Rethinking Test-Time Training: Tilting The Latent Distribution For Few-Shot Source-Free Adaptation

• URL: http://arxiv.org/abs/2602.02633v1
• Date: Mon, 02 Feb 2026 18:17:29 GMT
• Title: Rethinking Test-Time Training: Tilting The Latent Distribution For Few-Shot Source-Free Adaptation
• Authors: Tahir Qasim Syed, Behraj Khan,
• Abstract summary: We study test-time adaptation of foundation models for few-shot classification under a completely frozen-model regime.<n>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.
• Score: 3.5808917363708743
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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