PRO closes the indexing-decoding gap in multimodal generative retrieval via prefix ranking distillation, vocabulary scheduling, and geometric score fusion to improve beam search retention and retrieval accuracy.
Flow matching enables controllable generation via reference-guided mean shifts without fine-tuning, yielding training-free control and swappable semi-parametric guidance.
Post-hoc confidence remasking in masked diffusion language models offers little benefit under standard decoding and worsens diversity collapse under stochastic sampling, showing setting-dependent gains.
Kernel-gradient drifting replaces Euclidean drift with kernel-induced directions, yielding identifiable one-step generative models extending to manifolds and discrete data via Fisher-Rao geometry.
HypCBM grounds concept bottlenecks in hyperbolic space via asymmetric geometric containment to yield sparse, hierarchy-aware activations and coherent interventions without extra supervision.