In 1930, Keller conjectured that every gap-free tiling of ℝⁿ by n-dimensional unit cubes must contain cubes that fully share an (n - 1)-dimensional face. Keller’s conjecture holds for n ≤ 7 and fails for n ≥ 8. The final case, n = 7, was settled in 2020 using a mix of traditional and automated reasoning. The result was obtained by reducing the conjecture to a set of clique-existence problems, encoding those problems into propositional logic, breaking symmetries, and solving them with a SAT solver. In this paper, we present an end-to-end verification in Lean 4 of Keller’s conjecture for all dimensions. First, we simplify a prior reduction of Keller’s conjecture to the clique-existence problems. We then verify an improved SAT encoding of those problems, as well as some symmetry reasoning on the encoding. Throughout our work, we sought to maximize the synergy between interactive and automated techniques while minimizing human proof burden. In particular, the symmetry reasoning was split between Lean and a mechanically-checkable proof system, since neither was suitable on their own for verifying all of the symmetry reasoning. We discuss how and why we chose to split the reasoning across these systems based on their relative strengths and weaknesses.

Despite their sophisticated heuristics, boolean satisfiability (SAT) solvers are still vulnerable to symmetry, causing them to visit search regions that are symmetric to ones already explored. While symmetry handling is routine in other solving paradigms, integrating it into state-of-the-art proof-producing SAT solvers is difficult: added reasoning must be fast, non-interfering with solver heuristics, and compatible with formal proof logging. To address these issues, we present a practical static symmetry breaking approach based on orbitopal fixing , a technique adapted from mixed-integer programming. Our approach adds only unit clauses , which minimizes downstream slowdowns, and it emits succinct proof certificates in the substitution redundancy proof system. Implemented in the satsuma tool, our methods deliver consistent speedups on symmetry-rich benchmarks with negligible regressions elsewhere.

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.

Abstract We present , a powerful local search SAT solver that effectively solves hard combinatorial problems. Its unique approach of transferring clause weights in local minima enhances its efficiency in solving problem instances. Since it is implemented on top of , benefits from practical techniques such as restart strategies and thread parallelization. Our implementation includes a parallel version that shares data structures across threads, leading to a significant reduction in memory usage. Our experiments demonstrate that outperforms similar solvers on a vast set of SAT competition benchmarks. Notably, with the parallel configuration of , we improve lower bounds for several van der Waerden numbers.

The sample inefficiency of standard deep reinforcement learning methods precludes their application to many real-world problems. Methods which leverage human demonstrations require fewer samples but have been researched less. As demonstrated in the computer vision and natural language processing communities, large-scale datasets have the capacity to facilitate research by serving as an experimental and benchmarking platform for new methods. However, existing datasets compatible with reinforcement learning simulators do not have sufficient scale, structure, and quality to enable the further development and evaluation of methods focused on using human examples. Therefore, we introduce a comprehensive, large-scale, simulator-paired dataset of human demonstrations: MineRL. The dataset consists of over 60 million automatically annotated state-action pairs across a variety of related tasks in Minecraft, a dynamic, 3D, open-world environment. We present a novel data collection scheme which allows for the ongoing introduction of new tasks and the gathering of complete state information suitable for a variety of methods. We demonstrate the hierarchality, diversity, and scale of the MineRL dataset. Further, we show the difficulty of the Minecraft domain along with the potential of MineRL in developing techniques to solve key research challenges within it.