Pre-commitment Runtime AI Oversight: A Framework for Timing, Proxies, and Intervention Feasibility

This paper presents a theory-first framework for runtime AI oversight centered on pre-commitment intervention timing. Its core claim is narrow: in systems with an auditable commitment protocol, pre-commitment monitoring can improve intervention success over an output-only baseline when proxy quality and end-to-end latency are adequate. The framework focuses on four load-bearing elements: monitoring cadence, proxy usefulness, retained intervention feasibility, and commitment-relevant escalation. It develops an intervention-oriented phase model—contact, attention, recognition, impulse, and commitment—and introduces a minimal runtime escalation score, Vₛ (t), for phase-sensitive monitoring. The manuscript is not presented as a universal predictive law of AI safety. It is a structured runtime-oversight scaffold intended to clarify timing, proxy limits, burden structure, calibration, and falsification.