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Multi-Attractor GNNs: Set-Valued Expressivity Beyond Unique Equilibria
Recurrent and equilibrium graph neural networks (GNNs) often enforce a unique fixed point or use one training target per graph. Yet many combinatorial and scientific problems admit multiple valid solutions, with no preferred one. A designated target can then impose an arbitrary selection rule. For t
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