98 citations · 116 across the 12 of their papers we have counts for
12 papers
AgentFugue: Agent Scaling for Long-Horizon Tasks through Collective Reasoning
Yuyang Hu, Hongjin Qian, Shuting Wang +5
Recent progress on long-horizon agentic tasks has been driven largely by scaling up individual agents through stronger models, better tools, and more effective scaffolding. In cont…
SAM: State-Adaptive Memory for Long-Horizon Reasoning Agent
Yuyang Hu, Hongjin Qian, Shuting Wang +5
Long-horizon agentic reasoning requires large language models to act over long interaction histories containing thoughts, tool calls, observations, and partial conclusions. The cha…
Memory in the Age of AI Agents
Yuyang Hu, Shichun Liu, Yanwei Yue +44
Memory has emerged, and will continue to remain, a core capability of foundation model-based agents. As research on agent memory rapidly expands and attracts unprecedented attentio…
Memory Matters More: Event-Centric Memory as a Logic Map for Agent Searching and Reasoning
Yuyang Hu, Jiongnan Liu, Jiejun Tan +2
Large language models (LLMs) are increasingly deployed as intelligent agents that reason, plan, and interact with their environments. To effectively scale to long-horizon scenarios…
ORBIT -- Open Recommendation Benchmark for Reproducible Research with Hidden Tests
Jingyuan He, Jiongnan Liu, Vishan Vishesh Oberoi +7
Recommender systems are among the most impactful AI applications, interacting with billions of users every day, guiding them to relevant products, services, or information tailored…
Generate, Not Recommend: Personalized Multimodal Content Generation
Jiongnan Liu, Zhicheng Dou, Ning Hu +1
To address the challenge of information overload from massive web contents, recommender systems are widely applied to retrieve and present personalized results for users. However,…
LLMs + Persona-Plug = Personalized LLMs
Jiongnan Liu, Yutao Zhu, Shuting Wang +6
Personalization plays a critical role in numerous language tasks and applications, since users with the same requirements may prefer diverse outputs based on their individual inter…
DomainRAG: A Chinese Benchmark for Evaluating Domain-specific Retrieval-Augmented Generation
Shuting Wang, Jiongnan Liu, Shiren Song +6
Retrieval-Augmented Generation (RAG) offers a promising solution to address various limitations of Large Language Models (LLMs), such as hallucination and difficulties in keeping u…
Generalizing Conversational Dense Retrieval via LLM-Cognition Data Augmentation
Haonan Chen, Zhicheng Dou, Kelong Mao +2
Conversational search utilizes muli-turn natural language contexts to retrieve relevant passages. Existing conversational dense retrieval models mostly view a conversation as a fix…
Large Language Models for Information Retrieval: A Survey
Yutao Zhu, Huaying Yuan, Shuting Wang +7
As a primary means of information acquisition, information retrieval (IR) systems, such as search engines, have integrated themselves into our daily lives. These systems also serve…
RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit
Jiongnan Liu, Jiajie Jin, Zihan Wang +3
Although Large Language Models (LLMs) have demonstrated extraordinary capabilities in many domains, they still have a tendency to hallucinate and generate fictitious responses to u…
JDsearch: A Personalized Product Search Dataset with Real Queries and Full Interactions
Jiongnan Liu, Zhicheng Dou, Guoyu Tang +1
Recently, personalized product search attracts great attention and many models have been proposed. To evaluate the effectiveness of these models, previous studies mainly utilize th…