arXiv:2502.04671v3 Announce Type: replace-cross 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 often assistant-specific and oriented toward file-level execution, making repository-scale analysis and parallel experimentation challenging. We present ProofWala, a multilingual proof engineering framework built around \texttt{itp-interface}, a reusable library for programmatic interaction with ITPs. For Lean 4, we
The increasing sophistication and scale of neural approaches to theorem proving necessitate more robust and flexible tooling to manage complex proof data and experimentation.
This framework could significantly accelerate the development and application of AI in formal verification and automated reasoning, impacting software reliability, cybersecurity, and mathematical discovery.
The ability to synthesize multilingual proof data and interface programmatically with interactive theorem provers at scale becomes more accessible and standardized beyond assistant-specific solutions.
- · AI/ML researchers in formal methods
- · Developers of interactive theorem provers
- · Industries reliant on formal verification
- · Computer science education
- · Companies with proprietary, closed-source ITPs
- · Manual proof engineers
It becomes easier to train robust AI models for theorem proving across multiple prover systems and languages, leading to faster research cycles.
The improved accessibility and interoperability of ITPs could lead to a broader adoption of formal methods in software development and critical systems, enhancing reliability.
Automated theorem proving could reach a 'GPT moment' where complex mathematical proofs and formal verifications are generated and checked with minimal human intervention, accelerating scientific progress.
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