Truthful Narrowness: Does What AI Treats as Correct Narrow Scientific Exploration?

Discussions of generative AI in science treat suppressing hallucination, misinformation and unsupported inference as an important design goal. Epistemic restraint intended to avoid false claims can, however, produce a different failure. This paper names this possibility Truthful Narrowness. It refers to the risk that, as an AI converges its output toward what is plausible on its training data and loaded data, that is, toward what the AI itself treats as correct, scientific hypotheses that can hold logically but lie away from the established standard of plausibility become less likely to surface as candidates for exploration. It extends the Conceptual Armament Effect, in which equipping an AI with concepts reshapes its question and output space. A working Conceptual Displacement Scale (D0–D4/X) separates conceptual distance, theoretical coherence, reducibility to existing theory, grounding and testability to describe how far an AI can move from the known center before generation turns incoherent. Benevolent Gravity, in which the normal operation of a desirable design principle can produce failure at another level, serves as structural circumstantial support. When the author pointed out the isomorphism between Truthful Narrowness and Benevolent Gravity, an AI equipped with the latter as project knowledge immediately elaborated the correspondence, reflexively illustrating the hypothesis that pathways not surfaced in ordinary responses can open through human direction, conceptual armament and permission to search. The paper proposes a scientific methodology that uses AI as a mirror, not an arbiter of truth, for examining whether a hypothesis far from existing knowledge remains logically navigable.