LLM领域新突破：神经元感知主动少样本学习，精准挑选标注样本，大幅节省人力成本。
arXiv:2607.02423v1 Announce Type: cross Abstract: Active Few-Shot Learning (AFSL) adapts LLMs to specialized domains by identifying the most valuable …
新方法分解不确定性，让LLM代理主动提问澄清，提升交互可靠性。
arXiv:2606.19559v1 Announce Type: new Abstract: Recent position papers argue that the classical aleatoric/epistemic uncertainty framework is insuffici…
因果感知的错误诊断与交互澄清方法，让语音对话系统主动应对不确定性，提升鲁棒性。
arXiv:2605.25404v1 Announce Type: new Abstract: Cascaded Automatic Speech Recognition -- Large Language Model (ASR-LLM) pipelines remain popular for i…
提出Leveraging Diversity of Least方法，通过平衡不确定性与多样性优化主动学习样本选择，提升模型训练效率
arXiv:2605.22169v1 Announce Type: new Abstract: Deep learning models, including Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), h…
用主动学习算法加速光子晶体设计，Optics Express新研究验证效率提升
arXiv:2601.16287v3 Announce Type: replace-cross Abstract: Active learning for photonic crystals explores the integration of analytic approximate Bayes…
提出力感知神经切线核方法，攻克机器学习力场主动学习在大规模候选池及偏差下的扩展性与鲁棒性难题
arXiv:2605.13788v2 Announce Type: replace Abstract: Active learning for machine-learning interatomic potentials (MLIPs) must address several challenge…