DeFuture: Decoupling LLM Forecasting with Hindsight Evidence

Future event prediction tests whether a large language model (LLM) can turn information about the present into a judgment about the future. Current benchmarks score each system end-to-end, so a failed prediction cannot be traced to specific components, and an agentic forecaster cannot tell whether to improve its search, its processing, or its reasoning. We introduce **DeFuture**, which decouples future event prediction into evidence acquisition, interpretation, and prediction, and anchors this decomposition on golden evidence that a selector picks in hindsight from the same answer-blind evidence pool by reading the resolved answer. Golden evidence makes every phase measurable: it tops a ladder of four evidence tiers, carries known answer-bearing content for scoring each Interpreter, and gives all Predictors a shared input. On 1,254 FutureX-Past questions and 340 MIRAI relation queries, DeFuture shows that (1) decoupling measures each phase at almost no cost in accuracy, (2) evidence weighs more than model capability, since a 4B Interpreter and a 27B Predictor match a frontier model given golden evidence, and (3) even frontier Predictors miss over 25% of questions whose golden evidence contains the answer, with extra reasoning helping only at prediction. We release the golden evidence, evidence pools, model outputs, and code.