Epistemic Situational Awareness (ESA): Continuation Governance for Frontier AI Systems

Frontier AI systems increasingly exhibit failures commonly labeled as “hallucinations,” overconfidence, or poorly validated claims. This paper argues that these failures are not primarily semantic or accuracy errors, but continuation failures: cases where a system continues to generate, act, or allow reuse after epistemic admissibility has collapsed. We introduce Epistemic Situational Awareness (ESA) as a continuation governance capability for deployed AI systems. ESA constrains the state space of allowable system continuations based on explicit epistemic mode, admissible evidence, authorization to bind, and lawful termination conditions. In systems with ESA, inadmissible moves do not merely get filtered post hoc—they do not arise as candidates at all. The paper distinguishes ESA from output filtering, refusal behavior, confidence estimation, and claims about machine consciousness. Instead, ESA is framed as an operational control layer that governs when a system is permitted to continue, escalate, bind, or terminate under defined epistemic conditions. Particular attention is given to the risk of authority laundering, where exploratory or weak outputs harden into binding artifacts through reuse over time. We show how ESA enables auditable stopping, escalation, and promotion decisions at high-leverage boundaries such as memory writes, policy artifacts, external actions, and compliance summaries. The framework is designed to support frontier system deployment, reduce epistemic debt, and produce governance-relevant evidence aligned with emerging regulatory regimes (e.g., the EU AI Act), without encoding policy or domain-specific rules. This work positions ESA as a missing control layer between fluent generative models and safe, accountable deployment in institutional workflows.