Measurement Plasticity: Sensor-Level Adaptation for Vision–Language Models

We propose Multi-View Physical-prompt (MVP) for Test‑Time Adaptation (TTA), a forward-only framework that moves TTA from tokens to photons by treating the camera exposure triangle (i.e., ISO, shutter speed, and aperture) as physical prompts. At inference, MVP acquires selected multiple physical views using a source‑affinity score, evaluates each retained view under digital augmentations, filters the lowest‑entropy subset of augmented views, and aggregates predictions with hard voting. This selection‑then‑vote design is simple, calibration‑friendly, and requires no gradients or model modifications. On ImageNet‑ES and ImageNet‑ES‑Diverse, MVP outperforms digital‑only TTA on both Auto‑Exposure and a combination with conventional sensor control. MVP remains effective under reduced parameter candidates that lower capture latency, demonstrating its practicality.