arXiv:2603.25855v1 Announce Type: new
Abstract: Genome-Wide Association Studies (GWAS) identify associations between genetic variants and disease; however, moving beyond associations to causal mechanisms is critical for therapeutic target prioritizati…

arXiv:2603.26017v1 Announce Type: new
Abstract: Time series forecasting is critical across finance, healthcare, and cloud computing, yet progress is constrained by a fundamental bottleneck: the scarcity of large-scale, high-quality benchmarks. To addr…

arXiv:2510.00316v2 Announce Type: replace
Abstract: Identifying wireless modulation schemes is essential for cognitive radio, but standard supervised models often degrade under distribution shift, and training domain-specific wireless foundation model…

arXiv:2603.26177v1 Announce Type: new
Abstract: Recent work has questioned whether large language models (LLMs) can perform genuine in-context learning (ICL) for scientific experimental design, with prior studies suggesting that LLM-based agents exhib…

arXiv:2511.17339v2 Announce Type: replace
Abstract: Prompt learning has emerged as an effective technique for fine-tuning large-scale foundation models for downstream tasks. However, conventional prompt learning methods are prone to overfitting and ca…

arXiv:2603.26249v1 Announce Type: new
Abstract: Transformer-based reinforcement learning has emerged as a strong candidate for sequential control in residential energy management. In particular, the Decision Transformer can learn effective battery dis…

arXiv:2603.26254v1 Announce Type: new
Abstract: Hypertrophic cardiomyopathy (HCM) requires accurate risk stratification to inform decisions regarding ICD therapy and follow-up management. Current established models, such as the European Society of Car…