3 citations · 3 across the 12 of their papers we have counts for
12 papers
A Unified Framework for Context-Aware and Relation-Aware Graph Retrieval-Augmented Generation
Haoyang Zhong, Yifei Sun, Antong Zhang +3
Retrieval-Augmented Generation (RAG) has emerged as a paradigm for enhancing large language models (LLMs) with external knowledge, yet existing graph-based methods face a fundament…
QCFuse: Query-Aware Cache Fusion via Compressed View for Efficient RAG Serving
Jianxin Yan, Wangze Ni, Zhenxin Li +8
Retrieval-augmented generation (RAG) improves large language model (LLM) answer quality by grounding generation in external evidence, but processing retrieved contexts makes the pr…
Toward Autonomous Long-Horizon Engineering for ML Research
Guoxin Chen, Jie Chen, Lei Chen +7
Agentic systems increasingly automate pieces of AI research. Yet turning underspecified research objectives into runnable, experimentally validated ML systems remains a central bot…
ECHO: Elastic Speculative Decoding with Sparse Gating for High-Concurrency Scenarios
Xinyi Hu, Yuhao Shen, Baolin Zhang +6
Speculative Decoding promises to accelerate the inference of Large Language Models, yet its efficacy often degrades in production-grade serving. Existing evaluations typically over…
GRACE: A Dynamic Coreset Selection Framework for Large Language Model Optimization
Tianhao Tang, Haoyang Li, Lei Chen
Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation. However, their immense number of parameters and complex tra…
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…
QCFuse: Query-Centric Cache Fusion for Efficient RAG Inference
Jianxin Yan, Zeheng Qian, Wangze Ni +6
Cache fusion accelerates generation process of LLMs equipped with RAG through KV caching and selective token recomputation, thereby reducing computational costs and improving effic…
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…