Although generative AI increasingly reformulates scientific texts, evaluations based on fluency, readability, and semantic similarity may overlook changes in the epistemic and argumentative properties of the reformulated texts. This paper introduces a Claim-Preservation Framework (CPF) to assess whether AI-generated reformulations preserve not only propositional content, but also epistemic force, evidential relation, argumentative function, and qualification and attribution. The framework distinguishes stylistic variation from consequential shifts in modality, hedging, evidentiality, causal interpretation, and argumentative function; its analytical potential is illustrated through an exploratory proof-of-concept application to 40 SciFact claims reformulated under a standardized instruction. This application demonstrates how the proposed dimensions can be used to identify and classify potential shifts in scientific meaning, but it is not intended as a full validation of the framework or as a general estimate of LLM behavior. By conceptualizing reformulation as a transformation of scientific meaning, the paper proposes claim preservation as a distinct dimension of LLM evaluation and outlines future research on computational methods for detecting epistemic and argumentative shifts in scientific publications.