ProofWala framework enables multilingual theorem-proving research

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 →

• Amitayush Thakur
• ProofWala
• Rocq

• paper
• model release

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