12 citations · 22 across the 20 of their papers we have counts for
20 papers
SMetric: Rethink LLM Scheduling for Serving Agents with Balanced Session-centric Scheduling
Jiahao Wang, Kaizhan Lin, Kaixi Zhang +7
LLM scheduling is critical to serving, yet how well existing designs fit agentic serving--where agents, not humans, issue the requests--remains unclear. Agents shift the workload i…
Wan-Image: Pushing the Boundaries of Generative Visual Intelligence
Chaojie Mao, Chen-Wei Xie, Chongyang Zhong +55
We present Wan-Image, a unified visual generation system explicitly engineered to paradigm-shift image generation models from casual synthesizers into professional-grade productivi…
Efficient K-generalizable Learned Search
Yifan Peng, Jiafei Fan, Xingda Wei +7
Learned top-K search improves the accuracy-latency trade-off of graph-based vector search, but existing methods target a fixed K. Serving real-world workloads with various K values…
Simple is Better: Multiplication May Be All You Need for LLM Request Scheduling
Dingyan Zhang, Jinbo Han, Kaixi Zhang +6
High-quality LLM request scheduling requires meeting two key objectives: ensuring the routed instance has KVCache to accelerate request execution, and ensuring that the workload is…
Qwen3Guard Technical Report
Haiquan Zhao, Chenhan Yuan, Fei Huang +40
As large language models (LLMs) become more capable and widely used, ensuring the safety of their outputs is increasingly critical. Existing guardrail models, though useful in stat…
Revisiting Graph Analytics Benchmark
Lingkai Meng, Yu Shao, Long Yuan +7
The rise of graph analytics platforms has led to the development of various benchmarks for evaluating and comparing platform performance. However, existing benchmarks often fall sh…
KVCache Cache in the Wild: Characterizing and Optimizing KVCache Cache at a Large Cloud Provider
Jiahao Wang, Jinbo Han, Xingda Wei +6
Serving large language models (LLMs) is important for cloud providers, and caching intermediate results (KV$) after processing each request substantially improves serving throughp…
ServeGen: Workload Characterization and Generation of Large Language Model Serving in Production
Yuxing Xiang, Xue Li, Kun Qian +3
With the widespread adoption of Large Language Models (LLMs), serving LLM inference requests has become an increasingly important task, attracting active research advancements. Pra…
A Graph-native Optimization Framework for Complex Graph Queries
Bingqing Lyu, Xiaoli Zhou, Longbin Lai +4
This technical report extends the SIGMOD 2025 paper "A Modular Graph-Native Query Optimization Framework" by providing a comprehensive exposition of GOpt's advanced technical mecha…
Qwen2.5-1M Technical Report
An Yang, Bowen Yu, Chengyuan Li +25
We introduce Qwen2.5-1M, a series of models that extend the context length to 1 million tokens. Compared to the previous 128K version, the Qwen2.5-1M series have significantly enha…
Exact Acceleration of Subgraph Graph Neural Networks by Eliminating Computation Redundancy
Qian Tao, Xiyuan Wang, Muhan Zhang +3
Graph neural networks (GNNs) have become a prevalent framework for graph tasks. Many recent studies have proposed the use of graph convolution methods over the numerous subgraphs o…
LSMGraph: A High-Performance Dynamic Graph Storage System with Multi-Level CSR
Song Yu, Shufeng Gong, Qian Tao +9
The growing volume of graph data may exhaust the main memory. It is crucial to design a disk-based graph storage system to ingest updates and analyze graphs efficiently. However, e…
AsymKV: Enabling 1-Bit Quantization of KV Cache with Layer-Wise Asymmetric Quantization Configurations
Qian Tao, Wenyuan Yu, Jingren Zhou
Large language models have shown exceptional capabilities in a wide range of tasks, such as text generation and video generation, among others. However, due to their massive parame…
Towards a Converged Relational-Graph Optimization Framework
Yunkai Lou, Longbin Lai, Bingqing Lyu +5
The recent ISO SQL:2023 standard adopts SQL/PGQ (Property Graph Queries), facilitating graph-like querying within relational databases. This advancement, however, underscores a sig…
A Survey of Distributed Graph Algorithms on Massive Graphs
Lingkai Meng, Yu Shao, Long Yuan +7
Distributed processing of large-scale graph data has many practical applications and has been widely studied. In recent years, a lot of distributed graph processing frameworks and…
A Modular Graph-Native Query Optimization Framework
Bingqing Lyu, Xiaoli Zhou, Longbin Lai +4
Complex Graph Patterns (CGPs), which combine pattern matching with relational operations, are widely used in real-world applications. Existing systems rely on monolithic architectu…
Unicron: Economizing Self-Healing LLM Training at Scale
Tao He, Xue Li, Zhibin Wang +4
Training large-scale language models is increasingly critical in various domains, but it is hindered by frequent failures, leading to significant time and economic costs. Current f…
GraphScope Flex: LEGO-like Graph Computing Stack
Tao He, Shuxian Hu, Longbin Lai +18
Graph computing has become increasingly crucial in processing large-scale graph data, with numerous systems developed for this purpose. Two years ago, we introduced GraphScope as a…
GastCoCo: Graph Storage and Coroutine-Based Prefetch Co-Design for Dynamic Graph Processing
Hongfu Li, Qian Tao, Song Yu +6
An efficient data structure is fundamental to meeting the growing demands in dynamic graph processing. However, the dual requirements for graph computation efficiency (with contigu…
GraphAr: An Efficient Storage Scheme for Graph Data in Data Lakes
Xue Li, Weibin Zeng, Zhibin Wang +4
Data lakes, increasingly adopted for their ability to store and analyze diverse types of data, commonly use columnar storage formats like Parquet and ORC for handling relational ta…