Xixi WU 「吴茜茜」

Ph.D. Candidate,
The Chinese University of Hong Kong
Student Researcher, Google

Email / GitHub / Google Scholar / Twitter / LinkedIn

Short Bio

I am currently a Ph.D. candidate at The Chinese University of Hong Kong, advised by Prof. Hong CHENG, and a Student Researcher at Google DeepMind. Previously, I received my B.S. and M.S. in Computer Science from Fudan University in 2021 and 2024, respectively, under the supervision of Prof. Yun XIONG.

My research focuses on building autonomous agents capable of long-horizon reasoning, planning, and deep information-seeking, with an emphasis on reinforcement learning for search and tool-use agents. Before working on language agents, I conducted research on graph learning, including community detection and recommendation.

Recent News

  • [Jun 2026] I joined Google DeepMind as a Student Researcher 🚀
  • [May 2026] I passed my Ph.D. Thesis Proposal Defense and am now officially a Ph.D. candidate 🎓
  • [Mar 2026] Our paper Agent-STAR is out on arXiv, a practical training recipe for long-horizon tool-using agents [Code]
  • [Feb 2026] Our paper BrowseConf was accepted to ACL'2026 Findings 🎉
  • [Jan 2026] Our papers E-GRPO and WebSailor-V2 were accepted to ICLR'2026 ✨
  • [Aug 2025] Our paper MoLoRAG was accepted to EMNLP'2025. See you in Suzhou ✨
  • [May 2025] Our paper LLMNodeBed was accepted to ICML'2025. See you in Vancouver again 😄
  • [Nov 2024] I was awarded NeurIPS'2024 Top Reviewer 🥳

Research Highlights

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    ReSum: Context Summarization for Long-Horizon Reasoning

    ReSum studies the use of periodic context summarization to support longer reasoning processes in language systems. It is designed as a lightweight extension with minimal changes to existing ReAct workflows, and shows improved performance on challenging information-seeking benchmarks like BrowseComp and GAIA.

  • generation

    LLMNodeBed: Benchmarking LLMs for Node Classification (ICML'2025)

    We introduce LLMNodeBed, a PyG-based testbed for LLM-based node classification algorithms. It integrates 14 datasets, supports 8 LLM-based and 8 classic methods, and covers 3 learning configurations. With LLMNodeBed, we train and evaluate over 2,700 models to analyze the effects of factors like LLM type and size, prompt, learning paradigm, and dataset homophily. Our study reveals 8 key insights, including (1) optimal settings for each algorithm category, and (2) scenarios where LLMs significantly outperform LMs.

  • generation

    Graph Learning for Task Planning (NeurIPS'2024)

    In language agents, available tasks naturally form a task graph, where nodes represent tasks and edges denote dependencies. Under such context, task planning involves selecting a path within this graph to fulfill user requests. We find that the bottleneck in LLMs' planning abilities lies in their limited understanding of the task graph. Therefore, we introduce GNNs as a simple fix, available in both training-free and training-required variants. Extensive experiments demonstrate that GNN-based methods surpass existing solutions even without training.


Technical Reports

Selected Publications

[Show all publications]

Experience

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    Google DeepMind
    Student Researcher Jun. 2026 -
    Mountain View, CA, USA

  • generation

    Alibaba Group
    Research Intern, Deep Research Team, Tongyi Lab Jun. 2025 - Oct. 2025
    Hangzhou, China

  • generation

    Microsoft Research Asia
    Research Intern, Shanghai AI/ML Group Feb. 2024 - Jun. 2024
    Shanghai, China


Selected Awards

  • ACM Web Conference Student Travel Award 2023
  • Second Class Scholarship for Outstanding Student, Fudan University 2018 & 2021
  • Second Prize of Undergraduate Mathematical Contest in Modeling, Shanghai, China (CUMCM) 2019
  • First Prize in National Olympiad in Mathematics in Provinces, Jiangsu, China 2016

Professional Services

  • Conference Reviewer: NeurIPS'2024 (Top Reviewer Award🏆) - 2026, ICLR'2025 - 2026, ICML'2025 - 2026, WWW'2024 Graph Foundation Model (GFM) Workshop, SIGKDD'2024 - 2025, AAAI 2026
  • Journal Reviewer: IEEE Transactions on Knowledge and Data Engineering (TKDE), Transactions on Machine Learning Research (TMLR)

Miscellaneous

  • Goal for this year: run a half marathon! Let's see 💨
  • I love sports like swimming 🏊 and running 🏃. I also enjoy cooking Chinese food 🤣
  • During my undergraduate studies, I was interested in mobile app development (You can find all the source codes on my GitHub):

    Lose Weight, a Flutter App

    Hulv, a Mini-Program

    Chatroom, a Desktop App
  • I enjoy exploring the unknown and strive to keep moving forward on this path of discovery and learning ✨

Last updated at Jul 1, 2026 by Xixi