LLMs discover new theorems using in-context proof learning in Lean

Researchers have developed a new pipeline called the Conjecturing-Proving Loop (CPL) that uses Large Language Models (LLMs) to discover new mathematical theorems and generate formal proofs in Lean 4. CPL iteratively creates conjectures and attempts to prove them, leveraging previously generated theorems and proofs for in-context learning. This approach demonstrates improved discovery rates for complex theorems compared to simultaneous statement and proof generation, highlighting the effectiveness of self-generated context for neural theorem proving. AI

IMPACT Introduces a novel method for LLMs to discover mathematical theorems, potentially accelerating formal verification and mathematical research.

RANK_REASON This is a research paper detailing a new method for theorem discovery using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Conjecturing-Proving Loop
• Kazumi Kasaura
• Large Language Models

• paper
• model release

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →