arXiv:2604.01113v1 Announce Type: new
Abstract: Large language model (LLM) systems are increasingly used to support high-stakes decision-making, but they typically perform worse when the available evidence is internally inconsistent. Such a scenario e…
arXiv:2601.05144v2 Announce Type: replace
Abstract: Reasoning Large Language Models (RLLMs) excelling in complex tasks present unique challenges for digital watermarking, as existing methods often disrupt logical coherence or incur high computational …
arXiv:2604.00093v1 Announce Type: new
Abstract: Cameras capture scene-referred linear raw images, which are processed by onboard image signal processors (ISPs) into display-referred 8-bit sRGB outputs. Although raw data is more faithful for low-level …
arXiv:2306.14052v2 Announce Type: replace
Abstract: Graph neural networks (GNNs) are emerging for machine learning research on graph-structured data. GNNs achieve state-of-the-art performance on many tasks, but they face scalability challenges when it…
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: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…