Measure Less, Know More: Self-Supervised Test-Time Feature Acquisition

The paper introduces ECHO-$k$, a self-supervised, task-agnostic method for selecting which modalities to acquire at test time in multimodal, high-dimensional learning. By using a deep model’s pretrained representations as proxy targets, ECHO-$k$ learns a reinforcement‑learning policy that sequentially chooses informative modalities, providing theoretical guarantees in a linear setting. Experiments show that ECHO-$k$ consistently improves budgeted downstream performance across various foundation‑model backends, offering a principled approach to cost‑aware test‑time deployment when measurements are expensive or time‑constrained.

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• reinforcement-learning
• multimodal