11 papers
Retrieval-Augmented Generation for Natural Language Processing: A Survey
Shangyu Wu, Ying Xiong, Yufei Cui +8
Large language models (LLMs) have achieved strong empirical performance in various fields, benefiting from their huge amount of parameters that store knowledge. However, LLMs still…
ClawMobile: Rethinking Smartphone-Native Agentic Systems
Hongchao Du, Shangyu Wu, Qiao Li +4
Smartphones represent a uniquely challenging environment for agentic systems. Unlike cloud or desktop settings, mobile devices combine constrained execution contexts, fragmented co…
RAEE: A Robust Retrieval-Augmented Early Exit Framework for Efficient Inference
Lianming Huang, Shangyu Wu, Yufei Cui +6
Deploying large language model inference remains challenging due to their high computational overhead. Early exit optimizes model inference by adaptively reducing the number of inf…
ReFilter: Improving Robustness of Retrieval-Augmented Generation via Gated Filter
Yixin Chen, Ying Xiong, Shangyu Wu +3
Retrieval-augmented generation (RAG) has become a dominant paradigm for grounding large language models (LLMs) with external evidence in knowledge-intensive question answering. A c…
ContiguousKV: Accelerating LLM Prefill with Granularity-Aligned KV Cache Management
Jing Zou, Shangyu Wu, Hancong Duan +2
Efficiently serving Large Language Models (LLMs) with persistent Prefix Key-Value (KV) Cache is critical for applications like conversational search and multi-turn dialogue. Servin…
AD-EE: Early Exiting for Fast and Reliable Vision-Language Models in Autonomous Driving
Lianming Huang, Haibo Hu, Yufei Cui +4
With the rapid advancement of autonomous driving, deploying Vision-Language Models (VLMs) to enhance perception and decision-making has become increasingly common. However, the rea…