Theorem Proving

Theorem proving, the automated generation and verification of mathematical proofs, aims to leverage computational power to advance mathematical discovery and verification. Current research heavily utilizes large language models (LLMs) within various theorem proving environments (e.g., Lean, Isabelle, Coq), focusing on improving proof generation accuracy through techniques like subgoal-based learning, data augmentation (including synthetic data generation), and enhanced prompt engineering, often incorporating retrieval-augmented methods and multi-agent systems. This field is significant for its potential to automate complex mathematical reasoning, accelerate scientific discovery, and improve the reliability of software verification.