1 citations · 1 across the 14 of their papers we have counts for
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SmoothAgent: Efficient Long-Horizon LLM-Based Agent Serving with Lookahead Context Engineering
Zaifeng Pan, Qianxu Wang, Zhengding Hu +5
LLM-based agents execute multi-turn workflows with continuously growing contexts, where LLM calls are interleaved with tool invocations and environment feedback. To maintain model…
FlashCP: Load-Balanced Communication-Efficient Context Parallelism for LLM Training
Zheng Wang, Eric Liu, Linan Jiang +5
Context parallelism (CP) is essential for training large-scale, long-context language models, as it partitions sequences to reduce memory overhead. However, existing CP methods suf…
KVFlow: Efficient Prefix Caching for Accelerating LLM-Based Multi-Agent Workflows
Zaifeng Pan, Ajjkumar Patel, Zhengding Hu +6
Large language model (LLM) based agentic workflows have become a popular paradigm for coordinating multiple specialized agents to solve complex tasks. To improve serving efficiency…
WLB-LLM: Workload-Balanced 4D Parallelism for Large Language Model Training
Zheng Wang, Anna Cai, Xinfeng Xie +9
In this work, we present WLB-LLM, a workLoad-balanced 4D parallelism for large language model training. We first thoroughly analyze the workload imbalance issue in LLM training and…