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…
Securing Retrieval-Augmented Generation: A Taxonomy of Attacks, Defenses, and Future Directions
Yuming Xu, Mingtao Zhang, Zhuohan Ge +7
Retrieval-augmented generation (RAG) extends large language models (LLMs) with external knowledge, but this access path also introduces security risks that existing work often conf…
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…