Automatically discovering heuristics in a complex SAT solver with large language models Article Swipe
Shaowei Cai
,
Yiwen Sun
,
Furong Ye
,
Zhihan Chen
,
Ke Wei
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-7255349/v1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-7255349/v1
Satisfiability problem (SAT) is a cornerstone of computational complexity with broad industrial applications, and it remains challenging to optimize modern SAT solvers in real-world settings due to their intricate architectures. While automatic configuration frameworks have been developed, they rely on manually constrained search spaces and yield limited performance gains. This work introduces a novel paradigm which effectively optimizes complex SAT solvers via Large Language Models (LLMs), and a tool called AutoModSAT is developed. Three fundamental challenges are addressed in order to achieve superior performance: (1) LLM-friendly solver: Systematic guidelines are proposed for developing a modularized solver to meet LLMs' compatibility, emphasizing code simplification, information share and bug reduction; (2) Automatic prompt optimization: An unsupervised automatic prompt optimization method is introduced to advance the diversity of LLMs' output; (3) Efficient search strategy: We design a presearch strategy and an (1 + λ)EA evolutionary algorithm for the final efficient and effective discovery of heuristics. Extensive experiments across a wide range of datasets demonstrate that AutoModSAT achieves 50% performance improvement over the baseline solver and achieves 30% superiority against the state-of-the-art (SOTA) solvers. Moreover, AutoModSAT attains a 20% speedup on average compared to parameter-tuned alternatives of the SOTA solvers, showcasing the enhanced capability in handling complex problem instances. This work bridges the gap between AI-driven heuristics discovery and mission-critical system optimization, and provides both methodological advancements and empirically validated results for next-generation complex solver development.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-7255349/v1
- https://www.researchsquare.com/article/rs-7255349/latest.pdf
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412887300
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4412887300Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-7255349/v1Digital Object Identifier
- Title
-
Automatically discovering heuristics in a complex SAT solver with large language modelsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-08-04Full publication date if available
- Authors
-
Shaowei Cai, Yiwen Sun, Furong Ye, Zhihan Chen, Ke WeiList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-7255349/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-7255349/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.researchsquare.com/article/rs-7255349/latest.pdfDirect OA link when available
- Concepts
-
Heuristics, Problem solver, Solver, Computer science, Programming language, Theoretical computer science, Natural language processing, Software engineering, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Moreover, | 181 |
| abstract_inverted_index.addressed | 78 |
| abstract_inverted_index.algorithm | 143 |
| abstract_inverted_index.automatic | 32, 115 |
| abstract_inverted_index.discovery | 150, 214 |
| abstract_inverted_index.diversity | 124 |
| abstract_inverted_index.effective | 149 |
| abstract_inverted_index.efficient | 147 |
| abstract_inverted_index.intricate | 29 |
| abstract_inverted_index.optimizes | 58 |
| abstract_inverted_index.presearch | 135 |
| abstract_inverted_index.strategy: | 131 |
| abstract_inverted_index.validated | 226 |
| abstract_inverted_index.AutoModSAT | 71, 163, 182 |
| abstract_inverted_index.Systematic | 88 |
| abstract_inverted_index.capability | 200 |
| abstract_inverted_index.challenges | 76 |
| abstract_inverted_index.complexity | 9 |
| abstract_inverted_index.developed, | 37 |
| abstract_inverted_index.developed. | 73 |
| abstract_inverted_index.developing | 93 |
| abstract_inverted_index.frameworks | 34 |
| abstract_inverted_index.guidelines | 89 |
| abstract_inverted_index.heuristics | 213 |
| abstract_inverted_index.industrial | 12 |
| abstract_inverted_index.instances. | 205 |
| abstract_inverted_index.introduced | 120 |
| abstract_inverted_index.introduces | 52 |
| abstract_inverted_index.real-world | 24 |
| abstract_inverted_index.reduction; | 108 |
| abstract_inverted_index.showcasing | 197 |
| abstract_inverted_index.challenging | 17 |
| abstract_inverted_index.constrained | 42 |
| abstract_inverted_index.cornerstone | 6 |
| abstract_inverted_index.demonstrate | 161 |
| abstract_inverted_index.effectively | 57 |
| abstract_inverted_index.emphasizing | 101 |
| abstract_inverted_index.empirically | 225 |
| abstract_inverted_index.experiments | 154 |
| abstract_inverted_index.fundamental | 75 |
| abstract_inverted_index.heuristics. | 152 |
| abstract_inverted_index.improvement | 167 |
| abstract_inverted_index.information | 104 |
| abstract_inverted_index.modularized | 95 |
| abstract_inverted_index.performance | 48, 166 |
| abstract_inverted_index.superiority | 175 |
| abstract_inverted_index.LLM-friendly | 86 |
| abstract_inverted_index.advancements | 223 |
| abstract_inverted_index.alternatives | 192 |
| abstract_inverted_index.development. | 232 |
| abstract_inverted_index.evolutionary | 142 |
| abstract_inverted_index.optimization | 117 |
| abstract_inverted_index.performance: | 84 |
| abstract_inverted_index.unsupervised | 114 |
| abstract_inverted_index.applications, | 13 |
| abstract_inverted_index.computational | 8 |
| abstract_inverted_index.configuration | 33 |
| abstract_inverted_index.optimization, | 218 |
| abstract_inverted_index.optimization: | 112 |
| abstract_inverted_index.Satisfiability | 1 |
| abstract_inverted_index.architectures. | 30 |
| abstract_inverted_index.compatibility, | 100 |
| abstract_inverted_index.methodological | 222 |
| abstract_inverted_index.next-generation | 229 |
| abstract_inverted_index.parameter-tuned | 191 |
| abstract_inverted_index.simplification, | 103 |
| abstract_inverted_index.mission-critical | 216 |
| abstract_inverted_index.state-of-the-art | 178 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.36802544 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |