arXiv:2606. 26457v1 Announce Type: cross Abstract: This paper presents a probabilistic framework for online test-time adaptation problems.
This paper presents a probabilistic framework for online test-time adaptation problems. In them, a model is trained on labeled data but must adapt to unlabeled data at test time under the assumption that training and test distributions potentially differ, that is, there might have been a distributional shift.
arXiv:2606. 31420v1 Announce Type: new Abstract: Test-Time Adaptation (TTA) enables models trained on a source domain to adapt online to unlabeled test data under distribution shifts.
arXiv:2607. 23735v1 Announce Type: new Abstract: In many real-world scenarios, encountering continual shifts in domain during inference is very common.
This paper introduces TestDG, an online test-time domain generalization framework for continual test-time adaptation (CTTA). TestDG learns features invariant to both current and past test domains during testing, using a new model architecture, adaptation strategy, and prototype selection/update mechanisms. It achieves state‑of‑the‑art results on four CTTA benchmarks and demonstrates superior generalization to unseen test domains.
The paper introduces DGOTTA, a framework for temporal memory‑aware online test‑time adaptation on dynamic graphs. DGOTTA comprises three modules: temporal‑aware augmentation to diversify test graphs, memory‑aware model prediction to mitigate catastrophic forgetting, and consistency‑guided online adaptation to enforce temporal alignment and smoothness. Experiments on three real‑world datasets and four DGNN backbones show that DGOTTA improves generalization under diverse distribution shifts and across multiple model architectures.
The paper investigates the teacher‑student framework used in Test‑Time Adaptation (TTA) and questions the common practice of updating the teacher via an exponential moving average of the student. The authors demonstrate that error accumulation still occurs, especially over longer sequences, and propose an intransigent teacher that remains fixed. This modification yields significant performance gains across multiple datasets, longer scenarios, and various architectures, including semantic segmentation, while also improving robustness to hyperparameter changes.
The paper introduces query‑conditioned hypernetworks that predict distributions over LoRA weight updates for large language models. By learning a distribution rather than a single point estimate, the method allows sampling multiple adapted models for the same query, improving performance over deterministic hypernetworks and token‑sampling baselines. The study also shows that these learned updates can transfer across different queries, indicating reusable adaptation patterns.
The paper studies a two‑stage learning framework that first trains an offline model using approximate nonlinear‑least‑squares estimation and then adapts it online with a meta‑LMS algorithm to handle parameter drift in nonlinear stochastic dynamical systems. It provides an upper bound on the offline generalization error that accounts for strong data correlation and distribution shift via Kullback‑Leibler divergence, and it demonstrates that the combined offline‑online approach outperforms methods that rely solely on offline or online learning. Both theoretical analysis and empirical experiments support the claimed performance gains.
arXiv:2509.25692v2 Announce Type: replace-cross Abstract: Active Test-Time Adaptation (ATTA) improves model robustness under domain shift by selectively querying human annotations at deployment, but...
Hypernetworks have recently shown success in dynamically adapting the parameters of Large Language Models (LLMs) at runtime based on signals such as task descriptions or additional demostrations. Here...
arXiv:2607. 18899v1 Announce Type: new Abstract: Forecasting under real-world conditions is inherently non-stationary, as the conditional distribution of future observations evolves over time.
arXiv:2602. 06136v2 Announce Type: replace Abstract: Test-time adaptation (TTA) offers a compelling remedy for machine learning (ML) models that degrade under domain shifts, improving generalisation on-the-fly with only unlabelled samples.