Despite their successes, machine learning techniques are often stochastic, error-prone and
blackbox. How could they then be used in fields such as theoretical physics and pure …

We present an environment, benchmark, and deep learning driven automated theorem
prover for higher-order logic. Higher-order interactive theorem provers enable the …

This paper describes the TPTP problem library and associated infrastructure, from its use of
Clause Normal Form (CNF), via the First-Order Form (FOF) and Typed First-order Form …

We introduce a theorem proving algorithm that uses practically no domain heuristics for
guiding its connection-style proof search. Instead, it runs many Monte-Carlo simulations …

This paper presents a novel approach to premise selection, a crucial reasoning task in
automated theorem proving. Traditionally, symbolic methods that rely on extensive domain …

Hammers provide most powerful general purpose automation for proof assistants based on
HOL and set theory today. Despite the gaining popularity of the more advanced versions of …

Abstract Development of formal proofs of correctness of programs can increase actual and
perceived reliability and facilitate better understanding of program specifications and their …

ENIGMA is a learning-based method for guiding given clause selection in saturation-based
theorem provers. Clauses from many previous proof searches are classified as positive and …

We describe an implementation of gradient boosting and neural guidance of saturation-style
automated theorem provers that does not depend on consistent symbol names across …

We describe an efficient implementation of given clause selection in saturation-based
automated theorem provers, extending the previous ENIGMA approach. Unlike in the first …