1 citations · 1 across the 5 of their papers we have counts for
5 papers
Efficient LLM Serving for Agentic Workflows: A Data Systems Perspective
Noppanat Wadlom, Junyi Shen, Yao Lu
Agentic workflows are composed of sequences of interdependent Large Language Model (LLM) calls, and they have become a dominant workload in modern AI systems. These workflows exhib…
Double: Breaking the Acceleration Limit via Double Retrieval Speculative Parallelism
Yuhao Shen, Tianyu Liu, Junyi Shen +4
Parallel Speculative Decoding (PSD) accelerates traditional Speculative Decoding (SD) by overlapping draft generation with verification. However, it remains hampered by two fundame…
FlowMesh: A Service Fabric for Composable LLM Workflows
Junyi Shen, Noppanat Wadlom, Lingfeng Zhou +4
AI deployment increasingly resembles a pipeline of data transformation, fine-tuning, and agent interactions rather than a monolithic LLM job; recent examples include RLHF/RLAIF tra…
Batch Query Processing and Optimization for Agentic Workflows
Junyi Shen, Noppanat Wadlom, Yao Lu
Large Language Models (LLMs) in agentic workflows combine multi-step reasoning, heterogeneous tool use, and collaboration across multiple specialized agents. Existing LLM serving e…
SpecBranch: Speculative Decoding via Hybrid Drafting and Rollback-Aware Branch Parallelism
Yuhao Shen, Junyi Shen, Quan Kong +3
Recently, speculative decoding (SD) has emerged as a promising technique to accelerate LLM inference by employing a small draft model to propose draft tokens in advance, and valida…