👋 I am currently pursuing an M.S. in Computer Science at Northeastern University. My research interests include NLP, information retrieval, Retrieval-Augmented Generation, recommender systems, and multi-agent systems. You can find my Google Scholar profile here 
🎓 I received my B.S. in Computer Science from Harbin Engineering University. I am now a master’s student at NEUIR Lab, advised by Associate Professor Zhenghao Liu. I also collaborate closely with Qiyuan Lab 
, ModelBest 
, and TsinghuaNLP 
, and am mentored by Associate Researcher Yukun Yan.
🔥 News
- 2026.04: 🎉 Our paper MetaMem has been accepted at ACL 2026 (CCF-A)!
- 2026.04: 🎉 We released EdgeClaw 2.0
, an Edge-Cloud Collaborative AI Agent!
- 2026.02: 🎉 Our paper Knowledge Intensive Agents has been accepted by AI Open (IF=14.8)!
- 2026.01: 🎉 UltraRAG v3
reached #2 on GitHub Trending!
- 2026.01: 🎉 We released UltraRAG v3, making every step of the reasoning workflow visible!
- 2025.11: 🎉 Our paper LISRec has been accepted at KDD 2026 (CCF-A)!
- 2025.08: 🎉 Our paper LLMsPark has been accepted at EMNLP 2025 (CCF-B)!
- 2024.04: 🎉 We released heu-icicles
, an HEU course guide and resource-sharing platform.
- 2024.02: 🎉 Our paper MMAD has been accepted at COLING 2024 (CCF-B)!
🔨 Projects
PilotDeck: A WorkSpace-Centric Open-Source Agent Operating System
THUNLP, ModelBest, OpenBMB, and AI9Stars
PilotDeck is an open-source agent operating system built around WorkSpace-level isolation, white-box memory, smart routing, and always-on background execution for long-running productivity work.
UltraRAG 3.0: No More “Blind-Box” Development-Every Line of Reasoning Made Explicit
THUNLP, NEUIR, OpenBMB, and AI9Stars
UltraRAG is a low-code MCP-based RAG framework that standardizes retrieval and generation components as MCP servers, with visual orchestration, evaluation, and demo deployment workflows.
📝 Publications
* indicates equal contribution, and † indicates corresponding author.
MetaMem: Evolving Meta-Memory for Knowledge Utilization through Self-Reflective Symbolic Optimization
Haidong Xin$^{*}$, Xinze Li$^{*}$, Zhenghao Liu$^†$, Yukun Yan$^†$, Shuo Wang, Cheng Yang, Yu Gu, Ge Yu, Maosong Sun
This work proposes MetaMem, a meta-memory framework that evolves meta-memory through self-reflective symbolic optimization, enabling knowledge utilization in long-horizon human-LLM interactions.
LISRec: Modeling User Preferences with Learned Item Shortcuts for Sequential Recommendation
Haidong Xin, Zhenghao Liu$^†$, Sen Mei, Yukun Yan, Shi Yu, Shuo Wang, Zulong Chen, Yu Gu, Ge Yu, Chenyan Xiong
This work improves sequential recommendation systems by extracting personalized semantic shortcuts from user-item interaction histories, enhancing the capture of stable user preferences.
Knowledge Intensive Agents
Zhenghao Liu, Pengcheng Huang, Zhipeng Xu, Xinze Li, Shuliang Liu, Chunyi Peng, Haidong Xin, Yukun Yan$^†$, Shuo Wang, Xu Han, Zhiyuan Liu$^†$, Maosong Sun$^†$, Yu Gu, Ge Yu
This work provides a comprehensive overview of Retrieval-Augmented Generation from an agentic perspective, categorizing knowledge-intensive agents into acquisition and utilization roles, and highlighting future directions for joint optimization in multi-agent RAG systems.
Adapting Language Models to Text Matching based Recommendation Systems
Haidong Xin, Sen Mei, Zhenghao Liu$^†$, Xiaohua Li, Minghe Yu, Yu Gu, Ge Yu
This work has significantly improved the performance of text matching in recommendation systems by incorporating language model pretraining.
