3 citations · 6 across the 16 of their papers we have counts for
17 papers
Denoising Surface: Modeling and Predicting Inference Cost for Diffusion LLM Serving
Haoyu Zheng, Fangcheng Fu, Binhang Yuan +6
As diffusion large language models (dLLMs) become more capable, they are moving from research settings to real-world \textit{serving}, where request management (such as scheduling…
SiriusDeliver: Automating Data Warehouse Delivery at Tencent
Haining Xie, Xiaokai Zhou, Jiaming Yang +12
Enterprise data warehouses (DWs) support business-critical analytics, but warehouse task delivery remains a complicated production process involving context retrieval, workflow con…
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…
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…
MTGenRec: An Efficient Distributed Training System for Generative Recommendation Models in Meituan
Yuxiang Wang, Chi Ma, Xiao Yan +15
Recommendation is crucial for both user experience and company revenue in Meituan as a leading lifestyle company, and generative recommendation models (GRMs) are shown to produce q…
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…
Retrofitting Temporal Graph Neural Networks with Transformer
Qiang Huang, Xiao Yan, Xin Wang +5
Temporal graph neural networks (TGNNs) outperform regular GNNs by incorporating time information into graph-based operations. However, TGNNs adopt specialized models (e.g., TGN, TG…
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…
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning
Yuxiang Wang, Xiao Yan, Chuang Hu +5
For graph self-supervised learning (GSSL), masked autoencoder (MAE) follows the generative paradigm and learns to reconstruct masked graph edges or node features. Contrastive Learn…