Thermodynamic Advantage of Transient Wave Dynamics in Hierarchical Decision Architectures. Coherent Resonant Netting (CRN) via an Open-System Formalism

In this research, we examine, evaluate and propose Coherent Resonant Netting (CRN), a two-stage decision architecture designed for communication-dominant neural energetics. A transient wave-like pre-selection episode runs on an active hypothesis graph, pruning inconsistent candidates by phase-sensitive interference, before a thresholded fixation/broadcast stage commits to a discrete winner. The core engineering motivation is the strong asymmetry E_comm ≫ E_comp: long-range spiking and broadcast are metabolically expensive relative to local subthreshold dynamics, so the relevant objective is information-per-spike rather than raw wallclock speed in the reset-limited regime. Stage I is formalized as open-system wave transport on graphs using a GKSL/Lindblad generator (used here as a mesoscopic tool, not as a claim of microscopic quantum coherence). This approach adapts the established Environment-Assisted Quantum Transport (ENAQT) framework [REFS] to neural architectures. Unlike photosynthetic complexes where ENAQT optimizes speed, we propose that in neural substrates, this regime serves a different evolutionary function: metabolic robustness. It allows signal transmission through 'dirty' (high-disorder) and 'cold' (energy-starved) substrates where classical propagation fails. Reproducible simulations across toy and large graphs, and matched classical random-walk baselines, show interference trapping at low noise and its noise-assisted release within the NAT window. Finally, we outline falsifiable signatures (P1-P6) and dissociation protocols (Drosophila and mammalian/EEG/MEG extensions) to test CRN's architectural predictions. Numerical validation on the C. elegans touch circuit confirms this thermodynamic advantage: CRN maintains 99.7% signal fidelity under metabolic stress), where classical transport collapses.