IBM and NASA open-source the NASA-IBM Lunar Foundation Model, a foundation model trained on decades of multi-instrument lunar data, paired with a unified 30-layer dataset. It cuts ice-deposit identification error by up to 22% and joins the Prithvi family.

On September 9, 2026, IBM released Granite Time Series PatchTST-FM-r2, a 385M-parameter model that becomes the best zero-shot forecaster shipped under a permissive license on the GIFT-Eval benchmark, ahead of several larger models. For any team doing demand, load or telemetry forecasting, it is a production-ready zero-shot starting point.