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Structured and Compact: A Novel Encoding and Enhancement Paradigm for ML-based SAT Solving

Ziqi Zhang Lan Zhang
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Structured and Compact: A Novel Encoding and Enhancement Paradigm for ML-based SAT Solving

by Ziqi Zhang, Lan Zhang
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 130
Year of Publication: 2026
Authors: Ziqi Zhang, Lan Zhang
10.5120/ijca83017cd50825

Ziqi Zhang, Lan Zhang . Structured and Compact: A Novel Encoding and Enhancement Paradigm for ML-based SAT Solving. International Journal of Computer Applications. 187, 130 ( Jul 2026), 31-38. DOI=10.5120/ijca83017cd50825

@article{ 10.5120/ijca83017cd50825,
author = { Ziqi Zhang, Lan Zhang },
title = { Structured and Compact: A Novel Encoding and Enhancement Paradigm for ML-based SAT Solving },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2026 },
volume = { 187 },
number = { 130 },
month = { Jul },
year = { 2026 },
issn = { 0975-8887 },
pages = { 31-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number130/structured-and-compact-a-novel-encoding-and-enhancement-paradigm-for-ml-based-sat-solving/ },
doi = { 10.5120/ijca83017cd50825 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-08-01T02:11:43+05:30
%A Ziqi Zhang
%A Lan Zhang
%T Structured and Compact: A Novel Encoding and Enhancement Paradigm for ML-based SAT Solving
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 130
%P 31-38
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Machine Learning for SAT (ML4SAT) offers a data-driven alternative to traditional solvers. However, existing flat-vector paradigms face scalability bottlenecks, feature redundancy, and struggle to exploit the intrinsic logical structures of clauses. To address these deficiencies, a novel encoding and enhancement paradigm is proposed, comprising: (1) a lightweight Prime-Product Clause Encoding (PPCE) scheme leveraging unique prime factorization for mathematically lossless dimensionality compression; (2) a Structure-Aware Enhancement (SAE) module incorporating a localized subnetwork (ClauseNet) to better capture intraclause logic; and (3) a controlled benchmark evaluating four canonical models—Logistic Regression, Support Vector Machine, Multi-Layer Perceptron, and Transformer—under unified variables. Empirical results on two large-scale CNF datasets (183k and 480k samples) reveal high-dimensional semi-sparsity (S ranging from 73.81% to 75.20%). PPCE reduces feature dimensionality by 75%, while the SAE module can improve accuracy across models without inference overhead. These findings validate specialized encoding and structural modules in ML4SAT, providing practical, data-driven selection guidelines for exploring accuracy-efficiency trade-offs in industrial formal verification.

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Index Terms

Computer Science
Information Sciences

Keywords

Satisfiability Feature Engineering Prime-Product Clause Encoding Structure-Aware Enhancement Logistic Regression Support Vector Machine Multi-Layer Perceptron Transformer

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