SynBench benchmarks differentially private text generators across standardized datasets, revealing quality drops on out-of-distribution private data and invalidated privacy guarantees from pre-training contamination.
PrismFlow uses Koopman-inspired dynamical experts with a confidence-aware winner-take-all objective to learn residual flow corrections that recover fine-grained temporal dynamics and mitigate spectral contraction in flow matching.
CLP-DD distills synthetic datasets for frozen-feature linear probing via a closed-form kernel ridge solver, achieving near-state-of-the-art accuracy with roughly 14x faster training and far lower memory.
DECEIVE-AFC is an adversarial framework that attacks search-enabled LLM fact-checking by perturbing claims to disrupt retrieval and reasoning, reducing accuracy from 78.7% to 53.7%.
Attention transfer fails for four ViT families due to architectural mismatch, and adding the teacher's native components to students fully restores its effectiveness.
Slowly Annealed Langevin Dynamics tracks moving targets via time slowdown with non-asymptotic convergence guarantees, and velocity-aware extension enables training-free guided diffusion generation with convergence theory.