Ordinary transformations predict retelling chains

This proposed study asks whether stories retold by people and an artificial intelligence model follow ordinary rewriting patterns, or require an extra mechanism tied to the chain's history. The hypothesis freezes a prediction before the test: measure how each partner rewrites a text once, combine those steps, and use them to forecast person-only, model-only, person-then-model and model-then-person chains. The forecasts track changed claims, swapped causes and effects, lost exceptions, and memories of information kept private from the retellers. If held-out chains remain within the forecast's uncertainty range, ordinary transformations suffice for this setting. A repeatable gap beyond that range would justify searching for an added state, but would not say what it is.