AI Future Leakage: The Silent Flaw Breaking How We Test Whether Machines Can Predict the Future

Northwestern University researchers have demonstrated that the standard method for detecting training-data contamination in AI forecasting benchmarks is mathematically flawed, after a test they built falsely accused four of five flagship AI models of cheating. The test used questions resolved only after each model's training cutoff, meaning the answers could not have been memorized, yet the conventional contamination check still flagged the models as contaminated.