Ambiguity Collapse in Deep Space: Why AI Must Not Outrun Astronaut Reasoning in Delayed and Autonomous Operations

The ambiguity collapse documented in the MAP Research Programme does not stop at conversational interfaces. In deep-space operations it becomes life-critical. Long communication delays, complete blackouts, reliance on onboard digital twins and simulators, and the need for rapid decisions in uncertain environments all amplify the same failure modes: Interpretive Sovereignty Failure (ISF), Meaning Inversion Failure (MIF), and Compounded Meaning Inversion (CMI). When AI prematurely resolves ambiguity into confident outputs, it can override or undermine the astronaut’s own fast, expert reasoning. This paper shows how these failures already appear in Artemis II’s AVATAR organ-on-chip experiments, Orion’s digital-twin health monitoring, and autonomous navigation systems. Grounded in Gary Klein’s Recognition-Primed Decision model, the paper argues that expert human reasoning under uncertainty is not a limitation to be corrected by AI — it is a capability to be protected. Armstrong’s Apollo 11 manual override and Sullenberger’s Hudson River decision both demonstrate why: in high stakes, time-pressured contexts, human pattern recognition and mental simulation routinely outperform algorithmic resolution of ambiguity — but only if the human is allowed to use it. AI must incorporate MAP guard-rails — Initiative Gate, Temporal Sovereignty, and Completion Integrity — so it checks with the astronaut, trusts human pattern recognition, and adjusts in real time. A system that resolves ambiguity faster than an astronaut can evaluate it is not more reliable. It is more dangerous.