arXiv:2509.25210v1 Announce Type: new Abstract: To gain finer regional forecasts, many works have explored the regional integration from the global atmosphere, e.g., by solving boundary equations in physics-based methods or cropping regions from global forecasts in data-driven methods. However, the…
arXiv:2509.25211v1 Announce Type: new Abstract: This paper introduces Large Execution Models (LEMs), a novel deep learning framework that extends transformer-based architectures to address complex execution problems with flexible time boundaries and multiple execution constraints. Building upon recent advances in neural…
arXiv:2509.25207v1 Announce Type: new Abstract: Recent advancements in large language models (LLMs) have shown promise in feature engineering for tabular data, but concerns about their reliability persist, especially due to variability in generated outputs. We introduce a multi-level diagnosis and…
arXiv:2509.25208v1 Announce Type: new Abstract: Accurate and timely forecasting of heavy rainfall remains a critical challenge for modern society. Precipitation exhibits a highly imbalanced distribution: most observations record no or light rain, while heavy rainfall events are rare. Such an…
arXiv:2509.25206v1 Announce Type: new Abstract: This work explores optimization methods on hyperbolic manifolds. Building on Riemannian optimization principles, we extend the Hyperbolic Stochastic Gradient Descent (a specialization of Riemannian SGD) to a Hyperbolic Adam optimizer. While these methods are particularly…
arXiv:2509.25205v1 Announce Type: new Abstract: Self-supervised learning (SSL) has emerged as a powerful paradigm for learning representations on graph data without requiring manual labels. However, leading SSL methods like GRACE are fundamentally incompatible with privacy-preserving technologies such as Homomorphic Encryption…
arXiv:2509.25204v1 Announce Type: new Abstract: Entropy-based inference methods have gained traction for improving the reliability of Large Language Models (LLMs). However, many existing approaches, such as entropy minimization techniques, suffer from high computational overhead and fail to leverage historical token…
arXiv:2509.25926v1 Announce Type: cross Abstract: Large language models (LLMs) have become increasingly popular due to their ability to interact with unstructured content. As such, LLMs are now a key driver behind the automation of language processing systems, such as AI…
arXiv:2509.25213v1 Announce Type: new Abstract: The optimization of hyperparameters in convolutional neural networks (CNNs) remains a challenging and computationally expensive process, often requiring extensive trial-and-error approaches or exhaustive grid searches. This study introduces the application of Taguchi Design of Experiments…
arXiv:2509.26177v1 Announce Type: cross Abstract: Speaker diarization is the task of partitioning audio into segments according to speaker identity, answering the question of “who spoke when” in multi-speaker conversation recordings. While diarization is an essential task for many downstream applications,…
