Streamlined Constraint Reasoning via CNN Pattern Recognition on Enumerated Solutions
The paper presents a novel approach to streamline constraint reasoning using Convolutional Neural Networks (CNN) for pattern recognition on enumerated solutions. This method aims to enhance the efficiency of solving hard problems in constraint programming by leveraging structural patterns in feasible solutions. The proposed pipeline demonstrates significant time reductions across various benchmark models, showcasing its effectiveness in generating candidate streamliners.
- ▪The approach involves training a CNN contrastively against perturbed non-solutions to detect structural patterns.
- ▪The pipeline achieves up to 98.8% time reduction on hardened benchmark models.
- ▪Discovered streamliners include class-based packing constraints and layout-coordinate bounds.
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Computer Science > Artificial Intelligence arXiv:2605.19895 (cs) [Submitted on 19 May 2026] Title:Streamlined Constraint Reasoning via CNN Pattern Recognition on Enumerated Solutions Authors:Patrick Spracklen View a PDF of the paper titled Streamlined Constraint Reasoning via CNN Pattern Recognition on Enumerated Solutions, by Patrick Spracklen View PDF HTML (experimental) Abstract:Constraint programming practitioners accelerate hard problems through a layered set of techniques applied in order of risk. Standard hardening (symmetry-breaking and implied constraints) is applied first and preserves satisfiability. Streamliner constraints, which restrict search to a structural sub-family of solutions, do not preserve satisfiability and are reserved as a final lever.
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