February 2026 focused on delivering deterministic behavior in model training and reinforcing runtime reliability for production deployments. Key work included standardizing random state usage in the Random Forest implementation for tabpfn-extensions and eliminating race conditions in model downloads through a file locking mechanism in TabPFN, with accompanying tests.