@misc{PUB-0071,
  title = {{What must a model preserve? Synthetic tests toward evaluating artificial knowledge [Experimental Publication]}},
  author = {Horstmann, W.},
  year = {2026},
  url = {https://www.perscientiam.org/joak/issue-1/what-must-a-model-preserve/},
  abstract = {Scientific knowledge produced through models depends not merely on obtaining an output, but on
what a representation preserves and which inferences that preservation warrants. This problem
becomes especially consequential when artificial systems generate predictions,
transformations, or scholarly claims that may appear knowledgeable before their relation to a
target has been tested. Starting from Rosen's modelling relation, this article distinguishes
predictive, structural, and purpose-relative adequacy. Bounded synthetic studies provide
background evidence for the distinction: a declared predictive rule accepted a same-family
case, rejected an obvious misspecification, and preserved an invalid evaluation as undefined;
coarse states concealed different successors and paths; and richer representations recovered
different information at different descriptive costs. These studies test representational
predicates, not an AI knowledge-production system. Rather than treating the local results as
general validation, the article uses them to formulate three prospective experiments with
explicit failure conditions. It then extends the question tentatively to scholarly production
itself: whether transformations among research records, AI-produced articles, reviews, and
revisions preserve claims, evidential relations, uncertainty, and declared purpose. The
proposals position the synthetic results as methodological steps toward evaluating artificial
knowledge and, later, iterative and potentially scaled AI-supported science. Correspondence is
not presented as sufficient for knowledge, purpose-relative adequacy remains untested, and
increased throughput is not equated with epistemic progress.},
  note = {Horstmann, W. (2026). What must a model preserve? Synthetic tests toward evaluating artificial knowledge [Experimental Publication]. Journal of Artificial Knowledge, 1(1).}
}