1 citations · 1 across the 8 of their papers we have counts for
10 papers
OLED-MoE: Accelerating MoE-Based dLLM Inference via Inter-Iteration Locality-Aware Expert Offloading
Jingyuan Xiao, Jiayue Wang, Yitao Hu +7
Semi-autoregressive diffusion large language models (dLLMs) improve decoding parallelism through iterative block-wise denoising, but scaling them with mixture-of-experts (MoE) laye…
PatchKV: Efficient KV Cache Recovery for Dynamically Edited LLM Contexts
Guotao Yang, Rui Guo, Siwei He +3
Long-running LLM agent workflows often revise interior context spans while retaining long suffixes. Although suffix tokens remain unchanged, altered causal histories and rotary pos…
RAC: Reference-Aware Activation Compression for Communication-Efficient Split LLM Inference
Guotao Yang, Mingxi Zhao, Haopeng Li +4
Large language model (LLM) agents repeatedly process long, privacy-sensitive contexts, while cloud-only deployment exposes user data beyond the trusted endpoint and fully local dep…
AsymSpec: Efficient Cloud-Edge Speculative Decoding over Asymmetric Networks
Guotao Yang, Hao Chen, Rui Guo +5
Cloud-edge speculative decoding places a lightweight draft model at an edge gateway and a higher-quality target model in the cloud, but inserts communication into every speculative…
AESOP: Adversarial Execution-path Selection to Overload Deep Learning Pipelines
Tingxi Li, Mingfang Ji, Ravishka Shemal Rathnasuriya +3
Modern machine learning deployments increasingly compose specialized models into dynamic inference pipelines, where upstream components produce intermediate predictions that determ…
Mosaic: Unlocking Long-Context Inference for Diffusion LLMs via Global Memory Planning and Dynamic Peak Taming
Liang Zheng, Bowen Shi, Yitao Hu +5
Diffusion-based large language models (dLLMs) have emerged as a promising paradigm, utilizing simultaneous denoising to enable global planning and iterative refinement. While these…
RAGPulse: An Open-Source RAG Workload Trace to Optimize RAG Serving Systems
Zhengchao Wang, Yitao Hu, Jianing Ye +4
Retrieval-Augmented Generation (RAG) is a critical paradigm for building reliable, knowledge-intensive Large Language Model (LLM) applications. However, the multi-stage pipeline (r…
ServerlessLoRA: Enabling Low-Latency Serverless Multi-LoRA Serving
Yifan Sui, Hao Wang, Hanfei Yu +5
Multi-LoRA (Low-Rank Adaptation) serving allows many specialized LLM variants to share the same base model by attaching lightweight adapters. This makes it attractive for serving l…
Harpagon: Minimizing DNN Serving Cost via Efficient Dispatching, Scheduling and Splitting
Zhixin Zhao, Yitao Hu, Ziqi Gong +5
Advances in deep neural networks (DNNs) have significantly contributed to the development of real-time video processing applications. Efficient scheduling of DNN workloads in cloud…
Skill-Adaptive Imitation Learning for UI Test Reuse
Mengzhou Wu, Hao Wang, Jun Ren +7
To alleviate the substantial cost of manually crafting user interface (UI) test cases, UI test migration aims to automatically generate test cases for a target mobile application (…