arXiv:2604.00308v1 Announce Type: cross
Abstract: Objective: This study aimed to evaluate which voice features can predict health deterioration in patients with chronic HF.
Background: Heart failure (HF) is a chronic condition with progressive deter…
arXiv:2604.00547v1 Announce Type: new
Abstract: Unified Multimodal Large Models (UMLMs) integrate understanding and generation capabilities within a single architecture. While this architectural unification, driven by the deep fusion of multimodal fea…
arXiv:2604.01193v1 Announce Type: new
Abstract: Can a large language model (LLM) improve at code generation using only its own raw outputs, without a verifier, a teacher model, or reinforcement learning? We answer in the affirmative with simple self-d…
arXiv:2603.28804v1 Announce Type: cross
Abstract: Modern particle physics experiments face an increasing demand for high-fidelity detector simulation as luminosities rise and computational requirements approach the limits of available resources. Deep …
arXiv:2509.16139v4 Announce Type: replace
Abstract: Predicting the extreme hydrodynamic response of porous and architected lattice materials is a fundamental challenge in high energy density physics, where shock-induced pore collapse, baroclinic vorti…
arXiv:2603.29384v1 Announce Type: new
Abstract: Federated Graph Learning (FGL) has emerged as a powerful paradigm for decentralized training of graph neural networks while preserving data privacy. However, existing FGL methods are predominantly design…
arXiv:2603.29432v1 Announce Type: new
Abstract: Medical time-series data captures the dynamic progression of patient conditions, playing a vital role in modern clinical decision support systems. However, real-world clinical data is highly heterogeneou…
arXiv:2603.29654v1 Announce Type: new
Abstract: Aligning human-interpretable concepts with the internal representations learned by modern machine learning systems remains a central challenge for interpretable AI. We introduce a geometric framework for…
arXiv:2603.22455v3 Announce Type: replace
Abstract: Reusable skills let LLM agents package task-specific procedures, tool affordances, and execution guidance into modular building blocks. As skill ecosystems grow to tens of thousands of entries, expos…
arXiv:2602.14975v2 Announce Type: replace-cross
Abstract: Following our previous work (J. Phys. Chem. Lett., 2026, 17, 5, 1288-1295), we propose the DMTS-NC approach, a distilled multi-time-step (DMTS) strategy using non-conservative (NC) forces to fu…