Reproducible H100 benchmark of eager PyTorch, vLLM 0.17.1 and TensorRT-LLM on the autoregressive backbone of a multi-stage TTS model. vLLM reaches about 453 tok/s against 213 for TensorRT-LLM and 21 for eager, but the LLM is only about 21% of end-to-end latency; the flow-matching stage takes about 68%, which these engines do not cover.

A diagnosis of Direct Preference Optimization on a continuous-latent flow-matching TTS model. The automatic-metric reward consistently prefers the candidate the model finds less likely, so standard DPO pushes the model the wrong way and quality degrades. Shown with a held-out benchmark, a training-free mechanistic probe and a cross-architecture replication, followed by the alignment routes that do work.

Extracts the gyral net — the network of gyral crests whose three-way junctions are 3-hinge gyri — from cortical surfaces. Reads VTK surface meshes with sulcal depth, isolates gyral regions, skeletonizes them with shortest paths and repairs missing crest links; run on HCP 32k surfaces.

Can open, locally run LLMs remove protected health information from clinical notes? Compares LLaMA 2 7B, Falcon-7B and Flan-T5 under explicit and implicit prompts, plus fine-tuned variants, and scores how much PHI each leaves behind.