arXiv:2306.01270v2 Announce Type: replace-cross Abstract: Multi-robot path finding in dynamic environments is a highly challenging classic problem. In the movement process, robots need to avoid collisions with other moving robots while minimizing their travel distance. Previous methods for this problem…
arXiv:2512.10287v1 Announce Type: new Abstract: Surrogate models provide fast alternatives to costly aerodynamic simulations and are extremely useful in design and optimization applications. This study proposes the use of a recent kernel-based neural surrogate, KHRONOS. In this work, we blend…
arXiv:2412.12074v2 Announce Type: replace-cross Abstract: Generative networks are an exciting tool for fast LHC event fixed number of particles. Autoregressive transformers allow us to generate events containing variable numbers of particles, very much in line with the physics of QCD…
arXiv:2512.10308v1 Announce Type: new Abstract: Background. Treatment selection for low to intermediate risk patients with severe aortic stenosis between surgical (SAVR) and transcatheter (TAVR) aortic valve replacement remains variable in clinical practice, driven by patient heterogeneity and institutional preferences. While…
The technique can help scientists in economics, public health, and other fields understand whether to trust the results of their experiments.
arXiv:2512.10671v1 Announce Type: cross Abstract: Early-exit networks are effective solutions for reducing the overall energy consumption and latency of deep learning models by adjusting computation based on the complexity of input data. By incorporating intermediate exit branches into the architecture,…
arXiv:2508.02602v2 Announce Type: replace-cross Abstract: Generative artificial intelligence (AI) excels at producing complex data structures (text, images, videos) by learning patterns from training examples. Across scientific disciplines, researchers are now applying generative models to “inverse problems” to directly predict hidden…
arXiv:2512.09015v2 Announce Type: replace-cross Abstract: Frontier language model quality increasingly hinges on our ability to organize web-scale text corpora for training. Today’s dominant tools trade off speed and flexibility: lexical classifiers (e.g., FastText) are fast but limited to producing classification…
arXiv:2510.08169v3 Announce Type: replace Abstract: Autoregressive (AR) models, common in sequence generation, are limited in many biological tasks such as de novo peptide sequencing and protein modeling by their unidirectional nature, failing to capture crucial global bidirectional token dependencies. Non-Autoregressive…
arXiv:2411.14013v4 Announce Type: replace-cross Abstract: As speech generation technologies advance, so do risks of impersonation, misinformation, and spoofing. We present a lightweight, training-free approach for detecting synthetic speech and attributing it to its source model. Our method addresses three tasks:…
