新方法MTraining通过分布式动态稀疏注意力，大幅降低超长上下文训练的计算开销。
arXiv:2510.18830v2 Announce Type: replace-cross Abstract: The adoption of long context windows has become a standard feature in Large Language Models …
提出可微分自适应稀疏层次注意力机制，显著提升长序列建模效率与计算可扩展性
arXiv:2605.18753v1 Announce Type: cross Abstract: Current hierarchical attention methods, such as NSA and InfLLMv2, select the top-k relevant key-valu…
突破大模型长上下文推理瓶颈，百步内将全注意力高效转为稀疏，平衡效率与精度。
arXiv:2605.16928v1 Announce Type: new Abstract: Long-context inference in large language models is bottlenecked by the quadratic cost of full attentio…
提出无需重训练的稀疏注意力机制STS，通过推测性token稀疏性突破大模型长序列推理的算力和内存瓶颈。
arXiv:2605.15508v1 Announce Type: new Abstract: The quadratic complexity of attention imposes severe memory and computational bottlenecks on Large Lan…