arXiv:2511.15743v1 Announce Type: new Abstract: Operational forecasting of the ionosphere remains a critical space weather challenge due to sparse observations, complex coupling across geospatial layers, and a growing need for timely, accurate predictions that support Global Navigation Satellite System (GNSS),…
arXiv:2511.16622v1 Announce Type: cross Abstract: We develop a computational framework for classifying Galois groups of irreducible degree-7 polynomials over~$mathbb{Q}$, combining explicit resolvent methods with machine learning techniques. A database of over one million normalized projective septics is constructed, each annotated…
arXiv:2501.02584v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) have recently experienced significant advancements. However, challenges persist in the accurate recognition of fine details within high resolution images, which limits performance in multiple tasks. This work introduces Pheye, a novel architecture…
arXiv:2506.06941v3 Announce Type: replace-cross Abstract: Recent generations of language models have introduced Large Reasoning Models (LRMs) that generate detailed thinking processes before providing answers. While these models demonstrate improved performance on reasoning benchmarks, their fundamental capabilities, scaling properties, and limitations…
arXiv:2509.23928v2 Announce Type: replace Abstract: Speculative decoding has proven effective for accelerating inference in Large Language Models (LLMs), yet its extension to Vision-Language Models (VLMs) remains limited by the computational burden and semantic inconsistency introduced by visual tokens. Recent studies…
arXiv:2511.13984v2 Announce Type: replace Abstract: We present a practical framework for detecting errors in LLM-generated SQL by estimating uncertainty at the level of individual nodes in the query’s abstract syntax tree (AST). Our approach proceeds in two stages. First, we…
arXiv:2511.15854v1 Announce Type: new Abstract: We present discretize_distributions, a Python package that efficiently constructs discrete approximations of Gaussian mixture distributions and provides guarantees on the approximation error in Wasserstein distance. The package implements state-of-the-art quantization methods for Gaussian mixture models…
arXiv:2511.15856v1 Announce Type: new Abstract: We introduce GLOBE, a new neural surrogate for homogeneous PDEs that draws inductive bias from boundary-element methods and equivariant ML. GLOBE represents solutions as superpositions of learnable Green’s-function-like kernels evaluated from boundary faces to targets,…
arXiv:2511.15838v1 Announce Type: new Abstract: Online conformal prediction (OCP) wraps around any pre-trained predictor to produce prediction sets with coverage guarantees that hold irrespective of temporal dependencies or distribution shifts. However, standard OCP faces two key limitations: it operates in…
arXiv:2511.15847v1 Announce Type: new Abstract: Early identification of intensive care patients at risk of in-hospital mortality enables timely intervention and efficient resource allocation. Despite high predictive performance, existing machine learning approaches lack transparency and robustness, limiting clinical adoption. We present…
