Timing, Redirectability, and Runtime AI Oversight: The Sampling-Rate Hypothesis

This paper presents a theory-first framework for runtime AI oversight centered on pre-commitment monitoring, proxy faithfulness, and intervention feasibility. Its core claim is narrow: monitoring can improve intervention success only when a system can be observed, interpreted, and redirected before a hazardous trajectory reaches commitment. The framework organizes runtime oversight around three requirements: usable signal, sufficient remaining time, and retained intervention authority. It introduces Safety Slack, S_t, as a design margin comparing usable oversight capacity with effective hazard burden, and develops a phase-sensitive account of escalation through contact, attention, recognition, impulse, and…