minAction.net: Energy-First Neural Architecture Design -- From Biological Principles to Systematic Validation

In a sweeping study published on arXiv, Martin G. Frasch presents minAction.net, a novel approach to neural network design that prioritizes energy efficiency from the ground up. The research, spanning 2,203 experiments across vision, text, neuromorphic, and physiological datasets with 10 seeds per configuration, challenges the prevailing assumption that architecture alone drives accuracy. Instead, Frasch finds that architecture explains negligible variance in accuracy (partial eta^2 = 0.001), while the interaction between architecture and dataset is highly significant (partial eta^2 = 0.44, p < 0.001). This suggests that optimal architecture is critically dependent on task modality, undermining the search for a universal best architecture.

The core innovation is a single-parameter energy-regularized objective: L = L_CE + lambda * E(theta, x). A controlled lambda-sweep over four orders of magnitude shows that internal activation energy can be reduced to just 6% of baseline at moderate lambda values on MNIST, with zero accuracy degradation. Furthermore, energy-first architectures inspired by an action-principle framework deliver 5-33% within-modality training-efficiency gains over conventional baselines. Frasch frames this work as a design hypothesis, drawing a structural correspondence between the action functional in classical mechanics, free energy in statistical physics, and KL-regularized objectives in variational inference. This energy-aware approach could reshape how neural networks are designed for resource-constrained environments.