19 citations · 44 across the 19 of their papers we have counts for
7 papers · 1 filter
CRMWeaver: Building Powerful Business Agent via Agentic RL and Shared Memories
Yilong Lai, Yipin Yang, Ting Liang +4
Recent years have witnessed the rapid development of LLM-based agents, which shed light on using language agents to solve complex real-world problems. A prominent application lies…
AdaRewriter: Unleashing the Power of Prompting-based Conversational Query Reformulation via Test-Time Adaptation
Yilong Lai, Jialong Wu, Zhenglin Wang +1
Prompting-based conversational query reformulation has emerged as a powerful approach for conversational search, refining ambiguous user queries into standalone search queries. Bes…
SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement
Runnan Fang, Xiaobin Wang, Yuan Liang +8
In the interaction between agents and their environments, agents expand their capabilities by planning and executing actions. However, LLM-based agents face substantial challenges…
PROPER: A Progressive Learning Framework for Personalized Large Language Models with Group-Level Adaptation
Linhai Zhang, Jialong Wu, Deyu Zhou +1
Personalized large language models (LLMs) aim to tailor their outputs to user preferences. Recent advances in parameter-efficient fine-tuning (PEFT) methods have highlighted the ef…
Benchmarking Temporal Reasoning and Alignment Across Chinese Dynasties
Zhenglin Wang, Jialong Wu, Pengfei LI +2
Temporal reasoning is fundamental to human cognition and is crucial for various real-world applications. While recent advances in Large Language Models have demonstrated promising…
OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking
Zekun Xi, Wenbiao Yin, Jizhan Fang +7
Machine writing with large language models often relies on retrieval-augmented generation. However, these approaches remain confined within the boundaries of the model's predefined…
WebWalker: Benchmarking LLMs in Web Traversal
Jialong Wu, Wenbiao Yin, Yong Jiang +8
Retrieval-augmented generation (RAG) demonstrates remarkable performance across tasks in open-domain question-answering. However, traditional search engines may retrieve shallow co…