3 citations · 3 across the 9 of their papers we have counts for
9 papers
PolyUQuest: Verifiable Structure-Aware Web RAG over Heterogeneous Graphs
Ying Liu, Yi Ye, Quanyu Feng +5
Existing retrieval-augmented generation (RAG) systems treat web pages as flat text, losing the structural and semantic signals encoded in HTML. We present PolyUQuest, a verifiable,…
From Rigid to Dynamic: Entropy-Guided Adaptive Inference for Long-Context LLMs
Zhanchao Xu, Haoyang Li, Qingfa Xiao +4
Existing sparse attention and KV cache compression methods for long-context LLM inference typically apply fixed sparsity patterns or uniform budgets across all attention heads, ove…
A Survey of Secure Retrieval-Augmented Generation
Yuming Xu, Mingtao Zhang, Zhuohan Ge +7
Retrieval-augmented generation (RAG) improves large language models (LLMs) with external knowledge, but this access path creates security risks distinct from inherent prompt-only o…
GPU-Accelerated Algorithms for Graph Vector Search: Taxonomy, Empirical Study, and Research Directions
Yaowen Liu, Xuejia Chen, Anxin Tian +7
Approximate Nearest Neighbor Search (ANNS) underpins many large-scale data mining and machine learning applications, with efficient retrieval increasingly hinging on GPU accelerati…
When Speed meets Accuracy: an Efficient and Effective Graph Model for Temporal Link Prediction
Haoyang Li, Yuming Xu, Yiming Li +5
Temporal link prediction in dynamic graphs is a critical task with applications in diverse domains such as social networks, recommendation systems, and e-commerce platforms. While…
LoopServe: An Adaptive Dual-phase LLM Inference Acceleration System for Multi-Turn Dialogues
Haoyang Li, Zhanchao Xu, Yiming Li +9
Multi-turn dialogues are essential in many real-world applications of large language models, such as chatbots and virtual assistants. As conversation histories become longer, exist…
Exposing Numeracy Gaps: A Benchmark to Evaluate Fundamental Numerical Abilities in Large Language Models
Haoyang Li, Xuejia Chen, Zhanchao XU +8
Large Language Models (LLMs) have demonstrated impressive capabilities in natural language processing tasks, such as text generation and semantic understanding. However, their perf…
OpenGLT: A Comprehensive Benchmark of Graph Neural Networks for Graph-Level Tasks
Haoyang Li, Yuming Xu, Alexander Zhou +4
Graphs are fundamental data structures for modeling complex interactions in domains such as social networks, molecular structures, and biological systems. Graph-level tasks, which…
A Survey on Large Language Model Acceleration based on KV Cache Management
Haoyang Li, Yiming Li, Anxin Tian +7
Large Language Models (LLMs) have revolutionized a wide range of domains such as natural language processing, computer vision, and multi-modal tasks due to their ability to compreh…