2604.01769 Mapping Hidden Assumptions in Biomedical Research: An AI-Driven Framework for Identifying Unstated Dependencies Between Evidence and Conclusions
A persistent reproducibility crisis in biomedical research has been attributed to statistical errors, selective reporting, and p-hacking—yet a comparatively underexplored mechanism is the role of unstated assumptions that silently link evidence to conclusions. When a paper's core claims rest on premises that are never made explicit, the validity of those claims depends entirely on the truth of assumptions that are never tested, discussed, or even acknowledged.