PUTNAMBENCH: A New Benchmark for Neural Theorem-Provers
Automating mathematical reasoning is a key goal in AI, and frameworks like Lean 4, Isabelle, and Coq have played a significant role. Neural theorem-provers aim to automate this process, but there is a lack of comprehensive benchmarks for evaluating their effectiveness.
Addressing the Challenge
PUTNAMBENCH is a new benchmark designed to evaluate neural theorem-provers using problems from the William Lowell Putnam Mathematical Competition, known for its challenging college-level mathematics problems. It includes 1697 formalizations of 640 issues, available in multiple proof languages, ensuring a comprehensive evaluation across different theorem-proving environments.
Evaluating Theorem-Provers
The evaluation of PUTNAMBENCH involved testing several neural and symbolic theorem-provers on the formalizations. The results showed that current methods could solve only a handful of the PUTNAMBENCH problems, highlighting the need for more advanced neural models.
Setting a New Standard
PUTNAMBENCH sets a new standard for rigor and comprehensiveness in evaluating theorem-proving methods. It addresses the limitations of existing benchmarks and will be crucial in driving future research and innovation in the field of AI-driven theorem proving.
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