Designing reproducible large-language-model-assisted scientific analyses

Large language models (LLMs) increasingly write code, analyze data, and orchestrate scientific workflows. This can create reproducibility challenges because LLMs blur the boundary between how an analysis is built and what it depends on at run time. Guidelines for LLM-assisted science begin with a critical choice: whether the LLM sits on the data path of the published analysis, a live step results depend on, or off the data path, producing durable artifacts such as code. Reproducible analyses require preserving data, code, and runtime; an on-path LLM becomes part of the runtime, a dependency that may change, be deprecated, or become inaccessible. We derive six recommendations: (1) keep the LLM off the data path where possible, (2) preserve LLM-generated artifacts, (3) verify results by methods suited to the LLM's role, (4) consider open-weight models, (5) record the model version, and (6) assess determinism.