MedFlow: Class-Aware Multi-Scale Generation for Medical Time-Series Synthesis

arXiv:2605.04180v1 Announce Type: cross Abstract: Large Language Models exhibit strong reasoning and semantic understanding capabilities but often hallucinate in domains that

arXiv:2605.13046v1 Announce Type: new Abstract: Mental health disorders affect millions worldwide, and healthcare systems are increasingly overwhelmed by the volume of clinical

arXiv:2604.05364v1 Announce Type: new Abstract: We introduce TFRBench, the first benchmark designed to evaluate the reasoning capabilities of forecasting systems. Traditionally,

arXiv:2605.04071v1 Announce Type: cross Abstract: Autoregressive models can predict clinical events, but generating patient-conditioned multi-step trajectories that respond to

arXiv:2606.15038v1 Announce Type: new Abstract: Accurate time-to-event (TTE) prediction from multimodal clinical data remains challenging due to modality imbalance and

arXiv:2603.22294v1 Announce Type: cross Abstract: Synthetic Data Generation (SDG), leveraging Large Language Models (LLMs), has recently been recognized and broadly adopted as an