36 citations · 44 across the 11 of their papers we have counts for
6 papers · 1 filter
Optimizing Mixture of Block Attention
Guangxuan Xiao, Junxian Guo, Kasra Mazaheri +1
Mixture of Block Attention (MoBA) (Lu et al., 2025) is a promising building block for efficiently processing long contexts in LLMs by enabling queries to sparsely attend to a small…
BLASST: Dynamic BLocked Attention Sparsity via Softmax Thresholding
Jiayi Yuan, Cameron Shinn, Kai Xu +19
The growing demand for long-context inference capabilities in Large Language Models (LLMs) has intensified the computational and memory bottlenecks inherent to the self-attention m…
StreamingVLM: Real-Time Understanding for Infinite Video Streams
Ruyi Xu, Guangxuan Xiao, Yukang Chen +3
Vision-language models (VLMs) could power real-time assistants and autonomous agents, but they face a critical challenge: understanding near-infinite video streams without escalati…
XAttention: Block Sparse Attention with Antidiagonal Scoring
Ruyi Xu, Guangxuan Xiao, Haofeng Huang +2
Long-Context Transformer Models (LCTMs) are vital for real-world applications but suffer high computational costs due to attention's quadratic complexity. Block-sparse attention mi…
LServe: Efficient Long-sequence LLM Serving with Unified Sparse Attention
Shang Yang, Junxian Guo, Haotian Tang +7
Large language models (LLMs) have shown remarkable potential in processing long sequences and complex reasoning tasks, yet efficiently serving these models remains challenging due…
Twilight: Adaptive Attention Sparsity with Hierarchical Top- Pruning
Chaofan Lin, Jiaming Tang, Shuo Yang +6
Leveraging attention sparsity to accelerate long-context large language models (LLMs) has been a hot research topic. However, current algorithms such as sparse attention or key-val…