arXiv:2508.02473v3 Announce Type: replace-cross
Abstract: Code editing is a frequent yet cognitively demanding task in software development. Existing AI-powered tools often disrupt developer flow by requiring explicit natural language instructions and…
arXiv:2604.00835v1 Announce Type: new
Abstract: Large language models are increasingly being deployed as autonomous agents yet their real world effectiveness depends on reliable tools for information retrieval, computation and external action. Existin…
arXiv:2604.00892v1 Announce Type: new
Abstract: As LLM agents transition from short, static problem solving to executing complex, long-horizon tasks in dynamic environments, the ability to handle user interruptions, such as adding requirement or revis…
arXiv:2601.09504v2 Announce Type: replace
Abstract: Scientific surveys require not only summarizing large bodies of literature, but also organizing them into clear and coherent conceptual structures. However, existing automatic survey generation metho…
arXiv:2603.10305v2 Announce Type: replace
Abstract: Machine learning models can represent climate processes that are nonlocal in horizontal space, height, and time, often by combining information across these dimensions in highly nonlinear ways. While…
arXiv:2603.22455v4 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:2604.00205v1 Announce Type: new
Abstract: This work introduces an unsupervised Divergence and Aliasing-Free neural network (DAF-FlowNet) for 4D Flow Magnetic Resonance Imaging (4D Flow MRI) that jointly enhances noisy velocity fields and correct…
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: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…