Challenging the “Bigger Is Better” PhilosophyFor decades, neural networks have relied on fixed activation functions such as sigmoid, hyperbolic tangent, and Rectified Linear Units (ReLU). Rather than evaluating performance on a single benchmark, the study examined whether adaptive activation functions consistently improved prediction accuracy across varied problem types. Adaptive activation functions repeatedly outperformed their traditional static counterparts, with adaptive quadratic functions delivering particularly strong improvements in regression accuracy. One particularly noteworthy outcome involved layer-wide adaptive activation functions. Gupta’s research contributes to this emerging direction by demonstrating that adaptive neural behavior can offer an alternative path toward improving machine learning performance.