Order-Aware Test-Time Adaptation: Leveraging Temporal Dynamics for Robust Streaming Inference

• URL: http://arxiv.org/abs/2601.21012v1
• Date: Wed, 28 Jan 2026 20:07:40 GMT
• Title: Order-Aware Test-Time Adaptation: Leveraging Temporal Dynamics for Robust Streaming Inference
• Authors: Young Kyung Kim, Oded Schlesinger, Qiangqiang Wu, J. Matías Di Martino, Guillermo Sapiro,
• Abstract summary: Test-Time Adaptation (TTA) enables pre-trained models to adjust to distribution shift by learning from unlabeled test-time streams.<n>To address this, we introduce Order-Aware Test-Time Adaptation (OATTA)<n>OATTA consistently boosts established baselines, improving accuracy by up to 6.35%.
• Score: 18.524636088926425
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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