Large language models (LLMs) have shown promise in proving formal theorems using proof
assistants such as Lean. However, existing methods are difficult to reproduce or build on …

In theorem proving, the task of selecting useful premises from a large library to unlock the
proof of a given conjecture is crucially important. This presents a challenge for all theorem …

E 2.3 is a theorem prover for many-sorted first-order logic with equality. We describe the
basic logical and software architecture of the system, as well as core features of the …

The Mizar system is one of the pioneering systems aimed at supporting mathematical proof
development on a computer that have laid the groundwork for and eventually have evolved …

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 …

Labeled data for imitation learning of theorem proving in large libraries of formalized
mathematics is scarce as such libraries require years of concentrated effort by human …

We study the effectiveness of neural sequence models for premise selection in automated
theorem proving, a key bottleneck for progress in formalized mathematics. We propose a two …

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

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 …

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 …