ImProver: AI Agent for Automated Proof Optimization in Lean
Researchers have introduced ImProver, a large-language-model agent designed to automatically rewrite formal proofs in the Lean proof assistant to optimize user-defined criteria such as readability, conciseness, or modular structure. The work addresses the new problem of automated proof optimization, which aims to transform a correct proof into one that better suits downstream uses like learning tasks or style adherence. The study, published on arXiv (2410.04753), finds that naive application of LLMs to proof optimization is insufficient, and ImProver incorporates specific techniques to achieve effective rewriting. The agent can optimize arbitrary metrics defined by the user, making proofs more suitable for human consumption or further automated processing. This research highlights the potential of AI to refine formal mathematical proofs beyond mere correctness.
Key facts
- ImProver is an LLM agent for automated proof optimization in Lean.
- The problem involves rewriting proofs to optimize criteria like readability, conciseness, or modularity.
- Naive LLM application falls short for proof optimization.
- The work is published on arXiv with ID 2410.04753.
- Optimized proofs are important for learning tasks and downstream use.
- ImProver can handle arbitrary user-defined metrics.
- The agent ensures rewritten proofs remain correct.
- The research is from the field of AI and formal mathematics.
Entities
Institutions
- arXiv