2 citations · 2 across the 10 of their papers we have counts for
13 papers
Correct but Unselectable: The Hidden Interface Tax in Multi-Candidate Reasoning
Qianli Ma, Zhiqing Tang, Fanshuai Meng +3
Small language models deployed under cost, latency, or privacy constraints cannot simply switch to larger models when reasoning fails. Multi-candidate generate-then-rerank offers a…
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…
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…
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…
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…
Online Container Scheduling for Low-Latency IoT Services in Edge Cluster Upgrade: A Reinforcement Learning Approach
Hanshuai Cui, Zhiqing Tang, Jiong Lou +1
In Mobile Edge Computing (MEC), Internet of Things (IoT) devices offload computationally-intensive tasks to edge nodes, where they are executed within containers, reducing the reli…