Poisoning LLM pretraining requires only ~250 malicious documents regardless of dataset or model scale, revealing constant-cost backdoor injection risks for large models.
DRO-NPE trains neural posterior estimators with distributionally robust worst-case losses to reduce overconfidence and improve calibration under limited simulation budgets.
Affine tracing unifies probabilistic linear solvers by showing Bayesian methods are non-stationary affine iterative methods that are calibrated, and automatically generates probabilistic multigrid solvers via symbolic computation graphs.