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Litu Ou

ACL 2026ICLR 2026SIGIR 2026智能体与工具数据与预训练检索与知识强化学习与后训练推理
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一作贡献30
核心/参与21
方向广度16
合作网络12
1 一作 · 3 篇 · ACL 2026 / ICLR 2026 / SIGIR 2026

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2026 顶会论文

3
作者位次 #7/21ICLR 2026Poster

WebSailor-V2: Bridging the Chasm to Proprietary Agents via Synthetic Data and Scalable Reinforcement Learning

Kuan Li · Zhongwang Zhang · Huifeng Yin · Rui Ye · Yida Zhao · Liwen Zhang · Litu Ou · Ding-Chu Zhang · Xixi Wu · Xinmiao Yu · Jialong Wu · Xinyu Wang · Zile Qiao · Zhen Zhang · Yong Jiang · Pengjun Xie · Fei Huang · Zhiqin Xu · Shuai Wang · Minhao Cheng · Jingren Zhou

智能体与工具强化学习与后训练数据与预训练OpenReview
一作ACL 2026Findings· Findings of ACL

B rowse C onf: Confidence-Guided Test-Time Scaling for Web Agents

Litu Ou · Kuan Li · Huifeng Yin · Liwen Zhang · Zhongwang Zhang · Xixi Wu · Rui Ye · Zile Qiao · Yong Jiang · Pengjun Xie · Fei Huang · Jingren Zhou

摘要

Confidence in LLMs is a useful indicator of model uncertainty and answer reliability. Existing work mainly focused on single-turn scenarios, while research on confidence in complex multi-turn interactions is limited. In this paper, we investigate whether LLM-based search agents have the ability t...

检索与知识推理智能体与工具OpenReview 项目页
作者位次 #2/4SIGIR 2026Oral· Information Retrieval (Reproducibility)

Revisiting Text Ranking in Deep Research

Chuan Meng · Litu Ou · Sean MacAvaney · Jeffrey Dalton

摘要

Deep research has emerged as an important task that aims to address hard queries that need extensive open-web exploration. To tackle it, most prior work equips large language model (LLM)-based agents with opaque web search APIs, enabling agents to iteratively issue search queries, retrieve extern...

数据与预训练检索与知识多语言与语言学OpenReview

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