We design and conduct a simple experiment to study whether neural networks can perform several steps of approximate reasoning in a fixed dimensional latent space.
Our experiments show that the theorem prover trained with this exploration mechanism outperforms provers that are trained only on human proofs.
Ranked #3 on Automated Theorem Proving on HOList benchmark
We present an environment, benchmark, and deep learning driven automated theorem prover for higher-order logic.
Ranked #2 on Automated Theorem Proving on HOList benchmark