185 citations · 214 across the 25 of their papers we have counts for
30 papers
The Cost of an Unknown Horizon for the Last Iterate of Clipped SGD under Heavy-Tailed Noise
Yuxing Peng, Zhiqing Tang, Weijia Jia
We study the last iterate of clipped SGD, and of every rule that queries its current iterate and steps along the resulting stochastic gradient, when training is stopped at a data-d…
EchoFuzz: Empowering Smart Contract Fuzzing with Large Language Models
Juanen Li, Peng Qian, Guanyan Li +6
Smart contracts, serving as the cornerstone of decentralized applications, autonomously manage trillion-dollar digital assets, making them attractive targets for attacks. Fuzzing h…
Dense Weak Hiding: Closing Complexity Gaps in Nonconvex and PL Finite-Sum Optimization under Individual Smoothness
Yuxing Peng, Zhiqing Tang, Weijia Jia
Under individual smoothness, known algorithms for nonconvex finite-sum optimization use incremental first-order oracle (IFO) calls, while exis…
FeatFix: Reuse What You Verify through Local Exact-Feature Correction for Faster Cached Diffusion Inference
Hanshuai Cui, Zhiqing Tang, Zhi Yao +3
Diffusion models are widely used to generate high-quality images and videos, but their iterative denoising process remains computationally intensive. A growing class of training-fr…
MemTxn: A Transaction Boundary for Source-Supported Updates and Complete-State Recovery in Agent Memory
Hanshuai Cui, Zhiqing Tang, Zhi Yao +3
Persistent memory lets long-running large language model agents reuse information across sessions and tasks. Yet errors in writable memory can persist and corrupt future behavior.…
LASER: Load-Aware Serving with Early-Exit for Reasoning LLMs at the Edge
Zhiqing Tang, Size Li, Hanshuai Cui +5
Large reasoning models (LRMs) such as DeepSeek-R1 have achieved strong performance through extended chain-of-thought (CoT) generation. However, deploying them on edge devices raise…
RISE: Relay Inference and Online Scheduling for Efficient Edge-Device Collaborative Diffusion Model Services
Zilan Huang, Zhiqing Tang, Hanshuai Cui +4
Text-to-image diffusion models are increasingly deployed at the network edge to serve heterogeneous workloads with diverse quality and latency requirements. However, existing deplo…
ConCise: Training-Free Conclusion-Chain State Compression for Cost-Efficient Multi-Step RAG Services
Kuan Yan, Zhiqing Tang, Tian Wang +1
Multi-step retrieval-augmented generation (RAG) has been widely deployed as LLM-powered web services for complex question answering, where iterative retrieval-reasoning rounds deli…
CogGuard: Cognitive and Operational Profiling for Proactive Warning in Edge Intelligent Services
Zhi Yao, Weihao Chen, Zhiqing Tang +4
Proactive warning is an important capability for edge intelligent services, where the system predicts whether a subject will successfully complete an incoming task under strict lat…
CONCORD: Asynchronous Sparse Aggregation for Device-Cloud RAG under Document Isolation
Xuedong Hu, Zhiqing Tang, Zhi Yao +2
Retrieval-augmented generation (RAG) has emerged as a pivotal technique for improving language models by incorporating external knowledge at inference time. As device-cloud collabo…
HeraSys: Collaborative Serving of Multiple LLM Workflows via Fine-Grained End-to-End Optimization
Size Li, Zhiqing Tang, Hongrui Liang +4
The proliferation of Large Language Models (LLMs) has shifted serving systems from processing isolated requests to orchestrating high-concurrency, multi-tenant agentic workflows. H…
Semantic Cache Distillation: Efficient State Transfer via Reuse and Selective Patching
Qianli Ma, Zhiqing Tang, Hanshuai Cui +2
Disaggregated serving alleviates memory bottlenecks in Large Language Model (LLM) inference but creates a severe communication bottleneck: transmitting high-dimensional Key-Value (…
Not All Frames Deserve Full Computation: Accelerating Autoregressive Video Generation via Selective Computation and Predictive Extrapolation
Hanshuai Cui, Zhiqing Tang, Zhi Yao +3
Autoregressive (AR) video diffusion models enable long-form video generation but remain expensive due to repeated multi-step denoising. Existing training-free acceleration methods…
Predict to Skip: Linear Multistep Feature Forecasting for Efficient Diffusion Transformers
Hanshuai Cui, Zhiqing Tang, Qianli Ma +3
Diffusion Transformers (DiT) have emerged as a widely adopted backbone for high-fidelity image and video generation, yet their iterative denoising process incurs high computational…
Semantic-Aware Caching for Efficient Image Generation in Edge Computing
Hanshuai Cui, Zhiqing Tang, Zhi Yao +2
Text-to-image generation employing diffusion models has attained significant popularity due to its capability to produce high-quality images that adhere to textual prompts. However…
BWCache: Accelerating Video Diffusion Transformers through Block-Wise Caching
Hanshuai Cui, Zhiqing Tang, Zhifei Xu +3
Recent advancements in Diffusion Transformers (DiTs) have established them as the state-of-the-art method for video generation. However, their inherently sequential denoising proce…
Adaptive AI Agent Placement and Migration in Edge Intelligence Systems
Xingdan Wang, Jiayi He, Zhiqing Tang +5
The rise of LLMs such as ChatGPT and Claude fuels the need for AI agents capable of real-time task handling. However, migrating data-intensive, multi-modal edge workloads to cloud…
EAT: QoS-Aware Edge-Collaborative AIGC Task Scheduling via Attention-Guided Diffusion Reinforcement Learning
Zhifei Xu, Zhiqing Tang, Jiong Lou +5
The growth of Artificial Intelligence (AI) and large language models has enabled the use of Generative AI (GenAI) in cloud data centers for diverse AI-Generated Content (AIGC) task…
LRScheduler: A Layer-aware and Resource-adaptive Container Scheduler in Edge Computing
Zhiqing Tang, Wentao Peng, Jianxiong Guo +5
Lightweight containers provide an efficient approach for deploying computation-intensive applications in network edge. The layered storage structure of container images can further…
Adaptive Configuration Selection for Multi-Model Inference Pipelines in Edge Computing
Jinhao Sheng, Zhiqing Tang, Jianxiong Guo +1
The growing demand for real-time processing tasks is driving the need for multi-model inference pipelines on edge devices. However, cost-effectively deploying these pipelines while…
Hybrid Learning for Cold-Start-Aware Microservice Scheduling in Dynamic Edge Environments
Jingxi Lu, Wenhao Li, Jianxiong Guo +4
With the rapid growth of IoT devices and their diverse workloads, container-based microservices deployed at edge nodes have become a lightweight and scalable solution. However, exi…
Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Models
Xubin Wang, Zhiqing Tang, Jianxiong Guo +4
The rapid advancement of artificial intelligence (AI) technologies has led to an increasing deployment of AI models on edge and terminal devices, driven by the proliferation of the…
Low-Latency Layer-Aware Proactive and Passive Container Migration in Meta Computing
Mengjie Liu, Yihua Li, Fangyi Mou +4
Meta computing is a new computing paradigm that aims to efficiently utilize all network computing resources to provide fault-tolerant, personalized services with strong security an…
VELO: A Vector Database-Assisted Cloud-Edge Collaborative LLM QoS Optimization Framework
Zhi Yao, Zhiqing Tang, Jiong Lou +2
The Large Language Model (LLM) has gained significant popularity and is extensively utilized across various domains. Most LLM deployments occur within cloud data centers, where the…