arXiv:2511.06494v2 Announce Type: replace-cross
Abstract: Mixture-of-Experts (MoE) architectures scale large language models (LLMs) by activating only a subset of experts per token, but the standard TopK routing assigns the same fixed number of expert…
arXiv:2603.26231v1 Announce Type: new
Abstract: Synchronous federated learning scales poorly due to the straggler effect. Asynchronous algorithms increase the update throughput by processing updates upon arrival, but they introduce two fundamental cha…
arXiv:2603.26249v1 Announce Type: new
Abstract: Transformer-based reinforcement learning has emerged as a strong candidate for sequential control in residential energy management. In particular, the Decision Transformer can learn effective battery dis…
arXiv:2603.26254v1 Announce Type: new
Abstract: Hypertrophic cardiomyopathy (HCM) requires accurate risk stratification to inform decisions regarding ICD therapy and follow-up management. Current established models, such as the European Society of Car…
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: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…