NICER reformulates whole-slide image condensation as nonparametric distribution matching, improving self-supervised learning accuracy by 7.44% over heuristic methods.
Gated attention represents attention matrices as hierarchical mixtures of experts and achieves polynomial sample complexity versus exponential for multi-head self-attention.
GS-Power-UCT shares same-depth states in stochastic planning graphs to reuse samples while maintaining O(n^{-1/2}) convergence, with adaptive-horizon variants achieving optimal infinite-horizon values.
BSO recasts safety alignment as density ratio matching via Bregman divergence minimization, yielding a single-stage loss that improves the safety-helpfulness trade-off without auxiliary models.
Kernelized Activation Steering lifts activation steering into a reproducing kernel Hilbert space to induce locally adaptive, geometry-aware steering via implicit kernel evaluations, recovering Difference-in-Means as a linear special case and outperforming standard methods on LLM and image control ta