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ProofWala framework enables multilingual theorem-proving research

By PulseAugur Editorial · [1 sources] ·

Researchers have developed ProofWala, a new framework designed to facilitate multilingual proof data synthesis and theorem-proving for neural approaches. This framework includes a reusable library for interacting with interactive theorem provers (ITPs) and supports project-wide analysis and parallel experimentation. By training models multilingually across different ITPs like Lean 4 and Rocq, the system demonstrates improved cross-lingual and cross-domain transfer capabilities, showing statistically significant gains in specific mathematical domains. AI

IMPACT Enables more robust and scalable research in formal verification and automated theorem-proving by facilitating multilingual data synthesis and cross-lingual transfer.

RANK_REASON This is a research paper describing a new framework for theorem-proving. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

ProofWala framework enables multilingual theorem-proving research

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This is a research paper describing a new framework for theorem-proving. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Amitayush Thakur, George Tsoukalas, Greg Durrett, Swarat Chaudhuri ·

    ProofWala: A Framework for Multilingual Proof Data Synthesis and Theorem-Proving

    arXiv:2502.04671v3 Announce Type: replace Abstract: Neural approaches to theorem proving require robust infrastructure for interfacing with interactive theorem provers (ITPs), extracting structured proof data, and executing proof search at scale. However, existing tooling is ofte…

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