Calibration, confidence, and knowing how much to trust a model.
A portfolio can look safe on a return basis and still become extraordinarily expensive to exit. Illiquidity-at-Risk gives that danger its own tail-risk number — and finds it spikes in ways continuous models miss entirely.
arXiv:2609.00943
The standard ways we grade a model's confidence can be not just unhelpful but actively misleading — and the fix is to stop grading the forecast and start grading the decision it enables.
arXiv:2606.26990
Traditional weather uncertainty methods claim ninety percent confidence on rainfall but catch the truth only a third of the time. A distribution-free fix lands where forecasting begins.
arXiv:2606.27001
Prediction is turning into a commodity. The value is migrating to everything around it — calibration, execution, reusable representations, and the decision a forecast is actually meant to serve.
6 papers
AI weather models are brilliant on average and quietly overconfident at the tails. A cheap, distribution-free correction makes their stated uncertainty match reality.
arXiv:2606.19642