Learning-To-Measure: In-Context Active Feature Acquisition

ArXi:2510.12624v2 Announce Type: replace-cross Active feature acquisition (AFA) is a sequential decision-making problem where the goal is to improve model performance for test instances by adaptively selecting which features to acquire. In practice, AFA methods often learn from retrospective data with systematic missingness in the features and limited task-specific labels. Most prior work addresses acquisition for a single predetermined task, limiting scalability.