ACTIVE NEURAL MATTER: FINITE-WINDOW RESPONSE IN PHYSICAL SYSTEMS AND AI WORKFLOWS

Delayed interactions carry local perturbations through shared states before they alter observable outcomes. Endpoint changes alone leave their sources and propagation unresolved. We formulate Active Neural Matter (ANM) as a finite-window local response theory linking source perturbations, retained shared states carrying information between interactions, and system-specific readouts. Readout maps and declared endpoint criteria define operating boundaries on accessible states; dynamics determine responses relative to them. Readouts may be scalar or vector-valued, with continued workability and stricter endpoint exactness as a nested two-level case. In a driven delayed-consensus network, numerical response amplitudes and phases agree with analytic coherent-mode predictions; a polar flock illustrates nonlinear steady-state separation of alignment and collective order. AI examples comprise an endpoint-informed typed-evidence graph calculation, replay of model records, and fresh readout calls following imposed propagation through an externally constructed graph. Fresh calls resolve candidate-support changes in some directions; only a minority pass the finite-step linearity check, and the all-direction response criterion fails. In a retrospective offline example with previously identified defects, reference calculations replace selected program outputs. Replacing only the selected output or both readouts gives correct outcomes for both archived cases; selective replacement yields fewer correct outcomes on constructed controls. ANM provides an auditable response-measurement protocol with realization-specific states and response functions. These measurements leave causal attribution and retained-state mediation unresolved. Joint state formation and use by multiple live agents lie outside their scope.