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Documents authored by Bryant, Randal E.

Document
DOI: 10.4230/LIPIcs.SAT.2026.28
Factoring Learned Clauses

Authors: Florian Pollitt, Zachary Battleman, Mathias Fleury, Yakir Vizel, Marijn J. H. Heule, Armin Biere, and Randal E. Bryant

Published in: LIPIcs, Volume 377, 29th International Conference on Theory and Applications of Satisfiability Testing (SAT 2026)

Abstract
Modern SAT solvers are based on the conflict-driven clause learning (CDCL) paradigm, which can be simulated by the resolution proof system. This limits solver effectiveness on instances known to be hard for resolution. Certain approaches, such as parity reasoning, have been shown to be effective in this context, but are hard to integrate with CDCL, in particular, with mainstream proof certificates. The powerful yet simple Extended Resolution (ER) proof system provides an alternative but is not widely used in SAT solving despite having proof certificates for decades and using it effectively remains an open challenge. This paper revisits previous work on ER, which factors out repeated parts of learned clauses during conflict analysis, and explores how their original strategy benefits from 15 years of improvements in the state-of-the-art solver CaDiCaL. We further propose a new, less intrusive inprocessing approach based on factoring XOR and ITE gates from learned clauses globally. Previous work on bounded variable addition focused on AND gates and original clauses only. Our experimental evaluation shows substantial improvements on hard combinatorial benchmark families without performance degradation on the SAT Competition.
Cite as

Florian Pollitt, Zachary Battleman, Mathias Fleury, Yakir Vizel, Marijn J. H. Heule, Armin Biere, and Randal E. Bryant. Factoring Learned Clauses. In 29th International Conference on Theory and Applications of Satisfiability Testing (SAT 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 377, pp. 28:1-28:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{pollitt_et_al:LIPIcs.SAT.2026.28,
  author =	{Pollitt, Florian and Battleman, Zachary and Fleury, Mathias and Vizel, Yakir and Heule, Marijn J. H. and Biere, Armin and Bryant, Randal E.},
  title =	{{Factoring Learned Clauses}},
  booktitle =	{29th International Conference on Theory and Applications of Satisfiability Testing (SAT 2026)},
  pages =	{28:1--28:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-431-4},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{377},
  editor =	{Ignatiev, Alexey and Szeider, Stefan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2026.28},
  URN =		{urn:nbn:de:0030-drops-263343},
  doi =		{10.4230/LIPIcs.SAT.2026.28},
  annote =	{Keywords: SAT solving, Extended Resolution, CDCL, Inprocessing}
}
Document
DOI: 10.4230/LIPIcs.SAT.2025.8
Certifying Projected Knowledge Compilation

Authors: Randal E. Bryant, Yong Kiam Tan, and Marijn J. H. Heule

Published in: LIPIcs, Volume 341, 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)

Abstract
Knowledge compilers convert Boolean formulas, given in conjunctive normal form (CNF), into representations that enable efficient evaluation of unweighted and weighted model counts, as well as a variety of other useful properties. With projected knowledge compilation, the generated representation describes the restriction of the formula to a designated set of data variables, with the remaining ones eliminated by existential quantification. Projected knowledge compilation has applications in a variety of domains, including formal verification and synthesis. This paper describes a formally verified proof framework for certifying the output of a projected knowledge compiler. It builds on an earlier clausal proof framework for certifying the output of a standard knowledge compiler. Extending the framework to projected compilation requires a method to represent Skolem assignments, describing how the quantified variables can be assigned, given an assignment for the data variables. We do so by extending the representation generated by the knowledge compiler to also encode Skolem assignments. We also refine the earlier framework, moving beyond purely clausal proofs to enable scaling certification to larger formulas. We present experimental results obtained by making small modifications to the D4 projected knowledge compiler and extensions of our earlier proof generator. We detail a soundness argument stating that a compiler output that passes our certifier is logically equivalent to the quantified input formula; the soundness argument has been formally validated using the HOL4 proof assistant. The checker also ensures that the compiler output satisfies the properties required for efficient unweighted and weighted model counting. We have developed two proof checkers for the certification framework: one written in C and designed for high performance and one written in CakeML and formally verified in HOL4.
Cite as

Randal E. Bryant, Yong Kiam Tan, and Marijn J. H. Heule. Certifying Projected Knowledge Compilation. In 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 341, pp. 8:1-8:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{bryant_et_al:LIPIcs.SAT.2025.8,
  author =	{Bryant, Randal E. and Tan, Yong Kiam and Heule, Marijn J. H.},
  title =	{{Certifying Projected Knowledge Compilation}},
  booktitle =	{28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)},
  pages =	{8:1--8:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-381-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{341},
  editor =	{Berg, Jeremias and Nordstr\"{o}m, Jakob},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2025.8},
  URN =		{urn:nbn:de:0030-drops-237422},
  doi =		{10.4230/LIPIcs.SAT.2025.8},
  annote =	{Keywords: Knowledge Compilation, Propositional model counting, Proof checking}
}
Document
DOI: 10.4230/LIPIcs.SAT.2023.6
Certified Knowledge Compilation with Application to Verified Model Counting

Authors: Randal E. Bryant, Wojciech Nawrocki, Jeremy Avigad, and Marijn J. H. Heule

Published in: LIPIcs, Volume 271, 26th International Conference on Theory and Applications of Satisfiability Testing (SAT 2023)

Abstract
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 (dec-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 dec-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.
Cite as

Randal E. Bryant, Wojciech Nawrocki, Jeremy Avigad, and Marijn J. H. Heule. Certified Knowledge Compilation with Application to Verified Model Counting. In 26th International Conference on Theory and Applications of Satisfiability Testing (SAT 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 271, pp. 6:1-6:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{bryant_et_al:LIPIcs.SAT.2023.6,
  author =	{Bryant, Randal E. and Nawrocki, Wojciech and Avigad, Jeremy and Heule, Marijn J. H.},
  title =	{{Certified Knowledge Compilation with Application to Verified Model Counting}},
  booktitle =	{26th International Conference on Theory and Applications of Satisfiability Testing (SAT 2023)},
  pages =	{6:1--6:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-286-0},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{271},
  editor =	{Mahajan, Meena and Slivovsky, Friedrich},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2023.6},
  URN =		{urn:nbn:de:0030-drops-184685},
  doi =		{10.4230/LIPIcs.SAT.2023.6},
  annote =	{Keywords: Propositional model counting, Proof checking}
}
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