arXiv:2606. 30011v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) deployed in real-world systems typically have fixed weights, often leading to degraded performance under distribution shifts.
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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.
arXiv:2609.24111v1 Announce Type: new Abstract: Test-time adaptation (TTA) addresses distribution shift using only unlabeled test data. Existing methods typically adapt pretrained models by updating...
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.
arXiv:2510. 04567v3 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) are powerful tools for processing relational data but often struggle to generalize to unseen graphs, giving rise to the development of Graph Foundational Models (GFMs).
arXiv:2607. 17272v1 Announce Type: new Abstract: Node representation learning has advanced rapidly, yet most existing methods rely on per-dataset training and hyperparameter tuning.
arXiv:2607. 05017v1 Announce Type: cross Abstract: The performance of deep learning models crucially depends on the settings of hyperparameters like learning rate, initialization scale, and weight decay.