Hiprel Group

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Research Directions and Objectives

Our group is dedicated to advancing Neuro-Symbolic AI , bridging the strengths of learning-based and logic-based approaches. We focus on developing AI techniques that enhance the scalability and efficiency of automated reasoning systems, while also enabling AI models themselves to acquire reasoning and verification capabilities.

Beyond fundamental research, we develop specialized Neuro-Symbolic AI methods for reliable software, focusing on enhancing the reliability and robustness of systems ranging from large-scale cloud infrastructures to modern AI applications (which themselves can be viewed as complex software systems). We also explore verifiable code generation to make software development more trustworthy, transparent, and error-resistant.

Current Group Members

Faculty

Wenxi Wang

PhD Students

Zichen Xie (Summer 2025 - )

Research: Neuro-Symbolic AI for Verifiable Code Generation; Improving the Reasoning and Verification Capabilities of LLMs

Lize Shao (Summer 2025 - )

Research: Neuro-Symbolic AI for Verifiable Code Generation

Xiaoyan Guo (Summer 2026 - )

Research: Neuro-Symbolic AI and NLP for Software Testing

Master Students

Ao (David) Guo (Summer 2026 - )

Research: LLM for Detecting Ambiguities in Specifications

Derek Joseph Hansen (Fall 2025 - )

Research: LLM for Inline Testing

Undergraduate Students

Jerry Li (Fall 2024 - )

Research: LLM for Specifications

Carter Opperman (Fall 2024 - )

Research: AI for SAT Solving

Internship Students

Alumni

Mrigank Pawagi (Spring 2025 - Summer 2026)

Research: LLMs for Detecting Bugs in Model Specifications and Internet Protocol Specifications

Role: Research Intern

Next Position: PhD at UPenn

Aaditi Rai (Fall 2025)

Research: VeriContest Benchmark Generation

Role: Undergraduate

Tianyi Huang (Fall 2024 - Spring 2025)

Research: LLMs and Deep Learning for SAT Solving

Role: Research Intern

Next Position: PhD at NUS

Chaitanya Rajendra Shahane (Fall 2024 - Fall 2025)

Research: LLMs for Software Testing, Software Testing for Deep Learning Libraries

Role: Master

Next Position: AI Engineer at RootLogic Systems

Publications from Our Group

Chaitanya Shahane, Derek Hansen, Wenxi Wang, Pengyu Nie
Differential Inline Testing: Framework, Test Generation, and Application
The 36th IEEE International Conference on Collaborative Advances in Software and Computing (CASCON 2026) (to appear)
Arjun Tandon, Mehmet Fırat Dündar, Milkiyas Gebremichael Gebru, Darko Marinov, Yiling Lou, Wenxi Wang
Re-evaluating Detection of Equivalent Mutants Using LLMs: We Should Properly Measure How Far We Are
The 35th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2026)
PDF
Zichen Xie, Wenxi Wang
Can LLMs Reason Like Automated Theorem Provers for Rust Verification? VCoT-Bench: Evaluating via Verification Chain of Thought
The forty-third International Conference on Machine Learning (ICML 2026)
PDF
Mrigank Pawagi, Lize Shao, Hyeonmin Lee, Yixin Sun, Wenxi Wang
RFCScope: Detecting Logical Ambiguities in Internet Protocol Specifications
The 40th IEEE/ACM International Conference on Automated Software Engineering (ASE 2025)
PDF Slides Poster