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Life sciences · Preprint
arXiv · September 3, 2026
Posted before peer review. The findings may change or fail to hold.
This preprint reports a systematic empirical study showing that model compression degrades test-time adaptation performance across multiple compression methods and architectures, attributed to reduced representational diversity and structural constraints. The work is observational and does not yet establish causal mechanisms or provide quantified effect sizes; peer review status is unknown.
Empirical systems analysis. Pre-trained deep neural networks (ResNet-18, ViT-Base) subject to structured compression and evaluated under natural distribution shift (image corruptions).. Intervention: Structured model compression methods combined with test-time adaptation techniques. Compared with: Uncompressed (baseline) models with and without TTA.
Compressed models retain high accuracy under supervised adaptation but show significant TTA performance degradation with increasing compression Performance degradation stems from reduced representational diversity and structural constraints that limit recoverability Effects strongly depend on the compression method, indicating method-specific trade-offs between efficiency and adaptability
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This is an unrefereed preprint presenting a systematic empirical analysis of model compression and test-time adaptation interaction, with no peer review status reported.
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Deep neural networks deployed in the wild must be both efficient and adaptable, requiring model compression and test-time adaptation (TTA). While both are well studied in isolation, their interaction remains poorly understood. We systematically analyze how structured compression affects a model's ability to adapt under distribution shift. Using ResNet-18 and ViT-Base on CIFAR-10-C and ImageNet-C, we evaluate multiple compression methods combined with standard TTA techniques. We introduce a diagnostic framework that examines representational expressivity and adaptation subspace compatibility. Our results reveal a consistent gap: although compressed models retain high accuracy under supervised adaptation, their TTA performance degrades significantly with increasing compression. We show that this stems from reduced representational diversity and structural constraints that limit recoverability. These effects strongly depend on the compression method, highlighting the need to design compression strategies that preserve adaptability.
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