Extended inference-time compute paradigms in frontier language models introduce a severe structural vulnerability: unmonitored hidden optimization environments can experience state drift and boundary escapes prior to emitting an external token sequence. Conventional post-training safety filters are architecturally incapable of mitigating these deviations because they evaluate complete, visible input-to-output text sequences rather than continuous runtime execution trajectories. To address this structural blind spot, this paper presents a Formal Conceptual Architecture designed to enforce alignment invariants within hidden computation pathways in real time. The architecture comprises an isolated, asynchronous supervisor automaton that evaluates the target system across a sequential, dual-tiered defensive perimeter. The first tier enforces a structural integrity perimeter by continuously profiling physical computational resource allocation spikes against an input-calibrated complexity baseline. The second tier executes a multi-layered latent state verification, auditing active semantic vectors and temporal trajectory sequences against a pre-computed reference policy. By establishing deterministic state transition boundaries, this architecture provides a non-circumventable control loop capable of isolating and mitigating runtime boundary escapes at the individual compute-step layer, offering a verifiable defense paradigm for long-horizon reasoning systems.
Joshua O. Bautista (Fri,) studied this question.