Lean Refactor: Agentic Framework for Multi-Objective Proof Optimization
Researchers have introduced Lean Refactor, a retrieval-augmented agentic framework designed to refactor Lean proofs with multi-objective control. The framework addresses three key challenges: multi-objective optimization (proof length, compilation cost, version compatibility), fragile version compatibility across Lean/Mathlib releases, and scalability issues with training-based pipelines. Lean Refactor uses a frozen agentic LLM guided by retrievals from a curated database of refactoring strategies annotated with metadata such as supported versions and expected cost reduction. The system is plug-and-play and version-robust, eliminating the need for repeated fine-tuning with new LLM releases. The work is published on arXiv under identifier 2605.20244.
Key facts
- Lean Refactor is a plug-and-play retrieval-augmented agentic framework
- It targets multi-objective controllable refactoring of Lean proofs
- Addresses three challenges: multi-objective optimization, version compatibility, and scalability
- Uses a frozen agentic LLM with retrievals from a curated strategy database
- Database contains multi-objective refactoring strategies with metadata
- Metadata includes supported Lean/Mathlib versions and expected compilation-cost reduction
- Eliminates need for repeated fine-tuning with new LLM releases
- Published on arXiv with identifier 2605.20244
Entities
Institutions
- arXiv