Multifactorial N×L Phylogenomic Sensitivity & Clock-Time Curve: 3-Way RuNIE Comparison (100% Native Rust vs. 100% External Binaries vs. Unconstrained Hybrid) Mapping Minimum Alignment-Length Horizons, the 90% MAFFT Alignment Bottleneck, and Saturatio

25-cell factorial step-up sweep (N = 10, 25, 50, 75, 100 specimens × L = Alignment Length L = 1,548 bp [1 locus] to 80,553 bp *MAFFT / 180,536 bp Native Rust gotoh-msa [20 loci]) quantifying OLS CPU scaling laws, minimum N×L for optimal ln L, and the L4→L5 saturation boundary Reconstructing deep evolutionary relationships requires balancing specimen sampling against sequence alignment length, yet adding more genomic data does not monotonically improve phylogenetic accuracy. Here we perform a multifactorial sensitivity analysis across a Panarthropoda, Pancrustacea, and Hexapoda benchmark to compare a Native Rust pipeline, an External reference pipeline, and an unconstrained Hybrid RuNIE architecture across progressive steps in taxon density and alignment length [1] [2] . We demonstrate that purine-pyrimidine third-codon recoding enables both the Hybrid and Native Rust pipelines to resolve all benchmark clades using 50% of the sampled species across the protein-coding gene core , whereas expanding alignments into non-coding mitochondrial regions yields zero topological improvement while inflating computational cost and compositional drift [3] [4] . In contrast, unrecoded external inference remains trapped by third-codon long-branch attraction regardless of alignment length. Finally, empirical clock-time scaling curves reveal that progressive sequence alignment in *MAFFT scales near-quadratically with alignment length and consumes 89.8% of total runtime on full-length supermatrices, a computational bottleneck eliminated by Native Rust’s sub-linear Gotoh aligner ( 557.8× alignment speedup ) [1] [13] [17] . Reconstructing deep evolutionary relationships requires balancing specimen sampling against sequence alignment length, yet adding more genomic data does not monotonically improve phylogenetic accuracy. Here we perform a multifactorial sensitivity analysis across a Panarthropoda, Pancrustacea, and Hexapoda benchmark to compare a Native Rust pipeline, an External reference pipeline, and an unconstrained Hybrid RuNIE architecture across progressive steps in taxon density and alignment length. We demonstrate that purine-pyrimidine third-codon recoding enables both the Hybrid and Native Rust pipelines to resolve all benchmark clades using 50% of the sampled species across the protein-coding gene core, whereas expanding alignments into non-coding mitochondrial regions yields zero topological improvement while inflating computational cost and compositional drift. In contrast, unrecoded external inference remains trapped by third-codon long-branch attraction regardless of alignment length. Finally, empirical clock-time scaling curves reveal that progressive sequence alignment in *MAFFT scales near-quadratically with alignment length and consumes 89.8% of total runtime on full-length supermatrices, a computational bottleneck eliminated by Native Rust's sub-linear Gotoh aligner (557.8x alignment speedup). Canonical Preprint URL: https://crvscience.com/labs/preprints/multifactorial-nxl-clock-curve-preprint Master (Concept) Zenodo DOI: 10.5281/zenodo.24049860 Open-Source Software Repository (GNU GPL-3.0): https://github.com/bpwhite/crvscience-labs