-time streams. However, existing methods typically treat these streams as independent samples, overlooking the supervisory signal inherent in temporal dynamics. To address this, we introduce Order-Aware Test-Time Adaptation (OATTA). We formulate test-time adaptation as a gradient-free recursive Bayesian estimation task, using a learned dynamic transition matrix as a temporal prior to refine the base model's predictions. To ensure safety in weakly structured streams, we introduce a likelihood-ratio gate (LLR) that reverts to the base predictor when temporal evidence is absent. OATTA is a lightweight, model-agnostic module that incurs negligible computational overhead. Extensive experiments across image classification, wearable and physiological signal analysis, and language sentiment analysis demonstrate its universality; OATTA consistently boosts established baselines, improving accuracy by up to 6.35%. Our findings establish that modeling temporal dynamics provides a critical, orthogonal signal beyond standard order-agnostic TTA approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_21012 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Order-Aware Test-Time Adaptation: Leveraging Temporal Dynamics for Robust Streaming Inference Kim, Young Kyung Schlesinger, Oded Wu, Qiangqiang Di Martino, J. Matías Sapiro, Guillermo Machine Learning Test-Time Adaptation (TTA) enables pre-trained models to adjust to distribution shift by learning from unlabeled test-time streams. However, existing methods typically treat these streams as independent samples, overlooking the supervisory signal inherent in temporal dynamics. To address this, we introduce Order-Aware Test-Time Adaptation (OATTA). We formulate test-time adaptation as a gradient-free recursive Bayesian estimation task, using a learned dynamic transition matrix as a temporal prior to refine the base model's predictions. To ensure safety in weakly structured streams, we introduce a likelihood-ratio gate (LLR) that reverts to the base predictor when temporal evidence is absent. OATTA is a lightweight, model-agnostic module that incurs negligible computational overhead. Extensive experiments across image classification, wearable and physiological signal analysis, and language sentiment analysis demonstrate its universality; OATTA consistently boosts established baselines, improving accuracy by up to 6.35%. Our findings establish that modeling temporal dynamics provides a critical, orthogonal signal beyond standard order-agnostic TTA approaches. |
| title | Order-Aware Test-Time Adaptation: Leveraging Temporal Dynamics for Robust Streaming Inference |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2601.21012 |