Provenance of AI-assisted analysis: a disclosure and reproduction protocol for research involving language models

Preprint, 24 August 2026. A version of this manuscript is under consideration at a peer-reviewed journal of academic ethics. This record carries no peer-review status. Abstract. Language models entered research practice faster than the rules for disclosing them. The typical declaration today is a single sentence — “a language model was used in preparing this manuscript” — from which a reader can determine nothing: not where the model was applied, not what it did, not whether it touched the result, not whether any of it can be repeated. Alongside this sits a requirement nobody disputes, that human authors bear responsibility and a model cannot be an author. That requirement is satisfiable, and it does not help a reader check the work. This paper argues that the impasse rests on a conflated notion of reproducibility, and separates it into two: audit reproducibility — that a stated result was in fact obtained in the manner and at the time claimed — and procedural reproducibility — that the procedure can be re-run to yield a substantively comparable result. Bit-identical output is the wrong demand for a generative step and the right demand for a computation; the consequence is a division of labour in which published quantities are never produced by generation. On that basis the paper proposes a disclosure ladder (L0–L5) with working thresholds, a step log whose fields make audit possible, a contribution classification (presentation, search, construction) requiring authors to name the highest class reached, and a rule that rejected steps are retained on equal footing with accepted ones. Limits are stated plainly: provenance does not make a result correct, and the disappearance of a model version destroys procedural reproducibility while leaving audit reproducibility intact.