Prospective Learning Needs Physical Implementations

This article asks what kind of physical mechanism could implement prospective learning in biological networks. It separates a future-facing learning objective from neighboring computational and neurophysiological ideas, then introduces a receiver-relative Self-Aware Networks hypothesis only after explaining the ordinary adaptation problem and proposed operation. The paper reports an inspectable synthetic calibration benchmark and proposes two later biological tests. The first asks whether engineered phase structure improves prediction beyond locked recurrent and nonlinear raw-waveform comparators. The second asks whether separately localized phase conditions produce a selective receiver-transfer effect under matched perturbation. Its conclusions are limited to the assumptions, evidence, and testing conditions stated in the manuscript. It belongs to Micah Blumberg's governed research-paper corpus. This is a corrected preprint edition released under the Creative Commons Attribution 4.0 license.