Coherent-resonant netting: disorder-enhanced selectivity from transient wave-like dynamics on biological connectomes

Biological agents face an energy-information bottleneck: inference requires rapid exploration of large hypothesis spaces, yet high-gain spiking is metabolically expensive. We propose Coherent-Resonant Netting (CRN) as a two-regime decision architecture in which a low-amplitude Stage-I transport process filters candidate routes on a structural graph before a higher-cost Stage-II commitment step. In this manuscript, we model Stage-I only, using a mechanistically neutral GKSL open-system proxy with dephasing rate κ and diagonal disorder ε. The model does not imply microscopic quantum coherence in neural tissue. In two biological connectome benchmarks, selectivity improves under partial coherence. In a compact Caenorhabditis elegans touch-circuit benchmark, the wave proxy yields a 1.39 × improvement in peak target absorption over a matched low-temperature classical baseline. In a Drosophila larva mushroom-body motif (N = 243 active nodes), selectivity shows a pronounced non-monotonic disorder-enhanced selectivity peak at intermediate ε, strongest in the native topology and strongly attenuated by degree-preserving rewiring. A permutation-based reanalysis confirms the pre-specified DES contrast (ε = 3 versus ε = 0, p = 0.010), and the effect weakens progressively with increasing dephasing, becoming non-significant in the high-κ regime. We interpret these findings as evidence for a topology-sensitive, dephasing-dependent Stage-I routing effect on biological connectomes. Broader energetic and evolutionary implications remain conditional because Stage-II commitment is not explicitly modeled here.