arXiv:2604.00055v1 Announce Type: cross
Abstract: Existing robotic foundation policies are trained primarily via large-scale imitation learning. While such models demonstrate strong capabilities, they often struggle with long-horizon tasks due to dist…
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:2603.11558v3 Announce Type: replace-cross
Abstract: Vision-Language-Action (VLA) systems have shown strong potential for language-driven robotic manipulation. However, scaling them to long-horizon tasks remains challenging. Existing pipelines ty…
arXiv:2604.00175v1 Announce Type: cross
Abstract: Postural stability during movement is fundamental to independent living, fall prevention, and overall health, particularly among older adults who experience age-related declines in balance, muscle stre…
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:2604.01118v1 Announce Type: cross
Abstract: Leveraging the rich semantic features of vision-language models (VLMs) like CLIP for monocular depth estimation tasks is a promising direction, yet often requires extensive fine-tuning or lacks geometr…
arXiv:2603.17205v2 Announce Type: replace-cross
Abstract: Domain-specific finetuning is essential for dense retrievers, yet not all training pairs contribute equally to the learning process. We introduce OPERA, a data pruning framework that exploits t…
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…