arXiv:2602.11937v2 Announce Type: replace
Abstract: Reasoning-focused LLMs improve answer quality by generating longer reasoning traces, but the additional tokens dramatically increase serving cost, motivating inference optimization. We extend and app…
arXiv:2503.23866v3 Announce Type: replace-cross
Abstract: This paper investigates backdoor attacks in image-oriented semantic communications. The threat of backdoor attacks on symbol reconstruction in semantic communication (SemCom) systems has receiv…
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:2508.02835v2 Announce Type: replace
Abstract: Retrieval-Augmented Generation (RAG) has emerged as a powerful approach to boost the capabilities of large language models (LLMs) by incorporating external, up-to-date knowledge sources. However, thi…
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:2603.26231v1 Announce Type: new
Abstract: Synchronous federated learning scales poorly due to the straggler effect. Asynchronous algorithms increase the update throughput by processing updates upon arrival, but they introduce two fundamental cha…
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…