LeanMarathon: Toward Reliable AI Co-Mathematicians through Long-Horizon Lean Autoformalization
arXiv:2606.05400v1 Announce Type: cross Abstract: Long-horizon autoformalization of research mathematics fails not only at hard lemmas, but at scale: statements drift, dependencies tangle, context decays, and local repairs corrupt distant work. We present LeanMarathon, a multi-agent harness for reliable research-level Lean autoformalization. Its core abstraction is an evolving blueprint: a Lean file that serves simultaneously as formal proof skeleton, natural-language proof graph, and shared system of record. Four contract-scoped agents construct, audit, prove, and repair this blueprint. These
This development indicates significant progress in AI's capability to autonomously formalize complex mathematics, moving beyond basic tasks to research-level challenges, a long-standing goal within AI research.
Reliable AI co-mathematicians could dramatically accelerate scientific discovery and engineering by automating proof generation and verification, impacting fields from cryptography to material science.
The ability of AI to co-create and formalize mathematical proofs reliably at scale changes the collaboration paradigm between humans and AI in highly abstract and rigorous domains.
- · AI research labs
- · Mathematics community
- · Software verification industry
- · Science and engineering
- · Manual proof assistants
- · Traditional highly manual mathematical formalization processes
AI models will become more integrated into the early stages of mathematical and scientific discovery, not just execution.
Reduced bottlenecks in formal verification could accelerate the development of complex, provably correct software and hardware systems.
The development of new mathematical theories could be accelerated by AI, leading to unexpected breakthroughs in various scientific fields.
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Read at arXiv cs.CL