Thinking the Stability-Plasticity Dilemma in Non-Stationary Test-Time Adaptation from a Bayesian Perspective

Reliably adapting foundation models (FMs) to non-stationary medical environments remains unsolved, as test distributions evolve dynamically through changing acquisition protocols, patient populations, and disease prevalence. Existing test-time adaptation (TTA) methods often degrade below the frozen source model. We trace this failure to the stability-plasticity dilemma: plasticity for tracking the evolving target and stability for preserving generic knowledge compete for the same parameter space. From a Bayesian perspective, we decompose the predictive posterior on test data into a class prior that encodes stable class-level knowledge and a class-conditional likelihood that carries per-input evidence, with their balance governed by the uncertainty of online evidence. This yields a Bayesian Generalization Error Bound (BGEB) that factorizes target risk into finite-batch uncertainty, prior mismatch, conditional shift, and parameter drift, thereby turning the dilemma into a principled trade-off. Guided by the error bound, we propose an effective algorithm SynerTTA, which rectifies the online prior, restricts updates to a low-displacement subspace, and weights adaptation by instance reliability. Across eight medical benchmarks and three modalities, SynerTTA outperforms thirteen baselines, surpassing the second-best method by 7.75% in accuracy and 5.56% in macro-F1, while achieving the best calibration and robustness under unreliable online evidence.