Often, constraints arise in deployment settings where even lightweight parameter updates e.g. parameter-efficient fine-tuning could induce model shift or tuning instability. We study test-time adaptation of foundation models for few-shot classification under a completely frozen-model regime, where additionally, no upstream data are accessible. We propose arguably the first training-free inference method that adapts predictions to the new task by performing a change of measure over the latent embedding distribution induced by the encoder. Using task-similarity scores derived from a small labeled support set, exponential tilting reweights latent distributions in a KL-optimal manner without modifying model parameters. Empirically, the method consistently competes with parameter-update-based methods across multiple benchmarks and shot regimes, while operating under strictly and universally stronger constraints. These results demonstrate the viability of inference-level distributional correction for test-time adaptation even with a fully-frozen model pipeline.

翻译：在部署环境中，常常存在这样的约束：即使是轻量级的参数更新（例如参数高效微调）也可能导致模型偏移或调优不稳定性。我们研究在完全冻结模型的机制下，基础模型针对少样本分类任务的测试时适应问题，且在此过程中无法访问任何上游数据。我们提出了可论证的首个免训练推理方法，该方法通过对编码器诱导的潜在嵌入分布执行测度变换，使预测适应新任务。利用从少量带标签支持集导出的任务相似性分数，指数倾斜以KL最优的方式对潜在分布进行重新加权，而无需修改模型参数。实证结果表明，该方法在多个基准测试和样本数量设置下，始终与基于参数更新的方法相竞争，同时在严格且普遍更强的约束条件下运行。这些结果证明了即使采用完全冻结的模型流程，通过推理层面的分布校正来实现测试时适应是可行的。