arXiv:2512.07856v1 Announce Type: new Abstract: Accurate disease detection is of paramount importance for effective medical treatment and patient care. However, the process of disease detection is often associated with extensive medical testing and considerable costs, making it impractical to perform…
arXiv:2512.07853v1 Announce Type: new Abstract: As deep learning models in agentic AI systems grow in scale and complexity, GPU memory requirements increase and often exceed the available GPU memory capacity, so that out-of-memory (OoM) errors occur. It is well known…
arXiv:2512.07854v1 Announce Type: new Abstract: Traffic forecasting task is significant to modern urban management. Recently, there is growing attention on large-scale forecasting, as it better reflects the complexity of real-world traffic networks. However, existing models often exhibit quadratic computational complexity,…
arXiv:2512.07850v1 Announce Type: new Abstract: Despite rapid progress in LLM agents, performance on long-horizon, tool-using tasks remains fragile. To better understand this fragility, we ask a simple question: emph{do all actions contribute equally to failure?} Analyzing execution traces on $tau$-Bench…
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…
