Neural Theorem

Neural theorem proving aims to automate the process of mathematical proof discovery using artificial neural networks. Current research focuses on improving the ability of these networks to handle complex contexts and diverse problem types, employing techniques like large language models, Monte Carlo Tree Search, and dynamic sampling methods within interactive theorem provers. These advancements are evaluated on benchmarks like PutnamBench and miniF2F, pushing the boundaries of automated reasoning and potentially impacting fields such as formal verification and program synthesis. The ultimate goal is to create systems capable of solving complex mathematical problems and verifying the correctness of complex software.