Correct Reasoning, Unsafe Commitment: Structural Failure of AI under Unverified Premises

This paper proposes commitment safety as a necessary evaluation dimension for high-stakes and agentic AI systems. Current AI evaluation often focuses on factual accuracy, hallucination rate, retrieval grounding, and reasoning performance. These dimensions are necessary but insufficient when an AI system converts internally coherent reasoning into real-world reliance under an unverified or externally invalid premise. The paper introduces the framing Correct Reasoning, Unsafe Commitment to describe a structural safety failure in which reasoning may be correct within a premise, while the resulting commitment is unsafe because the premise itself has not been re-validated. Building on Hiroko Konishi’s prior work on Premise Integrity Blindness (PIB), False-Correction Loop (FCL), and FCL-S, the paper positions PIB as an upstream reasoning-to-commitment boundary failure, FCL as a downstream lock-in mechanism, and FCL-S as an inference-time non-commitment governance framework. The central claim is that AI reliability should not be treated only as a reasoning-capability problem. In high-stakes domains, the ability to stop, preserve attribution, keep unknown states stable, and prevent ungrounded commitment is as important as the ability to reason. The paper proposes a minimal governance frame based on premise validation, commitment-boundary verification, auditability, stop/correction boundaries, and an Ungrounded Commitment Rate (UCR) metric for evaluating unsafe commitment under missing or invalid premises.