3 citations · 5 across the 16 of their papers we have counts for
16 papers
Text-attributed Graph Condensation via Text Selection and Attribute Matching
Haowei Han, Yuxiang Wang, Guojia Wan +5
Text-Attributed Graph (TAG) is an important type of graph structured data, where each node has a text description. TAG models usually train a Graph Neural Network (GNN) and languag…
MiCU: End-to-End Smart Home Command Understanding with Large Language Model
Haowei Han, Kexin Hu, Weiwei Cai +6
Command understanding systems in smart home ecosystems can automate device control and substantially improve user experience. However, while they perform well on precise utterances…
Scheduling LLM Inference with Uncertainty-Aware Output Length Predictions
Haoyu Zheng, Yongqiang Zhang, Fangcheng Fu +7
To schedule LLM inference, the \textit{shortest job first} (SJF) principle is favorable by prioritizing requests with short output lengths to avoid head-of-line (HOL) blocking. Exi…
Efficient Serving for Dynamic Agent Workflows with Prediction-based KV-Cache Management
Haoyu Zheng, Fangcheng Fu, Jia Wu +6
LLM-based workflows compose specialized agents to execute complex tasks, and these agents usually share substantial context, allowing KV-Cache reuse to save computation. Existing a…
UniVA: Unified Value Alignment for Generative Recommendation in Online Advertising at Tencent
Xinxun Zhang, Yuling Xiong, Yangru Huang +14
Generative Recommendation (GR) reformulates recommendation as next-token generation over item Semantic IDs (SIDs) and has shown promise in industrial applications. However, extendi…
MTServe: Efficient Serving for Generative Recommendation Models with Hierarchical Caches
Xin Wang, Chi Ma, Shaobin Chen +14
Generative recommendation (GR) offers superior modeling capabilities but suffers from prohibitive inference costs due to the repeated encoding of long user histories. While cross-r…
FuxiShuffle: An Adaptive and Resilient Shuffle Service for Distributed Data Processing on Alibaba Cloud
Yuhao Lin, Zhipeng Tang, Jiayan Tong +11
Shuffle exchanges intermediate results between upstream and downstream operators in distributed data processing and is usually the bottleneck due to factors such as small random I/…
End-to-End Semantic ID Generation for Generative Advertisement Recommendation
Jie Jiang, Xinxun Zhang, Enming Zhang +8
Generative Recommendation (GR) has excelled by framing recommendation as next-token prediction. This paradigm relies on Semantic IDs (SIDs) to tokenize large-scale items into discr…
DevPiolt: Operation Recommendation for IoT Devices at Xiaomi Home
Yuxiang Wang, Siwen Wang, Haowei Han +10
Operation recommendation for IoT devices refers to generating personalized device operations for users based on their context, such as historical operations, environment informatio…
How Significant Are the Real Performance Gains? An Unbiased Evaluation Framework for GraphRAG
Qiming Zeng, Hao Luo, Yuhao Lin +5
By retrieving contexts from knowledge graphs, graph-based retrieval-augmented generation (GraphRAG) enhances large language models (LLMs) to generate quality answers for user quest…
Guiding LLM-based Smart Contract Generation with Finite State Machine
Hao Luo, Yuhao Lin, Xiao Yan +5
Smart contract is a kind of self-executing code based on blockchain technology with a wide range of application scenarios, but the traditional generation method relies on manual co…
Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph
Yuxiang Wang, Xiao Yan, Shiyu Jin +6
Text-attributed graph (TAG) provides a text description for each graph node, and few- and zero-shot node classification on TAGs have many applications in fields such as academia an…
RetroInfer: A Vector Storage Engine for Scalable Long-Context LLM Inference
Yaoqi Chen, Jinkai Zhang, Baotong Lu +16
Recent large language models (LLMs) are rapidly extending their context windows, yet inference throughput lags due to increasing GPU memory and bandwidth demands. This is because t…
Hound: Hunting Supervision Signals for Few and Zero Shot Node Classification on Text-attributed Graph
Yuxiang Wang, Xiao Yan, Shiyu Jin +6
Text-attributed graph (TAG) is an important type of graph structured data with text descriptions for each node. Few- and zero-shot node classification on TAGs have many application…
TreeCSS: An Efficient Framework for Vertical Federated Learning
Qinbo Zhang, Xiao Yan, Yukai Ding +4
Vertical federated learning (VFL) considers the case that the features of data samples are partitioned over different participants. VFL consists of two main steps, i.e., identify t…
Learning Diffusions under Uncertainty
Hao Huang, Qian Yan, Keqi Han +4
To infer a diffusion network based on observations from historical diffusion processes, existing approaches assume that observation data contain exact occurrence time of each node…