An intelligent system can recognize the present correctly and still fail to act intelligently if it cannot maintain what is changing, where that change may lead, which actions remain available, and when new evidence should revise its plan. This is the prospective-state problem. Existing work provides important parts of the answer through predictive processing, active inference, recurrent planning, traveling waves, oscillatory routing, fast weights, and test-time adaptation. What remains open is how these parts can be joined into a measurable state-maintenance architecture that operates while the future is still unfolding. This paper defines a prospective state as a coupled set of current context, trajectory estimate, candidate future policies, working state, and receiver-relative mismatch. 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.
Micah Blumberg (Wed,) studied this question.