Summary of Improver: Agent-based Automated Proof Optimization, by Riyaz Ahuja et al.
ImProver: Agent-Based Automated Proof Optimization
by Riyaz Ahuja, Jeremy Avigad, Prasad Tetali, Sean Welleck
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG); Logic in Computer Science (cs.LO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the problem of automated proof optimization in formal proof systems like Lean. Large language models (LLMs) have been used to generate formal proofs, but these models often lack the ability to optimize proofs for specific criteria such as length or readability. To address this limitation, the authors propose ImProver, a LLM-based agent that rewrites proofs to optimize arbitrary user-defined metrics in Lean. The authors incorporate various improvements into ImProver, including the use of symbolic Lean context and error-correction mechanisms. They test ImProver on real-world undergraduate, competition, and research-level mathematics theorems and find that it can rewrite proofs to make them shorter, more modular, and more readable. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computers to help humans write better math proofs. Right now, we have machines that can generate math proofs, but they don’t always do a good job of making those proofs easy to read or understand. The authors are trying to fix this by creating an AI agent called ImProver that can take an existing proof and make it better according to certain rules, like being shorter or more organized. |
Keywords
* Artificial intelligence * Optimization
