arXiv — Machine Learning · · 4 min read

Reliability-Gated Source Anchoring for Continual Test-Time Adaptation

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Computer Science > Machine Learning

arXiv:2605.14063 (cs)
[Submitted on 13 May 2026]

Title:Reliability-Gated Source Anchoring for Continual Test-Time Adaptation

View a PDF of the paper titled Reliability-Gated Source Anchoring for Continual Test-Time Adaptation, by Vikash Singh and Debargha Ganguly and Weicong Chen and Sabyasachi Sahoo and Sreehari Sankar and Biyao Zhang and Mohsen Harir and Shouren Wang and Osama Zafar and Christian Gagn\'e and Vipin Chaudhary
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Abstract: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 $1.13{\times}$ 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.
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2605.14063 [cs.LG]
  (or arXiv:2605.14063v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2605.14063
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Vikash Singh [view email]
[v1] Wed, 13 May 2026 19:38:08 UTC (383 KB)
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