arXiv:2512.07848v1 Announce Type: new Abstract: New York City reports over one hundred thousand motor vehicle collisions each year, creating substantial injury and public health burden. We present RaX-Crash, a resource efficient and explainable small model pipeline for structured injury severity…
arXiv:2512.07847v1 Announce Type: new Abstract: Benchmarking has been the cornerstone of progress in computer vision, natural language processing, and the broader deep learning domain, driving algorithmic innovation through standardized datasets and reproducible evaluation protocols. The growing availability of large-scale Computational…
arXiv:2512.08601v1 Announce Type: cross Abstract: Since the 1990s, considerable empirical work has been carried out to train statistical models, such as neural networks (NNs), as learned heuristics for combinatorial optimization (CO) problems. When successful, such an approach eliminates the need…
arXiv:2512.07857v1 Announce Type: new Abstract: We present Graph Foundation Models (GFMs) which have made significant progress in various tasks, but their robustness against domain noise, structural perturbations, and adversarial attacks remains underexplored. A key limitation is the insufficient modeling of…
arXiv:2512.08810v1 Announce Type: cross Abstract: As AI-based code generation becomes widespread, researchers are investigating the calibration of code LLMs – ensuring their confidence scores faithfully represent the true likelihood of code correctness. To do so, we investigate multicalibration, which can…
arXiv:2512.07858v1 Announce Type: new Abstract: Time series classification (TSC) is crucial in numerous real-world applications, such as environmental monitoring, medical diagnosis, and posture recognition. TSC tasks require models to effectively capture discriminative information for accurate class identification. Although deep learning…
arXiv:2410.17506v2 Announce Type: replace Abstract: Distribution shifts between training and testing datasets significantly impair the model performance on graph learning. A commonly-taken causal view in graph invariant learning suggests that stable predictive features of graphs are causally associated with labels,…
arXiv:2512.07863v1 Announce Type: new Abstract: Semi-supervised anomaly detection (AD) has shown great promise by effectively leveraging limited labeled data. However, existing methods are typically structured around scoring individual points or simple pairs. Such {point- or pair-centric} view not only overlooks…
arXiv:2506.07884v2 Announce Type: replace Abstract: We construct four Schauder bases for the space $C[0,1]$, one using ReLU functions, another using Softplus functions, and two more using sigmoidal versions of the ReLU and Softplus functions. This establishes the existence of a…
arXiv:2512.07864v1 Announce Type: new Abstract: New methods are needed to monitor environmental treaties, like the Montreal Protocol, by reviewing large, complex customs datasets. This paper introduces a framework using unsupervised machine learning to systematically detect suspicious trade patterns and highlight…
