arXiv:2605.20244v1 Announce Type: cross Abstract: We present Lean Refactor, a plug-and-play retrieval-augmented agentic framework for multi-objective, controllable, and version-robust refactoring of Lean proofs. LLM-generated proofs are notoriously correct-but-verbose and brittle across library versions, yet existing refactoring works overlook three practical challenges: 1) Lean refactoring is natively multi-objective (proof length, compilation cost, and version compatibility are often in tension); 2) Lean repositories have fragile compatibility, whereas LLM releases are unaware of Lean/Mathli
The proliferation of LLMs capable of generating foundational proofs is creating an urgent need for tools that can refine and maintain these complex artifacts across evolving software environments.
This development addresses a critical challenge in leveraging AI for formal verification, enabling more robust, efficient, and maintainable AI-generated proofs, which are foundational for complex software and hardware systems.
Proof generation and maintenance stand to become significantly more efficient and reliable through multi-objective optimization and agentic strategies, moving beyond simple correctness to practical utility.
- · AI-powered formal verification platforms
- · Software and hardware developers
- · Researchers in AI and formal methods
- · Cloud computing providers
- · Manual proof engineers
- · Systems with brittle, unoptimized proof implementations
Increased adoption of AI for complex formal verification tasks due to improved proof quality and maintainability.
Reduced incidence of critical bugs and security vulnerabilities in systems built with formally verified components.
Acceleration of autonomous system development and deployment, leveraging highly reliable and verifiable AI-generated code and logic.
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Read at arXiv cs.LG