Abstract Machine-assisted theorem proving refers to the process of conducting structured reasoning to automatically generate proofs for mathematical theorems. Recently, there has been a surge of interest in using machine learning models in conjunction with proof assistants to perform this task. In this paper, we introduce Pantograph, a tool that provides a versatile interface to the Lean 4 proof assistant and enables efficient proof search via powerful search algorithms such as Monte Carlo Tree Search. In addition, Pantograph enables high-level reasoning by enabling a more robust handling of Lean 4’s inference steps. We provide an overview of Pantograph’s architecture and features. We also report on an illustrative use case: using machine learning models and proof sketches to prove Lean 4 theorems. Pantograph’s innovative features pave the way for more advanced machine learning models to perform complex proof searches and high-level reasoning, equipping future researchers to design more versatile and powerful theorem provers.

Abstract Satisfiability modulo theories (SMT) solvers are widely used to ensure the correctness of safety- and security-critical applications. Therefore, being able to trust a solver’s results is crucial. One way to increase trust is to generate independently checkable proof certificates, which record the reasoning steps done by the solver. A key challenge with this approach is that it is difficult to efficiently and accurately produce proofs for reasoning steps involving term rewriting rules. Previous work showed how a domain-specific language, Rare, can be used to capture rewriting rules for the purposes of proof production. However, in that work, the Rare rules had to be trusted, as the correctness of the rules themselves was not checked by the proof checker. In this paper, we present IsaRare, a tool that can automatically translate Rare rules into Isabelle/HOL lemmas. The soundness of the rules can then be verified by proving the lemmas. Because an incorrect rule can put the entire soundness of a proof system in jeopardy, our solution closes an important gap in the trustworthiness of SMT proof certificates. The same tool also provides a necessary component for enabling full proof reconstruction of SMT proof certificates in Isabelle/HOL. We evaluate our approach by verifying an extensive set of rewrite rules used by the cvc5 SMT solver.

In probabilistic programming languages (PPLs), a critical step in optimization-based inference methods is constructing, for a given model program, a trainable guide program. Soundness and effectiveness of inference rely on constructing good guides, but the expressive power of a universal PPL poses challenges. This paper introduces an approach to automatically generating guides for deep amortized inference in a universal PPL. Guides are generated using a type-directed translation per a novel behavioral type system. Guide generation extracts and exploits independence structures using a syntactic approach to conditional independence, with a semantic account left to further work. Despite the control-flow expressiveness allowed by the universal PPL, generated guides are guaranteed to satisfy a critical soundness condition and moreover, consistently improve training and inference over state-of-the-art baselines for a suite of benchmarks.