The 3rd Workshop on Test-Time Updates (TTU)

The common paradigm of deep learning distinguishes the training stage, where model parameters are learnt on massive datasets, and deployment, during which the frozen models are tested on unseen data. In case the test-time data distribution changes, or the model needs to satisfy new requirements, a new training round is needed. Test-time updates (TTU), including test-time adaptation (TTA), post-training editing, in-context learning, and online continual learning, offer a complementary path to re-training: adapt when and where data shift occurs. Test-time updates are relevant across model size: they can be used to edit the knowledge in large foundation models for which re-training has prohibitive costs, as well as to adapt models on edge devices. Moreover, test-time adaption finds applications on a variety of tasks, from vision to natural language tasks or time series analysis, each presenting its specific challenges and methods. Finally, the goals of test-time approaches are multiple, spanning robustness, customization, and computational efficiency. In this workshop we want to bring together these different facets of test-time updates, connecting researchers focusing on topics typically treated as independent problems. We believe that this will offer a unique opportunity for cross-area collaborations. Sharing domain-specific challenges and solutions will bridge diverse communities, providing beneficial contamination. In fact, we will welcome works on methods, theory, systems, and evaluations for TTU/TTA across modalities (vision, language, audio, etc.), scales (from edge to cloud), and openness (open/closed models, black-/white-box scenarios). We will highlight principled objectives, safe/robust updates, practical parameterizations (inputs, features, adapters, heads), and cost-aware/green practices that respect latency, energy, and monetary budgets.