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Xinrui (Henry) Li
I am a first-year PhD student in the
Computer Science Department at
Carnegie Mellon University, advised by
Jeremy Avigad.
I work at the intersection of formal methods, automated reasoning, and machine learning.
I am especially interested in making AI systems that formalize and prove mathematics
trustworthy and their result more useful and understandable.
Before CMU, I received my B.A. in Applied Mathematics and Computer Science from
UC Berkeley, where I worked with
Kaiyu Yang on LLM-based theorem proving across
Lean, Coq, and Isabelle, and with
Venkatesan Guruswami on a Lean library
formalizing results in coding and information theory.
Email /
CV /
GitHub /
LinkedIn
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Research
I am interested in AI for mathematics and formal verification, and I am especially attracted by combination of neural and symbolic mehtods.
I am currently researching on autoformalization and how to evaluate it. As formalizatino library is growing drastically with the development of AI, we will need further judgement on
the faithfulness of a formalization beyond its correctness.
Representative work is highlighted.
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SemantEq: Faithful Bidirectional Equivalence of Formal Statements
Xinrui Li,
Pawan Sasanka Ammanamanchi,
Siddharth Bhat,
Rianna Jamdar,
Yongxi Lin,
Stella Biderman
arXiv preprint, 2026 · under review at ICLR 2027
arXiv
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code
Logical equivalence is too permissive a test of whether a formal statement faithfully
matches its reference, because a strong prover can connect any two provable statements
through P ↔ True ↔ Q. SemantEq is a finer relation under which each
statement must turn into the other by meaning-preserving steps alone, such as logical
rearrangement and rewriting with the library's own equations, and a proof is judged by the
term Lean checks rather than by its tactic script. SemantEq-Eval searches for such
chains with no model and reaches 100% precision on ProofNetVerif.
SemantEq-ProofAudit holds proofs written by a language model to the same standard,
accepting none of the non-equivalent pairs while raising recall from 65% to 81%.
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ProofGym: Unifying LLM-Based Theorem Proving Across Formal Systems
Xinrui Li,
Wenjie Ma,
Hangrui Bi,
Zhaoyu Li,
Xujie Si,
Kaiyu Yang
NeurIPS 2025 Workshop on Mathematical Reasoning and AI (MATH-AI), 2025
OpenReview
Infrastructure for LLM-based theorem proving that spans Lean, Coq, and Isabelle, so that the
same prover, data pipeline, and evaluation can be used across proof assistants instead of
being rebuilt for each one.
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Teaching
CS 170: Efficient Algorithms and Intractable Problems, UC Berkeley
Tutor, Spring 2025 and Fall 2025
CS 189: Introduction to Machine Learning, UC Berkeley
Reader, Spring 2025
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Misc
Outside research I play video games and flight simulators, and I watch anime.
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