3 papers
cs.LG2026
Fitting Multilinear Polynomials for Logic Gate Networks
Youngsung Kim
We study learnable logic gate networks that stack layers of 2-input Boolean gates to build combinational circuits. Every 2-input gate has a unique multilinear polynomial with 4 coe…
cs.LG2026
Align Forward, Adapt Backward: Closing the Discretization Gap in Logic Gate Networks
Youngsung Kim
In neural network models, soft mixtures of fixed candidate components (e.g., logic gates and sub-networks) are often used during training for stable optimization, while hard select…
cs.AI2025
Standard Neural Computation Alone Is Insufficient for Logical Intelligence
Youngsung Kim
Neural networks, as currently designed, fall short of achieving true logical intelligence. Modern AI models rely on standard neural computation-inner-product-based transformations…