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SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver

29 May 2019arXiv:1905.12149archive 2025-07-28

Po-Wei Wang, Priya L. Donti, Bryan Wilder, Zico Kolter

Integrating logical reasoning within deep learning architectures has been a major goal of modern AI systems. In this paper, we propose a new direction toward this goal by introducing a differentiable (smoothed) maximum satisfiability (MAXSAT) solver that can be integrated into the loop of larger deep learning systems. Our (approximate) solver is based upon a fast coordinate descent approach to solving the semidefinite program (SDP) associated with the MAXSAT problem. We show how to analytically differentiate through the solution to this SDP and efficiently solve the associated backward pass. We demonstrate that by integrating this solver into end-to-end learning systems, we can learn the logical structure of challenging problems in a minimally supervised fashion. In particular, we show that we can learn the parity function using single-bit supervision (a traditionally hard task for deep networks) and learn how to play 9x9 Sudoku solely from examples. We also solve a "visual Sudok" problem that maps images of Sudoku puzzles to their associated logical solutions by combining our MAXSAT solver with a traditional convolutional architecture. Our approach thus shows promise in integrating logical structures within deep learning.

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Kyubyong/sudoku officialmentioned in papermentioned on GitHubtfGPL-3.0 report
locuslab/SATNet officialmentioned in papermentioned on GitHubpytorch report
SeverTopan/SATNet mentioned on GitHubpytorchMIT report

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1ran · honoured contract
1ran · violated contract
2ran · fixture could not drive it
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computeErr locuslab/SATNet/exps/sudoku.py official repository ran · fixture could not drive it fingerprinted MIT (permissive) · 979489d150ea11b0 · report
computeErr locuslab/SATNet/exps/parity.py official repository ran · honoured contract fingerprinted MIT (permissive) · 83f53a6a79c8de4c · report
find_unperm locuslab/SATNet/exps/sudoku.py official repository ran · violated contract MIT (permissive) · ea38eb60359ba548 · report
process_inputs locuslab/SATNet/exps/sudoku.py official repository ran · fixture could not drive it fingerprinted MIT (permissive) · 95f02588dfc909d3 · report
apply_seq locuslab/SATNet/exps/parity.py official repository unverified MIT (permissive) · b65c4afca5a45ef2 · report

Tasks

Deep LearningGame of SudokuLogical Reasoning

Datasets

Introduced by this paper, per the archive.

satnet-sudoku

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Game of Sudoku Sudoku 9x9 SATNet Accuracy 98.3 #1 of 1 Archive leaderboard report

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