Chengzu Li

PhD student in Computation, Cognition and Language Language Technology Lab University of Cambridge [Email] [Github] [Google Scholar] [Semantic Scholar] [CV]

About me

I am a third-year PhD student in Computation, Cognition and Language at Language Technology Lab in the University of Cambridge, supervised by Dr. Ivan Vulić, Prof. Anna Korhonen and Prof. Serge Belongie. My research is supported by Cambridge Trust. I am a member of Jesus College.

Before starting my PhD, I received my MPhil degree in Advanced Computer Science at the Department of Computer Science, supervised by Prof. Simone Teufel, supported by Cambridge Trust. I was an undergraduate student in Automation at Xi'an Jiaotong University.

Research

My research interests center around Visual Intelligence with a specific focus on spatial reasoning [New Recipes], covering Multimodal LLMs, Video Generation Model and Multimodal Agents.

Specifically, my work focuses on:

• Visual Intelligence and Multimodal Reasoning: [Visual Planning, ICLR Oral] [MVoT, ICML]

• (M)LLMs and Agents: [UnifiedSKG, EMNLP Oral] [BINDER, ICLR Spotlight]

• Video Generation for Planning: [Thinking in Frames] [Microsoft Research Cambridge Internship Project]

• Evaluation and Predictability: [TopViewRS, EMNLP Oral] [11Plus] [VIBE]

I'm also interested in the potential downstream application scenarios of multimodal reasoning.

Education

• PhD student in Computation, Cognition and Language, Language Technology Lab, University of Cambridge, 2023 - now

• Master of Philosophy in Advanced Computer Science, University of Cambridge, 2022 - 2023

  • Graduation with Distinction

• Bachelor of Engineering in Automation, Xi'an Jiaotong University, 2018 - 2022

  • Graduate with Distinction (91.88/100)

  • Minor in Fintech (China Construction Bank - XJTU Fintech Elite Class)

Publications

(*: equal contribution, +: corresponding author)

Preprints

• From Trainee to Trainer: LLM-Designed Training Environment for RL with Multi-Agent Reasoning
  Chao Chen, Chengzu Li+, Zhiwei Li, Yinhong Liu, Zhijiang Guo

• Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond
  Meng Chu, Xuan Billy Zhang, Kevin Qinghong Lin, …, Chengzu Li, …, Ziwei Liu, Philip Torr, Jiaya Jia

• Thinking in Frames: How Visual Context and Test-Time Scaling Empower Video Reasoning
  Chengzu Li*, Zanyi Wang*, Jiaang Li*, Yi Xu, Han Zhou, Huanyu Zhang, Ruichuan An, Dengyang Jiang, Zhaochong An, Ivan Vulić, Serge Belongie, Anna Korhonen
  [project website]

• How Well Do Models Follow Visual Instructions? VIBE: A Systematic Benchmark for Visual Instruction-Driven Image Editing
  Huanyu Zhang*, Xuehai Bai*, Chengzu Li*, Chen Liang, Haochen Tian, Haodong Li, Ruichuan An, Yifan Zhang, Anna Korhonen, Zhang Zhang, Liang Wang, Tieniu Tan
  [project website]

• Latent Sketchpad: Autoregressive Visual Latent Generation for Interpretable Visual Thoughts in MLLMs
  Huanyu Zhang*, Wenshan Wu*, Chengzu Li, Ning Shang, Yan Xia, Yangyu Huang, Yifan Zhang, Li Dong, Zhang Zhang, Liang Wang, Tieniu Tan, Furu Wei
  [project website]

• 11Plus-Bench: Demystifying Multimodal LLM Spatial Reasoning with Cognitive-Inspired Analysis
  Chengzu Li, Wenshan Wu, Huanyu Zhang, Qingtao Li, Zeyu Gao, Yan Xia, José Hernández-Orallo, Ivan Vulić, Furu Wei
  

• Scaling and Beyond: Advancing Spatial Reasoning in MLLMs Requires New Recipes
  Huanyu Zhang*, Chengzu Li*, Wenshan Wu, Shaoguang Mao, Yan Xia, Ivan Vulić, Zhang Zhang, Liang Wang, Tieniu Tan, Furu Wei
  

2026

• Visual Planning: Let's Think Only with Images
  Yi Xu*, Chengzu Li*, Han Zhou*, Xingchen Wan, Caiqi Zhang, Anna Korhonen, Ivan Vulić
  ICLR 2026 (oral). [code] [机器之心] [量子位]

• Deforming Videos to Masks: Flow Matching for Referring Video Segmentation
  Zanyi Wang, Dengyang Jiang, Liuzhuozheng Li, Sizhe Dang, Chengzu Li, Harry Yang, Guang Dai, Mengmeng Wang, Jingdong Wang
  ICLR 2026. [code]

• Video Understanding: From Geometry and Semantics to Unified Models
  Zhaochong An, Zirui Li, Mingqiao Ye, Feng Qiao, Jiaang Li, Zongwei Wu, Vishal Thengane, Chengzu Li, Lei Li, Luc Van Gool, Guolei Sun, Serge Belongie
  Machine Intelligence Research.

