10 citations · 12 across the 7 of their papers we have counts for
9 papers
RAG-Stack: Co-Optimizing RAG Serving Performance and Quality
Haiqiang Zhang, Yuanqing Lei, Wanting Li +2
Retrieval-augmented generation (RAG), which augments large language model (LLM) generation with information retrieved from databases, has become a widely used approach for knowledg…
ROSA: A Robotics Foundation Model Serving System for Robot Factories
Wenqi Jiang, Jason Clemons, Rowland O'Flaherty +5
Robotics foundation models (RFMs) are making general-purpose robots increasingly practical for factory deployments. While RFM serving systems are central to this vision, existing s…
How Fast Can I Run My VLA? Demystifying VLA Inference Performance with VLA-Perf
Wenqi Jiang, Jason Clemons, Karu Sankaralingam +1
Vision-Language-Action (VLA) models have recently demonstrated impressive capabilities across various embodied AI tasks. While deploying VLA models on real-world robots imposes str…
Controlled Self-Evolution for Algorithmic Code Optimization
Tu Hu, Ronghao Chen, Shuo Zhang +9
Self-evolution methods enhance code generation through iterative "generate-verify-refine" cycles, yet existing approaches suffer from low exploration efficiency, failing to discove…
RAG-Stack: Co-Optimizing RAG Quality and Performance From the Vector Database Perspective
Wenqi Jiang
Retrieval-augmented generation (RAG) has emerged as one of the most prominent applications of vector databases. By integrating documents retrieved from a database into the prompt o…
Vector-Centric Machine Learning Systems: A Cross-Stack Approach
Wenqi Jiang
Today, two major trends are shaping the evolution of ML systems. First, modern AI systems are becoming increasingly complex, often integrating components beyond the model itself. A…
HEXGEN-FLOW: Optimizing LLM Inference Request Scheduling for Agentic Text-to-SQL
You Peng, Youhe Jiang, Wenqi Jiang +2
Recent advances in agentic large language models (LLMs) have substantially improved Text-to-SQL, enabling users without database expertise to query databases intuitively. However,…
MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labels
Qi Chen, Xiubo Geng, Corby Rosset +28
Recent breakthroughs in large models have highlighted the critical significance of data scale, labels and modals. In this paper, we introduce MS MARCO Web Search, the first large-s…
PipeRAG: Fast Retrieval-Augmented Generation via Algorithm-System Co-design
Wenqi Jiang, Shuai Zhang, Boran Han +3
Retrieval-augmented generation (RAG) can enhance the generation quality of large language models (LLMs) by incorporating external token databases. However, retrievals from large da…