LLMsPark: A Benchmark for Evaluating Large Language Models in Strategic Gaming Contexts
Junhao Chen, Jingbo Sun, Xiang Li, Haidong Xin, Yuhao Xue, Yibin Xu, Hao Zhao$^†$
This work introduces LLMsPark, a game theory-based platform for evaluating large language models’ strategic behaviors and decision-making abilities in multi-agent environments, providing a novel criterion for assessing their intelligence.
MMAD: Multi-modal Movie Audio Description
Xiaojun Ye, Junhao Chen, Xiang Li, Haidong Xin, Chao Li, Sheng Zhou$^†$, Jiajun Bu
This work has unlocked a whole new experience of watching movies for the visually impaired.
CAGMamba: Context-Aware Gated Cross-Modal Mamba Network for Multimodal Sentiment Analysis
Minghai Jiao, Jing Xiao$^†$, Peng Xiao, Ende Zhang, Shuang Kan, Wenyan Jiang, Jinyao Li, Yixian Liu, Haidong Xin
This work improves multimodal sentiment analysis by introducing a context-aware gated Mamba framework that models dialogue-level sentiment evolution and adaptively balances cross-modal fusion with modality-specific information.
Structured Knowledge Representation through Contextual Pages for Retrieval-Augmented Generation
Xinze Li, Zhenghao Liu$^†$, Haidong Xin, Yukun Yan$^†$, Shuo Wang, Zheni Zeng, Sen Mei, Ge Yu, Maosong Sun
This work enhances RAG systems by constructing structured cognitive outlines to guide iterative retrieval, effectively organizing multi-dimensional knowledge into coherent pages for more accurate answer generation.
Puzzle Game: Prediction and Classification of Wordle Solution Words
Haidong Xin$^{*†}$, Fang Wu$^{*}$, Zhitong Zhou$^{*}$
This work conducted a detailed numerical analysis of the Wordle game, revealing statistical patterns within it.
🏆 Awards
- 2025.11 🥇 First-Class Excellent Student Scholarship.
- 2024.11 🥇 First-Class Excellent Student Scholarship.
- 2023.07 🥈 National Second Prize, Chinese Collegiate Computing Competition (4C 2023).
- 2022.11 🥇 First Prize, China Undergraduate Mathematical Contest in Modeling (CUMCM 2022).
- 2022.10 🥈 Second Prize, Mathematical Modeling Competition of the Three Northeastern Provinces.
- 2022.05 🥇 First-Class Excellent Student Scholarship.
- 2021.05 🥇 First-Class Excellent Student Scholarship.
📖 Education
- 2024.09 - present, M.S. in Computer Science, School of Computer Science and Engineering, Northeastern University
- 2020.09 - 2024.06, B.S. in Computer Science, College of Computer Science and Technology, Harbin Engineering University
- 2017.09 - 2020.06, Yichun No. 1 Middle School
💬 Media Mentions
- 2026.04, 【TsinghuaNLP】成果 | MetaMem：赋予大模型自我进化的“元记忆”
- 2026.03, 【OpenBMB】EdgeClaw发布：安全高效端云协同智能体
- 2026.01, 【TsinghuaNLP】成果 | UltraRAG 3.0：拒绝“盲盒”开发，让每一行推理逻辑都清晰可见
- 2025.11, 【TsinghuaNLP】成果 | UltraRAG 2.1：强化知识接入与多模态支持，完善统一评估体系
- 2025.08, 【TsinghuaNLP】成果 | UltraRAG 2.0：几十行代码实现高性能 RAG，让科研专注思想创新
💻 Research and Internships
- 2024.04 - present, Qiyuan Lab, Beijing.
- 2024.10 - 2025.02, Language Technologies Institute (LTI), Carnegie Mellon University, Remote.
- 2023.10 - present, NEUIR Lab, Northeastern University, Shenyang.