6 citations · 8 across the 18 of their papers we have counts for
19 papers
LOCAL: Enabling Learning On-device Contiguously for Agent LLMs
Xinxin Liu, Jiaxin Li, Zibo Wang +7
On-device LLM agents interact repeatedly with users on local hardware, producing private traces that are valuable for adaptation but should not be sent to a remote trainer. Ideally…
TIDE-MC: Two-Sided Interpolative Decomposition for Billion-Scale GPU Matrix Completion
Chengying Huan, Yubo Wang, Pinhuan Wang +11
Matrix completion supports large-scale recommendation and scientific computing, yet existing GPU solvers commonly assume that the observed matrix or its dense factors fit in device…
SpecLA: Efficient Speculative Decoding for Linear-Attention Models
Zhibin Wang, Xuying Han, Zhaohua Yang +5
Linear-attention models replace the growing KV cache with recurrent states, but autoregressive decoding still reads, updates, and writes these states one token at a time. Speculati…
CacheWeaver: Cache-Aware Evidence Ordering for Efficient Grounded RAG Inference
Kaizhen Tan, Rong Gu, Mingyuan Li
Retrieval-Augmented Generation (RAG) improves factual grounding, but it also lengthens prompts and raises prefill cost. Prefix caching in serving engines such as vLLM reduces this…
SSV: Sparse Speculative Verification for Efficient LLM Inference
Zhibin Wang, Ziyu Zhong, Nuo Shen +3
Speculative decoding and dynamic sparse attention are two complementary approaches for accelerating long-context LLM inference: the former amortizes target-model execution across m…
MEPIC: Memory Efficient Position Independent Caching for LLM Serving
Qian Wang, Zahra Yousefijamarani, Morgan Lindsay Heisler +8
Modern LLM applications such as deep-research assistants, coding agents, and Retrieval-Augmented Generation (RAG) systems, repeatedly process long prompt histories containing share…
Scaling Graph Chain-of-Thought Reasoning: A Multi-Agent Framework with Efficient LLM Serving
Chengying Huan, Ziheng Meng, Yongchao Liu +11
Graph Chain-of-Thought (Graph-CoT) enables large language models (LLMs) to perform step-by-step reasoning over graph-structured knowledge, but existing pipelines suffer from low ac…
STAR: Decode-Phase Rescheduling for LLM Inference
Zhibin Wang, Zetao Hong, Xue Li +8
Large Language Model (LLM) inference has emerged as a fundamental paradigm, however, variations in output length cause severe workload imbalance in the decode phase, particularly f…
SmartSwap: Swap-Based Memory Optimization for LLM Training under Varying Operator Sequences
Zibo Wang, Yuhang Zhou, Zhibin Wang +13
The increasing size of large language models (LLMs) has led to a surge in memory requirements during training, often exceeding the capacity of high-bandwidth memory (HBM). Swap-bas…
BOASF: A Unified Framework for Speeding up Automatic Machine Learning via Adaptive Successive Filtering
Guanghui Zhu, Xin Fang, Feng Cheng +4
Machine learning has been making great success in many application areas. However, for the non-expert practitioners, it is always very challenging to address a machine learning tas…
Accelerating Mixture-of-Experts Inference by Hiding Offloading Latency with Speculative Decoding
Zhibin Wang, Zhonghui Zhang, Yuhang Zhou +8
Recent advancements in Mixture of Experts (MoE) models have significantly increased their parameter scale as well as model performance. Extensive offloading techniques have been pr…
Chameleon: Adaptive Fault Tolerance for Distributed Training via Real-time Policy Selection
Yuhang Zhou, Zhibin Wang, Peng Jiang +12
Training large language models faces frequent interruptions due to various faults, demanding robust fault-tolerance. Existing backup-free methods, such as redundant computation, dy…
Chordless Structure: A Pathway to Simple and Expressive GNNs
Hongxu Pan, Shuxian Hu, Mo Zhou +5
Researchers have proposed various methods of incorporating more structured information into the design of Graph Neural Networks (GNNs) to enhance their expressiveness. However, the…
CoDec: Prefix-Shared Decoding Kernel for LLMs
Zhibin Wang, Rui Ning, Chao Fang +12
Prefix-sharing among multiple prompts presents opportunities to combine the operations of the shared prefix, while attention computation in the decode stage, which becomes a critic…
Echo: Efficient Co-Scheduling of Hybrid Online-Offline Tasks for Large Language Model Serving
Zhibin Wang, Shipeng Li, Xue Li +7
Large language models have been widely deployed in various applications, encompassing both interactive online tasks and batched offline tasks. Given the burstiness and latency sens…
Revisiting Service Level Objectives and System Level Metrics in Large Language Model Serving
Zhibin Wang, Shipeng Li, Yuhang Zhou +7
User experience is a critical factor Large Language Model (LLM) serving systems must consider, where service level objectives (SLOs) considering the experience of individual reques…
Hash Adaptive Bloom Filter
Rongbiao Xie, Meng Li, Zheyu Miao +4
Bloom filter is a compact memory-efficient probabilistic data structure supporting membership testing, i.e., to check whether an element is in a given set. However, as Bloom filter…
Distributed Subgraph Enumeration via Backtracking-based Framework
Zhaokang Wang, Weiwei Hu, Chunfeng Yuan +2
Finding or monitoring subgraph instances that are isomorphic to a given pattern graph in a data graph is a fundamental query operation in many graph analytic applications, such as…
Semi-supervised Embedding Learning for High-dimensional Bayesian Optimization
Jingfan Chen, Guanghui Zhu, Chunfeng Yuan +1
Bayesian optimization is a broadly applied methodology to optimize the expensive black-box function. Despite its success, it still faces the challenge from the high-dimensional sea…