Long-Context Modeling with Dynamic Hierarchical Sparse Attention for Memory-Constrained LLM Inference 事件

Long-Context Modeling with Dynamic Hierarchical Sparse Attention for Memory-Constrained LLM Inference arXiv:2510.24606v2 Announce Type: replace Abstract: The quadratic cost of attention limits the scalability of long-context LLMs, especially under limited hardware memory budgets. While attention is often sparse, existing static sparse methods cannot adapt to task- or input-dependent variations, and recent dynamic approaches rely on predefined templates or heuristics that may sacrifice generalit