arXiv:2512.10187v3 Announce Type: replace Abstract: LLMs excel at reasoning, but validating their steps remains challenging. Formal verification offers a solution through mechanically checkable proofs. Interactive theorem provers (ITPs) dominate mathematical reasoning but require detailed low-level proof steps, while auto-active verifiers offer automation but focus on software verification. Recent work has begun bridging this divide by evaluating LLMs for software verification in ITPs, but the complementary direction, LLMs for mathematical theorem proving in auto-active verifiers, remains unex
The accelerating capabilities of large language models (LLMs) are enabling them to tackle increasingly complex cognitive tasks, including formal reasoning and mathematical proof generation, which is a frontier for AI development.
This breakthrough advances the reliability and explainability of AI reasoning by mechanizing proof validation, which is crucial for high-stakes applications in software verification and scientific discovery.
The explicit methodology for applying LLMs to mathematical theorem proving in auto-active verifiers offers a new pathway for AI to contribute to formal knowledge generation and software assurance.
- · AI researchers
- · Software verification industry
- · Formal methods community
- · Mathematics community
- · Manual proof assistants
Further integration of LLMs into formal verification tools will accelerate the development of provably correct software and complex systems.
Reduced human effort in mathematical proof generation could lead to a faster pace of discovery and validation in various scientific fields.
The development of highly reliable, AI-generated proofs could fundamentally alter the nature of formal education in logic and mathematics.
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Read at arXiv cs.LG