Xiao Chen

Ph.D. Candidate · The Hong Kong Polytechnic University

I am a Ph.D. candidate at The Hong Kong Polytechnic University, where I am fortunate to be advised by Professor Qing Li and Professor Zhaoxiang Zhang. I obtained my M.S. with honors from ZJU CS.

My industry experience at Tencent, Squirrel AI, and Amazon shaped my research vision. I aim to build personal agent systems from first principles. I am particularly interested in developing novel algorithms that help agents understand user intent. I also work towards more flexible human–agent interaction on next-generation smart devices.

Research focus: Reliable Self-Evolving Personal Agents.

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• [09/25/2026] IntentLens is accepted by NeurIPS 2026!
• [08/21/2026] RecToolBench is accepted by EMNLP 2026. Stay tuned!
• [06/11/2025] Delighted to be recognized as an Outstanding Reviewer for KDD 2025 February Cycle.
• [05/15/2025] Our paper on Small Language Model-based Recommendations is accepted by ACL 2025. Stay tuned!
• [12/20/2024] Grateful to receive AAAI-25 Student Scholarship.
• [12/10/2024] Our paper on flexible reflection removal is accepted by AAAI2025.
• [05/01/2023] Join us in the “Trustworthy Recommender Systems” tutorial in The Web Conference 2023. See you in Austin!
• [01/25/2023] Our work on Fairly adaptive negative sampling is accepted by The Web Conference 2023.

My PhD research centers on the design of reliable personal agent systems and the trustworthiness of personalization systems. I develop methods and evaluation frameworks to make recommendation systems and agents more grounded, faithful, fair, and efficient.

• Reliable Personal Agents: developing agents that actively use tools to understand user intent, adopt causal world models for reliable long-horizon planning, and use foundation models to interpret flexible multimodal human annotations. [NeurIPS 2026] [EMNLP 2026] [AAAI 2025]
• Efficient LLM-based Recommendation: developing off-policy and on-policy knowledge distillation for small language model-based recommendation. [ACL 2025]
• Trustworthy Personalization: evaluating whether user profiles are grounded in interaction histories and designing fairness-aware recommendation methods. [WWW 2023]

• [NeurIPS 2026] IntentLens: Grounding Underspecified Multimodal Queries for Recommendation via Tool-Augmented Reasoning.
  Xiao Chen, Dong Fang, Haitao Li, Li Qing.
• [EMNLP 2026] RecToolBench: Benchmarking Recommendation-Specific Tool Orchestration under Fuzzy User Intent.
  Xiao Chen, Zhendong Chu, Qingsong Wen.  [code]
• [ACL 2025] C2KD: Cross-Layer and Cross-Head Knowledge Distillation for Small Language Model-Based Recommendation.
  Xiao Chen, Wenqi Fan, Zhaoxiang Zhang, Qing Li.  [code]
• [AAAI 2025] FIRM: Flexible Interactive Reflection Removal.
  Xiao Chen, Xudong Jiang, Yunkang Tao, Zhen Lei, Qing Li, Chenyang Lei, Zhaoxiang Zhang.  [arXiv] [code]
• [WWW 2023] Fairly Adaptive Negative Sampling for Recommendations.
  Xiao Chen, Wenqi Fan, Jingfan Chen, Haochen Liu, Zitao Liu, Zhaoxiang Zhang, Qing Li.  [arXiv] [code]
• [arXiv 2023] A Comprehensive Survey on Trustworthy Recommender Systems.
  Wenqi Fan, Xiangyu Zhao, Xiao Chen, Jingtong Gao, Lin Wang, Qidong Liu, Yiqi Wang, Han Xu, Lei Chen, Qing Li. [arXiv]

• 2026: Internship at Nanyang Technological University (NTU), Singapore.
• 2025: Project UP Talent Intern at Tencent, Shenzhen, working on Agentic Recommender Systems.
• 2022 – Present: Ph.D. in Computer Science at The Hong Kong Polytechnic University, advised by Chair Professor Qing Li and Professor Zhaoxiang Zhang.
• 2020 – 2022: Full-time AI Algorithm Engineer.
• 2020: Outstanding Graduates, Zhejiang Province
• 2017 – 2020: MSc in Computer Science and Technology at Zhejiang University, China, advised by Prof. Lanfen Lin and Prof. Ruofeng Tong.
• 2018 – 2019: Visiting Student at Ritsumeikan University, Japan, advised by Prof. Yen-Wei Chen.
• 2016: China National Scholarship
• 2016: First Prize in Asian Super Computer Competition
• 2013 – 2017: BSc in Software Engineering at Northwestern Polytechnical University, China.

I write paper-reading notes and literature surveys. Click a title to read the full post.

• Continual Learning Memory Management: A Survey 2026-07-01

  A comprehensive survey of memory management methods in continual learning, organized by techniques: experience replay, generative replay, gradient constraints, dual memory, replay-free approaches, and LLM-Agent methods.

• Recommendation as Generation (RaG): Paper Reading 2026-07-01

Tutorial Co-organizer
Trustworthy Recommender Systems: Foundations and Frontiers — KDD, WWW, IJCAI 2023

Conference & Journal Reviewer
NeurIPS (2024–2026), ICLR (2025–2026), ICML (2025),
KDD (2025–2026), WWW (2025),
ACL ARR (2026),
AAAI (2023, 2026), ECCV (2024), ACM MM (2024, 2026), AISTATS (2025–2026),
TOIS (2025), TKDD (2024–2025), TAI (2024)

Student Volunteer
ICDE 2025, ACL 2025

• ⏳ AI Conference Deadlines: Countdowns to top CV/NLP/ML/Robotics/AI conference deadlines.
• 📝 Writing Perfect Papers: A long talk on how to write perfect papers (by Prof. Baochun Li @UofT).
• 🍑 Lilian Weng's Blog: Deep insights for many ML problems.
• 📡 Scientific Spaces: ML|NLP theory blog with deep explorations.

I enjoy hiking, tennis, ukulele, hip-hop, and photography in my spare time.

Thanks for Jon Barron's template