2 citations · 4 across the 21 of their papers we have counts for
14 papers · 1 filter
CORE: Reducing UI Exposure in Mobile Agents via Collaboration Between Cloud and Local LLMs
Gucongcong Fan, Chaoyue Niu, Chengfei Lyu +2
Mobile agents rely on Large Language Models (LLMs) to plan and execute tasks on smartphone user interfaces (UIs). While cloud-based LLMs achieve high task accuracy, they require up…
Optimizing Storage Overhead of User Behavior Log for ML-embedded Mobile Apps
Chen Gong, Yan Zhuang, Zhenzhe Zheng +4
Machine learning (ML) models are increasingly integrated into modern mobile apps to enable personalized and intelligent services. These models typically rely on rich input features…
TokenFlow: Responsive LLM Text Streaming Serving under Request Burst via Preemptive Scheduling
Junyi Chen, Chuheng Du, Renyuan Liu +6
Real-time LLM interactions demand streamed token generations, where text tokens are progressively generated and delivered to users while balancing two objectives: responsiveness (i…
Nova: Real-Time Agentic Vision-Language Model Serving with Adaptive Cross-Stage Parallelization
Yuhang Xu, Shengzhong Liu, Dong Zhang +3
This paper presents Nova, a real-time scheduling framework for serving agentic vision-language models (VLMs) on a single GPU with balanced per-request latency and overall request p…
ConfClip: Confidence-Weighted and Clipped Reward for Reinforcement Learning in LLMs
Bonan Zhang, Zhongqi Chen, Bowen Song +3
Reinforcement learning (RL) has become a standard paradigm for refining large language models (LLMs) beyond pre-training and instruction tuning. A prominent line of work is RL with…
MNN-AECS: Energy Optimization for LLM Decoding on Mobile Devices via Adaptive Core Selection
Zhengxiang Huang, Chaoyue Niu, Zhaode Wang +8
As the demand for on-device Large Language Model (LLM) inference grows, energy efficiency has become a major concern, especially for battery-limited mobile devices. Our analysis sh…
Pre: Enabling Deterministic Pushdown Automata for Faster Structured LLM Generation
Junyi Chen, Shihao Bai, Zaijun Wang +7
Extensive LLM applications demand efficient structured generations, particularly for LR(1) grammars, to produce outputs in specified formats (e.g., JSON). Existing methods primaril…
RAGRouter: Learning to Route Queries to Multiple Retrieval-Augmented Language Models
Jiarui Zhang, Xiangyu Liu, Yong Hu +3
Retrieval-Augmented Generation (RAG) significantly improves the performance of Large Language Models (LLMs) on knowledge-intensive tasks. However, varying response quality across L…
Automated Privacy Information Annotation in Large Language Model Interactions
Hang Zeng, Xiangyu Liu, Yong Hu +4
Users interacting with large language models (LLMs) under their real identifiers often unknowingly risk disclosing private information. Automatically notifying users whether their…
Responsive DNN Adaptation for Video Analytics against Environment Shift via Hierarchical Mobile-Cloud Collaborations
Maozhe Zhao, Shengzhong Liu, Fan Wu +1
Mobile video analysis systems often encounter various deploying environments, where environment shifts present greater demands for responsiveness in adaptations of deployed "expert…
ABO: Abandon Bayer Filter for Adaptive Edge Offloading in Responsive Augmented Reality
Yongxuan Han, Shengzhong Liu, Fan Wu +1
Bayer-patterned color filter array (CFA) has been the go-to solution for color image sensors. In augmented reality (AR), although color interpolation (i.e., demosaicing) of pre-dem…
Collaborative Learning of On-Device Small Model and Cloud-Based Large Model: Advances and Future Directions
Chaoyue Niu, Yucheng Ding, Junhui Lu +7
The conventional cloud-based large model learning framework is increasingly constrained by latency, cost, personalization, and privacy concerns. In this survey, we explore an emerg…
Efficient Distributed Retrieval-Augmented Generation for Enhancing Language Model Performance
Shangyu Liu, Zhenzhe Zheng, Xiaoyao Huang +3
Small language models (SLMs) support efficient deployments on resource-constrained edge devices, but their limited capacity compromises inference performance. Retrieval-augmented g…
Personalized Language Model Learning on Text Data Without User Identifiers
Yucheng Ding, Yangwenjian Tan, Xiangyu Liu +6
In many practical natural language applications, user data are highly sensitive, requiring anonymous uploads of text data from mobile devices to the cloud without user identifiers.…