Reliability-Gated Source Anchoring for Continual Test-Time Adaptation
Not on the map. Why?
Abstract (arXiv)
Continual test-time adaptation (CTTA) updates a pretrained model online on an unlabeled, non-stationary stream while anchoring it to a frozen source checkpoint. This anchor is useful only when the source remains reliable. On CCC-Hard, however, a ResNet-50 source falls to approximately 1.3% top-1 accuracy, while existing source-anchored CTTA methods continue applying the same anchor strength. We call this failure mode blind anchoring and propose RMemSafe, a reliability-gated extension of ROID that uses the frozen source's normalized predictive entropy to attenuate all explicit source-coupled uses in the objective. When the source posterior approaches uniformity, the gate closes: the source anchor and agreement filter vanish, and the objective reduces to a source-agnostic fallback comprising ROID's base losses plus marginal calibration. Combined with ASR, RMemSafe achieves the lowest error on 8 of 9 matched-split continual-corruption cells and is the best reset-based method on all 9, improving ROID+ASR by 1.05~pp on ResNet-50 and 0.48~pp on ViT-B/16. A controlled source-degradation sweep shows a 1.13× shallower harm slope than ROID+ASR, consistent with the graceful-decay prediction. The entropy gate detects high-entropy source collapse, not confidently wrong low-entropy sources; this scope is explicitly evaluated and discussed.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 28 of its 35 references that are in the map. First posted on arXiv 13 May 2026.
Builds on
28 of 35 references.
References · 28
- 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,877
- 2017Adversarial Discriminative Domain Adaptationcited by 1,311
- 2022Continual Test-Time Domain Adaptationcited by 525
- 2022Efficient Test-Time Model Adaptation without Forgettingcited by 469
- 2020Improving robustness against common corruptions by covariate shift adaptationcited by 431
- 2023Towards Stable Test-Time Adaptation in Dynamic Wild Worldcited by 415
- 2021MEMO: Test Time Robustness via Adaptation and Augmentationcited by 377
- 2023A Comprehensive Survey on Test-Time Adaptation under Distribution Shiftscited by 359
- 2023Robust Test-Time Adaptation in Dynamic Scenarioscited by 189
- 2022NOTE: Robust Continual Test-time Adaptation Against Temporal Correlationcited by 151
- 2023EcoTTA: Memory-Efficient Continual Test-time Adaptation via Self-distilled Regularizationcited by 123
- 2024Entropy is not Enough for Test-Time Adaptation: From the Perspective of Disentangled Factorscited by 106
16 more
- 2022Robust Mean Teacher for Continual and Gradual Test-Time Adaptationcited by 104
- 2021Test-Time Adaptation to Distribution Shift by Confidence Maximization and Input Transformationcited by 68
- 2023Universal Test-time Adaptation through Weight Ensembling, Diversity Weighting, and Prior Correctioncited by 65
- 2023RDumb: A simple approach that questions our progress in continual test-time adaptationcited by 53
- 2021If your data distribution shifts, use self-learningcited by 36
- 2024Proof of Thought : Neurosymbolic Program Synthesis allows Robust and Interpretable Reasoningcited by 19
- 2023Persistent Test-time Adaptation in Recurring Testing Scenarioscited by 18
- 2026VERGE: Formal Refinement and Guidance Engine for Verifiable LLM Reasoningcited by 12
- 2024Forte : Finding Outliers with Representation Typicality Estimationcited by 9
- 2025K4: Online Log Anomaly Detection Via Unsupervised Typicality Learningcited by 6
- 2026Mid-Think: Training-Free Intermediate-Budget Reasoning via Token-Level Triggerscited by 6
- 2026Toward Guarantees for Clinical Reasoning in Vision Language Models via Formal Verificationcited by 4
- 2026When and Where to Reset Matters for Long-Term Test-Time Adaptationcited by 4
- 2026Path-Lock Expert: Separating Reasoning Mode in Hybrid Thinking via Architecture-Level Separationcited by 3
- 2026CausalRAG2: Hierarchical Causal Knowledge Graph Design for RAGcited by 2
- 2026RDumb++: Drift-Aware Continual Test-Time Adaptationcited by 1
Built on by
Citing papers per year: 2026: 1.
1 paper.