R-DTLGN is a recurrent ternary logic network that hardens polynomial surrogates to monotone-gate circuits, links STL bounded operators to AND/OR connections for stability and principled abstention, and uses a formula-derived bound to size hidden state.
Polynomial surrogate training for differentiable ternary logic gate networks,
2 Pith papers cite this work. Polarity classification is still indexing.
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A modular neural architecture learns complete K3 logic and shows uncertainty-verdict asymmetric propagation plus a reliability spectrum for long discretized composition.
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On the Stability and Realizability of Recurrent Polynomial Surrogate Ternary Logic Gate Networks
R-DTLGN is a recurrent ternary logic network that hardens polynomial surrogates to monotone-gate circuits, links STL bounded operators to AND/OR connections for stability and principled abstention, and uses a formula-derived bound to size hidden state.
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THEIA: Learning Complete Kleene Three-Valued Logic in a Pure-Neural Modular Architecture
A modular neural architecture learns complete K3 logic and shows uncertainty-verdict asymmetric propagation plus a reliability spectrum for long discretized composition.