arXiv:2511.06696v2 Announce Type: replace Abstract: Pretrained equivariant graph neural networks based on spherical harmonics offer efficient and accurate alternatives to computationally expensive ab-initio methods, yet adapting them to new tasks and chemical environments still requires fine-tuning. Conventional parameter-efficient fine-tuning (PEFT)…
arXiv:2512.10258v1 Announce Type: new Abstract: Multi-output Gaussian process (MGP) models have attracted significant attention for their flexibility and uncertainty-quantification capabilities, and have been widely adopted in multi-source transfer learning scenarios due to their ability to capture inter-task correlations. However, they…
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…
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:…
