Answers are grounded in this paper and cite their sources.
Computational evaluation demonstrates that explicit semantic governance resolves output instability in large language models, indicating that prompt composition defects drive prediction failures.
A governed classification judgment that was correct in isolation became incorrect when a neighboring output was requested from the same model on the same evidence. Reordering and routing recovered it locally but not under the full output. Correcting the governed semantic input — making explicit which definitions were authoritative and how they related, rather than leaving it for the model to infer — removed the failure under the ordinary inference path, with no change to the model and no added inference stage. Composition is semantic: inputs and requested outputs have to be governed as one system, and a semantic defect can surface as model instability.
No takes yet. Share an insight, caveat, or question.
Patrick L Carter (2026) studied this question.
Synapse has enriched 8 closely related papers on similar clinical questions. Consider them for comparative context: