强化学习新框架“Connect the Dots”让LLM代理实现跨域泛化与长期自学习，迈向更智能的自主决策。
arXiv:2606.20002v1 Announce Type: cross Abstract: This work presents a general framework for training large language models (LLMs) to "Connect the Dot…
医疗图像分割中，跨域自监督方法智能挑选代表性样本，显著降低人工标注成本。
arXiv:2606.04301v1 Announce Type: new Abstract: Acquiring labeled medical image data is resource-intensive and a challenge further exacerbated in cros…
利用未标注目标数据与源域监督，实现稳健的跨域泛化，突破领域差异瓶颈。
arXiv:2605.29122v1 Announce Type: new Abstract: It is often desirable to generalize medical imaging AI models trained with dense annotations to data a…
揭秘视觉基础模型在面部深度伪造检测中的跨域泛化极限，为AI安全提供关键洞察。
arXiv:2605.24965v1 Announce Type: cross Abstract: The rapid evolution of generative models has enabled the creation of hyper-realistic facial deepfake…
ICML 2026新方法：用重要性-多样性数据选择提升Web智能体跨域泛化能力
arXiv:2605.20291v1 Announce Type: new Abstract: Large language models (LLMs) have enabled web agents that follow natural language goals through multi-…