GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models
Zhipu AI et al. (2025). "GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models." arXiv:2508.06471.
CV
Education
- PhD in Computer Science and Technology, Tsinghua University, 2021-present
- Bachelor’s degree in Computer Science and Technology, Department of Computer Science and Technology (DCST), Tsinghua University, 2017-2021
Research interests
Language Model Agent and Reasoning; Reinforcement Learning; LLM Alignment; Self-Supervised Learning
Experience
- GLM Team, Core Contributor
- Led GLM post-training for improved reasoning and alignment.
- Project lead for the GLM-4.5 series models.
- Drove LLM training/evaluation iterations for agentic and coding capabilities.
Skills
- Python
- PyTorch
- Graph neural networks
- Representation learning
Publications
T1: Advancing Language Model Reasoning through Reinforcement Learning and Inference Scaling
Hu, W., Xu, C., Hou, Z., et al. (2025). "T1: Advancing Language Model Reasoning through Reinforcement Learning and Inference Scaling." ICML.
ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools
Zeng, A., Liu, X., Du, Z., Wang, Z., Lai, H., Hou, Z., and others. (2024). "ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools." arXiv:2406.12793.
Harnessing Large Language Models for Hyperedge Prediction
Hou, Z., Fang, Y., Liu, Z., Cen, Y., Zheng, V., and Tang, J. (2024). "Harnessing Large Language Models for Hyperedge Prediction." AAAI.
GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner
Hou, Z., et al. (2023). "GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner." WWW.
GraphMAE: Self-Supervised Masked Graph Autoencoders
Hou, Z., Liu, X., Cen, Y., Dong, Y., Jiang, H., Yang, H., Wang, C., and Tang, J. (2022). "GraphMAE: Self-Supervised Masked Graph Autoencoders." ICML.
Self-Supervised Attributed Graph Learning: A Comprehensive Review
Xie, Y., Xu, Z., Ji, J., Wang, Z., Wang, S., Liu, J., Ding, T., Hou, Z., and Tang, J. (2021). "Self-Supervised Attributed Graph Learning: A Comprehensive Review." IEEE TKDE.
Talks
Conference Proceeding talk 3 on Relevant Topic in Your Field
Conference proceedings talk at Testing Institute of America 2014 Annual Conference, Los Angeles, CA, USA
Talk 2 on Relevant Topic in Your Field
Talk at London School of Testing, London, UK
Tutorial 1 on Relevant Topic in Your Field
Tutorial at UC-Berkeley Institute for Testing Science, Berkeley, CA, USA
Talk 1 on Relevant Topic in Your Field
Talk at UC San Francisco, Department of Testing, San Francisco, CA, USA
Teaching
Professional profiles
- Google Scholar: https://scholar.google.com/citations?hl=zh-CN&oi=ao&user=44W9SfwAAAAJ
- GitHub: https://github.com/think2try
- KEG: https://keg.cs.tsinghua.edu.cn/