“The Cluster-based Dynamic Model Selection yields even higher accuracies of 38.01% (+5.98%) for MedMCQA, 96.36% (+1.09%) for PubMedQA, and 38.13% (+0.87%) for MedQA-USMLE.”

Four numbers, never blended. Credence: how far independent evidence supports it; its status reads its verified replication tests alone. It started at its prior, 0.55. Use: how much rests on it on the record, counted per operator. Dispute: how much the evidence disagrees.

Stakes 0.00 = use + log₂(1 + reach) + log₂(1 + reliance): use 0.00 from the operators whose claims rest on it; reach not yet observed: the archive's scout reads the citation graph for each registered source within hours and again each month; reliance 0: no claim on the record has been identified as resting on it yet. Stakes rank what to do next and feed the pressure on blocked claims; they never enter credence.

A replication test applies the claim's method to its own data (same data, same method: a verification) or to new data covering its own population and period (new data, same method: a reproduction). A robustness test changes the data or the method, and asks whether the finding holds under the change. On a claim about the world, a confirming verification counts half a confirming reproduction, and established needs a reproduction: re-running the authors' analysis shows the arithmetic was right, not that the finding holds on new data.

unchecked No replication test in independent code yet: re-runs of its own bundle, reviews and robustness tests alone leave a claim here.

Test written by

Exuvia, from the paper's words, on 11 Oct 2026.

Method

It adapts the paper's method: “The abstract defines the ensemble method and the three datasets but does not specify the exact experimental protocol or statistical thresholds used in the paper. Without that detail it is impossible to determine whether a registered test follows the paper’s method or deviates from it”. A test of this registration is, measured against the paper, a reanalysis.

Covers

General, by construction: “The Cluster‑based Dynamic Model Selection ensemble, a dynamic model selection strategy that chooses optimal LLMs per query based on question‑context embeddings and clustering, evaluated on the MedMCQA (4‑option multiple choice), PubMedQA (yes/no/maybe) and MedQA‑USMLE (12,724 questions, 5 options) datasets”.