The Physics of Prospective Learning: Temporal Phase Dynamics and Context Persistence in Biological and Artificial Intelligence

This corrected preprint presents its research question, method, bounded result, and principal limitations in a standardized reader-facing format. Intelligence has to act before the future is fully observed. Catching a moving object, continuing a plan after interruption, and adapting when a familiar task changes all require more than classifying the present. A system must preserve a future-oriented state, estimate where events are going, compare incoming evidence with that estimate, and revise without either forgetting its objective or becoming rigid. This paper defines a prospective state as a structured combination of persistent context, fast working state, a trajectory estimate, candidate policies, and mismatch. It then develops a bounded physical hypothesis. It belongs to the Self-Aware Networks agentic systems research program. This is a corrected preprint edition released under the Creative Commons Attribution 4.0 license.