arXiv:2606.17581v1 Announce Type: cross Abstract: We present a dependent-type-based prover designed around the way LLMs (and humans) tend to write mathematics, complementing existing systems such as Lean and Rocq. Its core design choices are a surface that imitates mathematical natural language and a rule-driven automation layer that closes the routine steps a textbook would omit, so that an accepted proof can be re-emitted as a checked Lean file. Early experiments suggest that, even without any prover-specific training data, LLMs can learn to use it effectively on the miniF2F benchmark. Lean
The proliferation of Large Language Models (LLMs) in generating complex text, including mathematical proofs, necessitates robust verification tools.
This development suggests significant progress in enabling LLMs to produce verifiable and reliable mathematical content, crucial for scientific advancement and AI safety.
LLMs can now be paired with a specialized prover that imitates human mathematical reasoning, allowing for the verification of generated proofs and potentially expanding the scope of AI-assisted discovery.
- · AI/ML researchers
- · Mathematics community
- · Formal verification tooling providers
- · Manual proof verification (for routine steps)
Increased trust and utility of LLMs in generating and validating complex technical content.
Accelerated discovery of new mathematical theorems and scientific breakthroughs due to efficient AI-human collaboration.
Potential for an exponential increase in verifiable AI-generated knowledge, leading to new forms of scientific collaboration and education.
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Read at arXiv cs.AI