2 citations · 4 across the 13 of their papers we have counts for
7 papers · 1 filter
Prism Attention: Proposal-Refined Index Sharing Mechanism for Efficient LLMs Inference
Zhenxu Tian, Kebin Liu, Zhengwu Yang +8
Long-context decoding in large language models (LLMs) is increasingly bottlenecked by the memory and computation required to attend over an ever-growing Key-Value (KV) cache at eac…
Virtual Head Attention
Xibo Ding, Guoxia Wang, Jinle Zeng +4
The design of attention mechanisms in long-context large language models faces an ``impossible triangle'' among KV cache size, training and prefill FLOPs, and model quality. GQA ac…
Autonomy-of-Heads: Data-Free Sparse Attention from Frozen Query-Key Geometry
Yehan Yang, Junyuan Shang, Yang Li +3
Long-context LLM inference is bottlenecked by quadratic attention computation and growing KV-cache costs. Existing sparse attention and KV-compression methods typically decide whic…
ConSA: Controllable Sparsity in Hybrid Attention via Learnable Allocation
Yao Chen, Yinqi Yang, Junyuan Shang +6
Hybrid architectures combining full attention (FA) and sliding-window attention (SWA) are a promising paradigm for efficient LLM inference. However, existing methods typically rely…
RRAttention: Dynamic Block Sparse Attention via Per-Head Round-Robin Shifts for Long-Context Inference
Siran Liu, Guoxia Wang, Sa Wang +7
The quadratic complexity of attention mechanisms poses a critical bottleneck for large language models processing long contexts. While dynamic sparse attention methods offer input-…
ERNIE 5.0 Technical Report
Haifeng Wang, Hua Wu, Tian Wu +432
In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio…
NACL: A General and Effective KV Cache Eviction Framework for LLMs at Inference Time
Yilong Chen, Guoxia Wang, Junyuan Shang +7
Large Language Models (LLMs) have ignited an innovative surge of AI applications, marking a new era of exciting possibilities equipped with extended context windows. However, hosti…