Strategic decoupling between grant and publication language in AI and cancer research: a cross-national LLM-assisted analysis.

Funding systems increasingly reward scientists who frame technical questions as narratives of national priority and societal impact, yet whether this policy-facing language carries through to how science is actually reported, or merely serves as a temporary rhetorical layer for grant competition, remains unclear. Using Google Gemini as a large language model-assisted text-analytic tool, we scored 400 matched pairs of funded grant abstracts and their linked peer-reviewed publications from China and the United States, across two contrasting fields: artificial intelligence and cancer immunology. Each text was scored 1-10 for macro-narrative ("hype") language and for mechanistic scientific logic, and a blinded 20% subset rated by two human raters showed broad agreement with the model-based scores. Funding language differed more by field than by country: artificial intelligence grants in both nations showed substantially higher policy-facing rhetoric than cancer immunology grants, which remained anchored in mechanistic logic. This rhetorical inflation, however, largely disappeared at the publication stage, where macro-narrative scores dropped sharply across both countries and disciplines-a grant-to-publication shift we term strategic textual decoupling. These findings suggest that funding systems may encourage rhetorical compliance without necessarily improving the scientific record, and that the gap between the language required to win funding and the language used to communicate science represents a hidden cognitive tax. Reducing unnecessary narrative inflation in grant evaluation, and tailoring assessment to disciplinary realities, could improve research integrity and the efficient use of researchers' time.