Abstract This tool paper presents the latest (2026) version of Mallob – a distributed platform for automated reasoning on demand. Mallob features a world-leading distributed SAT solving engine, which is the first of its kind that supports proof checking, incremental SAT queries, and flexible (re-)scheduling of computational resources. Exploiting this technology, Mallob features further engines relevant for verification, such as MaxSAT and SMT solving. We present these use cases, discuss a wide range of experimental results, and reflect on the system’s impact.

Bit vectors with support for fast rank and select are a fundamental building block for compressed data structures. We close a gap between theory and practice by mapping a design space of promising data structures, analyzing it, and experimentally evaluating a promising region. The result are implementations of rank and select data structures for bit vectors with worst-case constant query time, leading practical performance, and a space-overhead reaching below 1 %. For difficult inputs, we are ≈ 8 times faster than the best previous implementations.

Distributed clause-sharing SAT solvers have recently been established as powerful automated reasoning tools that can conquer previously infeasible instances. A common design of distributed SAT solvers is to run many off-the-shelf sequential solvers in parallel, employ some diversification (e.g., restart intervals or decision orders), and share conflict clauses among the solver threads. This approach, naïvely, adopts all best practices of sequential solver design for distributed solving, where these practices may be less useful or even actively detrimental. In this work we diagnose such shortcomings in the state-of-the-art system MallobSat and propose first effective mitigations. In particular, we replace the redundant pre- and inprocessing at all threads with single-core preprocessing that runs next to the parallel search, remove LBD values from the clause-sharing operation, and slim down solver diversification to very few lightweight and uniform methods. Experimental evaluations on up to 3072 cores (64 nodes) confirm that our measures improve performance while also drastically simplifying the SAT solving program that is run in parallel.