arXiv:2610.34154v1 Announce Type: new Abstract: Within the broader landscape of artificial intelligence architectures, this paper introduces a compact, edge-centric neural framework that resolves the computational bottlenecks of modern edge-activated paradigms like Kolmogorov-Arnold Networks (KANs) while retaining their expressiveness over traditional Multi-Layer Perceptrons (MLPs). While KANs demonstrate remarkable representational capacity by placing learnable univariate functions on connection edges rather than hidden nodes, their reliance on non-local B-splines introduces severe optimization hurdles, high memory overhead, and poor hardware scaling. To bridge this gap, we propose the Parametric Tanh Connection Function, a smooth, bounded architectural primitive defined as $a_{\tanh}(x;\delta,\alpha,\beta,\gamma) = \delta + \alpha \tanh(\beta x + \gamma)$. This formulation unifies offset, amplitude, local slope, and horizontal displacement into a lightweight, 4-parameter differentiable mapping optimized edge-by-edge via backpropagation. Evaluated on dense architectures using MNIST and CIFAR-10, our parametric tanh connection model achieves significant absolute accuracy gains over traditional affine baselines-reaching 95.63% $\pm$ 0.08% on MNIST and 44.74% $\pm$ 0.18% on CIFAR-10. Crucially, it matches or exceeds the expressiveness of unconstrained 7-parameter exponential connection baselines while requiring up to 85% less training runtime. By replacing complex splines or hyper-flexible exponents with a bounded, analytically smooth hyperbolic tangent kernel, this work delivers a highly scalable, hardware-friendly compromise for heterogeneous connection-level deep learning.