decision-learning

This paper introduces a statistical framework showing that learning and certifying useful memory decisions can require quadratically fewer records than calibrating source reliability, and derives a finite-sample certificate for memory policy improvements that does not require source reliability recovery. Experiments on Nous Dimensions with 45,000 held-out histories and MiniGrid memory environments show the new certificate outperforms the earlier one, while strong inference baselines remain competitive.