arXiv:2502.17011v2 Announce Type: replace-cross
Abstract: Financial bond yield forecasting is challenging due to data scarcity, nonlinear macroeconomic dependencies, and evolving market conditions. In this paper, we propose a novel framework that leve…
arXiv:2604.21993v1 Announce Type: new
Abstract: We study the detection of transient liquidity erosion (“crumbling quotes”) in electronic limit order books, where observable quote deterioration may reflect either mechanical liquidity withdrawal or info…
arXiv:2505.17639v3 Announce Type: replace
Abstract: Mixture-of-Experts (MoE) models offer dynamic computation, but are typically deployed as static full-capacity models, missing opportunities for deployment-specific specialization. We introduce PreMoE…
arXiv:2511.21777v3 Announce Type: replace
Abstract: Methane is a potent greenhouse gas, responsible for roughly 30% of warming since pre-industrial times. A small number of large point sources account for a disproportionate share of emissions, creatin…
arXiv:2604.22579v1 Announce Type: cross
Abstract: We study whether deep networks for medical imaging learn useful nonrobust features – predictive input patterns that are not human interpretable and highly susceptible to small adversarial perturbations…
arXiv:2509.20979v2 Announce Type: replace
Abstract: In modern GPU inference, cache efficiency remains a major bottleneck, and heuristic policies such as \textsc{LRU} can perform far worse than the offline optimum. Existing learning-based caching syste…
arXiv:2510.12328v5 Announce Type: replace
Abstract: Accurate rainfall forecasting, particularly for extreme events, remains a significant challenge in climatology and the Earth system. This paper presents novel physics-informed Graph Neural Networks (…
arXiv:2604.22348v1 Announce Type: new
Abstract: Clinical risk prediction using longitudinal medical data supports individualized care. Self-supervised foundation models have emerged as a promising approach for leveraging large-scale unlabeled healthca…
arXiv:2604.22240v1 Announce Type: new
Abstract: Generative world models increasingly rely on 4D occupancy for realistic autonomous driving simulation. However, existing generation frameworks depend on rigid geometric conditions (e.g., explicit traject…
arXiv:2504.06148v3 Announce Type: replace
Abstract: Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in visual-text processing. However, existing static image-text benchmarks are insufficient fo…