57 citations · 75 across the 26 of their papers we have counts for
12 papers · 1 filter
AFORE: Attention-FFN Disaggregation with Overlapped Reconfiguration of Experts
Wenshuang Li, Youhe Jiang, You Peng +2
Efficient serving of Mixture-of-Experts (MoE) models is challenging due to large expert parameters, input-dependent expert activation, and dynamic workloads. Expert parallelism dis…
Coda: Exploiting Admission Flexibility for Coding-Agent Serving
Youhe Jiang, Fangcheng Fu, Binhang Yuan +5
Coding agents powered by large language models (LLMs) repeatedly alternate between model inference and tool calls, creating long-lived sessions with reusable key-value (KV) states…
Vidu S1: A Real-Time Interactive Video Generation Model
Jintao Zhang, Kai Jiang, Jintao Chen +24
We introduce Vidu S1, a real-time interactive video generation model supporting voice control of digital characters. Users can control video generation content at any moment throug…
Next-Generation Agentic Reinforcement Learning Systems Enable Self-Evolving Agents
Ran Yan, Wei Fu, Jiale Li +21
LLM agents are rapidly being deployed in production, including coding assistants, customer-support chatbots, and scientific research assistants, yet they remain fundamentally stati…
TurboServe: Serving Streaming Video Generation Efficiently and Economically
Youhe Jiang, Haoxu Wang, Haotong Bao +5
Streaming video generation is emerging as a new serving workload in which users interact with long-lived sessions that generate video progressively, chunk by chunk. Unlike offline…
HexAGenT: Efficient Agentic LLM Serving via Workflow- and Heterogeneity-Aware Scheduling
You Peng, Youhe Jiang, Wenshuang Li +5
Agentic LLM applications increasingly execute user requests as multi-step workflows involving planning, tool use, branching, refinement, and synthesis. In such settings, users expe…
HexiSeq: Accommodating Long Context Training of LLMs over Heterogeneous Hardware
Yan Liang, Youhe Jiang, Ran Yan +3
Long-context training of large language models (LLMs) is commonly distributed with Context Parallelism (CP) and Head Parallelism (HP), but existing training systems largely assume…
Autopoiesis: A Self-Evolving System Paradigm for LLM Serving Under Runtime Dynamics
Youhe Jiang, Ran Yan, You Peng +4
Modern Large Language Model (LLM) serving operates in highly volatile environments characterized by severe runtime dynamics, such as workload fluctuations and elastic cluster autos…
SLA2: Sparse-Linear Attention with Learnable Routing and Quantization-aware Training
Jintao Zhang, Haoxu Wang, Kai Jiang +6
Sparse-Linear Attention (SLA) combines sparse and linear attention to accelerate diffusion models and has shown strong performance in video generation. However, (i) SLA relies on a…
BOute: Cost-Efficient LLM Serving with Heterogeneous LLMs and GPUs via Multi-Objective Bayesian Optimization
Youhe Jiang, Fangcheng Fu, Eiko Yoneki
The rapid growth of large language model (LLM) deployments has made cost-efficient serving systems essential. Recent efforts to enhance system cost-efficiency adopt two main perspe…
Efficient Multi-round LLM Inference over Disaggregated Serving
Wenhao He, Youhe Jiang, Penghao Zhao +4
With the rapid evolution of Large Language Models (LLMs), multi-round workflows, such as autonomous agents and iterative retrieval, have become increasingly prevalent. However, thi…
OServe: Accelerating LLM Serving via Spatial-Temporal Workload Orchestration
Youhe Jiang, Fangcheng Fu, Taiyi Wang +2
Serving Large Language Models (LLMs) can benefit immensely from parallelizing both the model and input requests across multiple devices, but incoming workloads exhibit substantial…