Governed Cognition: CRF and MAED as Epistemic Stop Mechanisms for Decision-Capable AI Systems

As artificial intelligence systems increasingly operate in domains where their outputs implicitly authorize action—financial allocation, medical guidance, robotic behavior, operational decision-making—a critical gap has emerged in contemporary AI design. Current safety and alignment approaches focus primarily on correctness, uncertainty expression, or compliance with user intent, while largely neglecting a more fundamental question: when is an AI system epistemically permitted to continue reasoning or participate in a decision at all? This paper introduces a unified framework for governed cognition, formalizing the conditions under which continued AI reasoning, guidance, or action becomes illegitimate rather than merely uncertain. We integrate three complementary constructs into a coherent architectural model: the Cognitive Reflexivity Function (CRF), which specifies stop conditions for reasoning under epistemic incoherence or insufficiency; the Minimum Acceptable Evidence Doctrine (MAED), which governs the right to authorize action independent of analytical capability; and a graded intervention mechanism that translates epistemic state into proportional cognitive responses, including guidance, friction, refusal, or hard termination. Together, these mechanisms shift AI design from optimizing answer quality toward governing participation under uncertainty. Refusal, delay, disagreement, and silence are reframed not as failures of intelligence, but as first-class cognitive outcomes essential for responsible operation in high-stakes environments. Crucially, the framework preserves human agency by restricting the system’s own participation rather than overriding human decisions, positioning restraint as a form of ethical competence rather than limitation. Using examples from finance, robotics, and human–AI interaction, we demonstrate how governed cognition mitigates premature authorization, narrative-driven overconfidence, and cognitive degradation—failure modes that persist even in highly capable models. This work argues that alignment alone is insufficient for advanced AI systems and proposes governed cognition as a necessary complementary paradigm for trustworthy, decision-capable artificial intelligence.