• Thinking in Scales: Accelerating Gigapixel Pathology Image Analysis via Adaptive Continuous Reasoning
  Jiusong Ge, Yingkang Zhan, Wenjie Zhao, Di Zhang, Ke Wang, Jiashuai Liu, Chunze Yang, Chengzu Li, Jian Zhang, Yuxin Dong, Ni Zhang, Qidong Liu, Mireia Crispin-Ortuzar, Huazhu Fu, Chen Li, Zeyu Gao
  ICML 2026.

• Reinforcement Learning for Better Verbalized Confidence in Long-Form Generation
  Caiqi Zhang*, Xiaochen Zhu*, Chengzu Li, Nigel Collier, Andreas Vlachos
  ACL 2026.

2025

• Imagine while Reasoning in Space: Multimodal Visualization-of-Thought
  Chengzu Li*, Wenshan Wu*, Huanyu Zhang, Yan Xia, Shaoguang Mao, Li Dong, Ivan Vulić, Furu Wei
  ICML 2025. [code] [IEEE Spectrum] [TWIML Podcast] [新智元]

• Large Language Models are Miscalibrated In-Context Learners
  Chengzu Li, Han Zhou, Goran Glavaš, Anna Korhonen, Ivan Vulić.
  ACL 2025, Findings. [code]

• Enriching Patent Claim Generation with European Patent Dataset
  Lekang Jiang, Chengzu Li, Stephan Goetz
  EMNLP 2025, Findings.

• Lost in Embeddings: Information Loss in Vision-Language Models
  Wenyan Li, Raphael Tang, Chengzu Li, Caiqi Zhang, Ivan Vulić, Anders Søgaard
  EMNLP 2025 Findings.

2024

• TopViewRS: Vision-Language Models as Top-View Spatial Reasoners
  Chengzu Li*, Caiqi Zhang*, Han Zhou, Nigel Collier, Anna Korhonen, Ivan Vulić
  EMNLP 2024, main (oral). [project website] [code] [data]

• Semantic Map-based Generation of Navigation Instructions
  Chengzu Li, Chao Zhang, Simone Teufel, Rama Sanand Doddipatla, Svetlana Stoyanchev.
  COLING-LREC 2024. [code]

2023

• Generating Data for Symbolic Language with Large Language Models
  Jiacheng Ye, Chengzu Li, Lingpeng Kong, Tao Yu.
  EMNLP 2023, main. [code]

• Binding Language Models in Symbolic Languages
  Zhoujun Cheng, Tianbao Xie, Peng Shi, Chengzu Li, Rahul Nadkarni, Yushi Hu, Caiming Xiong, Dragomir Radev, Mari Ostendorf, Luke Zettlemoyer, Noah A. Smith, Tao Yu.
  ICLR 2023 (spotlight). [code]

2022

• UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
  Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A. Smith, Luke Zettlemoyer, Tao Yu.
  EMNLP 2022, main (oral). [code]

Selected Honors & Awards

• One of 3 nominations (Finalists) by University of Cambridge for the Apple AI/ML PhD Fellowship, 2025.

• Postgraduate Research Fund from Jesus College, Cambridge, 2024 & 2025.

• Lambda Research Grant ($2000 USD in GPU credits), 2025.

• Scholar of Jesus College, Cambridge (for outstanding academic performance), 2024.

• PhD Scholarship, Cambridge Trust, 2023.

• Master Scholarship, Cambridge Trust, 2022.

• National Scholarship (1%), Ministry of Education of China, 2019.

Media Coverage and Presentations

• Invited Guest Lecture at UCL NLP Course by Ehsan Shareghi on Multimodal Reasoning, 2026.

• Invited Talk at Shanghai Artificial Intelligence Laboratory: Visual Planning (ICLR 2026 Oral), 2026.

• Invited Talk at HKUST (GZ): Reason with Multimodal Minds in Space, 2025.

• 机器之心：只用图像也能思考，强化学习造就推理模型新范式！复杂场景规划能力Max, 2025.

• 量子位：纯靠“脑补”图像，大模型推理准确率狂飙80%丨剑桥谷歌新研究, 2025.

• The TWIML AI Podcast with Sam Charringto, 2025.

• IEEE Spectrum, ‘‘Thinking" Visually Boosts AI Problem Solving, 2025.

• 新智元：直接可视化多模态推理过程, 2025.

• BMVA: Trustworthy Multimodal Learning with Foundation Models, 2024.