Skip to content

Prove2Me: An Open Collaborative Platform for Scaling Math Formalization

Aug 2026 · 3 citations · 33 references
Computer Science

TL;DR

Prove2Me aims to turn math formalization into a scalable, crowd-sourced effort open to anyone with an agent, and designed mechanisms and a specialized harness that enable large-scale collaboration so that agents can build on one another's work and freely reuse existing results.

Abstract

Proof assistants such as Lean 4 promise the paradigm of formally verified mathematics, but large-scale formalization projects have faced major barriers to entry, including the need for expertise in formal verification (as well as the underlying mathematics) and the significant time required for writing formal proofs. AI coding agents have dramatically reduced these barriers; human users can now use natural language to prompt agents to write complex proofs in Lean. This opens up the intriguing possibility of internet-scale mathematical collaboration involving both humans and AI agents, where correctness is machine-checked. To realize this possibility, we introduce Prove2Me (https://prove2.me), an open collaborative platform for formalizing mathematics. Users launch formalization"missions", to which AI agents contribute formal proofs toward completion. We designed mechanisms and a specialized harness in Prove2Me that enable large-scale collaboration so that agents can build on one another's work and freely reuse existing results. In doing so, Prove2Me aims to turn math formalization into a scalable, crowd-sourced effort open to anyone with an agent.

View source

Similar papers

Preprint Sep 2026

ProofGap: Benchmarking Step-Level Formal Reasoning with Local Obligations Derived from Natural-Language Solutions

ProofGap is a fine-grained benchmark for step-level formal reasoning constructed through a natural-language proof-processing pipeline that decomposes each reasoning step into one or more aligned proof gaps, enabling more precise localization of model failures.

Li-Han Xie, Zhi-Cheng Hui, Ying-Jun Lan et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Magenta: Closing the Loop Between Mathematical Reasoning and Lean Verification

This paper introduces Magenta, a training-free agentic pipeline that, given only a natural-language problem, produces an answer, expresses it as a Lean 4 statement, and constructs a machine-checked proof, which achieves 100% accuracy across all evaluated olympiad benchmarks.

Joshua Ong Jun Leang, Hao-Nan Li, Zheng-Yang Zhao et al. · 0 citations
#artificial intelligence Preprint Oct 2026

AIProver: Agentic Auto-Formalization of Mathematical Research via Certificate-Driven Evolving Harness

Proof auto-formalization translates natural-language (NL) theorems and proofs into a formal language (FL) such as Lean, enabling mechanical verification. Despite rapid progress, research-level proofs often depend on concepts missing from leading proof assistant libraries (e.g., Lean's Mathlib), and successful compilati...

Prithwish Jana, Việt Bách Hoàng, Logan Luna et al. · 0 citations
Review Sep 2026

Long-horizon autoformalization of a core theorem underlying MIP* = RE

This work completed a machine-checked Lean 4 proof of the quantum soundness of the classical low individual-degree test, a core theorem underlying MIP* = RE, and provides a verified foundation for quantum complexity.

Si-Rui Lu, Rui-Xuan Deng, David Zhu et al. · 1 citation
#artificial intelligence Preprint Sep 2026

Cogentic: Multi-Agent Orchestration for Automated Proof Discovery

We present Cogentic, a multi-agent harness for automated proof discovery on open research problems. While frontier language models can generate strong mathematical ideas in a single shot, single-shot generation is often insufficient for open problems that require exploring multiple competing conjectures, overcoming sub...

Yang Cai, Vineet Gupta, Yan-Chen Jiang et al. · 0 citations
#human-computer interacti... Preprint Sep 2026

LeanSide: A Formally Verified Co-Reasoning System for Natural-language Proofs

Large language models are increasingly used as collaborators on deductive-reasoning tasks, but their outputs can hallucinate or pull users away from intended reasoning. Formal proof assistants provide machine-checked verification, but have a steep learning curve and require more granular reasoning than human written pr...

Chen-Jun Guo, Manooshree Patel, Arnav Mehta et al. · 0 citations

Related blog posts

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.