Prospective Learning Needs Physical Implementations: Phase-Structured State Dynamics as a Discriminating Neural Hypothesis

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. The synthetic benchmark performed well on its reference generators but lost sensitivity when planted effects were weak or noise was elevated, and it showed a small false-positive rate under a phase-locked intervention artifact. These failures define the operating limits of the current analysis. No biological experiment, animal count, achieved power, completed preregistration, clinical claim, or biological causal result is reported.