Does Accuracy Imply Correctness in Temporal Evolution Forecasting?

Claims of transferable temporal evolution require response evidence matched to the declared condition changes. Low error on natural trajectories alone does not supply that evidence. Temporal Evolution Forecasting (TEF) expresses this behavioral requirement through declared information interfaces and conditional-response diagnostics. Controlled experiments show why familiar evidence cannot substitute for these tests: longer histories can improve natural accuracy while amplifying response error, an autoregressive model's local advantage can disappear during rollout, and mechanism-matched positive controls can recover the intended response. Across seven real-world benchmarks, forecast revisions and input sensitivity distinguish properties that natural error alone does not describe, without supplying intervention-response ground truth. Authors should therefore state which evolution capability they claim, evaluate natural forecasts alongside responses to the relevant changes, and establish that their diagnostics reward the intended behavior. The six protocols studied here illustrate this requirement; the appropriate evidence depends on the claim and on which paired outcomes the setting can identify.