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Lean Refactor optimizes LLM-generated proofs for length and speed

Researchers have developed Lean Refactor, a new framework designed to optimize proofs generated by large language models (LLMs) in the Lean mathematical proof assistant. This system addresses key challenges such as proof length, compilation cost, and version compatibility, which are often in tension. By using a retrieval-augmented agentic approach with a curated database of refactoring strategies, Lean Refactor achieves significant compression rates and reduces compilation times, outperforming previous methods and demonstrating improved version transfer capabilities. AI

IMPACT Introduces a novel method for improving the efficiency and robustness of LLM-generated mathematical proofs, potentially accelerating formal verification efforts.

RANK_REASON Publication of an academic paper detailing a new framework for optimizing LLM-generated proofs.

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Jialin Lu, Soonho Kong, Rodrigo Stehling, Kaiyu Yang, Zhangyang Wang, Weiran Sun, Wuyang Chen ·

    Lean Refactor: Multi-Objective Controllable Proof Optimization via Agentic Strategy Search

    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…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Lean Refactor: Multi-Objective Controllable Proof Optimization via Agentic Strategy Search

    Lean Refactor presents a retrieval-augmented agentic framework that improves Lean proof refactoring by addressing multi-objective optimization, version compatibility, and scalability challenges through curated strategy databases and version-filtered retrieval.