The standard constraint-based paradigm for causal discovery with incomplete data -- impute first, test second -- is frequently miscalibrated: any consistent conditional independence (CI) test rejects a true null with probability approaching 1 when imputation error induces spurious conditional dependence. We introduce PAIR-CI, a nonparametric CI test that restores calibration by integrating multiple imputation directly into the inferential procedure via a paired permutation design. PAIR-CI compares cross-validated models that include and exclude the candidate variable while receiving the same imputed conditioning set, forcing imputation error to cancel in their loss difference rather than contaminate the test statistic. A provably consistent variance estimator jointly accounts for uncertainty arising from cross-validation and multiple imputation -- to our knowledge, the first formal unification of these two inferential frameworks. In simulations, existing imputation-based CI tests exhibit false positive rates of 28--45% when data are missing not at random (MNAR), whereas PAIR-CI averages below the nominal 5% level across data-generating processes and missingness mechanisms. These gains are largest in nonlinear settings and grow with causal graph size: when integrated into the PC algorithm, PAIR-CI reduces structural Hamming distance by 8% on 10-variable nonlinear graphs, 15% on 30-variable equivalents, and up to 44% on the 56-variable HAILFINDER network, with stable performance in all settings.

Membership inference attacks are designed to determine, using black box access to trained models, whether a particular example was used in training or not. Membership inference can be formalized as a hypothesis testing problem. The most effective existing attacks estimate the distribution of some test statistic (usually the model's confidence on the true label) on points that were (and were not) used in training by training many shadow models--i.e.

The Pope's Warnings About AI Were AI-Generated, a Detection Tool Claims Pangram Labs' updated Chrome extension puts warning labels on AI slop as you scroll your social feeds. On Monday, a brand-new Reddit account popped up on the widely read forum r/AmItheAsshole, where users have their personal disputes arbitrated by strangers. This particular user asked if they had crossed a line by "refusing to babysit my stepmother's kids because I have my own job and responsibilities." The post itself was succinct, straightforward, and grammatically clean, explaining a situation in which the person's stepmother and father often expected them to provide childcare on little notice, eventually leading to an argument. "Now there's tension at home, and I'm starting to wonder if I handled it the wrong way," the redditor concluded.

One such filter, the partitioned learned Bloom filter (PLBF), achieves excellent memory efficiency.