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 …

The decision problem for Boolean satisfiability, generally referred to as SAT, is the
archetypal NP-complete problem, and encodings of many problems of practical interest exist …

Self-supervised learning has been shown to be very effective in learning useful
representations, and yet much of the success is achieved in data types such as images …

The goal of machine learning is to facilitate a computer to execute a specific task without
explicit instruction by an external party. Quantum foundations seek to explain the conceptual …

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

Sum-product networks (SPNs) are expressive probabilistic models with a rich set of exact
and efficient inference routines. However, in order to guarantee exact inference, they require …

We propose a deep learning-based approach to the problem of premise selection: selecting
mathematical statements relevant for proving a given conjecture. We represent a higher …

Humans prove theorems by relying on substantial high-level reasoning and problem-
specific insights. Proof assistants offer a formalism that resembles human mathematical …

The main ingredients underlying this approach are efficient automatic theorem provers that
can cope with hundreds of axioms, suitable translations of the proof assistant's logic to the …

We introduce an environment that allows interaction with an Isabelle server in an
incremental manner. With this environment, we mined the Isabelle standard library and the …