# Informational Friction

Autonomous systems usually monitor motion and whether a task is complete, leaving the information-gathering process itself unchecked, so a system can keep operating without resolving outstanding requirements. Acquisition-State Telemetry (AST) maps raw observations onto task-requirement resolution coordinates, and a fixed causal monitor emits two continuous signals: prospective progress across unresolved requirements and informational friction when recurrent evidence yields negligible gain. One unretuned configuration ran across eight domains; the friction channel separated unproductive stagnation from purposeful exploration before completion and stayed muted afterward, and in closed-loop reinforcement learning under early termination, penalizing friction sustained high mission success.

Autonomous systems usually monitor motion and whether a task is complete, leaving the information-gathering process itself unchecked, so a system can keep operating without resolving outstanding requirements. Acquisition-State Telemetry (AST) maps raw observations onto task-requirement resolution coordinates, and a fixed causal monitor emits two continuous signals: prospective progress across unresolved requirements and informational friction when recurrent evidence yields negligible gain. One unretuned configuration ran across eight domains; the friction channel separated unproductive stagnation from purposeful exploration before completion and stayed muted afterward, and in closed-loop reinforcement learning under early termination, penalizing friction sustained high mission success.