AI-Assisted Autoformalization of Combinatorics Problems in Proof Assistants

Proof assistants such as Coq and LEAN have been increasingly used by renowned mathematicians to formalize and prove mathematical theorems. Despite their growing use, writing formal proofs is challenging, and even the first step of stating the problem formally is difficult as it requires a deep understanding of these systems’ languages. Recent advancements in AI, especially large language models (LLMs), have shown promise in automating this formalization task. However, domains such as combinatorics pose significant challenges for AI-assisted proof assistant systems due to their cryptic nature and the lack of existing data to train AI models. We introduce AutoForm4Lean, a system designed to leverage LLMs to aid in formalizing combinatorics problems for LEAN. By combining LLMs with techniques from software engineering and formal methods such as validation and synthesis, AutoForm4Lean generates formalizations of combinatorics problems more effectively than the current state-of-the-art LLMs. Moreover, this project seeks to provide a comprehensive collection of formalized combinatorics problems, theorems, and lemmas, which would enrich the LEAN library and provide valuable training data for LLMs. Preliminary results demonstrate the effectiveness of AutoForm4Lean in formalizing combinatorics problems in LEAN, making a step forward in AI-based theorem proving.

Authors

Institutions

Publication Details

Published
2025-04-27
DOI
https://doi.org/10.1109/icse-nier66352.2025.00006
Citations
1
Primary Topic
Logic, programming, and type systems
Type
article
Field-Weighted Citation Impact
2.48

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AI-Assisted Autoformalization of Combinatorics Problems in Proof Assistants

Long Doan, ThanhVu Nguyen
1 citations
Logic, programming, and type systems
2.48
article

AI-Assisted Autoformalization of Combinatorics Problems in Proof Assistants

Long Doan, ThanhVu Nguyen
article en
1 citations

Abstract

Proof assistants such as Coq and LEAN have been increasingly used by renowned mathematicians to formalize and prove mathematical theorems. Despite their growing use, writing formal proofs is challenging, and even the first step of stating the problem formally is difficult as it requires a deep understanding of these systems’ languages. Recent advancements in AI, especially large language models (LLMs), have shown promise in automating this formalization task. However, domains such as combinatorics pose significant challenges for AI-assisted proof assistant systems due to their cryptic nature and the lack of existing data to train AI models. We introduce AutoForm4Lean, a system designed to leverage LLMs to aid in formalizing combinatorics problems for LEAN. By combining LLMs with techniques from software engineering and formal methods such as validation and synthesis, AutoForm4Lean generates formalizations of combinatorics problems more effectively than the current state-of-the-art LLMs. Moreover, this project seeks to provide a comprehensive collection of formalized combinatorics problems, theorems, and lemmas, which would enrich the LEAN library and provide valuable training data for LLMs. Preliminary results demonstrate the effectiveness of AutoForm4Lean in formalizing combinatorics problems in LEAN, making a step forward in AI-based theorem proving.

George Mason University (US)
National Science Foundation
Openalex Percentile: Top 10%
Logic, programming, and type systems
2.48
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.