Synthetic theories such as homotopy type theory axiomatize classical mathematical objects such as spaces up to homotopy. Although theorems in synthetic theories translate to theorems about the axiomatized structures on paper, this fact has not yet been exploited in proof assistants. This makes it challenging to formalize results in classical mathematics using synthetic methods. For example, Cubical Agda supports reasoning about cubical types, but cubical proofs have not been translated to proofs about cubical set models, let alone their topological realizations. To bridge this gap, we present SynthLean: a proof assistant that combines reasoning using synthetic theories with reasoning about their models. SynthLean embeds Martin-Löf type theory as a domain-specific language in Lean, supporting a bidirectional workflow: constructions can be made internally in Martin-Löf type theory as well as externally in a model of the theory. A certifying normalization-by-evaluation typechecker automatically proves that internal definitions have sound interpretations in any model; conversely, semantic entities can be axiomatized in the syntax. Our implementation handles universes, Σ, Π, and identity types, as well as arbitrary axiomatized constants. To provide a familiar experience for Lean users, we reuse Lean’s tactic language and syntax in the internal mode, and base our formalization of natural model semantics on Mathlib. By taking a generic approach, SynthLean can be used to mechanize various interpretations of internal languages such as the groupoid, cubical, or simplicial models of homotopy type theory in HoTTLean.

Computing many useful properties of Boolean formulas, such as their weighted or unweighted model count, is intractable on general representations. It can become tractable when formulas are expressed in a special form, such as the decision decomposable negation normal form (decision-DNNF). Knowledge compilation is the process of converting a formula into such a form. Unfortunately existing knowledge compilers provide no guarantee that their output correctly represents the original formula, and therefore they cannot validate a model count, or any other computed value. We present Partitioned-Operation Graphs (POGs), a form that can encode all of the representations used by existing knowledge compilers. We have designed CPOG, a framework that can express proofs of equivalence between a POG and a Boolean formula in conjunctive normal form (CNF). We have developed a program that generates POG representations from decision-DNNF graphs produced by the state-of-the-art knowledge compiler D4, as well as checkable CPOG proofs certifying that the output POGs are equivalent to the input CNF formulas. Our toolchain for generating and verifying POGs scales to all but the largest graphs produced by D4 for formulas from a recent model counting competition. Additionally, we have developed a formally verified CPOG checker and model counter for POGs in the Lean 4 proof assistant. In doing so, we proved the soundness of our proof framework. These programs comprise the first formally verified toolchain for weighted and unweighted model counting.

A recent breakthrough in computer-assisted mathematics showed that every set of 30 points in the plane in general position (i.e., no three points on a common line) contains an empty convex hexagon. Heule and Scheucher solved this problem with a combination of geometric insights and automated reasoning techniques by constructing CNF formulas ϕ_n, with O(n⁴) clauses, such that if ϕ_n is unsatisfiable then every set of n points in general position must contain an empty convex hexagon. An unsatisfiability proof for n = 30 was then found with a SAT solver using 17 300 CPU hours of parallel computation. In this paper, we formalize and verify this result in the Lean theorem prover. Our formalization covers ideas in discrete computational geometry and SAT encoding techniques by introducing a framework that connects geometric objects to propositional assignments. We see this as a key step towards the formal verification of other SAT-based results in geometry, since the abstractions we use have been successfully applied to similar problems. Overall, we hope that our work sets a new standard for the verification of geometry problems relying on extensive computation, and that it increases the trust the mathematical community places in computer-assisted proofs.