36 citations · 44 across the 11 of their papers we have counts for
12 papers
SparDA: Sparse Decoupled Attention for Efficient Long-Context LLM Inference
Yaosheng Fu, Guangxuan Xiao, Xin Dong +2
Sparse attention reduces compute and memory bandwidth for long-context LLM inference. However, two key challenges remain: (1) KV cache capacity still grows with sequence length, an…
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…
DuoAttention: Efficient Long-Context LLM Inference with Retrieval and Streaming Heads
Guangxuan Xiao, Jiaming Tang, Jingwei Zuo +5
Deploying long-context large language models (LLMs) is essential but poses significant computational and memory challenges. Caching all Key and Value (KV) states across all attenti…
Quest: Query-Aware Sparsity for Efficient Long-Context LLM Inference
Jiaming Tang, Yilong Zhao, Kan Zhu +3
As the demand for long-context large language models (LLMs) increases, models with context windows of up to 128K or 1M tokens are becoming increasingly prevalent. However, long-con…
BitDelta: Your Fine-Tune May Only Be Worth One Bit
James Liu, Guangxuan Xiao, Kai Li +4
Large Language Models (LLMs) are typically trained in two phases: pre-training on large internet-scale datasets, and fine-tuning for downstream tasks. Given the higher computationa…
InfLLM: Training-Free Long-Context Extrapolation for LLMs with an Efficient Context Memory
Chaojun Xiao, Pengle Zhang, Xu Han +5
Large language models (LLMs) have emerged as a cornerstone in real-world applications with lengthy streaming inputs (e.g., LLM-driven agents). However, existing LLMs, pre-trained o…
Efficient Streaming Language Models with Attention Sinks
Guangxuan Xiao, Yuandong Tian, Beidi Chen +2
Deploying Large Language Models (LLMs) in streaming applications such as multi-round dialogue, where long interactions are expected, is urgently needed but poses two major challeng…