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arXiv:2603.15696v1 Announce Type: new Abstract: Hypergraph neural networks (HGNNs) have demonstrated strong capabilities in modeling complex higher-order relationships. However, existing HGNNs often suffer from over-smoothing …
arXiv:2603.15708v1 Announce Type: new Abstract: Imbalanced data distribution remains a critical challenge in sequential learning, leading models to easily recognize frequent categories while failing to …
arXiv:2603.15713v1 Announce Type: new Abstract: Industrial financial systems operate on temporal event sequences such as transactions, user actions, and system logs. While recent research emphasizes …
arXiv:2603.15724v1 Announce Type: new Abstract: Existing test-time scaling (TTS) methods for unified multimodal models (UMMs) in text-to-image (T2I) generation primarily rely on search or sampling …
arXiv:2603.15797v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated exceptional logical reasoning capabilities but frequently struggle with the continuous spatiotemporal dynamics governed by …
arXiv:2603.15803v1 Announce Type: new Abstract: Discrete diffusion models offer global context awareness and flexible parallel generation. However, uniform random noise schedulers in standard DLLM training …
arXiv:2603.15814v1 Announce Type: new Abstract: Breast cancer remains a leading cause of cancer-related mortality worldwide. Longitudinal mammography risk prediction models improve multi-year breast cancer risk …
arXiv:2603.15821v1 Announce Type: new Abstract: The assumption that prediction-equivalent models produce equivalent explanations underlies many practices in explainable AI, including model selection, auditing, and regulatory …
arXiv:2603.15840v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as decision-support tools in data-constrained scientific workflows, where correctness and validity are critical. …
arXiv:2603.15842v1 Announce Type: new Abstract: Modern machine learning systems increasingly rely on sensitive data, creating significant privacy, security, and regulatory risks that existing privacy-preserving machine …
arXiv:2603.15854v1 Announce Type: new Abstract: Sampling from a categorical distribution is mathematically simple, but in large-vocabulary decoding, it often triggers extra memory traffic and extra …
arXiv:2603.15867v1 Announce Type: new Abstract: The massive use of Machine Learning (ML) tools in industry comes with critical challenges, such as the lack of explainable …