A target-aware solver-free data generation pipeline plus an LPGNN that uses linear-programming residuals produces fast, correctly labeled training data and improves GNN-based SAT prediction.
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A branch-and-bound algorithm with custom node selection, branching rules, and conflict definitions solves the logic-constrained shortest path problem for flight planning with traffic flow restrictions, showing order-of-magnitude speedups on a public global dataset with 20000 real constraints.
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Target-Aware Data Augmentation for SAT Prediction
A target-aware solver-free data generation pipeline plus an LPGNN that uses linear-programming residuals produces fast, correctly labeled training data and improves GNN-based SAT prediction.
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Logic-Constrained Shortest Paths for Flight Planning
A branch-and-bound algorithm with custom node selection, branching rules, and conflict definitions solves the logic-constrained shortest path problem for flight planning with traffic flow restrictions, showing order-of-magnitude speedups on a public global dataset with 20000 real constraints.