195 citations · 734 across the 42 of their papers we have counts for
42 papers
BlockServe: Block-Grained Continuous Batching for High-Throughput Diffusion LLM Serving
Yuanjie Zhu, Liangwei Yang, Ke Xu +4
Efficient serving of diffusion large language models (dLLMs) is hindered by convergence heterogeneity: when batching multiple requests, different sequences converge at different ra…
LLM-MemCluster: Empowering Large Language Models with Dynamic Memory for Text Clustering
Yuanjie Zhu, Liangwei Yang, Ke Xu +4
Large Language Models (LLMs) are reshaping unsupervised learning by offering an unprecedented ability to perform text clustering based on their deep semantic understanding. However…
SGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation
Weizhi Zhang, Liangwei Yang, Zihe Song +4
Recommender systems (RecSys) are essential for online platforms, providing personalized suggestions to users within a vast sea of information. Self-supervised graph learning seeks…
From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents
Weizhi Zhang, Yangning Li, Yuanchen Bei +20
Information retrieval is a cornerstone of modern knowledge acquisition, enabling billions of queries each day across diverse domains. However, traditional keyword-based search engi…
LLMInit: A Free Lunch from Large Language Models for Selective Initialization of Recommendation
Weizhi Zhang, Liangwei Yang, Wooseong Yang +5
Collaborative filtering (CF) is widely adopted in industrial recommender systems (RecSys) for modeling user-item interactions across numerous applications, but often struggles with…
Training Large Recommendation Models via Graph-Language Token Alignment
Mingdai Yang, Zhiwei Liu, Liangwei Yang +4
Recommender systems (RS) have become essential tools for helping users efficiently navigate the overwhelming amount of information on e-commerce and social platforms. However, trad…
PersonaBench: Evaluating AI Models on Understanding Personal Information through Accessing (Synthetic) Private User Data
Juntao Tan, Liangwei Yang, Zuxin Liu +11
Personalization is critical in AI assistants, particularly in the context of private AI models that work with individual users. A key scenario in this domain involves enabling AI m…
Benchmarking LLMs for Political Science: A United Nations Perspective
Yueqing Liang, Liangwei Yang, Chen Wang +6
Large Language Models (LLMs) have achieved significant advances in natural language processing, yet their potential for high-stake political decision-making remains largely unexplo…
Graph-Sequential Alignment and Uniformity: Toward Enhanced Recommendation Systems
Yuwei Cao, Liangwei Yang, Zhiwei Liu +5
Graph-based and sequential methods are two popular recommendation paradigms, each excelling in its domain but lacking the ability to leverage signals from the other. To address thi…
Cold-Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmap
Weizhi Zhang, Yuanchen Bei, Liangwei Yang +15
Cold-start problem is one of the long-standing challenges in recommender systems, focusing on accurately modeling new or interaction-limited users or items to provide better recomm…
ToolScan: A Benchmark for Characterizing Errors in Tool-Use LLMs
Shirley Kokane, Ming Zhu, Tulika Awalgaonkar +15
Evaluating Large Language Models (LLMs) is one of the most critical aspects of building a performant compound AI system. Since the output from LLMs propagate to downstream steps, i…
PRACT: Optimizing Principled Reasoning and Acting of LLM Agent
Zhiwei Liu, Weiran Yao, Jianguo Zhang +13
We introduce the Principled Reasoning and Acting (PRAct) framework, a novel method for learning and enforcing action principles from trajectory data. Central to our approach is the…
Personalized Multi-task Training for Recommender System
Liangwei Yang, Zhiwei Liu, Jianguo Zhang +5
In the vast landscape of internet information, recommender systems (RecSys) have become essential for guiding users through a sea of choices aligned with their preferences. These s…
Do We Really Need Graph Convolution During Training? Light Post-Training Graph-ODE for Efficient Recommendation
Weizhi Zhang, Liangwei Yang, Zihe Song +4
The efficiency and scalability of graph convolution networks (GCNs) in training recommender systems (RecSys) have been persistent concerns, hindering their deployment in real-world…
APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets
Zuxin Liu, Thai Hoang, Jianguo Zhang +14
The advancement of function-calling agent models requires diverse, reliable, and high-quality datasets. This paper presents APIGen, an automated data generation pipeline designed t…
MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases
Rithesh Murthy, Liangwei Yang, Juntao Tan +15
The deployment of Large Language Models (LLMs) and Large Multimodal Models (LMMs) on mobile devices has gained significant attention due to the benefits of enhanced privacy, stabil…
Taxonomy-Guided Zero-Shot Recommendations with LLMs
Yueqing Liang, Liangwei Yang, Chen Wang +3
With the emergence of large language models (LLMs) and their ability to perform a variety of tasks, their application in recommender systems (RecSys) has shown promise. However, we…
Mixed Supervised Graph Contrastive Learning for Recommendation
Weizhi Zhang, Liangwei Yang, Zihe Song +4
Recommender systems (RecSys) play a vital role in online platforms, offering users personalized suggestions amidst vast information. Graph contrastive learning aims to learn from h…
Instruction-based Hypergraph Pretraining
Mingdai Yang, Zhiwei Liu, Liangwei Yang +4
Pretraining has been widely explored to augment the adaptability of graph learning models to transfer knowledge from large datasets to a downstream task, such as link prediction or…
Against Filter Bubbles: Diversified Music Recommendation via Weighted Hypergraph Embedding Learning
Chaoguang Luo, Liuying Wen, Yong Qin +3
Recommender systems serve a dual purpose for users: sifting out inappropriate or mismatched information while accurately identifying items that align with their preferences. Numero…
AgentLite: A Lightweight Library for Building and Advancing Task-Oriented LLM Agent System
Zhiwei Liu, Weiran Yao, Jianguo Zhang +10
The booming success of LLMs initiates rapid development in LLM agents. Though the foundation of an LLM agent is the generative model, it is critical to devise the optimal reasoning…
AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning
Jianguo Zhang, Tian Lan, Rithesh Murthy +15
Autonomous agents powered by large language models (LLMs) have garnered significant research attention. However, fully harnessing the potential of LLMs for agent-based tasks presen…
Cyclic Neural Network
Liangwei Yang, Hengrui Zhang, Zihe Song +4
This paper answers a fundamental question in artificial neural network (ANN) design: We do not need to build ANNs layer-by-layer sequentially to guarantee the Directed Acyclic Grap…
Multi-view Graph Convolution for Participant Recommendation
Xiaolong Liu, Liangwei Yang, Chen Wang +3
Social networks have become essential for people's lives. The proliferation of web services further expands social networks at an unprecedented scale, leading to immeasurable comme…