arXiv:2603.26528v1 Announce Type: new
Abstract: Hyperspectral sensing provides rich spectral information for scene understanding in urban driving, but its high dimensionality poses challenges for interpretation and efficient learning. We introduce Lea…
arXiv:2603.26324v1 Announce Type: cross
Abstract: Most existing approaches to AI in pharmacy collapse three epistemologically distinct operations into a single technical layer: document preservation, semantic interpretation, and contextual presentatio…
arXiv:2603.25051v2 Announce Type: replace
Abstract: This study presents a computational analysis of the Slovene historical newspapers \textit{Slovenec} and \textit{Slovenski narod} from the sPeriodika corpus, combining topic modelling, large language …
arXiv:2601.13222v2 Announce Type: replace-cross
Abstract: RAGE systems integrate ideas from automatic evaluation (E) into Retrieval-augmented Generation (RAG). As one such example, we present Crucible, a Nugget-Augmented Generation System that preserv…
arXiv:2510.02898v4 Announce Type: replace
Abstract: Zero-shot captioners are recently proposed models that utilize common-space vision-language representations to caption images without relying on paired image-text data. To caption an image, they proc…
arXiv:2603.26359v1 Announce Type: cross
Abstract: Designing quantum algorithms is a complex and counterintuitive task, making it an ideal candidate for AI-driven algorithm discovery. To this end, we employ the Hive, an AI platform for program synthesi…
arXiv:2602.13151v2 Announce Type: replace-cross
Abstract: Large Language Model (LLM) unlearning aims to remove targeted knowledge from a trained model, but practical deployments often require post-training quantization (PTQ) for efficient inference. H…
arXiv:2603.26164v1 Announce Type: cross
Abstract: Data-centric training has emerged as a promising direction for improving large language models (LLMs) by optimizing not only model parameters but also the selection, composition, and weighting of train…
arXiv:2603.26389v1 Announce Type: new
Abstract: The Triplet Margin Ranking Loss is one of the most widely used loss functions in Siamese Networks for solving Distance Metric Learning (DML) problems. This loss function depends on a margin parameter {\m…
arXiv:2511.00255v2 Announce Type: replace
Abstract: In entomology and ecology research, biologists often need to collect a large number of insects, among which beetles are the most common species. A common practice for biologists to organize beetles i…