About Me
I am a direct doctoral student at DSA Thrust, HKUST(GZ) supervised by Prof. Zhijiang Guo. Currently, I am a Visiting Researcher at ETH Zürich (D-INFK), working with Prof. Rasmus Kyng and Dr. Sorrachai Yingchareonthawornchai on verifiable autoformalization and formal reasoning in Theoretical Computer Science. Previously, I was a Student Researcher at ByteDance Seed and DeepSeek AI.
I graduated with a Bachelor's degree (Honours) in Mathematics and Applied Mathematics from Xiamen University in 2025. My undergraduate thesis focused on formalizing Auction Theory in Lean 4 with Mathlib4, supervised by Prof. Ma Jiajun.
Research Interests
My research focuses on:
- Formal Mathematical Reasoning: Developing LLMs for automated theorem proving through Supervised Fine-Tuning and Reinforcement Learning approaches
- Autoformalization: Building multimodal agents and pipelines to translate natural language mathematics into formal proofs
- AI for Mathematics: Creating benchmarks, tools, and methods to advance AI's capability in mathematical reasoning
Featured Projects
Z.Z. Ren, Zhihong Shao, Junxiao Song, Huajian Xin, Haocheng Wang, Wanjia Zhao, Liyue Zhang, Zhe Fu, Qihao Zhu, Dejian Yang, Z.F. Wu, Zhibin Gou, Shirong Ma, Hongxuan Tang, Yuxuan Liu, Wenjun Gao, Daya Guo, Chong Ruan
ArXiv Preprint
DeepSeek-Prover-V2: Advancing Formal Mathematical Reasoning via Reinforcement Learning for Subgoal Decomposition
DeepSeek-Prover-V2, a 671B-sized model achieving SOTA results on miniF2F (88.9%) and PutnamBench (47 out of 658). Our approach combines reinforcement learning with subgoal decomposition to enhance formal mathematical reasoning capabilities.
Huajian Xin, Z.Z. Ren, Junxiao Song, Zhihong Shao, Wanjia Zhao, Haocheng Wang, Bo Liu, Liyue Zhang, Xuan Lu, Qiushi Du, Wenjun Gao, Qihao Zhu, Dejian Yang, Zhibin Gou, Z.F. Wu, Fuli Luo, Chong Ruan
ICLR 2025
DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search
A novel hybrid approach combining LLMs and Monte-Carlo tree search for automated theorem proving. Achieved SOTA results on miniF2F (63.5%) and ProofNet (25.3%).
ByteDance Seed Team
ArXiv Preprint
Solving Formal Math Problems by Decomposition and Iterative Reflection
An agent-based training-free framework that enables general-purpose Large Language Models (gLLMs) to solve complex formal proofs in Lean 4.
Professional Experience
Scientific Assistant I
ETH Zürich
Aug 2025 - Present · Zürich, Switzerland
- Focusing on building Multi-gLLM Agent for formal reasoning in Theoretical Computer Science
Student Researcher
ByteDance Co., Ltd.
Jan 2025 - Jul 2025 · Shanghai, China
- Built Scoring & Self-refinement agent pipeline for Natural Language Proof
- Proposed sketch-incorporated long Chain-of-Thought formal reasoning method
- Led data initiatives for DeltaProver
Student Researcher
DeepSeek AI Co., Ltd.
June 2024 - Sep 2024 · Beijing, China
- Developed ProverBench, a domain-categorized benchmark with 325 problems for evaluating LLM theorem proving
- Contributed to DeepSeek-Prover-V1.5 and V2 model development
Education
Xiamen University
Sep 2020 - Jun 2025
Bachelor of Science in Mathematics and Applied Mathematics (Honours)
Final Thesis: Formalization of Auction Theory using Lean 4 and Mathlib4
[GitHub]
Academia Service
Teaching Assistant
Invited Talks
- Verifiable Theorem Proving with Large Language Models - Xi'an Jiaotong-Liverpool University (Dec. 2025 )
- AI for Theoretical Computer Science in Lean - ETH Zürich (Aug. 2025 )
- Frontier of Formal Theorem Proving with LLMs - Xiamen University (Dec. 2024)
Conference Reviewer
- ICLR 2026
- ACL 2025
- 2nd AI4Math Workshop @ ICML 2025
Workshops
Contact
Feel free to reach out to me via email at haocheng.wang [at] inf.ethz.ch or connect with me on GitHub.