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A comprehensive study of activation-steering-induced emergent misalignment reveals activation steering as a significant yet underexamined source of emergent misalignment and provides an activation-space perspective on emergent misalignment and its safety risks.
TimeNet provides the data and systems foundation needed to move beyond task- and dataset-specific TFMs toward models that can learn jointly across heterogeneous domains, modalities, temporal scales, and forms of supervision from a common data model.
AdaPCLA framework is proposed, which enables generative models to adaptively fit and generate EHR data through a data distribution-aware training strategy; this is achieved by internalizing data knowledge parameters by simulated annealing training.
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