arXiv:2603.16513v2 Announce Type: replace Abstract: Structured data is foundational to healthcare, finance, e-commerce, and scientific data management. Large structured-data models (LDMs) extend the foundation model paradigm to unify heterogeneous datasets for tasks such as classification, regression, and decision support. However,…
arXiv:2603.19994v1 Announce Type: cross Abstract: Deep learning models often struggle under natural distribution shifts, a common challenge in real-world deployments. Test-Time Adaptation (TTA) addresses this by adapting models during inference without labeled source data. We present the first evaluation of…
arXiv:2603.19299v1 Announce Type: new Abstract: In recent years, progress in medical informatics and machine learning has been accelerated by the availability of openly accessible benchmark datasets. However, patient-level electronic medical record (EMR) data are rarely available for teaching or methodological…
arXiv:2603.19302v1 Announce Type: new Abstract: Machine unlearning is increasingly important for clinical language models, where privacy regulations and institutional policies may require removing sensitive information from deployed systems without retraining from scratch. In practice, deletion requests must balance effective forgetting…
arXiv:2603.19297v1 Announce Type: new Abstract: The static knowledge representations of large language models (LLMs) inevitably become outdated or incorrect over time. While model-editing techniques offer a promising solution by modifying a model’s factual associations, they often produce unpredictable ripple effects,…
arXiv:2603.19298v1 Announce Type: new Abstract: Operator situation awareness is a pivotal yet elusive determinant of human reliability in complex nuclear control environments. Existing assessment methods, such as SAGAT and SART, remain static, retrospective, and detached from the evolving cognitive dynamics…
arXiv:2603.19296v1 Announce Type: new Abstract: To tackle the huge computational demand of large foundation models, activation-aware compression techniques without retraining have been introduced. However, since these methods highly rely on calibration data, domain shift issues may arise for unseen downstream…
arXiv:2603.19295v1 Announce Type: new Abstract: Mental disorder populations exhibit pronounced heterogeneity — that is, the significant differences between samples — poses a significant challenge to the definition of positive pairs in contrastive learning. To address this, we propose a subtype-guided…
arXiv:2603.19294v1 Announce Type: new Abstract: While post-training has successfully improved large language models (LLMs) across a variety of domains, these gains heavily rely on human-labeled data or external verifiers. Existing data has already been exploited, and new high-quality data is…
arXiv:2603.19687v1 Announce Type: cross Abstract: Modern generative modelling systems are increasingly improved by expanding model capacity, training data, and computational resources. While empirical studies have documented such scaling behaviour across architectures including generative adversarial networks, variational autoencoders, transformer-based models, and…
