RECAST: Recent & Context-Aware Sampling for Test-Time Adaptation in Streaming Biosignals

• URL: http://arxiv.org/abs/2608.28271v1
• Date: Fri, 28 Aug 2026 12:35:06 GMT
• Title: RECAST: Recent & Context-Aware Sampling for Test-Time Adaptation in Streaming Biosignals
• Abstract summary: Streaming biosignals vary across subjects and drift over time, so population-trained models lose accuracy during long-term monitoring.<n>Test-time adaptation (TTA) enables online personalization by updating the model on incoming samples.<n>We propose textbfRECAST (REcent & Context-Aware Sampling for TTA), a lightweight sampling module for buffered TTA frameworks.
• Score: 4.510104714573981
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Streaming biosignals vary across subjects and drift over time, so population-trained models lose accuracy during long-term monitoring. Test-time adaptation (TTA) enables online personalization by updating the model on incoming samples. But in a stream, a basic question is left open: \emph{which samples should drive each update?} Using all buffered samples blurs the update with irrelevant segments. Using only the latest segment makes the update noisy and unstable. The most useful samples are recent, aligned with the current physiological state, and reliable enough to learn from. We propose \textbf{RECAST} (REcent \& Context-Aware Sampling for TTA), a lightweight sampling module for buffered TTA frameworks. RECAST builds each adaptation batch from three signals: temporal recency, contextual similarity, and predictive reliability. It changes only which samples are used, leaving the model and the training objective unchanged. On two blood-pressure datasets, RECAST improves estimation accuracy and trend tracking over baselines and ablations. The per-patient gains are statistically significant on both datasets, with broad improvement on the regular benchmark and gains concentrated on the hardest patients in the emergency-department setting. RECAST stays practical, adding only sub-second latency per segment on a single GPU and CPU core.

Related papers

• TTA-Vid: Generalized Test-Time Adaptation for Video Reasoning [54.70019148172847]
  We leverage the paradigm of Test-Time Reinforcement Learning on video-language data to allow for adapting a pretrained model to incoming video samples at test-time without explicit labels.<n>The proposed test-time adaptation for video approach (TTA-Vid) combines two components that work simultaneously.<n>TTA-Vid yields consistent improvements across various video reasoning tasks and is able to outperform current state-of-the-art methods trained on large-scale data.
  arXiv  Detail & Related papers  (2026-04-01T09:52:57Z)
• Adapting Point Cloud Analysis via Multimodal Bayesian Distribution Learning [47.975618905252354]
  Multimodal 3D vision-language models show strong generalization across diverse 3D tasks, but their performance still degrades notably under domain shifts.<n>This has motivated recent studies on test-time adaptation, which enables models to adapt online using test-time data.<n>We propose BayesMM, a Multimodal Distribution Learning framework for test-time point cloud analysis.
  arXiv  Detail & Related papers  (2026-03-23T15:03:47Z)
• Test-Time Adaptation with Binary Feedback [50.20923012663613]
  BiTTA is a novel dual-path optimization framework that balances binary feedback-guided adaptation on uncertain samples with agreement-based self-adaptation on confident predictions.<n> Experiments show BiTTA achieves 13.3%p accuracy improvements over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-05-24T05:24:10Z)
• New Test-Time Scenario for Biosignal: Concept and Its Approach [2.5828021866862683]
  Online Test-Time Adaptation (OTTA) enhances model robustness by updating pre-trained models with unlabeled data during testing.<n>We introduce a new test-time scenario with streams of unlabeled samples and occasional labeled samples.<n>Our framework combines supervised and self-supervised learning, employing a dual-queue buffer and weighted batch sampling to balance data types.
  arXiv  Detail & Related papers  (2024-11-26T14:54:02Z)
• DOTA: Distributional Test-Time Adaptation of Vision-Language Models [69.41389326333771]
  Vision-language foundation models can be unreliable when significant distribution gaps exist between training and test data.<n>We propose DOTA (DistributiOnal Test-time Adaptation), a simple yet effective method addressing this limitation.<n>This distribution-centric approach enables the model to continually learn and adapt to the deployment environment.
  arXiv  Detail & Related papers  (2024-09-28T15:03:28Z)
• Diversity-aware Buffer for Coping with Temporally Correlated Data Streams in Online Test-time Adaptation [3.1265626879839923]
  Test data streams are not always independent and identically distributed (i.i.d.) We propose a diversity-aware and category-balanced buffer that can simulate an i.i.d. data stream, even in non-i.i.d. scenarios. We achieve state-of-the-art results on most considered benchmarks.
  arXiv  Detail & Related papers  (2024-01-02T01:56:25Z)
• SoTTA: Robust Test-Time Adaptation on Noisy Data Streams [9.490557638324084]
  Test-time adaptation (TTA) aims to address distributional shifts between training and testing data. Most TTA methods assume benign test streams, while test samples could be unexpectedly diverse in the wild. We present Screening-out Test-Time Adaptation (SoTTA), a novel TTA algorithm that is robust to noisy samples.
  arXiv  Detail & Related papers  (2023-10-16T05:15:35Z)
• Robust Continual Test-time Adaptation: Instance-aware BN and Prediction-balanced Memory [58.72445309519892]
  We present a new test-time adaptation scheme that is robust against non-i.i.d. test data streams. Our novelty is mainly two-fold: (a) Instance-Aware Batch Normalization (IABN) that corrects normalization for out-of-distribution samples, and (b) Prediction-balanced Reservoir Sampling (PBRS) that simulates i.i.d. data stream from non-i.i.d. stream in a class-balanced manner.
  arXiv  Detail & Related papers  (2022-08-10T03:05:46Z)
• Efficient Test-Time Model Adaptation without Forgetting [60.36499845014649]
  Test-time adaptation seeks to tackle potential distribution shifts between training and testing data. We propose an active sample selection criterion to identify reliable and non-redundant samples. We also introduce a Fisher regularizer to constrain important model parameters from drastic changes.
  arXiv  Detail & Related papers  (2022-04-06T06:39:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.