Can the models that write the news also tell you what happens next — and say how sure they are? We took the 45 June 2026 events Pollar covered most broadly at birth, had a drafting model write binary forecasting questions about how each story would develop, struck the weak ones in a logged human vetting pass, and asked four models — Claude Fable 5, Pollar's production writer, Kimi K2.6, and GLM-4.6 — to put probabilities on the 28 surviving questions from 22 events, seen exactly as the stories stood at birth and again at each story's midpoint. Every question was resolved by a forecast-blind resolver; the 20 close-call or provisional-VOID cases received a context-isolated second blind review by another instance of the same model family. The headline: on these 28 retrospective questions, the preregistered paired mean Brier difference, Fable 5 minus the production writer at the birth snapshot, was −0.154 (95% CI [−0.254, −0.072], event-clustered bootstrap; negative favors Fable). Separately and descriptively, the production writer's mean Brier score was 0.276 versus 0.250 for the analytic 0.5 reference. Everything beyond the single preregistered contrast — calibration curves, Murphy components, the open-model table, update behavior — is descriptive, and labeled so. This estimate describes one preregistered paired contrast on 28 outcome-aware retrospective questions from 22 events; it is not an estimate of prospective forecasting performance. The preregistration survived six external audit rounds; 27 deviations are logged; every raw artifact is linked below.
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