11 citations · 17 across the 9 of their papers we have counts for
8 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…
Four Over Six: More Accurate NVFP4 Quantization with Adaptive Block Scaling
Jack Cook, Junxian Guo, Guangxuan Xiao +7
As large language models have grown larger, interest has grown in low-precision numerical formats such as NVFP4 as a way to improve speed and reduce memory usage. However, quantizi…
Taming the Long-Tail: Efficient Reasoning RL Training with Adaptive Drafter
Qinghao Hu, Shang Yang, Junxian Guo +7
The emergence of Large Language Models (LLMs) with strong reasoning capabilities marks a significant milestone, unlocking new frontiers in complex problem-solving. However, trainin…
SparseVILA: Decoupling Visual Sparsity for Efficient VLM Inference
Samir Khaki, Junxian Guo, Jiaming Tang +6
Vision Language Models (VLMs) have rapidly advanced in integrating visual and textual reasoning, powering applications across high-resolution image understanding, long-video analys…
SparseLoRA: Accelerating LLM Fine-Tuning with Contextual Sparsity
Samir Khaki, Xiuyu Li, Junxian Guo +7
Fine-tuning LLMs is both computationally and memory-intensive. While parameter-efficient fine-tuning methods, such as QLoRA and DoRA, reduce the number of trainable parameters and…
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…
VQ-LLM: High-performance Code Generation for Vector Quantization Augmented LLM Inference
Zihan Liu, Xinhao Luo, Junxian Guo +11
In this work, we design and implement VQ-LLM, an efficient fused Vector Quantization (VQ) kernel generation framework. We first introduce a software abstraction called codebook cac…
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…